Prediction

ID

2ywaqkw3a5rm40cjb6q9btgwbr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a cat smiling and looking directly at the camera, wearing a white t-shirt with the word "HYPER" printed on it.
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \"HYPER\" printed on it.",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \"HYPER\" printed on it.",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \"HYPER\" printed on it.",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \\"HYPER\\" printed on it.",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:37:46.075036Z",
  "created_at": "2024-10-04T22:37:42.097000Z",
  "data_removed": false,
  "error": null,
  "id": "2ywaqkw3a5rm40cjb6q9btgwbr",
  "input": {
    "prompt": "a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \"HYPER\" printed on it.",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 45151\nPrompt: a cat smiling and looking directly at the camera, wearing a white t-shirt with the word \"HYPER\" printed on it.\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.32it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.47it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.89it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.52it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.44it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.41it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.38it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.36it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.35it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.35it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.34it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.33it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.33it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.32it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.32it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.42it/s]",
  "metrics": {
    "predict_time": 3.970947661,
    "total_time": 3.978036
  },
  "output": [
    "https://replicate.delivery/yhqm/0sXEmUlSeFWxWiiod5UYRbOHet2aneSDx94CAe4UHhDoDIOOB/out-0.webp"
  ],
  "started_at": "2024-10-04T22:37:42.104088Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2ywaqkw3a5rm40cjb6q9btgwbr",
    "cancel": "https://api.replicate.com/v1/predictions/2ywaqkw3a5rm40cjb6q9btgwbr/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

q808tp2e9hrm40cjb6rbcp3c88

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:39:43.598691Z",
  "created_at": "2024-10-04T22:39:39.596000Z",
  "data_removed": false,
  "error": null,
  "id": "q808tp2e9hrm40cjb6rbcp3c88",
  "input": {
    "prompt": "a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 27837\nPrompt: a digital portrait of a woman with a pensive expression, her hair styled in a messy bun adorned with splashes of color\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.33it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.50it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.88it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.49it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.42it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.39it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.36it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.33it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.31it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.31it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.30it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.30it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.29it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.29it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.29it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.39it/s]",
  "metrics": {
    "predict_time": 3.9953830630000002,
    "total_time": 4.002691
  },
  "output": [
    "https://replicate.delivery/yhqm/K8ooWF9W1zprCF4Dt7XD6tWYFwHTS8BybcCLeKIGps6XBxxJA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:39:39.603308Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/q808tp2e9hrm40cjb6rbcp3c88",
    "cancel": "https://api.replicate.com/v1/predictions/q808tp2e9hrm40cjb6rbcp3c88/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

5sh1r34kt1rm20cjb6ra8matcg

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:40:01.390742Z",
  "created_at": "2024-10-04T22:39:57.392000Z",
  "data_removed": false,
  "error": null,
  "id": "5sh1r34kt1rm20cjb6ra8matcg",
  "input": {
    "prompt": "a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 34094\nPrompt: a heart-shaped glass object, filled with green plants, rests on a mossy surface, surrounded by rocks and other greenery, with sunlight filtering through the foliage.\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.32it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.47it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.88it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.50it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.43it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.40it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.36it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.34it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.33it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.32it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.32it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.31it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.30it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.40it/s]",
  "metrics": {
    "predict_time": 3.9920524569999998,
    "total_time": 3.998742
  },
  "output": [
    "https://replicate.delivery/yhqm/2vlwkHBV2fQlZix2LQoCNl5GjOyes0edSLeMhqWJZB8FMIOOB/out-0.webp"
  ],
  "started_at": "2024-10-04T22:39:57.398690Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/5sh1r34kt1rm20cjb6ra8matcg",
    "cancel": "https://api.replicate.com/v1/predictions/5sh1r34kt1rm20cjb6ra8matcg/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

fcmzravrpsrm40cjb6rsjbrvrr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: womens street skateboarding final in Paris Olympics 2024
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "womens street skateboarding final in Paris Olympics 2024",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "womens street skateboarding final in Paris Olympics 2024",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "womens street skateboarding final in Paris Olympics 2024",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "womens street skateboarding final in Paris Olympics 2024",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:40:59.979452Z",
  "created_at": "2024-10-04T22:40:55.990000Z",
  "data_removed": false,
  "error": null,
  "id": "fcmzravrpsrm40cjb6rsjbrvrr",
  "input": {
    "prompt": "womens street skateboarding final in Paris Olympics 2024",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 57819\nPrompt: womens street skateboarding final in Paris Olympics 2024\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.33it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.50it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.89it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.49it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.43it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.39it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.36it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.34it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.33it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.32it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.31it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.30it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.30it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.40it/s]",
  "metrics": {
    "predict_time": 3.984317216,
    "total_time": 3.989452
  },
  "output": [
    "https://replicate.delivery/yhqm/OB6eQcqOkvQfrkTSW4ogewaSg2OkEEe9cIDsAQecRof8eBxxJA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:40:55.995135Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/fcmzravrpsrm40cjb6rsjbrvrr",
    "cancel": "https://api.replicate.com/v1/predictions/fcmzravrpsrm40cjb6rsjbrvrr/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

xa0y2zxw61rm40cjb6rs0vntmm

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:41:17.270625Z",
  "created_at": "2024-10-04T22:41:13.264000Z",
  "data_removed": false,
  "error": null,
  "id": "xa0y2zxw61rm40cjb6rs0vntmm",
  "input": {
    "prompt": "a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 49014\nPrompt: a deer in a suit with antlers standing amidst a forest of orange leaves, with a light source shining from above\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.32it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.49it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.88it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.64it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.50it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.42it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.38it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.37it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.34it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.32it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.32it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.31it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.31it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.31it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.40it/s]",
  "metrics": {
    "predict_time": 4.000136739,
    "total_time": 4.006625
  },
  "output": [
    "https://replicate.delivery/yhqm/p5cIEj633bbgPhpM9NA3KiMTFqcuzw6vSdfY9MokbnxGCxxJA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:41:13.270489Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xa0y2zxw61rm40cjb6rs0vntmm",
    "cancel": "https://api.replicate.com/v1/predictions/xa0y2zxw61rm40cjb6rs0vntmm/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

pvtqr9qnq9rm40cjb6rstq9tq4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a blue paradise bird in the jungle
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a blue paradise bird in the jungle",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a blue paradise bird in the jungle",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a blue paradise bird in the jungle",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a blue paradise bird in the jungle",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:41:31.983365Z",
  "created_at": "2024-10-04T22:41:27.994000Z",
  "data_removed": false,
  "error": null,
  "id": "pvtqr9qnq9rm40cjb6rstq9tq4",
  "input": {
    "prompt": "a blue paradise bird in the jungle",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 58375\nPrompt: a blue paradise bird in the jungle\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.32it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.48it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.89it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.51it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.43it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.40it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.38it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.35it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.32it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.30it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.31it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.32it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.32it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.32it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.31it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.41it/s]",
  "metrics": {
    "predict_time": 3.977563356,
    "total_time": 3.989365
  },
  "output": [
    "https://replicate.delivery/yhqm/hTYl9dfZmVwfeIhqQokEYtE375FifftCS0IIxeapeyu2NCxxJA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:41:28.005801Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/pvtqr9qnq9rm40cjb6rstq9tq4",
    "cancel": "https://api.replicate.com/v1/predictions/pvtqr9qnq9rm40cjb6rstq9tq4/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

3wjmv31fchrm20cjb6s9p7hdvm

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: a serene landscape with mountains and a lake at sunset
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "a serene landscape with mountains and a lake at sunset",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "a serene landscape with mountains and a lake at sunset",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "a serene landscape with mountains and a lake at sunset",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "a serene landscape with mountains and a lake at sunset",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:41:46.763211Z",
  "created_at": "2024-10-04T22:41:42.756000Z",
  "data_removed": false,
  "error": null,
  "id": "3wjmv31fchrm20cjb6s9p7hdvm",
  "input": {
    "prompt": "a serene landscape with mountains and a lake at sunset",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 10454\nPrompt: a serene landscape with mountains and a lake at sunset\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.31it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.49it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.88it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.65it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.51it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.45it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.40it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.37it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.35it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.33it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.33it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.32it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.31it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.30it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.41it/s]",
  "metrics": {
    "predict_time": 3.998226791,
    "total_time": 4.007211
  },
  "output": [
    "https://replicate.delivery/yhqm/OxiNdVd6C04lKlZqwtFnmTNICdfJkmytHKD2EaGt1GfqEijTA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:41:42.764984Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/3wjmv31fchrm20cjb6s9p7hdvm",
    "cancel": "https://api.replicate.com/v1/predictions/3wjmv31fchrm20cjb6s9p7hdvm/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds

bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7

Prediction

Model

ID

bn14m6hb4xrm00cjb6srne29sc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

4.0s

Created

10 months ago

Input

prompt: A chocolate cookie
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 16

{
  "prompt": "A chocolate cookie",
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 16
}

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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
  {
    input: {
      prompt: "A chocolate cookie",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      num_inference_steps: 16
    }
  }
);

// 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 bytedance/hyper-flux-16step using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    input={
        "prompt": "A chocolate cookie",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 16
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run bytedance/hyper-flux-16step 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": "bytedance/hyper-flux-16step:382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7",
    "input": {
      "prompt": "A chocolate cookie",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "num_inference_steps": 16
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-04T22:42:51.219885Z",
  "created_at": "2024-10-04T22:42:47.207000Z",
  "data_removed": false,
  "error": null,
  "id": "bn14m6hb4xrm00cjb6srne29sc",
  "input": {
    "prompt": "A chocolate cookie",
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 16
  },
  "logs": "Using seed: 45466\nPrompt: A chocolate cookie\ntxt2img mode\n  0%|          | 0/16 [00:00<?, ?it/s]\n  6%|▋         | 1/16 [00:00<00:03,  4.32it/s]\n 12%|█▎        | 2/16 [00:00<00:02,  5.47it/s]\n 19%|█▉        | 3/16 [00:00<00:02,  4.87it/s]\n 25%|██▌       | 4/16 [00:00<00:02,  4.63it/s]\n 31%|███▏      | 5/16 [00:01<00:02,  4.46it/s]\n 38%|███▊      | 6/16 [00:01<00:02,  4.40it/s]\n 44%|████▍     | 7/16 [00:01<00:02,  4.37it/s]\n 50%|█████     | 8/16 [00:01<00:01,  4.36it/s]\n 56%|█████▋    | 9/16 [00:02<00:01,  4.33it/s]\n 62%|██████▎   | 10/16 [00:02<00:01,  4.32it/s]\n 69%|██████▉   | 11/16 [00:02<00:01,  4.31it/s]\n 75%|███████▌  | 12/16 [00:02<00:00,  4.31it/s]\n 81%|████████▏ | 13/16 [00:02<00:00,  4.31it/s]\n 88%|████████▊ | 14/16 [00:03<00:00,  4.30it/s]\n 94%|█████████▍| 15/16 [00:03<00:00,  4.29it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.30it/s]\n100%|██████████| 16/16 [00:03<00:00,  4.39it/s]",
  "metrics": {
    "predict_time": 4.004228095,
    "total_time": 4.012885
  },
  "output": [
    "https://replicate.delivery/yhqm/zpmRSBEKZjZlF5jwSam8jDuBEzffHGlRs1JB6roUe69WLEHnA/out-0.webp"
  ],
  "started_at": "2024-10-04T22:42:47.215657Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/bn14m6hb4xrm00cjb6srne29sc",
    "cancel": "https://api.replicate.com/v1/predictions/bn14m6hb4xrm00cjb6srne29sc/cancel"
  },
  "version": "382cf8959fb0f0d665b26e7e80b8d6dc3faaef1510f14ce017e8c732bb3d1eb7"
}

Generated in

4.0 seconds