Prediction

prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f

Model

prunaai/flux.1-omini-juiced:490a3e1f

ID

z80fj2e76drj40cpc7y8xpqegm

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

9.2s

Created

4 months ago

Input

seed: 1
prompt: In a beautifully furnished living room, this item is placed in the middle
condition
image_size: 512
speed_mode: Extra Juiced 🔥
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 1.5

{
  "seed": 1,
  "prompt": "In a beautifully furnished living room, this item is placed in the middle",
  "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
  "image_size": 512,
  "speed_mode": "Extra Juiced 🔥",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 1.5
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
  {
    input: {
      seed: 1,
      prompt: "In a beautifully furnished living room, this item is placed in the middle",
      condition: "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      image_size: 512,
      speed_mode: "Extra Juiced 🔥",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 1.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
    input={
        "seed": 1,
        "prompt": "In a beautifully furnished living room, this item is placed in the middle",
        "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
        "image_size": 512,
        "speed_mode": "Extra Juiced 🔥",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 1.5
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
    "input": {
      "seed": 1,
      "prompt": "In a beautifully furnished living room, this item is placed in the middle",
      "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      "image_size": 512,
      "speed_mode": "Extra Juiced 🔥",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 1.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

9.2 seconds

Tweak it Iterate in playground ShareReport

Prediction

prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f

Model

prunaai/flux.1-omini-juiced:490a3e1f

ID

pqxyfded9hrj40cpc80aex2r90

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

9.2s

Created

4 months ago

Input

seed: 1
prompt: In a beautifully furnished living room, this item is placed in the middle
condition
image_size: 512
speed_mode: Extra Juiced 🔥
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 1.5

{
  "seed": 1,
  "prompt": "In a beautifully furnished living room, this item is placed in the middle",
  "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
  "image_size": 512,
  "speed_mode": "Extra Juiced 🔥",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 1.5
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
  {
    input: {
      seed: 1,
      prompt: "In a beautifully furnished living room, this item is placed in the middle",
      condition: "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      image_size: 512,
      speed_mode: "Extra Juiced 🔥",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 1.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
    input={
        "seed": 1,
        "prompt": "In a beautifully furnished living room, this item is placed in the middle",
        "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
        "image_size": 512,
        "speed_mode": "Extra Juiced 🔥",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 1.5
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:490a3e1f7c42ba11d4b07960c070f585f02931d2b1067885c8b1a57ab2562f5f",
    "input": {
      "seed": 1,
      "prompt": "In a beautifully furnished living room, this item is placed in the middle",
      "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      "image_size": 512,
      "speed_mode": "Extra Juiced 🔥",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 1.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

9.2 seconds

Tweak it Iterate in playground ShareReport

Prediction

prunaai/flux.1-omini-juiced:908ad1e9a0799d91a006b110c20be660af3abe42809f4d9d776ae7d57395e804

Model

prunaai/flux.1-omini-juiced:908ad1e9

ID

af1mq8ejfxrj60cpbw1rsgb3bg

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

6.6s

Created

4 months ago

Input

seed: 10
prompt: On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.
condition
image_size: 512
speed_mode: Lightly Juiced 🍊 (more consistent)
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 1

{
  "seed": 10,
  "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.",
  "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/penguin.jpg",
  "image_size": 512,
  "speed_mode": "Lightly Juiced 🍊 (more consistent)",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 1
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:908ad1e9a0799d91a006b110c20be660af3abe42809f4d9d776ae7d57395e804",
  {
    input: {
      seed: 10,
      prompt: "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.",
      condition: "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/penguin.jpg",
      image_size: 512,
      speed_mode: "Lightly Juiced 🍊 (more consistent)",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:908ad1e9a0799d91a006b110c20be660af3abe42809f4d9d776ae7d57395e804",
    input={
        "seed": 10,
        "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.",
        "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/penguin.jpg",
        "image_size": 512,
        "speed_mode": "Lightly Juiced 🍊 (more consistent)",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 1
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:908ad1e9a0799d91a006b110c20be660af3abe42809f4d9d776ae7d57395e804",
    "input": {
      "seed": 10,
      "prompt": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat.",
      "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/penguin.jpg",
      "image_size": 512,
      "speed_mode": "Lightly Juiced 🍊 (more consistent)",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

6.6 seconds

Tweak it Iterate in playground ShareReport

Prediction

prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793

Model

prunaai/flux.1-omini-juiced:4abbe496

ID

0ves383qqxrj60cpbwntybqns4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

6.8s

Created

4 months ago

Input

seed: 1
prompt: In a beautifully furnished living room, this item is placed in the middle
condition
image_size: 512
speed_mode: Juiced 🔥 (default)
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 1.5

{
  "seed": 1,
  "prompt": "In a beautifully furnished living room, this item is placed in the middle",
  "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
  "image_size": 512,
  "speed_mode": "Juiced 🔥 (default)",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 1.5
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
  {
    input: {
      seed: 1,
      prompt: "In a beautifully furnished living room, this item is placed in the middle",
      condition: "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      image_size: 512,
      speed_mode: "Juiced 🔥 (default)",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 1.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    input={
        "seed": 1,
        "prompt": "In a beautifully furnished living room, this item is placed in the middle",
        "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
        "image_size": 512,
        "speed_mode": "Juiced 🔥 (default)",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 1.5
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    "input": {
      "seed": 1,
      "prompt": "In a beautifully furnished living room, this item is placed in the middle",
      "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      "image_size": 512,
      "speed_mode": "Juiced 🔥 (default)",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 1.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2025-04-22T20:23:33.159878Z",
  "created_at": "2025-04-22T20:23:26.399000Z",
  "data_removed": false,
  "error": null,
  "id": "0ves383qqxrj60cpbwntybqns4",
  "input": {
    "seed": 1,
    "prompt": "In a beautifully furnished living room, this item is placed in the middle",
    "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
    "image_size": 512,
    "speed_mode": "Juiced 🔥 (default)",
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "output_quality": 80,
    "num_inference_steps": 28,
    "image_guidance_scale": 1.5
  },
  "logs": "Using txt2img pipeline\nRunning prediction with args: ['prompt', 'conditions', 'height', 'width', 'num_inference_steps', 'generator', 'image_guidance_scale']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:12,  2.19it/s]\n  7%|▋         | 2/28 [00:00<00:10,  2.43it/s]\n 11%|█         | 3/28 [00:01<00:10,  2.31it/s]\n 14%|█▍        | 4/28 [00:01<00:10,  2.26it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  3.01it/s]\n 25%|██▌       | 7/28 [00:02<00:05,  3.68it/s]\n 29%|██▊       | 8/28 [00:02<00:04,  4.25it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  4.42it/s]\n 39%|███▉      | 11/28 [00:03<00:03,  4.90it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  4.80it/s]\n 50%|█████     | 14/28 [00:03<00:02,  5.21it/s]\n 57%|█████▋    | 16/28 [00:04<00:02,  4.99it/s]\n 61%|██████    | 17/28 [00:04<00:02,  5.38it/s]\n 68%|██████▊   | 19/28 [00:04<00:01,  5.09it/s]\n 71%|███████▏  | 20/28 [00:04<00:01,  5.46it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  5.13it/s]\n 82%|████████▏ | 23/28 [00:05<00:00,  5.50it/s]\n 89%|████████▉ | 25/28 [00:05<00:00,  5.15it/s]\n 93%|█████████▎| 26/28 [00:05<00:00,  5.51it/s]\n100%|██████████| 28/28 [00:06<00:00,  5.15it/s]\n100%|██████████| 28/28 [00:06<00:00,  4.48it/s]",
  "metrics": {
    "predict_time": 6.749236056,
    "total_time": 6.760878
  },
  "output": "https://replicate.delivery/yhqm/FTZAya8ZSSLeOS4IR5RqDSFbwM0KOnsqZ6O8EOi8VbpiZtSKA/output_1_0.webp",
  "started_at": "2025-04-22T20:23:26.410642Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/yswh-sbeyd27oyhmcgqcf7mkk3u44ptgoxz3jqndzfmp6m5e34zd36wqq",
    "get": "https://api.replicate.com/v1/predictions/0ves383qqxrj60cpbwntybqns4",
    "cancel": "https://api.replicate.com/v1/predictions/0ves383qqxrj60cpbwntybqns4/cancel"
  },
  "version": "4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793"
}

Generated in

6.8 seconds

Tweak it Iterate in playground ShareReport

Prediction

prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793

Model

prunaai/flux.1-omini-juiced:4abbe496

ID

rdfv2p074hrj00cpbwp9bwq2er

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

6.7s

Created

4 months ago

Input

seed: 1
prompt: In a beautifully furnished living room, this item is placed in the middle
condition
image_size: 512
speed_mode: Juiced 🔥 (default)
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 1.5

{
  "seed": 1,
  "prompt": "In a beautifully furnished living room, this item is placed in the middle",
  "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
  "image_size": 512,
  "speed_mode": "Juiced 🔥 (default)",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 1.5
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
  {
    input: {
      seed: 1,
      prompt: "In a beautifully furnished living room, this item is placed in the middle",
      condition: "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      image_size: 512,
      speed_mode: "Juiced 🔥 (default)",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 1.5
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    input={
        "seed": 1,
        "prompt": "In a beautifully furnished living room, this item is placed in the middle",
        "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
        "image_size": 512,
        "speed_mode": "Juiced 🔥 (default)",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 1.5
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    "input": {
      "seed": 1,
      "prompt": "In a beautifully furnished living room, this item is placed in the middle",
      "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
      "image_size": 512,
      "speed_mode": "Juiced 🔥 (default)",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 1.5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2025-04-22T20:24:09.832237Z",
  "created_at": "2025-04-22T20:24:03.108000Z",
  "data_removed": false,
  "error": null,
  "id": "rdfv2p074hrj00cpbwp9bwq2er",
  "input": {
    "seed": 1,
    "prompt": "In a beautifully furnished living room, this item is placed in the middle",
    "condition": "https://replicate.delivery/yhqm/IItye7fbhRuEVkobFA7C103dtBn0AIfQAwaIHEOEw54BhICpA/output.png",
    "image_size": 512,
    "speed_mode": "Juiced 🔥 (default)",
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "output_quality": 80,
    "num_inference_steps": 28,
    "image_guidance_scale": 1.5
  },
  "logs": "Using txt2img pipeline\nRunning prediction with args: ['prompt', 'conditions', 'height', 'width', 'num_inference_steps', 'generator', 'image_guidance_scale']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:12,  2.19it/s]\n  7%|▋         | 2/28 [00:00<00:10,  2.45it/s]\n 11%|█         | 3/28 [00:01<00:10,  2.32it/s]\n 14%|█▍        | 4/28 [00:01<00:10,  2.27it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  3.03it/s]\n 25%|██▌       | 7/28 [00:02<00:05,  3.69it/s]\n 29%|██▊       | 8/28 [00:02<00:04,  4.27it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  4.44it/s]\n 39%|███▉      | 11/28 [00:02<00:03,  4.91it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  4.82it/s]\n 50%|█████     | 14/28 [00:03<00:02,  5.23it/s]\n 57%|█████▋    | 16/28 [00:03<00:02,  4.94it/s]\n 61%|██████    | 17/28 [00:04<00:02,  5.34it/s]\n 68%|██████▊   | 19/28 [00:04<00:01,  5.04it/s]\n 71%|███████▏  | 20/28 [00:04<00:01,  5.41it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  5.09it/s]\n 82%|████████▏ | 23/28 [00:05<00:00,  5.47it/s]\n 89%|████████▉ | 25/28 [00:05<00:00,  5.14it/s]\n 93%|█████████▎| 26/28 [00:05<00:00,  5.51it/s]\n100%|██████████| 28/28 [00:06<00:00,  5.16it/s]\n100%|██████████| 28/28 [00:06<00:00,  4.48it/s]",
  "metrics": {
    "predict_time": 6.712976199,
    "total_time": 6.724237
  },
  "output": "https://replicate.delivery/yhqm/EHVALpeV1UWUGqLWmP8uNpUCgfkegzPHroD9NdK1hL5Tn1KpA/output_1_0.webp",
  "started_at": "2025-04-22T20:24:03.119261Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/yswh-6hc7vy4oiqvef3z43rcxlwjzhyiedd3ilc57b2rswfbkyamjxktq",
    "get": "https://api.replicate.com/v1/predictions/rdfv2p074hrj00cpbwp9bwq2er",
    "cancel": "https://api.replicate.com/v1/predictions/rdfv2p074hrj00cpbwp9bwq2er/cancel"
  },
  "version": "4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793"
}

Generated in

6.7 seconds

Tweak it Iterate in playground ShareReport

Prediction

prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793

Model

prunaai/flux.1-omini-juiced:4abbe496

ID

8fj4b5gf45rj20cpbwy95f6tec

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

7.4s

Created

4 months ago

Input

seed: 2
prompt: A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.
condition
image_size: 512
speed_mode: Extra Juiced 🔥 (more speed)
aspect_ratio: 1:1
output_format: webp
output_quality: 80
num_inference_steps: 28
image_guidance_scale: 2

{
  "seed": 2,
  "prompt": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.",
  "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/oranges.jpg",
  "image_size": 512,
  "speed_mode": "Extra Juiced 🔥 (more speed)",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "output_quality": 80,
  "num_inference_steps": 28,
  "image_guidance_scale": 2
}

Install Replicate’s Node.js client library:

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 prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
  {
    input: {
      seed: 2,
      prompt: "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.",
      condition: "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/oranges.jpg",
      image_size: 512,
      speed_mode: "Extra Juiced 🔥 (more speed)",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 28,
      image_guidance_scale: 2
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

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

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run prunaai/flux.1-omini-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    input={
        "seed": 2,
        "prompt": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.",
        "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/oranges.jpg",
        "image_size": 512,
        "speed_mode": "Extra Juiced 🔥 (more speed)",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 28,
        "image_guidance_scale": 2
    }
)

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

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

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

Run prunaai/flux.1-omini-juiced 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": "prunaai/flux.1-omini-juiced:4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793",
    "input": {
      "seed": 2,
      "prompt": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.",
      "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/oranges.jpg",
      "image_size": 512,
      "speed_mode": "Extra Juiced 🔥 (more speed)",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 28,
      "image_guidance_scale": 2
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2025-04-22T20:41:41.101617Z",
  "created_at": "2025-04-22T20:41:33.729000Z",
  "data_removed": false,
  "error": null,
  "id": "8fj4b5gf45rj20cpbwy95f6tec",
  "input": {
    "seed": 2,
    "prompt": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show.",
    "condition": "https://raw.githubusercontent.com/Yuanshi9815/OminiControl/refs/heads/main/assets/oranges.jpg",
    "image_size": 512,
    "speed_mode": "Extra Juiced 🔥 (more speed)",
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "output_quality": 80,
    "num_inference_steps": 28,
    "image_guidance_scale": 2
  },
  "logs": "Using txt2img pipeline\nRunning prediction with args: ['prompt', 'conditions', 'height', 'width', 'num_inference_steps', 'generator', 'image_guidance_scale']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:12,  2.19it/s]\n  7%|▋         | 2/28 [00:00<00:10,  2.45it/s]\n 11%|█         | 3/28 [00:01<00:10,  2.32it/s]\n 14%|█▍        | 4/28 [00:01<00:10,  2.26it/s]\n 18%|█▊        | 5/28 [00:02<00:10,  2.23it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.94it/s]\n 29%|██▊       | 8/28 [00:02<00:03,  5.05it/s]\n 32%|███▏      | 9/28 [00:02<00:04,  4.10it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  4.50it/s]\n 43%|████▎     | 12/28 [00:03<00:02,  6.67it/s]\n 50%|█████     | 14/28 [00:03<00:02,  5.27it/s]\n 57%|█████▋    | 16/28 [00:03<00:01,  7.10it/s]\n 64%|██████▍   | 18/28 [00:04<00:01,  5.56it/s]\n 71%|███████▏  | 20/28 [00:04<00:01,  7.24it/s]\n 79%|███████▊  | 22/28 [00:04<00:01,  5.71it/s]\n 86%|████████▌ | 24/28 [00:04<00:00,  7.31it/s]\n 93%|█████████▎| 26/28 [00:05<00:00,  5.78it/s]\n100%|██████████| 28/28 [00:05<00:00,  5.40it/s]\n100%|██████████| 28/28 [00:05<00:00,  4.77it/s]",
  "metrics": {
    "predict_time": 6.228237958,
    "total_time": 7.372617
  },
  "output": "https://replicate.delivery/yhqm/kXcNYOhtsvpUBVDhpFXEe8UbMeehapPimKFyf0cs1i0XQsVSB/output_2_0.webp",
  "started_at": "2025-04-22T20:41:34.873379Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/yswh-eo5fousqllw7xgtossxa33k3d7wmo5ppyn6xbs7dtwl7iwewxwfq",
    "get": "https://api.replicate.com/v1/predictions/8fj4b5gf45rj20cpbwy95f6tec",
    "cancel": "https://api.replicate.com/v1/predictions/8fj4b5gf45rj20cpbwy95f6tec/cancel"
  },
  "version": "4abbe496540d006abafbe61d5d81839fc94090cd809fc04ef0181bea22d5e793"
}

Generated in

6.2 seconds

Tweak it Iterate in playground ShareReport

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