Prediction

5vdj1y725srm00chkbtspdcm2m

Status

Succeeded

Source

Web

Hardware

H100

Total duration

18.7s

Created

8 months ago

Input

seed: 75757504940878
model: dev
width: 768
height: 1440
prompt: 1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance
lora_scale: 0.47
num_outputs: 1
aspect_ratio: custom
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "seed": 75757504940878,
  "model": "dev",
  "width": 768,
  "height": 1440,
  "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
  "lora_scale": 0.47,
  "num_outputs": 1,
  "aspect_ratio": "custom",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      seed: 75757504940878,
      model: "dev",
      width: 768,
      height: 1440,
      prompt: "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
      lora_scale: 0.47,
      num_outputs: 1,
      aspect_ratio: "custom",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "seed": 75757504940878,
        "model": "dev",
        "width": 768,
        "height": 1440,
        "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
        "lora_scale": 0.47,
        "num_outputs": 1,
        "aspect_ratio": "custom",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "seed": 75757504940878,
      "model": "dev",
      "width": 768,
      "height": 1440,
      "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
      "lora_scale": 0.47,
      "num_outputs": 1,
      "aspect_ratio": "custom",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:48:29.896780Z",
  "created_at": "2024-08-28T21:48:11.182000Z",
  "data_removed": false,
  "error": null,
  "id": "5vdj1y725srm00chkbtspdcm2m",
  "input": {
    "seed": 75757504940878,
    "model": "dev",
    "width": 768,
    "height": 1440,
    "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
    "lora_scale": 0.47,
    "num_outputs": 1,
    "aspect_ratio": "custom",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 75757504940878\nPrompt: 1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.91s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:08,  3.36it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.77it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.58it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.50it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.45it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.42it/s]\n 25%|██▌       | 7/28 [00:02<00:06,  3.40it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.39it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.38it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.39it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  3.38it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.38it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.38it/s]\n 50%|█████     | 14/28 [00:04<00:04,  3.38it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.37it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.37it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.38it/s]\n 64%|██████▍   | 18/28 [00:05<00:02,  3.37it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.37it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.35it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.37it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.36it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.37it/s]\n 86%|████████▌ | 24/28 [00:07<00:01,  3.37it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.37it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.37it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.36it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.36it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.39it/s]",
  "metrics": {
    "predict_time": 18.7098564,
    "total_time": 18.71478
  },
  "output": [
    "https://replicate.delivery/yhqm/he1mgreJFvulIELs607DoWutvzPOJZWrdGrWgQmbM4Qt0UXTA/out-0.webp"
  ],
  "started_at": "2024-08-28T21:48:11.186924Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/5vdj1y725srm00chkbtspdcm2m",
    "cancel": "https://api.replicate.com/v1/predictions/5vdj1y725srm00chkbtspdcm2m/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

18.7 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Prediction

Model

vvwecytq7srm20chkbwrvtg2xc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

17.8s

Created

8 months ago

Input

seed: 75757504940878
model: dev
width: 768
height: 1440
prompt: 1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance
lora_scale: 0.68
num_outputs: 1
aspect_ratio: custom
output_format: png
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "seed": 75757504940878,
  "model": "dev",
  "width": 768,
  "height": 1440,
  "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
  "lora_scale": 0.68,
  "num_outputs": 1,
  "aspect_ratio": "custom",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      seed: 75757504940878,
      model: "dev",
      width: 768,
      height: 1440,
      prompt: "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
      lora_scale: 0.68,
      num_outputs: 1,
      aspect_ratio: "custom",
      output_format: "png",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "seed": 75757504940878,
        "model": "dev",
        "width": 768,
        "height": 1440,
        "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
        "lora_scale": 0.68,
        "num_outputs": 1,
        "aspect_ratio": "custom",
        "output_format": "png",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "seed": 75757504940878,
      "model": "dev",
      "width": 768,
      "height": 1440,
      "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
      "lora_scale": 0.68,
      "num_outputs": 1,
      "aspect_ratio": "custom",
      "output_format": "png",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:52:15.594177Z",
  "created_at": "2024-08-28T21:51:57.758000Z",
  "data_removed": false,
  "error": null,
  "id": "vvwecytq7srm20chkbwrvtg2xc",
  "input": {
    "seed": 75757504940878,
    "model": "dev",
    "width": 768,
    "height": 1440,
    "prompt": "1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance ",
    "lora_scale": 0.68,
    "num_outputs": 1,
    "aspect_ratio": "custom",
    "output_format": "png",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 75757504940878\nPrompt: 1MARK1, a space ship car drivin by a massive megafauna creature in the open landscape, busy, full of action and nuance\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.51s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:08,  3.34it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.73it/s]\n 11%|█         | 3/28 [00:00<00:07,  3.54it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.45it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.41it/s]\n 21%|██▏       | 6/28 [00:01<00:06,  3.38it/s]\n 25%|██▌       | 7/28 [00:02<00:06,  3.37it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.35it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.35it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.34it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  3.34it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.33it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.33it/s]\n 50%|█████     | 14/28 [00:04<00:04,  3.33it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.33it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.32it/s]\n 61%|██████    | 17/28 [00:05<00:03,  3.32it/s]\n 64%|██████▍   | 18/28 [00:05<00:03,  3.32it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.32it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.32it/s]\n 75%|███████▌  | 21/28 [00:06<00:02,  3.33it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.32it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.32it/s]\n 86%|████████▌ | 24/28 [00:07<00:01,  3.32it/s]\n 89%|████████▉ | 25/28 [00:07<00:00,  3.33it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.33it/s]\n 96%|█████████▋| 27/28 [00:08<00:00,  3.33it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.33it/s]\n100%|██████████| 28/28 [00:08<00:00,  3.35it/s]",
  "metrics": {
    "predict_time": 17.826356823,
    "total_time": 17.836177
  },
  "output": [
    "https://replicate.delivery/yhqm/CECKAGmerekM00Ht7T1HyaWrxHHhnxBi74AXFSFgooRP4UXTA/out-0.png"
  ],
  "started_at": "2024-08-28T21:51:57.767820Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vvwecytq7srm20chkbwrvtg2xc",
    "cancel": "https://api.replicate.com/v1/predictions/vvwecytq7srm20chkbwrvtg2xc/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

17.8 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Prediction

Model

4ahbbtfbt1rm60chkcc881r0wc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

26.8s

Created

8 months ago

Input

model: dev
width: 1440
height: 398
prompt: 1MARK1, a mountain landscape, photorealistic
lora_scale: 1
num_outputs: 1
aspect_ratio: 9:16
output_format: png
guidance_scale: 3.5
output_quality: 100
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "width": 1440,
  "height": 398,
  "prompt": "1MARK1, a mountain landscape, photorealistic",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "9:16",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 100,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      width: 1440,
      height: 398,
      prompt: "1MARK1, a mountain landscape, photorealistic",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "9:16",
      output_format: "png",
      guidance_scale: 3.5,
      output_quality: 100,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "width": 1440,
        "height": 398,
        "prompt": "1MARK1, a mountain landscape, photorealistic",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "9:16",
        "output_format": "png",
        "guidance_scale": 3.5,
        "output_quality": 100,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "width": 1440,
      "height": 398,
      "prompt": "1MARK1, a mountain landscape, photorealistic",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "9:16",
      "output_format": "png",
      "guidance_scale": 3.5,
      "output_quality": 100,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T22:26:54.214297Z",
  "created_at": "2024-08-28T22:26:27.408000Z",
  "data_removed": false,
  "error": null,
  "id": "4ahbbtfbt1rm60chkcc881r0wc",
  "input": {
    "model": "dev",
    "width": 1440,
    "height": 398,
    "prompt": "1MARK1, a mountain landscape, photorealistic",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "9:16",
    "output_format": "png",
    "guidance_scale": 3.5,
    "output_quality": 100,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 49376\nPrompt: 1MARK1, a mountain landscape, photorealistic\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.06s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.73it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.29it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.01it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.90it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.83it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.80it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.77it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.76it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.75it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.74it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.73it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.73it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.73it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.73it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.73it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.73it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.73it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.73it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.71it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.72it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.72it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.72it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.72it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.72it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.72it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.75it/s]",
  "metrics": {
    "predict_time": 16.541714836,
    "total_time": 26.806297
  },
  "output": [
    "https://replicate.delivery/yhqm/qMpIfJIcgy1efoPdnlwSt0Oke1szFEEOv2NS1JCw6xfuFr6aC/out-0.png"
  ],
  "started_at": "2024-08-28T22:26:37.672582Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4ahbbtfbt1rm60chkcc881r0wc",
    "cancel": "https://api.replicate.com/v1/predictions/4ahbbtfbt1rm60chkcc881r0wc/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

16.6 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Prediction

Model

8yajpwmyrhrm40chkcctjddc4w

Status

Succeeded

Source

Web

Hardware

H100

Total duration

17.8s

Created

8 months ago

Input

model: dev
width: 1440
height: 398
prompt: 1MARK1, a mountain landscape, photorealistic, 1MARK1,
lora_scale: 1
num_outputs: 1
aspect_ratio: 9:16
output_format: png
guidance_scale: 3.5
output_quality: 100
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "width": 1440,
  "height": 398,
  "prompt": "1MARK1, a mountain landscape, photorealistic, 1MARK1, ",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "9:16",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 100,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      width: 1440,
      height: 398,
      prompt: "1MARK1, a mountain landscape, photorealistic, 1MARK1, ",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "9:16",
      output_format: "png",
      guidance_scale: 3.5,
      output_quality: 100,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "width": 1440,
        "height": 398,
        "prompt": "1MARK1, a mountain landscape, photorealistic, 1MARK1, ",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "9:16",
        "output_format": "png",
        "guidance_scale": 3.5,
        "output_quality": 100,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "width": 1440,
      "height": 398,
      "prompt": "1MARK1, a mountain landscape, photorealistic, 1MARK1, ",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "9:16",
      "output_format": "png",
      "guidance_scale": 3.5,
      "output_quality": 100,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T22:27:31.045505Z",
  "created_at": "2024-08-28T22:27:13.220000Z",
  "data_removed": false,
  "error": null,
  "id": "8yajpwmyrhrm40chkcctjddc4w",
  "input": {
    "model": "dev",
    "width": 1440,
    "height": 398,
    "prompt": "1MARK1, a mountain landscape, photorealistic, 1MARK1, ",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "9:16",
    "output_format": "png",
    "guidance_scale": 3.5,
    "output_quality": 100,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 40565\nPrompt: 1MARK1, a mountain landscape, photorealistic, 1MARK1,\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.52s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.72it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.27it/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<00:06,  3.83it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.79it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.77it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.76it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.75it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.74it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.73it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.73it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.73it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.73it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.72it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.73it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.73it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.72it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.72it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.72it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.72it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.72it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.72it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.72it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.72it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.75it/s]",
  "metrics": {
    "predict_time": 17.816925145,
    "total_time": 17.825505
  },
  "output": [
    "https://replicate.delivery/yhqm/bODtick7Hs6pMROft5OEWrmvLLOaRAo0qkfzYPigi2gSZVXTA/out-0.png"
  ],
  "started_at": "2024-08-28T22:27:13.228580Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/8yajpwmyrhrm40chkcctjddc4w",
    "cancel": "https://api.replicate.com/v1/predictions/8yajpwmyrhrm40chkcctjddc4w/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

17.8 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Prediction

Model

0gnm6dc8wsrm60chkch8qhs3f8

Status

Succeeded

Source

Web

Hardware

H100

Total duration

20.9s

Created

8 months ago

Input

model: dev
width: 1440
height: 398
prompt: 1MARK1, a battle between man and machine
lora_scale: 0.83
num_outputs: 1
aspect_ratio: 9:16
output_format: png
guidance_scale: 3.5
output_quality: 100
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "width": 1440,
  "height": 398,
  "prompt": "1MARK1, a battle between man and machine",
  "lora_scale": 0.83,
  "num_outputs": 1,
  "aspect_ratio": "9:16",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 100,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      width: 1440,
      height: 398,
      prompt: "1MARK1, a battle between man and machine",
      lora_scale: 0.83,
      num_outputs: 1,
      aspect_ratio: "9:16",
      output_format: "png",
      guidance_scale: 3.5,
      output_quality: 100,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "width": 1440,
        "height": 398,
        "prompt": "1MARK1, a battle between man and machine",
        "lora_scale": 0.83,
        "num_outputs": 1,
        "aspect_ratio": "9:16",
        "output_format": "png",
        "guidance_scale": 3.5,
        "output_quality": 100,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "width": 1440,
      "height": 398,
      "prompt": "1MARK1, a battle between man and machine",
      "lora_scale": 0.83,
      "num_outputs": 1,
      "aspect_ratio": "9:16",
      "output_format": "png",
      "guidance_scale": 3.5,
      "output_quality": 100,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T22:37:18.307505Z",
  "created_at": "2024-08-28T22:36:57.446000Z",
  "data_removed": false,
  "error": null,
  "id": "0gnm6dc8wsrm60chkch8qhs3f8",
  "input": {
    "model": "dev",
    "width": 1440,
    "height": 398,
    "prompt": "1MARK1, a battle between man and machine",
    "lora_scale": 0.83,
    "num_outputs": 1,
    "aspect_ratio": "9:16",
    "output_format": "png",
    "guidance_scale": 3.5,
    "output_quality": 100,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 25142\nPrompt: 1MARK1, a battle between man and machine\ntxt2img mode\nUsing dev model\nfree=9343880232960\nDownloading weights\n2024-08-28T22:36:57Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp03xec5gt/weights url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar\n2024-08-28T22:37:00Z | INFO  | [ Complete ] dest=/tmp/tmp03xec5gt/weights size=\"172 MB\" total_elapsed=2.164s url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar\nDownloaded weights in 2.20s\nLoaded LoRAs in 11.85s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.58it/s]\n  7%|▋         | 2/28 [00:00<00:06,  3.99it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.78it/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,  3.59it/s]\n 36%|███▌      | 10/28 [00:02<00:05,  3.57it/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,  3.57it/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,  3.56it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.56it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.57it/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,  3.57it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.59it/s]",
  "metrics": {
    "predict_time": 20.536591537,
    "total_time": 20.861505
  },
  "output": [
    "https://replicate.delivery/yhqm/YwEUykItMC5ebi6TIGWoG73LShWxboZWTe6xTDZTJbaeErumA/out-0.png"
  ],
  "started_at": "2024-08-28T22:36:57.770913Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/0gnm6dc8wsrm60chkch8qhs3f8",
    "cancel": "https://api.replicate.com/v1/predictions/0gnm6dc8wsrm60chkch8qhs3f8/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

20.5 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Using seed: 25142
Prompt: 1MARK1, a battle between man and machine
txt2img mode
Using dev model
free=9343880232960
Downloading weights
2024-08-28T22:36:57Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp03xec5gt/weights url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar
2024-08-28T22:37:00Z | INFO  | [ Complete ] dest=/tmp/tmp03xec5gt/weights size="172 MB" total_elapsed=2.164s url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar
Downloaded weights in 2.20s
Loaded LoRAs in 11.85s
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Prediction

Model

gchzcmdt15rm20chkcjrs4yph4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

17.5s

Created

8 months ago

Input

model: dev
width: 1440
height: 398
prompt: 1MARK1, a battle between man and machine
lora_scale: 0.77
num_outputs: 1
aspect_ratio: 9:16
output_format: png
guidance_scale: 3.5
output_quality: 100
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "width": 1440,
  "height": 398,
  "prompt": "1MARK1, a battle between man and machine",
  "lora_scale": 0.77,
  "num_outputs": 1,
  "aspect_ratio": "9:16",
  "output_format": "png",
  "guidance_scale": 3.5,
  "output_quality": 100,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      width: 1440,
      height: 398,
      prompt: "1MARK1, a battle between man and machine",
      lora_scale: 0.77,
      num_outputs: 1,
      aspect_ratio: "9:16",
      output_format: "png",
      guidance_scale: 3.5,
      output_quality: 100,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "width": 1440,
        "height": 398,
        "prompt": "1MARK1, a battle between man and machine",
        "lora_scale": 0.77,
        "num_outputs": 1,
        "aspect_ratio": "9:16",
        "output_format": "png",
        "guidance_scale": 3.5,
        "output_quality": 100,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "width": 1440,
      "height": 398,
      "prompt": "1MARK1, a battle between man and machine",
      "lora_scale": 0.77,
      "num_outputs": 1,
      "aspect_ratio": "9:16",
      "output_format": "png",
      "guidance_scale": 3.5,
      "output_quality": 100,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T22:40:44.137729Z",
  "created_at": "2024-08-28T22:40:26.633000Z",
  "data_removed": false,
  "error": null,
  "id": "gchzcmdt15rm20chkcjrs4yph4",
  "input": {
    "model": "dev",
    "width": 1440,
    "height": 398,
    "prompt": "1MARK1, a battle between man and machine",
    "lora_scale": 0.77,
    "num_outputs": 1,
    "aspect_ratio": "9:16",
    "output_format": "png",
    "guidance_scale": 3.5,
    "output_quality": 100,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 30781\nPrompt: 1MARK1, a battle between man and machine\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.80s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.60it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.02it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.81it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.72it/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,  3.61it/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,  3.57it/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,  3.58it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.57it/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,  3.57it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.58it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.58it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.57it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.58it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.58it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.58it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.58it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.58it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.58it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.60it/s]",
  "metrics": {
    "predict_time": 17.495422321,
    "total_time": 17.504729
  },
  "output": [
    "https://replicate.delivery/yhqm/iPvtogCt7i5NN9LLpkMC0ekGeNvj8Q7tFmnyy7S3nYmrlVXTA/out-0.png"
  ],
  "started_at": "2024-08-28T22:40:26.642307Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/gchzcmdt15rm20chkcjrs4yph4",
    "cancel": "https://api.replicate.com/v1/predictions/gchzcmdt15rm20chkcjrs4yph4/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

17.5 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Prediction

Model

2vaxfwe6z1rm60chkbdad8pk48

Status

Succeeded

Source

Web

Hardware

H100

Total duration

28.4s

Created

8 months ago

Input

model: dev
prompt: 1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      prompt: "1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "prompt": "1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "prompt": "1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:19:03.085118Z",
  "created_at": "2024-08-28T21:18:34.744000Z",
  "data_removed": false,
  "error": null,
  "id": "2vaxfwe6z1rm60chkbdad8pk48",
  "input": {
    "model": "dev",
    "prompt": "1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 14990\nPrompt: 1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.83s\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['( style : inspired by scientific photography and abstract art ) ( lighting : high - contrast lighting to emphasize textures and depth ) ( colors : monochrome, grayscale with high contrast ) ( composition : extreme close - up shot using an fei quanta 2 5 0 sem, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.70it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.26it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.98it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.86it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.80it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.76it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.74it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.72it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.72it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.71it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.70it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.70it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.70it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.70it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.69it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.69it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.69it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.69it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.69it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.69it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.69it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.69it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.69it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.72it/s]",
  "metrics": {
    "predict_time": 16.94351611,
    "total_time": 28.341118
  },
  "output": [
    "https://replicate.delivery/yhqm/z6a8TgvxvRr3LliS6RkAG8lwDJSDi5OyK8K0EerIa7NjMqrJA/out-0.webp"
  ],
  "started_at": "2024-08-28T21:18:46.141602Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2vaxfwe6z1rm60chkbdad8pk48",
    "cancel": "https://api.replicate.com/v1/predictions/2vaxfwe6z1rm60chkbdad8pk48/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

16.9 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Using seed: 14990
Prompt: 1MARK1, A close-up view of a butterfly wing captured through scanning electron microscopy, showcasing the intricate scales and their unique patterns. The image reveals the microscopic texture and the overlapping layers, resembling a detailed mosaic. The monochromatic tones enhance the fine structures, giving the scene an abstract, otherworldly feel. (Medium: digital micrograph) (Style: inspired by scientific photography and abstract art) (Lighting: high-contrast lighting to emphasize textures and depth) (Colors: monochrome, grayscale with high contrast) (Composition: extreme close-up shot using an FEI Quanta 250 SEM, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)
txt2img mode
Using dev model
Loaded LoRAs in 8.83s
The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['( style : inspired by scientific photography and abstract art ) ( lighting : high - contrast lighting to emphasize textures and depth ) ( colors : monochrome, grayscale with high contrast ) ( composition : extreme close - up shot using an fei quanta 2 5 0 sem, focusing on a specific area of the wing to highlight the repetitive patterns and textures.)']
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Prediction

Model

2e71jjepqnrm00chkbds00rgn4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

33.2s

Created

8 months ago

Input

model: dev
prompt: 1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      model: "dev",
      prompt: "1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "model": "dev",
        "prompt": "1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "model": "dev",
      "prompt": "1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine\'s eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:20:17.507260Z",
  "created_at": "2024-08-28T21:19:44.317000Z",
  "data_removed": false,
  "error": null,
  "id": "2e71jjepqnrm00chkbds00rgn4",
  "input": {
    "model": "dev",
    "prompt": "1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 37950\nPrompt: 1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.\ntxt2img mode\nUsing dev model\nfree=9694836862976\nDownloading weights\n2024-08-28T21:19:58Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp60it78_f/weights url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar\n2024-08-28T21:20:00Z | INFO  | [ Complete ] dest=/tmp/tmp60it78_f/weights size=\"172 MB\" total_elapsed=2.339s url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar\nDownloaded weights in 2.37s\nLoaded LoRAs in 11.37s\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['. the interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.']\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.68it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.22it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.95it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.83it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.77it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.73it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.71it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.70it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.68it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.67it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.67it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.67it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.66it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.66it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.65it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.66it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.66it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.66it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.65it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.66it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.66it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.66it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.66it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.65it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.65it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.65it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.69it/s]",
  "metrics": {
    "predict_time": 19.505964364,
    "total_time": 33.19026
  },
  "output": [
    "https://replicate.delivery/yhqm/0NukmDWR5mLGNpuglrf7itAfifsSANHHfk8WMGuZNsUGpRdNB/out-0.webp"
  ],
  "started_at": "2024-08-28T21:19:58.001296Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2e71jjepqnrm00chkbds00rgn4",
    "cancel": "https://api.replicate.com/v1/predictions/2e71jjepqnrm00chkbds00rgn4/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

19.5 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Using seed: 37950
Prompt: 1MARK1, A high-definition profile shot of a colossal homunculi machine in motion, set within a dramatic action scene. Focus on the machine's eye and face, revealing its elaborate construction in ultra-detailed clarity. The scene should be rendered in a matte photographic style, similar to a giclee print, to emphasize the texture and detail of the machine. The interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.
txt2img mode
Using dev model
free=9694836862976
Downloading weights
2024-08-28T21:19:58Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp60it78_f/weights url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar
2024-08-28T21:20:00Z | INFO  | [ Complete ] dest=/tmp/tmp60it78_f/weights size="172 MB" total_elapsed=2.339s url=https://replicate.delivery/yhqm/gvS9aqomyPaYMRkDBf82VhArkonDGBfhkGGVXPFRNZP6dRXTA/trained_model.tar
Downloaded weights in 2.37s
Loaded LoRAs in 11.37s
The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['. the interaction with an adjacent machine should be depicted with fine accuracy, showcasing the complex mechanics and intense action of the moment.']
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Prediction

Model

dar0b8tnksrm00chkbmsz9z9am

Status

Succeeded

Source

Web

Hardware

H100

Total duration

34.3s

Created

8 months ago

Input

seed: 1984
model: dev
width: 768
height: 1440
prompt: 1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography
lora_scale: 0.45
num_outputs: 1
aspect_ratio: 2:3
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "seed": 1984,
  "model": "dev",
  "width": 768,
  "height": 1440,
  "prompt": "1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography",
  "lora_scale": 0.45,
  "num_outputs": 1,
  "aspect_ratio": "2:3",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      seed: 1984,
      model: "dev",
      width: 768,
      height: 1440,
      prompt: "1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography",
      lora_scale: 0.45,
      num_outputs: 1,
      aspect_ratio: "2:3",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "seed": 1984,
        "model": "dev",
        "width": 768,
        "height": 1440,
        "prompt": "1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography",
        "lora_scale": 0.45,
        "num_outputs": 1,
        "aspect_ratio": "2:3",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "seed": 1984,
      "model": "dev",
      "width": 768,
      "height": 1440,
      "prompt": "1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography",
      "lora_scale": 0.45,
      "num_outputs": 1,
      "aspect_ratio": "2:3",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:35:03.084177Z",
  "created_at": "2024-08-28T21:34:28.766000Z",
  "data_removed": false,
  "error": null,
  "id": "dar0b8tnksrm00chkbmsz9z9am",
  "input": {
    "seed": 1984,
    "model": "dev",
    "width": 768,
    "height": 1440,
    "prompt": "1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography",
    "lora_scale": 0.45,
    "num_outputs": 1,
    "aspect_ratio": "2:3",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 1984\nPrompt: 1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.30s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.64it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.06it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.84it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.76it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.71it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.68it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.66it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.65it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.63it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.63it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.63it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.63it/s]\n 46%|████▋     | 13/28 [00:03<00:04,  3.63it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.63it/s]\n 54%|█████▎    | 15/28 [00:04<00:03,  3.62it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.62it/s]\n 61%|██████    | 17/28 [00:04<00:03,  3.63it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.63it/s]\n 68%|██████▊   | 19/28 [00:05<00:02,  3.63it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.63it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.63it/s]\n 79%|███████▊  | 22/28 [00:06<00:01,  3.63it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.62it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.63it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.63it/s]\n 93%|█████████▎| 26/28 [00:07<00:00,  3.63it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.63it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.63it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.65it/s]",
  "metrics": {
    "predict_time": 17.499290013,
    "total_time": 34.318177
  },
  "output": [
    "https://replicate.delivery/yhqm/gywb0aSfvnToaaPDUqyQcYiAl668BUtR0D792aIeuJYGoUXTA/out-0.webp"
  ],
  "started_at": "2024-08-28T21:34:45.584887Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dar0b8tnksrm00chkbmsz9z9am",
    "cancel": "https://api.replicate.com/v1/predictions/dar0b8tnksrm00chkbmsz9z9am/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

17.5 seconds

markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1

Using seed: 1984
Prompt: 1MARK1, Megafauna walking through the landscape, ultra detailed, unique characteristics, symmetrical, compound microscopic, intensely vivid, soft tones, dynamic distance and ultra macro photography
txt2img mode
Using dev model
Loaded LoRAs in 9.30s
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Prediction

Model

8pr29er2hxrm00chkbptp40t6c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

50.6s

Created

8 months ago

Input

seed: 1984
model: dev
width: 768
height: 1440
prompt: 1MARK1, Megafauna in its natural habitat
lora_scale: 1
num_outputs: 1
aspect_ratio: 2:3
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 28

{
  "seed": 1984,
  "model": "dev",
  "width": 768,
  "height": 1440,
  "prompt": "1MARK1, Megafauna in its natural habitat",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "2:3",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 0.8,
  "num_inference_steps": 28
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
  {
    input: {
      seed: 1984,
      model: "dev",
      width: 768,
      height: 1440,
      prompt: "1MARK1, Megafauna in its natural habitat",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "2:3",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 0.8,
      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 markbland82/mjbstyle1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "markbland82/mjbstyle1:d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    input={
        "seed": 1984,
        "model": "dev",
        "width": 768,
        "height": 1440,
        "prompt": "1MARK1, Megafauna in its natural habitat",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "2:3",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

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

Run markbland82/mjbstyle1 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": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1",
    "input": {
      "seed": 1984,
      "model": "dev",
      "width": 768,
      "height": 1440,
      "prompt": "1MARK1, Megafauna in its natural habitat",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "2:3",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 0.8,
      "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": "2024-08-28T21:39:20.239329Z",
  "created_at": "2024-08-28T21:38:29.647000Z",
  "data_removed": false,
  "error": null,
  "id": "8pr29er2hxrm00chkbptp40t6c",
  "input": {
    "seed": 1984,
    "model": "dev",
    "width": 768,
    "height": 1440,
    "prompt": "1MARK1, Megafauna in its natural habitat",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "2:3",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "extra_lora_scale": 0.8,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 1984\nPrompt: 1MARK1, Megafauna in its natural habitat\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 38.96s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.78it/s]\n  7%|▋         | 2/28 [00:00<00:05,  4.33it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.05it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.93it/s]\n 18%|█▊        | 5/28 [00:01<00:05,  3.87it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.83it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.81it/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,  3.77it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.77it/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 [00:04<00:02,  3.75it/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": 46.924611821,
    "total_time": 50.592329
  },
  "output": [
    "https://replicate.delivery/yhqm/zAQHgcMjqPIZP11secRJ4FpTvuzQWh34wwekK6l85mgIsUXTA/out-0.webp"
  ],
  "started_at": "2024-08-28T21:38:33.314717Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/8pr29er2hxrm00chkbptp40t6c",
    "cancel": "https://api.replicate.com/v1/predictions/8pr29er2hxrm00chkbptp40t6c/cancel"
  },
  "version": "d39163e7f7af729dd71b02b59d15ad199a2b00843c3c94ad7ed2983a7e8932e1"
}

Generated in

46.9 seconds