Prediction

jmeyd2z2b5rgg0cg05qbcd3y64

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

5.3s

Created

about 1 year ago

Input

width: 1024
height: 1024
prompt: MSPaint beautiful landscape
sampler: Default
scheduler: Default
lora_strength: 1
output_format: webp
output_quality: 80
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "MSPaint beautiful landscape",
  "sampler": "Default",
  "scheduler": "Default",
  "lora_strength": 1,
  "output_format": "webp",
  "output_quality": 80,
  "number_of_images": 1
}

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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "MSPaint beautiful landscape",
      sampler: "Default",
      scheduler: "Default",
      lora_strength: 1,
      output_format: "webp",
      output_quality: 80,
      number_of_images: 1
    }
  }
);

// 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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "MSPaint beautiful landscape",
        "sampler": "Default",
        "scheduler": "Default",
        "lora_strength": 1,
        "output_format": "webp",
        "output_quality": 80,
        "number_of_images": 1
    }
)

# 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 fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "MSPaint beautiful landscape",
      "sampler": "Default",
      "scheduler": "Default",
      "lora_strength": 1,
      "output_format": "webp",
      "output_quality": 80,
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-06-10T09:16:07.829931Z",
  "created_at": "2024-06-10T09:16:02.521000Z",
  "data_removed": false,
  "error": null,
  "id": "jmeyd2z2b5rgg0cg05qbcd3y64",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "MSPaint beautiful landscape",
    "sampler": "Default",
    "scheduler": "Default",
    "lora_strength": 1,
    "output_format": "webp",
    "output_quality": 80,
    "number_of_images": 1
  },
  "logs": "Random seed set to: 933701224\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength:1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 10, title: Load LoRA, class type: LoraLoader\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load SDXL\nLoading 1 new model\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:03,  5.30it/s]\n 10%|█         | 2/20 [00:00<00:03,  5.24it/s]\n 15%|█▌        | 3/20 [00:00<00:03,  5.23it/s]\n 20%|██        | 4/20 [00:00<00:03,  5.23it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  5.22it/s]\n 30%|███       | 6/20 [00:01<00:02,  5.22it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  5.22it/s]\n 40%|████      | 8/20 [00:01<00:02,  5.22it/s]\n 45%|████▌     | 9/20 [00:01<00:02,  5.21it/s]\n 50%|█████     | 10/20 [00:01<00:01,  5.22it/s]\n 55%|█████▌    | 11/20 [00:02<00:01,  5.23it/s]\n 60%|██████    | 12/20 [00:02<00:01,  5.24it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  5.25it/s]\n 70%|███████   | 14/20 [00:02<00:01,  5.26it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  5.26it/s]\n 80%|████████  | 16/20 [00:03<00:00,  5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00,  5.25it/s]\n 90%|█████████ | 18/20 [00:03<00:00,  5.26it/s]\n 95%|█████████▌| 19/20 [00:03<00:00,  5.29it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.32it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.25it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.59 seconds\noutputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png",
  "metrics": {
    "predict_time": 5.299896,
    "total_time": 5.308931
  },
  "output": [
    "https://replicate.delivery/pbxt/ED09UfWhu0QCUyW71hUB95e90YZ67U45Y1c0e695JNkuyO6lA/R8__00001_.webp"
  ],
  "started_at": "2024-06-10T09:16:02.530035Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/jmeyd2z2b5rgg0cg05qbcd3y64",
    "cancel": "https://api.replicate.com/v1/predictions/jmeyd2z2b5rgg0cg05qbcd3y64/cancel"
  },
  "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f"
}

Generated in

5.3 seconds

fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f

Random seed set to: 933701224
==============================
Generation settings
Sampler: dpmpp_2m_sde_gpu
Scheduler: karras
Steps: 20
CFG: 7.0
LORA: Using a lora. Lora strength:1.0
==============================
Running workflow
got prompt
Executing node 10, title: Load LoRA, class type: LoraLoader
Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode
Requested to load SDXLClipModel
Loading 1 new model
Executing node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode
Executing node 3, title: KSampler, class type: KSampler
Requested to load SDXL
Loading 1 new model
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100%|██████████| 20/20 [00:03<00:00,  5.25it/s]
Executing node 8, title: VAE Decode, class type: VAEDecode
Executing node 9, title: Save Image, class type: SaveImage
Prompt executed in 4.59 seconds
outputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}
====================================
R8__00001_.png

Prediction

Model

qr4vfa78bnrgp0cg05qv6vbfz4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

5.0s

Created

about 1 year ago

Input

width: 1024
height: 1024
prompt: MSPaint astronaut
sampler: Default
scheduler: Default
lora_strength: 1
output_format: webp
output_quality: 80
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "MSPaint astronaut",
  "sampler": "Default",
  "scheduler": "Default",
  "lora_strength": 1,
  "output_format": "webp",
  "output_quality": 80,
  "number_of_images": 1
}

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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "MSPaint astronaut",
      sampler: "Default",
      scheduler: "Default",
      lora_strength: 1,
      output_format: "webp",
      output_quality: 80,
      number_of_images: 1
    }
  }
);

// 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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "MSPaint astronaut",
        "sampler": "Default",
        "scheduler": "Default",
        "lora_strength": 1,
        "output_format": "webp",
        "output_quality": 80,
        "number_of_images": 1
    }
)

# 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 fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "MSPaint astronaut",
      "sampler": "Default",
      "scheduler": "Default",
      "lora_strength": 1,
      "output_format": "webp",
      "output_quality": 80,
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-06-10T09:17:14.543095Z",
  "created_at": "2024-06-10T09:17:09.597000Z",
  "data_removed": false,
  "error": null,
  "id": "qr4vfa78bnrgp0cg05qv6vbfz4",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "MSPaint astronaut",
    "sampler": "Default",
    "scheduler": "Default",
    "lora_strength": 1,
    "output_format": "webp",
    "output_quality": 80,
    "number_of_images": 1
  },
  "logs": "Random seed set to: 305983986\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:03,  5.32it/s]\n 10%|█         | 2/20 [00:00<00:03,  5.27it/s]\n 15%|█▌        | 3/20 [00:00<00:03,  5.27it/s]\n 20%|██        | 4/20 [00:00<00:03,  5.27it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  5.27it/s]\n 30%|███       | 6/20 [00:01<00:02,  5.26it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  5.26it/s]\n 40%|████      | 8/20 [00:01<00:02,  5.26it/s]\n 45%|████▌     | 9/20 [00:01<00:02,  5.26it/s]\n 50%|█████     | 10/20 [00:01<00:01,  5.25it/s]\n 55%|█████▌    | 11/20 [00:02<00:01,  5.26it/s]\n 60%|██████    | 12/20 [00:02<00:01,  5.26it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  5.26it/s]\n 70%|███████   | 14/20 [00:02<00:01,  5.27it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  5.27it/s]\n 80%|████████  | 16/20 [00:03<00:00,  5.27it/s]\n 85%|████████▌ | 17/20 [00:03<00:00,  5.27it/s]\n 90%|█████████ | 18/20 [00:03<00:00,  5.25it/s]\n 95%|█████████▌| 19/20 [00:03<00:00,  5.27it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.30it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.16 seconds\noutputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png",
  "metrics": {
    "predict_time": 4.937003,
    "total_time": 4.946095
  },
  "output": [
    "https://replicate.delivery/pbxt/Wmxf4hRDoetdCk6YpwMpoOcWyXxwbpr7eKWFOhLKQgk00O6lA/R8__00001_.webp"
  ],
  "started_at": "2024-06-10T09:17:09.606092Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/qr4vfa78bnrgp0cg05qv6vbfz4",
    "cancel": "https://api.replicate.com/v1/predictions/qr4vfa78bnrgp0cg05qv6vbfz4/cancel"
  },
  "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f"
}

Generated in

4.9 seconds

fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f

Prediction

Model

df9mjbxpzsrgm0cg05st5bxmqc

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

4.9s

Created

about 1 year ago

Input

width: 1024
height: 1024
prompt: MSPaint ryan gosling and margot robbie
sampler: Default
scheduler: Default
lora_strength: 1
output_format: webp
output_quality: 80
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "MSPaint ryan gosling and margot robbie",
  "sampler": "Default",
  "scheduler": "Default",
  "lora_strength": 1,
  "output_format": "webp",
  "output_quality": 80,
  "number_of_images": 1
}

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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "MSPaint ryan gosling and margot robbie",
      sampler: "Default",
      scheduler: "Default",
      lora_strength: 1,
      output_format: "webp",
      output_quality: 80,
      number_of_images: 1
    }
  }
);

// 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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "MSPaint ryan gosling and margot robbie",
        "sampler": "Default",
        "scheduler": "Default",
        "lora_strength": 1,
        "output_format": "webp",
        "output_quality": 80,
        "number_of_images": 1
    }
)

# 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 fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "MSPaint ryan gosling and margot robbie",
      "sampler": "Default",
      "scheduler": "Default",
      "lora_strength": 1,
      "output_format": "webp",
      "output_quality": 80,
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-06-10T09:21:23.996216Z",
  "created_at": "2024-06-10T09:21:19.102000Z",
  "data_removed": false,
  "error": null,
  "id": "df9mjbxpzsrgm0cg05st5bxmqc",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "MSPaint ryan gosling and margot robbie",
    "sampler": "Default",
    "scheduler": "Default",
    "lora_strength": 1,
    "output_format": "webp",
    "output_quality": 80,
    "number_of_images": 1
  },
  "logs": "Random seed set to: 4089181157\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength:1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 5, title: Empty Latent Image, class type: EmptyLatentImage\nExecuting node 3, title: KSampler, class type: KSampler\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:03,  5.28it/s]\n 10%|█         | 2/20 [00:00<00:03,  5.26it/s]\n 15%|█▌        | 3/20 [00:00<00:03,  5.25it/s]\n 20%|██        | 4/20 [00:00<00:03,  5.25it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  5.27it/s]\n 30%|███       | 6/20 [00:01<00:02,  5.26it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  5.26it/s]\n 40%|████      | 8/20 [00:01<00:02,  5.26it/s]\n 45%|████▌     | 9/20 [00:01<00:02,  5.26it/s]\n 50%|█████     | 10/20 [00:01<00:01,  5.25it/s]\n 55%|█████▌    | 11/20 [00:02<00:01,  5.26it/s]\n 60%|██████    | 12/20 [00:02<00:01,  5.26it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  5.26it/s]\n 70%|███████   | 14/20 [00:02<00:01,  5.27it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  5.26it/s]\n 80%|████████  | 16/20 [00:03<00:00,  5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00,  5.26it/s]\n 90%|█████████ | 18/20 [00:03<00:00,  5.27it/s]\n 95%|█████████▌| 19/20 [00:03<00:00,  5.29it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.33it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.17 seconds\noutputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png",
  "metrics": {
    "predict_time": 4.883241,
    "total_time": 4.894216
  },
  "output": [
    "https://replicate.delivery/pbxt/r8reSFWMderWfov1MmoeQe01Ine27LOOoNPxiHFdGi39k3RvE/R8__00001_.webp"
  ],
  "started_at": "2024-06-10T09:21:19.112975Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/df9mjbxpzsrgm0cg05st5bxmqc",
    "cancel": "https://api.replicate.com/v1/predictions/df9mjbxpzsrgm0cg05st5bxmqc/cancel"
  },
  "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f"
}

Generated in

4.9 seconds

fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f

Prediction

Model

dahcwxtb61rgp0cg05tak61hpg

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

4.9s

Created

about 1 year ago

Input

width: 1024
height: 1024
prompt: MSPaint yoda
sampler: Default
scheduler: Default
lora_strength: 1
output_format: webp
output_quality: 80
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "MSPaint yoda",
  "sampler": "Default",
  "scheduler": "Default",
  "lora_strength": 1,
  "output_format": "webp",
  "output_quality": 80,
  "number_of_images": 1
}

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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "MSPaint yoda",
      sampler: "Default",
      scheduler: "Default",
      lora_strength: 1,
      output_format: "webp",
      output_quality: 80,
      number_of_images: 1
    }
  }
);

// 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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "MSPaint yoda",
        "sampler": "Default",
        "scheduler": "Default",
        "lora_strength": 1,
        "output_format": "webp",
        "output_quality": 80,
        "number_of_images": 1
    }
)

# 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 fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "MSPaint yoda",
      "sampler": "Default",
      "scheduler": "Default",
      "lora_strength": 1,
      "output_format": "webp",
      "output_quality": 80,
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-06-10T09:22:01.890158Z",
  "created_at": "2024-06-10T09:21:57.040000Z",
  "data_removed": false,
  "error": null,
  "id": "dahcwxtb61rgp0cg05tak61hpg",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "MSPaint yoda",
    "sampler": "Default",
    "scheduler": "Default",
    "lora_strength": 1,
    "output_format": "webp",
    "output_quality": 80,
    "number_of_images": 1
  },
  "logs": "Random seed set to: 2666338119\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler:karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:03,  5.32it/s]\n 10%|█         | 2/20 [00:00<00:03,  5.26it/s]\n 15%|█▌        | 3/20 [00:00<00:03,  5.25it/s]\n 20%|██        | 4/20 [00:00<00:03,  5.24it/s]\n 25%|██▌       | 5/20 [00:00<00:02,  5.24it/s]\n 30%|███       | 6/20 [00:01<00:02,  5.23it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  5.24it/s]\n 40%|████      | 8/20 [00:01<00:02,  5.25it/s]\n 45%|████▌     | 9/20 [00:01<00:02,  5.25it/s]\n 50%|█████     | 10/20 [00:01<00:01,  5.25it/s]\n 55%|█████▌    | 11/20 [00:02<00:01,  5.26it/s]\n 60%|██████    | 12/20 [00:02<00:01,  5.26it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  5.26it/s]\n 70%|███████   | 14/20 [00:02<00:01,  5.26it/s]\n 75%|███████▌  | 15/20 [00:02<00:00,  5.26it/s]\n 80%|████████  | 16/20 [00:03<00:00,  5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00,  5.26it/s]\n 90%|█████████ | 18/20 [00:03<00:00,  5.27it/s]\n 95%|█████████▌| 19/20 [00:03<00:00,  5.30it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.34it/s]\n100%|██████████| 20/20 [00:03<00:00,  5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.16 seconds\noutputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png",
  "metrics": {
    "predict_time": 4.839576,
    "total_time": 4.850158
  },
  "output": [
    "https://replicate.delivery/pbxt/g9KZGaqtjI5rCVfeH84DU15Z2cgzvpRrSbZKfD0nqvTz9O6lA/R8__00001_.webp"
  ],
  "started_at": "2024-06-10T09:21:57.050582Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dahcwxtb61rgp0cg05tak61hpg",
    "cancel": "https://api.replicate.com/v1/predictions/dahcwxtb61rgp0cg05tak61hpg/cancel"
  },
  "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f"
}

Generated in

4.8 seconds

fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f

Prediction

Model

dq5z1emxm1rgj0cfz7vvskbt14

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

76.3s

Created

about 1 year ago

Input

width: 1024
height: 1024
prompt: MSPaint portrait of a dog
sampler: Default
scheduler: Default
lora_strength: 1
output_format: webp
output_quality: 80
negative_prompt
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "MSPaint portrait of a dog",
  "sampler": "Default",
  "scheduler": "Default",
  "lora_strength": 1,
  "output_format": "webp",
  "output_quality": 80,
  "negative_prompt": "",
  "number_of_images": 1
}

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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "MSPaint portrait of a dog",
      sampler: "Default",
      scheduler: "Default",
      lora_strength: 1,
      output_format: "webp",
      output_quality: 80,
      negative_prompt: "",
      number_of_images: 1
    }
  }
);

// 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 fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "MSPaint portrait of a dog",
        "sampler": "Default",
        "scheduler": "Default",
        "lora_strength": 1,
        "output_format": "webp",
        "output_quality": 80,
        "negative_prompt": "",
        "number_of_images": 1
    }
)

# 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 fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "MSPaint portrait of a dog",
      "sampler": "Default",
      "scheduler": "Default",
      "lora_strength": 1,
      "output_format": "webp",
      "output_quality": 80,
      "negative_prompt": "",
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-06-08T22:29:41.930129Z",
  "created_at": "2024-06-08T22:28:25.632000Z",
  "data_removed": false,
  "error": null,
  "id": "dq5z1emxm1rgj0cfz7vvskbt14",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "MSPaint portrait of a dog",
    "sampler": "Default",
    "scheduler": "Default",
    "lora_strength": 1,
    "output_format": "webp",
    "output_quality": 80,
    "negative_prompt": "",
    "number_of_images": 1
  },
  "logs": "Random seed set to: 2783082920\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 4, title: Load Checkpoint, class type: CheckpointLoaderSimple\nmodel_type EPS\nUsing pytorch attention in VAE\nUsing pytorch attention in VAE\nclip missing: ['clip_l.logit_scale', 'clip_l.transformer.text_projection.weight']\nloaded straight to GPU\nRequested to load SDXL\nLoading 1 new model\nExecuting node 10, title: Load LoRA, class type: LoraLoader\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 5, title: Empty Latent Image, class type: EmptyLatentImage\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load SDXL\nLoading 1 new model\n  0%|          | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=14.614643096923828 and t1=14.614643.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n  5%|▌         | 1/20 [00:00<00:11,  1.61it/s]\n 10%|█         | 2/20 [00:00<00:06,  2.70it/s]\n 15%|█▌        | 3/20 [00:01<00:04,  3.46it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.99it/s]\n 25%|██▌       | 5/20 [00:01<00:03,  4.36it/s]\n 30%|███       | 6/20 [00:01<00:03,  4.61it/s]\n 35%|███▌      | 7/20 [00:01<00:02,  4.79it/s]\n 40%|████      | 8/20 [00:01<00:02,  4.90it/s]\n 45%|████▌     | 9/20 [00:02<00:02,  4.99it/s]\n 50%|█████     | 10/20 [00:02<00:01,  5.05it/s]\n 55%|█████▌    | 11/20 [00:02<00:01,  5.10it/s]\n 60%|██████    | 12/20 [00:02<00:01,  5.14it/s]\n 65%|██████▌   | 13/20 [00:02<00:01,  5.15it/s]\n 70%|███████   | 14/20 [00:03<00:01,  5.17it/s]\n 75%|███████▌  | 15/20 [00:03<00:00,  5.17it/s]\n 80%|████████  | 16/20 [00:03<00:00,  5.18it/s]\n 85%|████████▌ | 17/20 [00:03<00:00,  5.19it/s]\n 90%|█████████ | 18/20 [00:03<00:00,  5.20it/s]\n 95%|█████████▌| 19/20 [00:04<00:00,  5.24it/s]\n100%|██████████| 20/20 [00:04<00:00,  5.26it/s]\n100%|██████████| 20/20 [00:04<00:00,  4.69it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 8.21 seconds\noutputs:  {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png",
  "metrics": {
    "predict_time": 10.870445,
    "total_time": 76.298129
  },
  "output": [
    "https://replicate.delivery/pbxt/Kf3ix6vdRp0xbiR5139W1CeUtLzwJfyMAiyilmGNcrsqqR5lA/R8__00001_.webp"
  ],
  "started_at": "2024-06-08T22:29:31.059684Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dq5z1emxm1rgj0cfz7vvskbt14",
    "cancel": "https://api.replicate.com/v1/predictions/dq5z1emxm1rgj0cfz7vvskbt14/cancel"
  },
  "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f"
}

Generated in

10.9 seconds