Prediction

Model

lucataco/flux-vlta:e0aeb648

6naq7mw1mhrm20cjeb8s9b72e8

Status

Succeeded

Source

Web

Hardware

H100

Total duration

12.7s

Created

about 1 year ago

Webhook

–

Input

prompt: portrait photo of VLTA with purple hair
aspect_ratio: 1:1
prompt_strength: 0.8
model: dev
num_outputs: 1
num_inference_steps: 28
guidance_scale: 3.5
output_format: webp
output_quality: 90
lora_scale: 1
extra_lora_scale: 1

{
  "aspect_ratio": "1:1",
  "extra_lora_scale": 1,
  "guidance_scale": 3.5,
  "lora_scale": 1,
  "model": "dev",
  "num_inference_steps": 28,
  "num_outputs": 1,
  "output_format": "webp",
  "output_quality": 90,
  "prompt": "portrait photo of VLTA with purple hair",
  "prompt_strength": 0.8
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_EZM**********************************

This is your API token. Keep it to yourself.

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

const output = await replicate.run(
  "lucataco/flux-vlta:e0aeb648e5d574ad729269b07dfb80765f5b169b19aecc9b416a5ce14b43b29a",
  {
    input: {
      aspect_ratio: "1:1",
      extra_lora_scale: 1,
      guidance_scale: 3.5,
      lora_scale: 1,
      model: "dev",
      num_inference_steps: 28,
      num_outputs: 1,
      output_format: "webp",
      output_quality: 90,
      prompt: "portrait photo of VLTA with purple hair",
      prompt_strength: 0.8
    }
  }
);

// 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.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_EZM**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/flux-vlta:e0aeb648e5d574ad729269b07dfb80765f5b169b19aecc9b416a5ce14b43b29a",
    input={
        "aspect_ratio": "1:1",
        "extra_lora_scale": 1,
        "guidance_scale": 3.5,
        "lora_scale": 1,
        "model": "dev",
        "num_inference_steps": 28,
        "num_outputs": 1,
        "output_format": "webp",
        "output_quality": 90,
        "prompt": "portrait photo of VLTA with purple hair",
        "prompt_strength": 0.8
    }
)

# 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.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_EZM**********************************

This is your API token. Keep it to yourself.

Run lucataco/flux-vlta 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": "lucataco/flux-vlta:e0aeb648e5d574ad729269b07dfb80765f5b169b19aecc9b416a5ce14b43b29a",
    "input": {
      "aspect_ratio": "1:1",
      "extra_lora_scale": 1,
      "guidance_scale": 3.5,
      "lora_scale": 1,
      "model": "dev",
      "num_inference_steps": 28,
      "num_outputs": 1,
      "output_format": "webp",
      "output_quality": 90,
      "prompt": "portrait photo of VLTA with purple hair",
      "prompt_strength": 0.8
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "6naq7mw1mhrm20cjeb8s9b72e8",
  "model": "lucataco/flux-vlta",
  "version": "e0aeb648e5d574ad729269b07dfb80765f5b169b19aecc9b416a5ce14b43b29a",
  "input": {
    "aspect_ratio": "1:1",
    "extra_lora_scale": 1,
    "guidance_scale": 3.5,
    "lora_scale": 1,
    "model": "dev",
    "num_inference_steps": 28,
    "num_outputs": 1,
    "output_format": "webp",
    "output_quality": 90,
    "prompt": "portrait photo of VLTA with purple hair",
    "prompt_strength": 0.8
  },
  "logs": "Using seed: 30002\nPrompt: portrait photo of VLTA with purple hair\n[!] txt2img mode\nUsing dev model\nfree=6941966012416\nDownloading weights\n2024-10-09T19:46:25Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmptj37z586/weights url=https://replicate.delivery/yhqm/M6dMdD4uIHqPGBBlpbdAJp1uge9x1DHe8v63nzEYfiNA4azmA/trained_model.tar\n2024-10-09T19:46:27Z | INFO  | [ Complete ] dest=/tmp/tmptj37z586/weights size=\"172 MB\" total_elapsed=1.853s url=https://replicate.delivery/yhqm/M6dMdD4uIHqPGBBlpbdAJp1uge9x1DHe8v63nzEYfiNA4azmA/trained_model.tar\nDownloaded weights in 1.88s\nLoaded LoRAs in 2.46s\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,  2.90it/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,  2.88it/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,  2.89it/s]",
  "output": [
    "https://replicate.delivery/yhqm/6Hvx5t8qs27gOh5G7jcuLdbmdJ3UA1D9efpL4jLyJcGe8RKnA/out-0.webp"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-10-09T19:46:25.828Z",
  "started_at": "2024-10-09T19:46:25.829124Z",
  "completed_at": "2024-10-09T19:46:38.512107Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/6naq7mw1mhrm20cjeb8s9b72e8/cancel",
    "get": "https://api.replicate.com/v1/predictions/6naq7mw1mhrm20cjeb8s9b72e8",
    "web": "https://replicate.com/p/6naq7mw1mhrm20cjeb8s9b72e8"
  },
  "metrics": {
    "predict_time": 12.682982625,
    "total_time": 12.684107
  }
}

Generated in

12.7 seconds

Tweak it Report

Using seed: 30002 Prompt: portrait photo of VLTA with purple hair [!] txt2img mode Using dev model free=6941966012416 Downloading weights 2024-10-09T19:46:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmptj37z586/weights url=https://replicate.delivery/yhqm/M6dMdD4uIHqPGBBlpbdAJp1uge9x1DHe8v63nzEYfiNA4azmA/trained_model.tar 2024-10-09T19:46:27Z | INFO | [ Complete ] dest=/tmp/tmptj37z586/weights size="172 MB" total_elapsed=1.853s url=https://replicate.delivery/yhqm/M6dMdD4uIHqPGBBlpbdAJp1uge9x1DHe8v63nzEYfiNA4azmA/trained_model.tar Downloaded weights in 1.88s Loaded LoRAs in 2.46s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:09, 2.87it/s] 7%|▋ | 2/28 [00:00<00:08, 3.21it/s] 11%|█ | 3/28 [00:00<00:08, 3.05it/s] 14%|█▍ | 4/28 [00:01<00:08, 2.98it/s] 18%|█▊ | 5/28 [00:01<00:07, 2.94it/s] 21%|██▏ | 6/28 [00:02<00:07, 2.92it/s] 25%|██▌ | 7/28 [00:02<00:07, 2.90it/s] 29%|██▊ | 8/28 [00:02<00:06, 2.90it/s] 32%|███▏ | 9/28 [00:03<00:06, 2.89it/s] 36%|███▌ | 10/28 [00:03<00:06, 2.88it/s] 39%|███▉ | 11/28 [00:03<00:05, 2.88it/s] 43%|████▎ | 12/28 [00:04<00:05, 2.88it/s] 46%|████▋ | 13/28 [00:04<00:05, 2.88it/s] 50%|█████ | 14/28 [00:04<00:04, 2.88it/s] 54%|█████▎ | 15/28 [00:05<00:04, 2.88it/s] 57%|█████▋ | 16/28 [00:05<00:04, 2.88it/s] 61%|██████ | 17/28 [00:05<00:03, 2.88it/s] 64%|██████▍ | 18/28 [00:06<00:03, 2.88it/s] 68%|██████▊ | 19/28 [00:06<00:03, 2.88it/s] 71%|███████▏ | 20/28 [00:06<00:02, 2.88it/s] 75%|███████▌ | 21/28 [00:07<00:02, 2.88it/s] 79%|███████▊ | 22/28 [00:07<00:02, 2.88it/s] 82%|████████▏ | 23/28 [00:07<00:01, 2.88it/s] 86%|████████▌ | 24/28 [00:08<00:01, 2.88it/s] 89%|████████▉ | 25/28 [00:08<00:01, 2.88it/s] 93%|█████████▎| 26/28 [00:08<00:00, 2.88it/s] 96%|█████████▋| 27/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.88it/s] 100%|██████████| 28/28 [00:09<00:00, 2.89it/s]