Prediction

ID

w3hycztb6m2lcgihtne5bb5fby

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 38s

Created

almost 2 years ago by @kornesh

Input

width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic chicken dressed as an officer"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

2.9 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

x76ltplbe6ufxucocdwtdjasq4

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 52s

Created

almost 2 years ago by @kornesh

Input

width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic chicken dressed as an officer"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a

Prediction

Model

pagebrain/wuerstchen-v2:38331cbf

ID

565afkbbosrogqcmmozwajopna

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 38s

Created

almost 2 years ago by @kornesh

Input

width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic chicken dressed as an officer"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-09-14T08:26:35.134417Z",
  "created_at": "2023-09-14T08:23:57.235411Z",
  "data_removed": false,
  "error": null,
  "id": "565afkbbosrogqcmmozwajopna",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "Anthropomorphic chicken dressed as an officer",
    "num_outputs": 1,
    "num_inference_steps": 12,
    "prior_guidance_scale": 4,
    "decoder_guidance_scale": 0,
    "prior_num_inference_steps": 30
  },
  "logs": "Using seed: 34461\nPrompt: Anthropomorphic chicken dressed as an officer\n  0%|          | 0/30 [00:00<?, ?it/s]\n  7%|▋         | 2/30 [00:00<00:01, 16.46it/s]\n 13%|█▎        | 4/30 [00:00<00:01, 17.82it/s]\n 20%|██        | 6/30 [00:00<00:01, 17.74it/s]\n 27%|██▋       | 8/30 [00:00<00:01, 18.26it/s]\n 33%|███▎      | 10/30 [00:00<00:01, 18.71it/s]\n 40%|████      | 12/30 [00:00<00:00, 19.05it/s]\n 47%|████▋     | 14/30 [00:00<00:00, 19.17it/s]\n 53%|█████▎    | 16/30 [00:00<00:00, 19.40it/s]\n 60%|██████    | 18/30 [00:00<00:00, 19.42it/s]\n 70%|███████   | 21/30 [00:01<00:00, 19.67it/s]\n 80%|████████  | 24/30 [00:01<00:00, 19.80it/s]\n 87%|████████▋ | 26/30 [00:01<00:00, 19.64it/s]\n 97%|█████████▋| 29/30 [00:01<00:00, 19.87it/s]\n100%|██████████| 30/30 [00:01<00:00, 19.28it/s]\n  0%|          | 0/12 [00:00<?, ?it/s]\n  8%|▊         | 1/12 [00:00<00:01,  6.13it/s]\n 25%|██▌       | 3/12 [00:00<00:01,  8.90it/s]\n 42%|████▏     | 5/12 [00:00<00:00,  9.83it/s]\n 58%|█████▊    | 7/12 [00:00<00:00, 10.24it/s]\n 75%|███████▌  | 9/12 [00:00<00:00, 10.36it/s]\n 92%|█████████▏| 11/12 [00:01<00:00, 10.47it/s]\n100%|██████████| 12/12 [00:01<00:00, 10.07it/s]",
  "metrics": {
    "predict_time": 4.201766,
    "total_time": 157.899006
  },
  "output": [
    "https://pbxt.replicate.delivery/AEUi668tms6wH9U7ihi49lRLh5yWYxs55haEHj9DTHuu1BZE/out-0.png"
  ],
  "started_at": "2023-09-14T08:26:30.932651Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/565afkbbosrogqcmmozwajopna",
    "cancel": "https://api.replicate.com/v1/predictions/565afkbbosrogqcmmozwajopna/cancel"
  },
  "version": "38331cbfd47332a3969b52a196ab26dcc8978209af6cbe7a74dbcf3a8ca7872a"
}

Generated in

4.2 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

e6rckjdbcneiggx4lkxcugezbi

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

8.4s

Created

almost 2 years ago by @kornesh

Input

seed: 10352
width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 10352,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 10352,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 10352,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 10352,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=10352' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic chicken dressed as an officer"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 10352,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

reiun63bn2h32xhrfphquwkfqa

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.0s

Created

almost 2 years ago by @kornesh

Input

seed: 50402
width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 50402,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 50402,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 50402,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 50402,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=50402' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic chicken dressed as an officer"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 50402,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

b45x4x3bivf4vfpw6mzqwbjmae

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.0s

Created

almost 2 years ago by @kornesh

Input

seed: 42626
width: 1024
height: 1024
prompt: Anthropomorphic cat dressed as a firefighter
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 42626,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic cat dressed as a firefighter",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 42626,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 42626,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 42626,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=42626' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 42626,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

u3ziuttbmskqjc5l67lnvzfdsm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.0s

Created

almost 2 years ago by @kornesh

Input

seed: 20138
width: 1024
height: 1024
prompt: Anthropomorphic cat dressed as a firefighter
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 20138,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic cat dressed as a firefighter",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 20138,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 20138,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 20138,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=20138' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 20138,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

3h6ziktbttybf25gomguv6olj4

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

7.1s

Created

almost 2 years ago by @kornesh

Input

seed: 60496
width: 1024
height: 1024
prompt: Anthropomorphic cat dressed as a firefighter
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 60496,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic cat dressed as a firefighter",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 60496,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 60496,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 60496,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=60496' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 60496,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

vrxx7qtbjr7pquls5s46sihbzm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.0s

Created

almost 2 years ago by @kornesh

Input

seed: 40951
width: 1024
height: 1024
prompt: Anthropomorphic cat dressed as a firefighter
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 40951,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic cat dressed as a firefighter",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 40951,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 40951,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 40951,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=40951' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 40951,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.0 seconds

pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f

Prediction

Model

ID

pfk6sytbqbd7g6lz2xud7clnyu

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.1s

Created

almost 2 years ago by @kornesh

Input

seed: 10352
width: 1024
height: 1024
prompt: Anthropomorphic cat dressed as a firefighter
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 10352,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic cat dressed as a firefighter",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
  {
    input: {
      seed: 10352,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    input={
        "seed": 10352,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f",
    "input": {
      "seed": 10352,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f \
  -i 'seed=10352' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'num_outputs=1' \
  -i 'num_inference_steps=12' \
  -i 'prior_guidance_scale=4' \
  -i 'decoder_guidance_scale=0' \
  -i 'prior_num_inference_steps=30'

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/pagebrain/wuerstchen-v2@sha256:fba2b69c67341c193ffda8710f484d79ee93846ba5c539d1c9af515145f9bd3f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 10352,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.1 seconds

pagebrain/wuerstchen-v2:0725c5f70905b87ffadd7d6fe4cfae1b5457784d911eebd8521022892deb481a

Prediction

Model

pagebrain/wuerstchen-v2:0725c5f7

ID

yetdezjbedldgfhnmq2oe5x4yq

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.9s

Created

almost 2 years ago by @kornesh

Input

seed: 39564
width: 1024
height: 1024
prompt: Anthropomorphic chicken dressed as an officer
num_outputs: 1
num_inference_steps: 12
prior_guidance_scale: 4
decoder_guidance_scale: 0
prior_num_inference_steps: 30

{
  "seed": 39564,
  "width": 1024,
  "height": 1024,
  "prompt": "Anthropomorphic chicken dressed as an officer",
  "num_outputs": 1,
  "num_inference_steps": 12,
  "prior_guidance_scale": 4,
  "decoder_guidance_scale": 0,
  "prior_num_inference_steps": 30
}

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

const output = await replicate.run(
  "pagebrain/wuerstchen-v2:0725c5f70905b87ffadd7d6fe4cfae1b5457784d911eebd8521022892deb481a",
  {
    input: {
      seed: 39564,
      width: 1024,
      height: 1024,
      prompt: "Anthropomorphic chicken dressed as an officer",
      num_outputs: 1,
      num_inference_steps: 12,
      prior_guidance_scale: 4,
      decoder_guidance_scale: 0,
      prior_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.

pip install replicate

Import the client:

import replicate

Run pagebrain/wuerstchen-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pagebrain/wuerstchen-v2:0725c5f70905b87ffadd7d6fe4cfae1b5457784d911eebd8521022892deb481a",
    input={
        "seed": 39564,
        "width": 1024,
        "height": 1024,
        "prompt": "Anthropomorphic chicken dressed as an officer",
        "num_outputs": 1,
        "num_inference_steps": 12,
        "prior_guidance_scale": 4,
        "decoder_guidance_scale": 0,
        "prior_num_inference_steps": 30
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run pagebrain/wuerstchen-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": "pagebrain/wuerstchen-v2:0725c5f70905b87ffadd7d6fe4cfae1b5457784d911eebd8521022892deb481a",
    "input": {
      "seed": 39564,
      "width": 1024,
      "height": 1024,
      "prompt": "Anthropomorphic chicken dressed as an officer",
      "num_outputs": 1,
      "num_inference_steps": 12,
      "prior_guidance_scale": 4,
      "decoder_guidance_scale": 0,
      "prior_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": "2023-09-16T06:43:30.217513Z",
  "created_at": "2023-09-16T06:43:26.376094Z",
  "data_removed": false,
  "error": null,
  "id": "yetdezjbedldgfhnmq2oe5x4yq",
  "input": {
    "seed": 39564,
    "width": 1024,
    "height": 1024,
    "prompt": "Anthropomorphic chicken dressed as an officer",
    "num_outputs": 1,
    "num_inference_steps": 12,
    "prior_guidance_scale": 4,
    "decoder_guidance_scale": 0,
    "prior_num_inference_steps": 30
  },
  "logs": "Using seed: 39564\nPrompt: Anthropomorphic chicken dressed as an officer\n  0%|          | 0/30 [00:00<?, ?it/s]\n 10%|█         | 3/30 [00:00<00:01, 20.60it/s]\n 20%|██        | 6/30 [00:00<00:01, 21.28it/s]\n 30%|███       | 9/30 [00:00<00:00, 21.40it/s]\n 40%|████      | 12/30 [00:00<00:00, 21.46it/s]\n 50%|█████     | 15/30 [00:00<00:00, 21.38it/s]\n 60%|██████    | 18/30 [00:00<00:00, 21.50it/s]\n 70%|███████   | 21/30 [00:00<00:00, 21.32it/s]\n 80%|████████  | 24/30 [00:01<00:00, 21.55it/s]\n 90%|█████████ | 27/30 [00:01<00:00, 21.61it/s]\n100%|██████████| 30/30 [00:01<00:00, 21.70it/s]\n100%|██████████| 30/30 [00:01<00:00, 21.50it/s]\n  0%|          | 0/12 [00:00<?, ?it/s]\n 17%|█▋        | 2/12 [00:00<00:00, 12.24it/s]\n 33%|███▎      | 4/12 [00:00<00:00, 12.83it/s]\n 50%|█████     | 6/12 [00:00<00:00, 13.03it/s]\n 67%|██████▋   | 8/12 [00:00<00:00, 13.19it/s]\n 83%|████████▎ | 10/12 [00:00<00:00, 13.21it/s]\n100%|██████████| 12/12 [00:00<00:00, 13.30it/s]\n100%|██████████| 12/12 [00:00<00:00, 13.14it/s]",
  "metrics": {
    "predict_time": 3.879448,
    "total_time": 3.841419
  },
  "output": [
    "https://pbxt.replicate.delivery/e2U2mWZ6NTQhFqZeeX8U8TxLIuffsNBSxGKSSHlpJ6COSAmMC/out-0.png"
  ],
  "started_at": "2023-09-16T06:43:26.338065Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/yetdezjbedldgfhnmq2oe5x4yq",
    "cancel": "https://api.replicate.com/v1/predictions/yetdezjbedldgfhnmq2oe5x4yq/cancel"
  },
  "version": "0725c5f70905b87ffadd7d6fe4cfae1b5457784d911eebd8521022892deb481a"
}

Generated in

3.9 seconds