Prediction

a86ksbegw9rm60cjacg9vdr2nc

Status

Succeeded

Source

Web

Hardware

H100

Total duration

14.2s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a punk punk style shoe with gold highlights
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a punk punk style shoe with gold highlights",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a punk punk style shoe with gold highlights",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a punk punk style shoe with gold highlights",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a punk punk style shoe with gold highlights",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T16:05:26.699923Z",
  "created_at": "2024-10-03T16:05:12.546000Z",
  "data_removed": false,
  "error": null,
  "id": "a86ksbegw9rm60cjacg9vdr2nc",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a punk punk style shoe with gold highlights",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 21370\nPrompt: generate image in style of AF1L of a punk punk style shoe with gold highlights\n[!] txt2img mode\nUsing dev model\nfree=6946119098368\nDownloading weights\n2024-10-03T16:05:12Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpg91oxri8/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\n2024-10-03T16:05:16Z | INFO  | [ Complete ] dest=/tmp/tmpg91oxri8/weights size=\"172 MB\" total_elapsed=3.498s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\nDownloaded weights in 3.53s\nLoaded LoRAs in 4.11s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.21it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.94it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.92it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.89it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.88it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.88it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.88it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 14.146740875,
    "total_time": 14.153923
  },
  "output": [
    "https://replicate.delivery/yhqm/jCaeHDcafooBZEs4RKQHtTVNAfAsJVmrxBOVIqsaYtBNWOGnA/out-0.webp"
  ],
  "started_at": "2024-10-03T16:05:12.553182Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/a86ksbegw9rm60cjacg9vdr2nc",
    "cancel": "https://api.replicate.com/v1/predictions/a86ksbegw9rm60cjacg9vdr2nc/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

14.2 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Using seed: 21370
Prompt: generate image in style of AF1L of a punk punk style shoe with gold highlights
[!] txt2img mode
Using dev model
free=6946119098368
Downloading weights
2024-10-03T16:05:12Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpg91oxri8/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
2024-10-03T16:05:16Z | INFO  | [ Complete ] dest=/tmp/tmpg91oxri8/weights size="172 MB" total_elapsed=3.498s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
Downloaded weights in 3.53s
Loaded LoRAs in 4.11s
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Prediction

Model

86qqhpjkh9rm40cjacnvmjrzkw

Status

Succeeded

Source

Web

Hardware

H100

Total duration

16.0s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a shiny, blood red colored shoe
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a shiny, blood red colored  shoe",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a shiny, blood red colored  shoe",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a shiny, blood red colored  shoe",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a shiny, blood red colored  shoe",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T16:16:57.307952Z",
  "created_at": "2024-10-03T16:16:41.354000Z",
  "data_removed": false,
  "error": null,
  "id": "86qqhpjkh9rm40cjacnvmjrzkw",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a shiny, blood red colored  shoe",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 61014\nPrompt: generate image in style of AF1L of a shiny, blood red colored  shoe\n[!] txt2img mode\nUsing dev model\nfree=6898680532992\nDownloading weights\n2024-10-03T16:16:44Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmprzarxrnt/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\n2024-10-03T16:16:46Z | INFO  | [ Complete ] dest=/tmp/tmprzarxrnt/weights size=\"172 MB\" total_elapsed=1.725s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\nDownloaded weights in 1.75s\nLoaded LoRAs in 2.49s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.20it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.04it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.97it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.93it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.91it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.89it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.88it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.88it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.87it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.87it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.87it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.87it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.87it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.87it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:09<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 12.511229114,
    "total_time": 15.953952
  },
  "output": [
    "https://replicate.delivery/yhqm/NDe1UM61EPwAZ6itWZxX4dLweTaLTkg6PEb2PJuKabN5VHjTA/out-0.webp"
  ],
  "started_at": "2024-10-03T16:16:44.796723Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/86qqhpjkh9rm40cjacnvmjrzkw",
    "cancel": "https://api.replicate.com/v1/predictions/86qqhpjkh9rm40cjacnvmjrzkw/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

12.5 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Using seed: 61014
Prompt: generate image in style of AF1L of a shiny, blood red colored  shoe
[!] txt2img mode
Using dev model
free=6898680532992
Downloading weights
2024-10-03T16:16:44Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmprzarxrnt/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
2024-10-03T16:16:46Z | INFO  | [ Complete ] dest=/tmp/tmprzarxrnt/weights size="172 MB" total_elapsed=1.725s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
Downloaded weights in 1.75s
Loaded LoRAs in 2.49s
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Prediction

Model

szzfvvkensrm60cjacpbgg0mtm

Status

Succeeded

Source

Web

Hardware

H100

Total duration

12.1s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a dripping, blue colored shoe on wet pavement
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T16:18:05.939907Z",
  "created_at": "2024-10-03T16:17:53.838000Z",
  "data_removed": false,
  "error": null,
  "id": "szzfvvkensrm60cjacpbgg0mtm",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 26727\nPrompt: generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement\n[!] txt2img mode\nUsing dev model\nfree=6625813741568\nDownloading weights\n2024-10-03T16:17:53Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmphdu8qq92/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\n2024-10-03T16:17:55Z | INFO  | [ Complete ] dest=/tmp/tmphdu8qq92/weights size=\"172 MB\" total_elapsed=1.306s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\nDownloaded weights in 1.34s\nLoaded LoRAs in 2.09s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.88it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.21it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.93it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.91it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.90it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.90it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.89it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.89it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.89it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.89it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.89it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.89it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.89it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.89it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.89it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.89it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.89it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.89it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.89it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.89it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.89it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.89it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.89it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.89it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]",
  "metrics": {
    "predict_time": 12.088870828,
    "total_time": 12.101907
  },
  "output": [
    "https://replicate.delivery/yhqm/7Mr34u8oWfV6EinAigbvP19pTqIIZkETdWigiFAnD13eWHjTA/out-0.webp"
  ],
  "started_at": "2024-10-03T16:17:53.851036Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/szzfvvkensrm60cjacpbgg0mtm",
    "cancel": "https://api.replicate.com/v1/predictions/szzfvvkensrm60cjacpbgg0mtm/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

12.1 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Using seed: 26727
Prompt: generate image in style of AF1L of a dripping, blue colored  shoe on wet pavement
[!] txt2img mode
Using dev model
free=6625813741568
Downloading weights
2024-10-03T16:17:53Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmphdu8qq92/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
2024-10-03T16:17:55Z | INFO  | [ Complete ] dest=/tmp/tmphdu8qq92/weights size="172 MB" total_elapsed=1.306s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
Downloaded weights in 1.34s
Loaded LoRAs in 2.09s
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Prediction

Model

8ekdz4p6fhrm20cjacrsdhcn6c

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.7s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a white & "Where is Waldo?" shoe on brick sidewalk
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a white &  \"Where is Waldo?\" shoe on brick sidewalk",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a white &  \"Where is Waldo?\" shoe on brick sidewalk",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a white &  \"Where is Waldo?\" shoe on brick sidewalk",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a white &  \\"Where is Waldo?\\" shoe on brick sidewalk",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T16:23:54.698039Z",
  "created_at": "2024-10-03T16:23:43.996000Z",
  "data_removed": false,
  "error": null,
  "id": "8ekdz4p6fhrm20cjacrsdhcn6c",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a white &  \"Where is Waldo?\" shoe on brick sidewalk",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 51015\nPrompt: generate image in style of AF1L of a white &  \"Where is Waldo?\" shoe on brick sidewalk\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.71s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.88it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.06it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.99it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.93it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.91it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.91it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.90it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.90it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.89it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.89it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.89it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.89it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.89it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.89it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.89it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.89it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.89it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.89it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.89it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.89it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.89it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.89it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]",
  "metrics": {
    "predict_time": 10.695203759,
    "total_time": 10.702039
  },
  "output": [
    "https://replicate.delivery/yhqm/hBxicxd7ka53M9OzeLXoVxYg75huYFH0PpOQCXqoLTCNujxJA/out-0.webp"
  ],
  "started_at": "2024-10-03T16:23:44.002835Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/8ekdz4p6fhrm20cjacrsdhcn6c",
    "cancel": "https://api.replicate.com/v1/predictions/8ekdz4p6fhrm20cjacrsdhcn6c/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

10.7 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Prediction

Model

3aqd8vp4ysrm00cjacsay69px8

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.7s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T16:24:59.790271Z",
  "created_at": "2024-10-03T16:24:49.142000Z",
  "data_removed": false,
  "error": null,
  "id": "3aqd8vp4ysrm00cjacsay69px8",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 22786\nPrompt: generate image in style of AF1L of a green plaid shoe in grass with four leaf clovers\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.59s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.86it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.19it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.04it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.97it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.93it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.91it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.89it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.88it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.87it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.87it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.87it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.87it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.87it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.87it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.87it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.87it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.87it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.87it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.87it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.87it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.87it/s]\n 93%|█████████▎| 26/28 [00:09<00:00,  2.87it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.87it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]",
  "metrics": {
    "predict_time": 10.640663271,
    "total_time": 10.648271
  },
  "output": [
    "https://replicate.delivery/yhqm/k4dPMKLOYLY5B1LLdQl340Qee9nQpwQDJCkS4gHDqhe36OGnA/out-0.webp"
  ],
  "started_at": "2024-10-03T16:24:49.149607Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/3aqd8vp4ysrm00cjacsay69px8",
    "cancel": "https://api.replicate.com/v1/predictions/3aqd8vp4ysrm00cjacsay69px8/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

10.7 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Prediction

Model

pdd8qwzb89rm20cjad9b1xfbgg

Status

Succeeded

Source

Web

Hardware

H100

Total duration

61.7s

Created

10 months ago

Input

model: dev
prompt: generate image in style of AF1L of a matte black diamond encrusted
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate image in style of AF1L of a matte black diamond encrusted",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate image in style of AF1L of a matte black diamond encrusted",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate image in style of AF1L of a matte black diamond encrusted",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate image in style of AF1L of a matte black diamond encrusted",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T17:00:57.830347Z",
  "created_at": "2024-10-03T16:59:56.098000Z",
  "data_removed": false,
  "error": null,
  "id": "pdd8qwzb89rm20cjad9b1xfbgg",
  "input": {
    "model": "dev",
    "prompt": "generate image in style of AF1L of a matte black diamond encrusted",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 4964\nPrompt: generate image in style of AF1L of a matte black diamond encrusted\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.75s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.89it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.05it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.98it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.92it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.91it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.90it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.89it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.89it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.89it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.88it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.88it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.88it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.88it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.88it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.88it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]",
  "metrics": {
    "predict_time": 10.725069148,
    "total_time": 61.732347
  },
  "output": [
    "https://replicate.delivery/yhqm/1b1L0XeBfwvWSUL56x6h6EGmrEXWwXoyfbY6aZ3Y8oUTefYcC/out-0.webp"
  ],
  "started_at": "2024-10-03T17:00:47.105278Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/pdd8qwzb89rm20cjad9b1xfbgg",
    "cancel": "https://api.replicate.com/v1/predictions/pdd8qwzb89rm20cjad9b1xfbgg/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

10.7 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Prediction

Model

xt1q76nft9rm00cjags9fvb4yg

Status

Succeeded

Source

Web

Hardware

H100

Total duration

12.5s

Created

10 months ago

Input

model: dev
prompt: generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T21:04:33.401314Z",
  "created_at": "2024-10-03T21:04:20.946000Z",
  "data_removed": false,
  "error": null,
  "id": "xt1q76nft9rm00cjags9fvb4yg",
  "input": {
    "model": "dev",
    "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 52816\nPrompt: generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285\n[!] txt2img mode\nUsing dev model\nfree=7116320325632\nDownloading weights\n2024-10-03T21:04:21Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmprotrngop/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\n2024-10-03T21:04:22Z | INFO  | [ Complete ] dest=/tmp/tmprotrngop/weights size=\"172 MB\" total_elapsed=1.623s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\nDownloaded weights in 1.66s\nLoaded LoRAs in 2.40s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.87it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.20it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.04it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.97it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.94it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.91it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.90it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.88it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.89it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.88it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.88it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.88it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.88it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.88it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.88it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.88it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.88it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.88it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.88it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.87it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.87it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.89it/s]",
  "metrics": {
    "predict_time": 12.445415795,
    "total_time": 12.455314
  },
  "output": [
    "https://replicate.delivery/yhqm/PlR8fyNVrNRpLCzeqyj2IGLrUe6iAnYxIjYiQUJeO5ZEOuMOB/out-0.webp"
  ],
  "started_at": "2024-10-03T21:04:20.955899Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xt1q76nft9rm00cjags9fvb4yg",
    "cancel": "https://api.replicate.com/v1/predictions/xt1q76nft9rm00cjags9fvb4yg/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

12.4 seconds

henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5

Using seed: 52816
Prompt: generate photograph in style of AF1L of a punk punk style low top shoe, on a slick break dance floor. colors of HEX ##00F80C and #1B3285
[!] txt2img mode
Using dev model
free=7116320325632
Downloading weights
2024-10-03T21:04:21Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmprotrngop/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
2024-10-03T21:04:22Z | INFO  | [ Complete ] dest=/tmp/tmprotrngop/weights size="172 MB" total_elapsed=1.623s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar
Downloaded weights in 1.66s
Loaded LoRAs in 2.40s
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Prediction

Model

m1ga0mcwnhrm00cjah28zym42r

Status

Succeeded

Source

Web

Hardware

H100

Total duration

16.7s

Created

10 months ago

Input

model: dev
prompt: generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor. neon colors.
lora_scale: 1
num_outputs: 1
aspect_ratio: 1:1
output_format: webp
guidance_scale: 3.5
output_quality: 90
prompt_strength: 0.8
extra_lora_scale: 1
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 90,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}

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 henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
  {
    input: {
      model: "dev",
      prompt: "generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 90,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

pip install replicate

Import the client:

import replicate

Run henn0124/af1-l using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    input={
        "model": "dev",
        "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 90,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# 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 henn0124/af1-l 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": "henn0124/af1-l:447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5",
    "input": {
      "model": "dev",
      "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 90,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-10-03T21:24:12.408556Z",
  "created_at": "2024-10-03T21:23:55.692000Z",
  "data_removed": false,
  "error": null,
  "id": "m1ga0mcwnhrm00cjah28zym42r",
  "input": {
    "model": "dev",
    "prompt": "generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 90,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 65227\nPrompt: generate photograph in style of AF1L of a punk punk style low top shoe, moving on a slick break dance floor.  neon colors.\n[!] txt2img mode\nUsing dev model\nfree=9159026405376\nDownloading weights\n2024-10-03T21:23:59Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp5dcfxv68/weights url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\n2024-10-03T21:24:01Z | INFO  | [ Complete ] dest=/tmp/tmp5dcfxv68/weights size=\"172 MB\" total_elapsed=2.087s url=https://replicate.delivery/yhqm/Tir32koSx6I3JhgVkoCnOzsjxJV5wwhWfCYu0yMGsvlpeGjTA/trained_model.tar\nDownloaded weights in 2.12s\nLoaded LoRAs in 2.86s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:09,  2.88it/s]\n  7%|▋         | 2/28 [00:00<00:08,  3.22it/s]\n 11%|█         | 3/28 [00:00<00:08,  3.06it/s]\n 14%|█▍        | 4/28 [00:01<00:08,  2.99it/s]\n 18%|█▊        | 5/28 [00:01<00:07,  2.95it/s]\n 21%|██▏       | 6/28 [00:02<00:07,  2.93it/s]\n 25%|██▌       | 7/28 [00:02<00:07,  2.91it/s]\n 29%|██▊       | 8/28 [00:02<00:06,  2.90it/s]\n 32%|███▏      | 9/28 [00:03<00:06,  2.90it/s]\n 36%|███▌      | 10/28 [00:03<00:06,  2.89it/s]\n 39%|███▉      | 11/28 [00:03<00:05,  2.89it/s]\n 43%|████▎     | 12/28 [00:04<00:05,  2.89it/s]\n 46%|████▋     | 13/28 [00:04<00:05,  2.89it/s]\n 50%|█████     | 14/28 [00:04<00:04,  2.89it/s]\n 54%|█████▎    | 15/28 [00:05<00:04,  2.89it/s]\n 57%|█████▋    | 16/28 [00:05<00:04,  2.89it/s]\n 61%|██████    | 17/28 [00:05<00:03,  2.89it/s]\n 64%|██████▍   | 18/28 [00:06<00:03,  2.89it/s]\n 68%|██████▊   | 19/28 [00:06<00:03,  2.88it/s]\n 71%|███████▏  | 20/28 [00:06<00:02,  2.88it/s]\n 75%|███████▌  | 21/28 [00:07<00:02,  2.88it/s]\n 79%|███████▊  | 22/28 [00:07<00:02,  2.88it/s]\n 82%|████████▏ | 23/28 [00:07<00:01,  2.88it/s]\n 86%|████████▌ | 24/28 [00:08<00:01,  2.88it/s]\n 89%|████████▉ | 25/28 [00:08<00:01,  2.88it/s]\n 93%|█████████▎| 26/28 [00:08<00:00,  2.88it/s]\n 96%|█████████▋| 27/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.88it/s]\n100%|██████████| 28/28 [00:09<00:00,  2.90it/s]",
  "metrics": {
    "predict_time": 12.840519816,
    "total_time": 16.716556
  },
  "output": [
    "https://replicate.delivery/yhqm/kLc7oxEYMW4KFtcXleHTuUoYeTftBc0pFEO5Z9xxrdD4rXGnA/out-0.webp"
  ],
  "started_at": "2024-10-03T21:23:59.568036Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/m1ga0mcwnhrm00cjah28zym42r",
    "cancel": "https://api.replicate.com/v1/predictions/m1ga0mcwnhrm00cjah28zym42r/cancel"
  },
  "version": "447e1eddcaba74f45bd268f3546718afc63e61c8ef93f99d93d853e4afae8ca5"
}

Generated in

12.8 seconds