Prediction

ID

favsxj7r9srm60cj4xdtzkeprc

Status

Succeeded

Source

API

Hardware

H100

Total duration

9.7s

Created

9 months ago

Input

model: dev
prompt: a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "a book with the words \'Don\'t Panic!\' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:06:41.467987Z",
  "created_at": "2024-09-25T04:06:31.758000Z",
  "data_removed": false,
  "error": null,
  "id": "favsxj7r9srm60cj4xdtzkeprc",
  "input": {
    "model": "dev",
    "prompt": "a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 27193\nPrompt: a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.55it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.03it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.80it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.70it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.65it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.62it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.60it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.59it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.58it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.58it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.57it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.57it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.57it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.57it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.56it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.56it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.56it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.56it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.56it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.56it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.56it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.56it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.56it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.56it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.56it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.56it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.56it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.59it/s]",
  "metrics": {
    "predict_time": 8.401203221,
    "total_time": 9.709987
  },
  "output": [
    "https://replicate.delivery/yhqm/Vv5FL5OZU7ZqJ15bgF0PZe9g3fzXrmoIG5DhjzDma8iR5TgTA/out-0.webp"
  ],
  "started_at": "2024-09-25T04:06:33.066784Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/favsxj7r9srm60cj4xdtzkeprc",
    "cancel": "https://api.replicate.com/v1/predictions/favsxj7r9srm60cj4xdtzkeprc/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

8.4 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Using seed: 27193
Prompt: a book with the words 'Don't Panic!' written on cover, an homage to the hitchhikers guide to the galaxy, LNTP cartoon style
[!] txt2img mode
Using dev model
Weights already loaded
Loaded LoRAs in 0.03s
  0%|          | 0/28 [00:00<?, ?it/s]
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Prediction

Model

ID

p4edkvha49rm20cj4x6vezb84w

Status

Succeeded

Source

API

Hardware

H100

Total duration

29.7s

Created

9 months ago

Input

model: dev
prompt: a robot, blue background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 2.35
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 20
disable_safety_checker

{
  "model": "dev",
  "prompt": "a robot, blue background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 2.35,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 20,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "a robot, blue background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 2.35,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "a robot, blue background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 2.35,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "a robot, blue background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 2.35,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T03:50:51.129366Z",
  "created_at": "2024-09-25T03:50:21.474000Z",
  "data_removed": false,
  "error": null,
  "id": "p4edkvha49rm20cj4x6vezb84w",
  "input": {
    "model": "dev",
    "prompt": "a robot, blue background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 2.35,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 45178\nPrompt: a robot, blue background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.72it/s]\n 10%|█         | 2/20 [00:00<00:04,  4.21it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.97it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.87it/s]\n 25%|██▌       | 5/20 [00:01<00:03,  3.81it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.78it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.76it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.75it/s]\n 45%|████▌     | 9/20 [00:02<00:02,  3.74it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.73it/s]\n 55%|█████▌    | 11/20 [00:02<00:02,  3.73it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.73it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.72it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.73it/s]\n 75%|███████▌  | 15/20 [00:03<00:01,  3.72it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.72it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.72it/s]\n 90%|█████████ | 18/20 [00:04<00:00,  3.72it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.72it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.72it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.76it/s]",
  "metrics": {
    "predict_time": 5.85348959,
    "total_time": 29.655366
  },
  "output": [
    "https://replicate.delivery/yhqm/BuMthXA6fQ3CIKSqJpbtqxefm3U9IQFMDIa5Lfu9DtuqpOBOB/out-0.webp"
  ],
  "started_at": "2024-09-25T03:50:45.275876Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/p4edkvha49rm20cj4x6vezb84w",
    "cancel": "https://api.replicate.com/v1/predictions/p4edkvha49rm20cj4x6vezb84w/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

5.9 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

gq7e2nfg0srm20cj4wvvw4dn40

Status

Succeeded

Source

API

Hardware

H100

Total duration

34.8s

Created

9 months ago

Input

model: dev
prompt: two cats doing research, blue background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 2.35
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 20
disable_safety_checker

{
  "model": "dev",
  "prompt": "two cats doing research, blue background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 2.35,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 20,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "two cats doing research, blue background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 2.35,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "two cats doing research, blue background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 2.35,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "two cats doing research, blue background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 2.35,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T03:27:45.085511Z",
  "created_at": "2024-09-25T03:27:10.342000Z",
  "data_removed": false,
  "error": null,
  "id": "gq7e2nfg0srm20cj4wvvw4dn40",
  "input": {
    "model": "dev",
    "prompt": "two cats doing research, blue background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 2.35,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 14281\nPrompt: two cats doing research, blue background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 9.85s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.53it/s]\n 10%|█         | 2/20 [00:00<00:04,  3.99it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.77it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.67it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.62it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.59it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.57it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.56it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.55it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.54it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.54it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.54it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.53it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.53it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.53it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.53it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.53it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.53it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.53it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.53it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.56it/s]",
  "metrics": {
    "predict_time": 15.991911446,
    "total_time": 34.743511
  },
  "output": [
    "https://replicate.delivery/yhqm/l6N0OFfgFuXSF69OpZUo4mnoaGMCq5HNn7FPzFxSPfawUTgTA/out-0.webp"
  ],
  "started_at": "2024-09-25T03:27:29.093599Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/gq7e2nfg0srm20cj4wvvw4dn40",
    "cancel": "https://api.replicate.com/v1/predictions/gq7e2nfg0srm20cj4wvvw4dn40/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

16.0 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

d478b4ryanrm20cj4wra52wqzm

Status

Succeeded

Source

API

Hardware

H100

Total duration

89.2s

Created

9 months ago

Input

model: dev
prompt: a t-shirt featuring a cat, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 2.5
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 20
disable_safety_checker

{
  "model": "dev",
  "prompt": "a t-shirt featuring a cat, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 20,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "a t-shirt featuring a cat, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "a t-shirt featuring a cat, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "a t-shirt featuring a cat, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T03:20:07.104511Z",
  "created_at": "2024-09-25T03:18:37.909000Z",
  "data_removed": false,
  "error": null,
  "id": "d478b4ryanrm20cj4wra52wqzm",
  "input": {
    "model": "dev",
    "prompt": "a t-shirt featuring a cat, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 60689\nPrompt: a t-shirt featuring a cat, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 83.03s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.54it/s]\n 10%|█         | 2/20 [00:00<00:04,  4.00it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.78it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.68it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.63it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.60it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.58it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.56it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.56it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.55it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.55it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.54it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.54it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.54it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.54it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.54it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.54it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.54it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.54it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.54it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.57it/s]",
  "metrics": {
    "predict_time": 89.168728492,
    "total_time": 89.195511
  },
  "output": [
    "https://replicate.delivery/yhqm/HDN8SNL7CTpLOJX6JUb61Bu68vLLRmNhgLs8ZEadRPoZzE4E/out-0.webp"
  ],
  "started_at": "2024-09-25T03:18:37.935782Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/d478b4ryanrm20cj4wra52wqzm",
    "cancel": "https://api.replicate.com/v1/predictions/d478b4ryanrm20cj4wra52wqzm/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

89.2 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

y6rp6rx0pnrm40cj4wpscsxe80

Status

Succeeded

Source

API

Hardware

H100

Total duration

17.9s

Created

9 months ago

Input

model: dev
prompt: a t-shirt, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 2.8
output_quality: 80
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 20
disable_safety_checker

{
  "model": "dev",
  "prompt": "a t-shirt, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 2.8,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 20,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "a t-shirt, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 2.8,
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "a t-shirt, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 2.8,
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "a t-shirt, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 2.8,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T03:16:12.559204Z",
  "created_at": "2024-09-25T03:15:54.677000Z",
  "data_removed": false,
  "error": null,
  "id": "y6rp6rx0pnrm40cj4wpscsxe80",
  "input": {
    "model": "dev",
    "prompt": "a t-shirt, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 2.8,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 53681\nPrompt: a t-shirt, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 11.74s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.53it/s]\n 10%|█         | 2/20 [00:00<00:04,  3.98it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.77it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.67it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.62it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.59it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.58it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.56it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.56it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.55it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.55it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.55it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.54it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.54it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.54it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.54it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.54it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.54it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.54it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.54it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.57it/s]",
  "metrics": {
    "predict_time": 17.869774939,
    "total_time": 17.882204
  },
  "output": [
    "https://replicate.delivery/yhqm/OCzIYAihfc3BOql92yYxGO7tW80kxtXNXmFXF2uUPwe8JTgTA/out-0.webp"
  ],
  "started_at": "2024-09-25T03:15:54.689429Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/y6rp6rx0pnrm40cj4wpscsxe80",
    "cancel": "https://api.replicate.com/v1/predictions/y6rp6rx0pnrm40cj4wpscsxe80/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

17.9 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

ggzrka5fv5rm40cj4xga7bx6dr

Status

Succeeded

Source

API

Hardware

H100

Total duration

79.6s

Created

9 months ago

Input

model: dev
prompt: A neon-lit diner floating in a pastel nebula, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "A neon-lit diner floating in a pastel nebula, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "A neon-lit diner floating in a pastel nebula, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "A neon-lit diner floating in a pastel nebula, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "A neon-lit diner floating in a pastel nebula, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:13:00.524076Z",
  "created_at": "2024-09-25T04:11:40.889000Z",
  "data_removed": false,
  "error": null,
  "id": "ggzrka5fv5rm40cj4xga7bx6dr",
  "input": {
    "model": "dev",
    "prompt": "A neon-lit diner floating in a pastel nebula, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 1597\nPrompt: A neon-lit diner floating in a pastel nebula, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 10.73s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.57it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.04it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.81it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.71it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.66it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.63it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.61it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.60it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.59it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.58it/s]\n 39%|███▉      | 11/28 [00:03<00:04,  3.58it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.58it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.57it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.57it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.57it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.57it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.57it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.57it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.57it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.57it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.57it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.57it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.57it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.57it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.57it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.57it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.57it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.57it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.59it/s]",
  "metrics": {
    "predict_time": 19.093468676,
    "total_time": 79.635076
  },
  "output": [
    "https://replicate.delivery/yhqm/eNjb7FD8GMVRb6V0eFPmecUhitJZdOgm8fvyegSj8VWk5fE4E/out-0.webp"
  ],
  "started_at": "2024-09-25T04:12:41.430607Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ggzrka5fv5rm40cj4xga7bx6dr",
    "cancel": "https://api.replicate.com/v1/predictions/ggzrka5fv5rm40cj4xga7bx6dr/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

19.1 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

674d1dkjc1rm20cj4xha53hb0r

Status

Succeeded

Source

API

Hardware

H100

Total duration

66.7s

Created

9 months ago

Input

model: dev
prompt: A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:14:42.920270Z",
  "created_at": "2024-09-25T04:13:36.224000Z",
  "data_removed": false,
  "error": null,
  "id": "674d1dkjc1rm20cj4xha53hb0r",
  "input": {
    "model": "dev",
    "prompt": "A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 47437\nPrompt: A nostalgic robot ice cream vendor from the 1950s, yellow background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.75it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.24it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.00it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.90it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.84it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.81it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.79it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.78it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.77it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.76it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.76it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.75it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  3.75it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.75it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.75it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.75it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.75it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.75it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.75it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.75it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.75it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.75it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.75it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.75it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.75it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.75it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.78it/s]",
  "metrics": {
    "predict_time": 7.992696934,
    "total_time": 66.69627
  },
  "output": [
    "https://replicate.delivery/yhqm/sWM0wTlYJH42FhTmIdyE20qdffWvrw2J9aRJhMQeYGLkBoAnA/out-0.webp"
  ],
  "started_at": "2024-09-25T04:14:34.927573Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/674d1dkjc1rm20cj4xha53hb0r",
    "cancel": "https://api.replicate.com/v1/predictions/674d1dkjc1rm20cj4xha53hb0r/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

8.0 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

2tfm6h7v0hrm60cj4xjreakyfc

Status

Succeeded

Source

API

Hardware

H100

Total duration

52.1s

Created

9 months ago

Input

model: dev
prompt: astronaut, orange background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "astronaut, orange background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "astronaut, orange background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "astronaut, orange background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "astronaut, orange background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:18:19.944969Z",
  "created_at": "2024-09-25T04:17:27.812000Z",
  "data_removed": false,
  "error": null,
  "id": "2tfm6h7v0hrm60cj4xjreakyfc",
  "input": {
    "model": "dev",
    "prompt": "astronaut, orange background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 51721\nPrompt: astronaut, orange background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.74it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.24it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.00it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.90it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.84it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.81it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.79it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.78it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.77it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.76it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.76it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.76it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  3.75it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.75it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.75it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.75it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.75it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.75it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.75it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.75it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.75it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.75it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.75it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.75it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.75it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.75it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.78it/s]",
  "metrics": {
    "predict_time": 7.979377737,
    "total_time": 52.132969
  },
  "output": [
    "https://replicate.delivery/yhqm/aTO58xRixp5AFZlFJuWtlU3CQNP7esAZIrfsdnd6fifvQQBOB/out-0.webp"
  ],
  "started_at": "2024-09-25T04:18:11.965592Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2tfm6h7v0hrm60cj4xjreakyfc",
    "cancel": "https://api.replicate.com/v1/predictions/2tfm6h7v0hrm60cj4xjreakyfc/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

8.0 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

j8spzqq2khrm20cj4xjt3j16n8

Status

Succeeded

Source

API

Hardware

H100

Total duration

42.4s

Created

9 months ago

Input

model: dev
prompt: girl, orange background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "girl, orange background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "girl, orange background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "girl, orange background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "girl, orange background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:18:03.963901Z",
  "created_at": "2024-09-25T04:17:21.564000Z",
  "data_removed": false,
  "error": null,
  "id": "j8spzqq2khrm20cj4xjt3j16n8",
  "input": {
    "model": "dev",
    "prompt": "girl, orange background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 45794\nPrompt: girl, orange background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.74it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.24it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.00it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.89it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.84it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.80it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.78it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.77it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.76it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.76it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.76it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.76it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  3.76it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.75it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.75it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.75it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.75it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.75it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.75it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.75it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.75it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.75it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.75it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.75it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.75it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.75it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.75it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.77it/s]",
  "metrics": {
    "predict_time": 7.9631889319999996,
    "total_time": 42.399901
  },
  "output": [
    "https://replicate.delivery/yhqm/KShfD9UJSc3zIiYUnWtr0zgDSxV0XINar23ZJYitl169BKwJA/out-0.webp"
  ],
  "started_at": "2024-09-25T04:17:56.000712Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/j8spzqq2khrm20cj4xjt3j16n8",
    "cancel": "https://api.replicate.com/v1/predictions/j8spzqq2khrm20cj4xjt3j16n8/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

8.0 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

nzfm5rpwedrm40cj4xksb6abgw

Status

Succeeded

Source

API

Hardware

H100

Total duration

51.0s

Created

9 months ago

Input

model: dev
prompt: man, orange background, LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 4:5
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28
disable_safety_checker

{
  "model": "dev",
  "prompt": "man, orange background, LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "4:5",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "man, orange background, LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "4:5",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "man, orange background, LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "4:5",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "man, orange background, LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "4:5",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T04:20:22.052599Z",
  "created_at": "2024-09-25T04:19:31.059000Z",
  "data_removed": false,
  "error": null,
  "id": "nzfm5rpwedrm40cj4xksb6abgw",
  "input": {
    "model": "dev",
    "prompt": "man, orange background, LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "4:5",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 42882\nPrompt: man, orange background, LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 15.02s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.76it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.26it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.02it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.91it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.86it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.82it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.80it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.79it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.78it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.77it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.77it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.77it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  3.76it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.76it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.76it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.76it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.76it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.76it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.76it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.76it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.76it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.76it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.76it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.76it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.76it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.76it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.76it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.76it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.79it/s]",
  "metrics": {
    "predict_time": 22.919689963,
    "total_time": 50.993599
  },
  "output": [
    "https://replicate.delivery/yhqm/hI7PQzxtoBoTDNic6d4yf70eBLR157OMCw7YP59BLAxFGUgTA/out-0.webp"
  ],
  "started_at": "2024-09-25T04:19:59.132909Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/nzfm5rpwedrm40cj4xksb6abgw",
    "cancel": "https://api.replicate.com/v1/predictions/nzfm5rpwedrm40cj4xksb6abgw/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

22.9 seconds

jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc

Prediction

Model

ID

0g92prkys9rm20cj5cv8qenjr0

Status

Succeeded

Source

API

Hardware

H100

Total duration

25.6s

Created

9 months ago

Input

model: dev
prompt: screenprint tshirt design, a happy cat holding a sign that says "I LOVE REPLICATE", LNTP illustration style
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 2.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 20
disable_safety_checker

{
  "model": "dev",
  "prompt": "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 2.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 20,
  "disable_safety_checker": false
}

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

const output = await replicate.run(
  "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
  {
    input: {
      model: "dev",
      prompt: "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 2.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    input={
        "model": "dev",
        "prompt": "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 2.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 20
    }
)
print(output)

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

Run jakedahn/flux-latentpop 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": "jakedahn/flux-latentpop:c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc",
    "input": {
      "model": "dev",
      "prompt": "screenprint tshirt design, a happy cat holding a sign that says \\"I LOVE REPLICATE\\", LNTP illustration style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 2.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-09-25T22:04:30.262889Z",
  "created_at": "2024-09-25T22:04:04.682000Z",
  "data_removed": false,
  "error": null,
  "id": "0g92prkys9rm20cj5cv8qenjr0",
  "input": {
    "model": "dev",
    "prompt": "screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 2.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 20,
    "disable_safety_checker": false
  },
  "logs": "Using seed: 4485\nPrompt: screenprint tshirt design, a happy cat holding a sign that says \"I LOVE REPLICATE\", LNTP illustration style\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.77s\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.51it/s]\n 10%|█         | 2/20 [00:00<00:04,  3.97it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.74it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.64it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.59it/s]\n 30%|███       | 6/20 [00:01<00:03,  3.56it/s]\n 35%|███▌      | 7/20 [00:01<00:03,  3.55it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.53it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.53it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.52it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.52it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.51it/s]\n 65%|██████▌   | 13/20 [00:03<00:01,  3.51it/s]\n 70%|███████   | 14/20 [00:03<00:01,  3.51it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.51it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.51it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.51it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.50it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.50it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.50it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.54it/s]",
  "metrics": {
    "predict_time": 14.983668764,
    "total_time": 25.580889
  },
  "output": [
    "https://replicate.delivery/yhqm/6oe5Mae5xNrptUvEdwdWLxKkiEIWdPpgfJd44JkXSmUdXHBnA/out-0.webp"
  ],
  "started_at": "2024-09-25T22:04:15.279220Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/0g92prkys9rm20cj5cv8qenjr0",
    "cancel": "https://api.replicate.com/v1/predictions/0g92prkys9rm20cj5cv8qenjr0/cancel"
  },
  "version": "c5e4432e01d30a523f9ebf1af1ad9f7ce82adc6709ec3061a817d53ff3bb06cc"
}

Generated in

15.0 seconds