Prediction

Model

fermatresearch/sdxl-lcm-lora-controlnet:d9db096a

szk7rylbfq6aosmfuvkfczlogm

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

3.8s

Created

about 2 years ago

Webhook

–

Input

prompt: shot in the style of sksfer, a boy playing with some toys
negative_prompt: Empty string
batched_prompt: false
controlnet_image: https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png
width: 1024
height: 1024
num_outputs: 1
scheduler: LCM
num_inference_steps: 6
guidance_scale: 2
prompt_strength: 0.8
apply_watermark: true
lora_scale: 0.9
condition_scale: 0.45
lora_weights: https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar

{
  "apply_watermark": true,
  "batched_prompt": false,
  "condition_scale": 0.45,
  "controlnet_image": "https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png",
  "guidance_scale": 2,
  "height": 1024,
  "lora_scale": 0.9,
  "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar",
  "negative_prompt": "",
  "num_inference_steps": 6,
  "num_outputs": 1,
  "prompt": "shot in the style of sksfer, a boy playing with some toys",
  "prompt_strength": 0.8,
  "scheduler": "LCM",
  "width": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_OSX**********************************

This is your API token. Keep it to yourself.

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 fermatresearch/sdxl-lcm-lora-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fermatresearch/sdxl-lcm-lora-controlnet:d9db096aec79855d1f42d2bd4fc89f2b1ddf7dcd00118a0e21b8f1d5c03be099",
  {
    input: {
      apply_watermark: true,
      batched_prompt: false,
      condition_scale: 0.45,
      controlnet_image: "https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png",
      guidance_scale: 2,
      height: 1024,
      lora_scale: 0.9,
      lora_weights: "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar",
      negative_prompt: "",
      num_inference_steps: 6,
      num_outputs: 1,
      prompt: "shot in the style of sksfer, a boy playing with some toys",
      prompt_strength: 0.8,
      scheduler: "LCM",
      width: 1024
    }
  }
);

// 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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_OSX**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run fermatresearch/sdxl-lcm-lora-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/sdxl-lcm-lora-controlnet:d9db096aec79855d1f42d2bd4fc89f2b1ddf7dcd00118a0e21b8f1d5c03be099",
    input={
        "apply_watermark": True,
        "batched_prompt": False,
        "condition_scale": 0.45,
        "controlnet_image": "https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png",
        "guidance_scale": 2,
        "height": 1024,
        "lora_scale": 0.9,
        "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar",
        "negative_prompt": "",
        "num_inference_steps": 6,
        "num_outputs": 1,
        "prompt": "shot in the style of sksfer, a boy playing with some toys",
        "prompt_strength": 0.8,
        "scheduler": "LCM",
        "width": 1024
    }
)

# 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.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_OSX**********************************

This is your API token. Keep it to yourself.

Run fermatresearch/sdxl-lcm-lora-controlnet 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": "fermatresearch/sdxl-lcm-lora-controlnet:d9db096aec79855d1f42d2bd4fc89f2b1ddf7dcd00118a0e21b8f1d5c03be099",
    "input": {
      "apply_watermark": true,
      "batched_prompt": false,
      "condition_scale": 0.45,
      "controlnet_image": "https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png",
      "guidance_scale": 2,
      "height": 1024,
      "lora_scale": 0.9,
      "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar",
      "negative_prompt": "",
      "num_inference_steps": 6,
      "num_outputs": 1,
      "prompt": "shot in the style of sksfer, a boy playing with some toys",
      "prompt_strength": 0.8,
      "scheduler": "LCM",
      "width": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "szk7rylbfq6aosmfuvkfczlogm",
  "model": "fermatresearch/sdxl-lcm-lora-controlnet",
  "version": "d9db096aec79855d1f42d2bd4fc89f2b1ddf7dcd00118a0e21b8f1d5c03be099",
  "input": {
    "apply_watermark": true,
    "batched_prompt": false,
    "condition_scale": 0.45,
    "controlnet_image": "https://replicate.delivery/pbxt/JsNziYC0Ha190bMKRDwkR5FpRGDrewOXzGH7mneGnHG4jREV/1f8edf0a-6ae1-4021-8ad2-e866e98d6efd.png",
    "guidance_scale": 2,
    "height": 1024,
    "lora_scale": 0.9,
    "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar",
    "negative_prompt": "",
    "num_inference_steps": 6,
    "num_outputs": 1,
    "prompt": "shot in the style of sksfer, a boy playing with some toys",
    "prompt_strength": 0.8,
    "scheduler": "LCM",
    "width": 1024
  },
  "logs": "Using seed: 59453\nskipping loading .. weights already loaded\nPrompt: shot in the style of <s0><s1>, a boy playing with some toys\nThe config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n  0%|          | 0/6 [00:00<?, ?it/s]\n 17%|█▋        | 1/6 [00:00<00:01,  4.77it/s]\n 33%|███▎      | 2/6 [00:00<00:00,  4.77it/s]\n 50%|█████     | 3/6 [00:00<00:00,  4.77it/s]\n 67%|██████▋   | 4/6 [00:00<00:00,  4.76it/s]\n 83%|████████▎ | 5/6 [00:01<00:00,  4.76it/s]\n100%|██████████| 6/6 [00:01<00:00,  4.76it/s]\n100%|██████████| 6/6 [00:01<00:00,  4.76it/s]",
  "output": [
    "https://replicate.delivery/pbxt/URStBauvk2KTHl0Be5UaczWxPPdeX5cDm4fyvQYYQ876dowjA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-11-14T15:26:50.711437Z",
  "started_at": "2023-11-14T15:26:50.768442Z",
  "completed_at": "2023-11-14T15:26:54.48443Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/szk7rylbfq6aosmfuvkfczlogm/cancel",
    "get": "https://api.replicate.com/v1/predictions/szk7rylbfq6aosmfuvkfczlogm"
  },
  "metrics": {
    "predict_time": 3.715988,
    "total_time": 3.772993
  }
}

Generated in

3.7 seconds

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