Prediction

Model

ugleh/flux-dev-lora-dixit:fee2d6d6

w5rc5rkvb9rm40chjqmam3q9e4

Status

Succeeded

Source

Web

Hardware

H100

Total duration

34.3s

Created

over 1 year ago

Webhook

–

Input

prompt: A DIXIT image of toys in a toy store
aspect_ratio: 1:1
model: dev
num_outputs: 1
num_inference_steps: 28
guidance_scale: 3.5
output_format: webp
output_quality: 80
lora_scale: 1
extra_lora_scale: 0.8

{
  "aspect_ratio": "1:1",
  "extra_lora_scale": 0.8,
  "guidance_scale": 3.5,
  "lora_scale": 1,
  "model": "dev",
  "num_inference_steps": 28,
  "num_outputs": 1,
  "output_format": "webp",
  "output_quality": 80,
  "prompt": "A DIXIT image of toys in a toy store"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_XsF**********************************

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 ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92",
  {
    input: {
      aspect_ratio: "1:1",
      extra_lora_scale: 0.8,
      guidance_scale: 3.5,
      lora_scale: 1,
      model: "dev",
      num_inference_steps: 28,
      num_outputs: 1,
      output_format: "webp",
      output_quality: 80,
      prompt: "A DIXIT image of toys in a toy store"
    }
  }
);

// 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_XsF**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run ugleh/flux-dev-lora-dixit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92",
    input={
        "aspect_ratio": "1:1",
        "extra_lora_scale": 0.8,
        "guidance_scale": 3.5,
        "lora_scale": 1,
        "model": "dev",
        "num_inference_steps": 28,
        "num_outputs": 1,
        "output_format": "webp",
        "output_quality": 80,
        "prompt": "A DIXIT image of toys in a toy store"
    }
)

# 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_XsF**********************************

This is your API token. Keep it to yourself.

Run ugleh/flux-dev-lora-dixit 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": "ugleh/flux-dev-lora-dixit:fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92",
    "input": {
      "aspect_ratio": "1:1",
      "extra_lora_scale": 0.8,
      "guidance_scale": 3.5,
      "lora_scale": 1,
      "model": "dev",
      "num_inference_steps": 28,
      "num_outputs": 1,
      "output_format": "webp",
      "output_quality": 80,
      "prompt": "A DIXIT image of toys in a toy store"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "w5rc5rkvb9rm40chjqmam3q9e4",
  "model": "ugleh/flux-dev-lora-dixit",
  "version": "fee2d6d63e93e4b11d3a7a1b6c07321eb36ac9d8d77e12255d040cd82d8c3a92",
  "input": {
    "aspect_ratio": "1:1",
    "extra_lora_scale": 0.8,
    "guidance_scale": 3.5,
    "lora_scale": 1,
    "model": "dev",
    "num_inference_steps": 28,
    "num_outputs": 1,
    "output_format": "webp",
    "output_quality": 80,
    "prompt": "A DIXIT image of toys in a toy store"
  },
  "logs": "Using seed: 60021\nPrompt: A DIXIT image of toys in a toy store\ntxt2img mode\nUsing dev model\nfree=9507482099712\nDownloading weights\n2024-08-27T22:15:26Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmpsv3u1d4f/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\n2024-08-27T22:15:28Z | INFO  | [ Complete ] dest=/tmp/tmpsv3u1d4f/weights size=\"172 MB\" total_elapsed=1.669s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar\nDownloaded weights in 1.70s\nLoaded LoRAs in 26.16s\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.67it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.23it/s]\n 11%|█         | 3/28 [00:00<00:06,  3.94it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.81it/s]\n 18%|█▊        | 5/28 [00:01<00:06,  3.76it/s]\n 21%|██▏       | 6/28 [00:01<00:05,  3.73it/s]\n 25%|██▌       | 7/28 [00:01<00:05,  3.70it/s]\n 29%|██▊       | 8/28 [00:02<00:05,  3.68it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.67it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.67it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.66it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.65it/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,  3.66it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.65it/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,  3.66it/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,  3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.66it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.66it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.69it/s]",
  "output": [
    "https://replicate.delivery/yhqm/GLu9EK8hZp4YHZ9yDnfC4BKrR5v8f0fSetioTeU0ZwkGEB4aC/out-0.webp"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-08-27T22:15:26.81Z",
  "started_at": "2024-08-27T22:15:26.818208Z",
  "completed_at": "2024-08-27T22:16:01.145977Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/w5rc5rkvb9rm40chjqmam3q9e4/cancel",
    "get": "https://api.replicate.com/v1/predictions/w5rc5rkvb9rm40chjqmam3q9e4",
    "web": "https://replicate.com/p/w5rc5rkvb9rm40chjqmam3q9e4"
  },
  "metrics": {
    "predict_time": 34.32776936,
    "total_time": 34.335977
  }
}

Generated in

34.3 seconds

Tweak it Report

Using seed: 60021 Prompt: A DIXIT image of toys in a toy store txt2img mode Using dev model free=9507482099712 Downloading weights 2024-08-27T22:15:26Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsv3u1d4f/weights url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar 2024-08-27T22:15:28Z | INFO | [ Complete ] dest=/tmp/tmpsv3u1d4f/weights size="172 MB" total_elapsed=1.669s url=https://replicate.delivery/yhqm/Axejgm3ZkP37ZSExv53jfyfDeKo733UDWhe8THzJyhZfDefWTA/trained_model.tar Downloaded weights in 1.70s Loaded LoRAs in 26.16s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.94it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.81it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.76it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.70it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.68it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.67it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.67it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.66it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.65it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.66it/s] 50%|█████ | 14/28 [00:03<00:03, 3.66it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.65it/s] 61%|██████ | 17/28 [00:04<00:03, 3.66it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.66it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]