Prediction

ID

knc6znwmgnrj20chs7nvkytm10

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.5s

Created

8 months ago by @lucataco

Input

image
prompt: portrait photo of TOK with purple hair
hf_lora: https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.7
num_inference_steps: 4

{
  "image": "https://replicate.delivery/pbxt/LZwbGwE2kG8XVL5vUUlAZMnlexg9zCsNfn3yAgUUZ8rHk3K4/image.png",
  "prompt": "portrait photo of TOK with purple hair",
  "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.7,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      image: "https://replicate.delivery/pbxt/LZwbGwE2kG8XVL5vUUlAZMnlexg9zCsNfn3yAgUUZ8rHk3K4/image.png",
      prompt: "portrait photo of TOK with purple hair",
      hf_lora: "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.7,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "image": "https://replicate.delivery/pbxt/LZwbGwE2kG8XVL5vUUlAZMnlexg9zCsNfn3yAgUUZ8rHk3K4/image.png",
        "prompt": "portrait photo of TOK with purple hair",
        "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.7,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "image": "https://replicate.delivery/pbxt/LZwbGwE2kG8XVL5vUUlAZMnlexg9zCsNfn3yAgUUZ8rHk3K4/image.png",
      "prompt": "portrait photo of TOK with purple hair",
      "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.7,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.5 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

z3dryvt569rj00chs7pbxxtqf8

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.7s

Created

8 months ago

Input

prompt: pnt style Illustration of a latina woman
hf_lora: https://civitai.com/api/download/models/735262?type=Model&format=SafeTensor
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "pnt style Illustration of a latina woman",
  "hf_lora": "https://civitai.com/api/download/models/735262?type=Model&format=SafeTensor",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "pnt style Illustration of a latina woman",
      hf_lora: "https://civitai.com/api/download/models/735262?type=Model&format=SafeTensor",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "pnt style Illustration of a latina woman",
        "hf_lora": "https://civitai.com/api/download/models/735262?type=Model&format=SafeTensor",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "pnt style Illustration of a latina woman",
      "hf_lora": "https://civitai.com/api/download/models/735262?type=Model&format=SafeTensor",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.7 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

x9jxf1dv7nrj20chs7jvaprbng

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.8s

Created

8 months ago by @lucataco

Input

prompt: a beautiful castle frstingln illustration
hf_lora: alvdansen/frosting_lane_flux
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "a beautiful castle frstingln illustration",
  "hf_lora": "alvdansen/frosting_lane_flux",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "a beautiful castle frstingln illustration",
      hf_lora: "alvdansen/frosting_lane_flux",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "a beautiful castle frstingln illustration",
        "hf_lora": "alvdansen/frosting_lane_flux",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "a beautiful castle frstingln illustration",
      "hf_lora": "alvdansen/frosting_lane_flux",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.7 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

s7s62cg37hrj00chs7ktna875c

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.6s

Created

8 months ago

Input

prompt: a boat in the style of TOK
hf_lora: lucataco/ReplicateFluxLoRA
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "a boat in the style of TOK",
  "hf_lora": "lucataco/ReplicateFluxLoRA",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "a boat in the style of TOK",
      hf_lora: "lucataco/ReplicateFluxLoRA",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "a boat in the style of TOK",
        "hf_lora": "lucataco/ReplicateFluxLoRA",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "a boat in the style of TOK",
      "hf_lora": "lucataco/ReplicateFluxLoRA",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.6 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

q8v8efn1tdrj60chs7ktgpf4v4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.6s

Created

8 months ago

Input

prompt: an elegant and timeless portrait of Julia, exuding grace and sophistication
hf_lora: ptx0/flux-dreambooth-lora-r16-dev
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "an elegant and timeless portrait of Julia, exuding grace and sophistication",
  "hf_lora": "ptx0/flux-dreambooth-lora-r16-dev",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "an elegant and timeless portrait of Julia, exuding grace and sophistication",
      hf_lora: "ptx0/flux-dreambooth-lora-r16-dev",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "an elegant and timeless portrait of Julia, exuding grace and sophistication",
        "hf_lora": "ptx0/flux-dreambooth-lora-r16-dev",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "an elegant and timeless portrait of Julia, exuding grace and sophistication",
      "hf_lora": "ptx0/flux-dreambooth-lora-r16-dev",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.6 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

me3m2tn3ghrj00chs7mv3zpfgm

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.7s

Created

8 months ago

Input

prompt: photo of TOK with purple hair
hf_lora: https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar
lora_scale: 0.9
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "photo of TOK with purple hair",
  "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
  "lora_scale": 0.9,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "photo of TOK with purple hair",
      hf_lora: "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
      lora_scale: 0.9,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "photo of TOK with purple hair",
        "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
        "lora_scale": 0.9,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "photo of TOK with purple hair",
      "hf_lora": "https://replicate.delivery/yhqm/9vSmRCa8Vv7bFtKfCfXTRzTq4X71tZW0LtLCb1l49bTSo8TTA/trained_model.tar",
      "lora_scale": 0.9,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.7 seconds

lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574

Prediction

Model

ID

w7pyq9mbmxrj00chs7naeq3dvr

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.7s

Created

8 months ago

Input

prompt: a photo of TOK dog
hf_lora: https://replicate.delivery/yhqm/NBUAeV9jWoSfD0vh6Dfc5JKS4osMUpQSV8RRu62JJLEUvmnmA/trained_model.tar
lora_scale: 0.8
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
output_quality: 80
prompt_strength: 0.8
num_inference_steps: 4

{
  "prompt": "a photo of TOK dog",
  "hf_lora": "https://replicate.delivery/yhqm/NBUAeV9jWoSfD0vh6Dfc5JKS4osMUpQSV8RRu62JJLEUvmnmA/trained_model.tar",
  "lora_scale": 0.8,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
  {
    input: {
      prompt: "a photo of TOK dog",
      hf_lora: "https://replicate.delivery/yhqm/NBUAeV9jWoSfD0vh6Dfc5JKS4osMUpQSV8RRu62JJLEUvmnmA/trained_model.tar",
      lora_scale: 0.8,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 lucataco/flux-schnell-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/flux-schnell-lora:2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    input={
        "prompt": "a photo of TOK dog",
        "hf_lora": "https://replicate.delivery/yhqm/NBUAeV9jWoSfD0vh6Dfc5JKS4osMUpQSV8RRu62JJLEUvmnmA/trained_model.tar",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run lucataco/flux-schnell-lora 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": "2a6b576af31790b470f0a8442e1e9791213fa13799cbb65a9fc1436e96389574",
    "input": {
      "prompt": "a photo of TOK dog",
      "hf_lora": "https://replicate.delivery/yhqm/NBUAeV9jWoSfD0vh6Dfc5JKS4osMUpQSV8RRu62JJLEUvmnmA/trained_model.tar",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

2.7 seconds