apolinario / flux-tarot-v1

Flux lora, use "in the style of TOK a trtcrd tarot style" to trigger image generation

Warm

Public
31.2K runs
H100
Paper

Run with an API

Playground API Examples README Versions

Prediction

apolinario/flux-tarot-v1:6c4ebdf049df552f8c02b3a7bbb3afec3d37b20924282bab8744f1168b6de470

Model

apolinario/flux-tarot-v1:6c4ebdf0

djrsws8zm9rm20chc2g9hv8zy0

Status

Succeeded

Source

Web

Hardware

H100

Total duration

64.8s

Created

9 months ago

Input

model: dev
prompt: a person giving a ted talk on a TED stage with the TED logo, "the speaker" in the style of TOK a trtcrd, tarot style
lora_scale: 1
num_outputs: 1
aspect_ratio: 2:3
output_format: webp
guidance_scale: 3.5
output_quality: 80
num_inference_steps: 28

{
  "model": "dev",
  "prompt": "a person giving a ted talk on a TED stage with the TED logo, \"the speaker\" in the style of TOK a trtcrd, tarot style",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "2:3",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "num_inference_steps": 28
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

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

Run apolinario/flux-tarot-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "apolinario/flux-tarot-v1:6c4ebdf049df552f8c02b3a7bbb3afec3d37b20924282bab8744f1168b6de470",
  {
    input: {
      model: "dev",
      prompt: "a person giving a ted talk on a TED stage with the TED logo, \"the speaker\" in the style of TOK a trtcrd, tarot style",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "2:3",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      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.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run apolinario/flux-tarot-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "apolinario/flux-tarot-v1:6c4ebdf049df552f8c02b3a7bbb3afec3d37b20924282bab8744f1168b6de470",
    input={
        "model": "dev",
        "prompt": "a person giving a ted talk on a TED stage with the TED logo, \"the speaker\" in the style of TOK a trtcrd, tarot style",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "2:3",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "num_inference_steps": 28
    }
)
print(output)

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

Run apolinario/flux-tarot-v1 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": "6c4ebdf049df552f8c02b3a7bbb3afec3d37b20924282bab8744f1168b6de470",
    "input": {
      "model": "dev",
      "prompt": "a person giving a ted talk on a TED stage with the TED logo, \\"the speaker\\" in the style of TOK a trtcrd, tarot style",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "2:3",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "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": "2024-08-17T13:57:37.080412Z",
  "created_at": "2024-08-17T13:56:32.290000Z",
  "data_removed": false,
  "error": null,
  "id": "djrsws8zm9rm20chc2g9hv8zy0",
  "input": {
    "model": "dev",
    "prompt": "a person giving a ted talk on a TED stage with the TED logo, \"the speaker\" in the style of TOK a trtcrd, tarot style",
    "lora_scale": 1,
    "num_outputs": 1,
    "aspect_ratio": "2:3",
    "output_format": "webp",
    "guidance_scale": 3.5,
    "output_quality": 80,
    "num_inference_steps": 28
  },
  "logs": "Using seed: 38164\nPrompt: a person giving a ted talk on a TED stage with the TED logo, \"the speaker\" in the style of TOK a trtcrd, tarot style\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9873027907584\nDownloading weights\n2024-08-17T13:57:18Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/f39242990b811c57 url=https://replicate.delivery/yhqm/P0f0U8kSZX3WPyee7NQHScd7S3IwjvC2tWKfiKG7nIOQdXONB/trained_model.tar\n2024-08-17T13:57:19Z | INFO  | [ Complete ] dest=/src/weights-cache/f39242990b811c57 size=\"172 MB\" total_elapsed=1.525s url=https://replicate.delivery/yhqm/P0f0U8kSZX3WPyee7NQHScd7S3IwjvC2tWKfiKG7nIOQdXONB/trained_model.tar\nb''\nDownloaded weights in 1.5542120933532715 seconds\nLoRA weights loaded successfully\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:07,  3.75it/s]\n  7%|▋         | 2/28 [00:00<00:06,  4.31it/s]\n 11%|█         | 3/28 [00:00<00:06,  4.05it/s]\n 14%|█▍        | 4/28 [00:01<00:06,  3.94it/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,  3.80it/s]\n 32%|███▏      | 9/28 [00:02<00:05,  3.79it/s]\n 36%|███▌      | 10/28 [00:02<00:04,  3.78it/s]\n 39%|███▉      | 11/28 [00:02<00:04,  3.77it/s]\n 43%|████▎     | 12/28 [00:03<00:04,  3.78it/s]\n 46%|████▋     | 13/28 [00:03<00:03,  3.77it/s]\n 50%|█████     | 14/28 [00:03<00:03,  3.76it/s]\n 54%|█████▎    | 15/28 [00:03<00:03,  3.77it/s]\n 57%|█████▋    | 16/28 [00:04<00:03,  3.77it/s]\n 61%|██████    | 17/28 [00:04<00:02,  3.77it/s]\n 64%|██████▍   | 18/28 [00:04<00:02,  3.76it/s]\n 68%|██████▊   | 19/28 [00:04<00:02,  3.77it/s]\n 71%|███████▏  | 20/28 [00:05<00:02,  3.77it/s]\n 75%|███████▌  | 21/28 [00:05<00:01,  3.77it/s]\n 79%|███████▊  | 22/28 [00:05<00:01,  3.77it/s]\n 82%|████████▏ | 23/28 [00:06<00:01,  3.77it/s]\n 86%|████████▌ | 24/28 [00:06<00:01,  3.77it/s]\n 89%|████████▉ | 25/28 [00:06<00:00,  3.77it/s]\n 93%|█████████▎| 26/28 [00:06<00:00,  3.77it/s]\n 96%|█████████▋| 27/28 [00:07<00:00,  3.77it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.78it/s]\n100%|██████████| 28/28 [00:07<00:00,  3.80it/s]",
  "metrics": {
    "predict_time": 18.747860465,
    "total_time": 64.790412
  },
  "output": [
    "https://replicate.delivery/yhqm/ntpCLc1reMXKTKAipFQCgpD4qYB6LkIoqiFJNUWll2Ko8ypJA/out-0.webp"
  ],
  "started_at": "2024-08-17T13:57:18.332551Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/djrsws8zm9rm20chc2g9hv8zy0",
    "cancel": "https://api.replicate.com/v1/predictions/djrsws8zm9rm20chc2g9hv8zy0/cancel"
  },
  "version": "6c4ebdf049df552f8c02b3a7bbb3afec3d37b20924282bab8744f1168b6de470"
}

Generated in

18.8 seconds

Tweak it ShareReport

Using seed: 38164
Prompt: a person giving a ted talk on a TED stage with the TED logo, "the speaker" in the style of TOK a trtcrd, tarot style
txt2img mode
Using dev model
Loading LoRA weights
Ensuring enough disk space...
Free disk space: 9873027907584
Downloading weights
2024-08-17T13:57:18Z | INFO  | [ Initiating ] chunk_size=150M dest=/src/weights-cache/f39242990b811c57 url=https://replicate.delivery/yhqm/P0f0U8kSZX3WPyee7NQHScd7S3IwjvC2tWKfiKG7nIOQdXONB/trained_model.tar
2024-08-17T13:57:19Z | INFO  | [ Complete ] dest=/src/weights-cache/f39242990b811c57 size="172 MB" total_elapsed=1.525s url=https://replicate.delivery/yhqm/P0f0U8kSZX3WPyee7NQHScd7S3IwjvC2tWKfiKG7nIOQdXONB/trained_model.tar
b''
Downloaded weights in 1.5542120933532715 seconds
LoRA weights loaded successfully
  0%|          | 0/28 [00:00<?, ?it/s]
  4%|▎         | 1/28 [00:00<00:07,  3.75it/s]
  7%|▋         | 2/28 [00:00<00:06,  4.31it/s]
 11%|█         | 3/28 [00:00<00:06,  4.05it/s]
 14%|█▍        | 4/28 [00:01<00:06,  3.94it/s]
 18%|█▊        | 5/28 [00:01<00:05,  3.87it/s]
 21%|██▏       | 6/28 [00:01<00:05,  3.83it/s]
 25%|██▌       | 7/28 [00:01<00:05,  3.81it/s]
 29%|██▊       | 8/28 [00:02<00:05,  3.80it/s]
 32%|███▏      | 9/28 [00:02<00:05,  3.79it/s]
 36%|███▌      | 10/28 [00:02<00:04,  3.78it/s]
 39%|███▉      | 11/28 [00:02<00:04,  3.77it/s]
 43%|████▎     | 12/28 [00:03<00:04,  3.78it/s]
 46%|████▋     | 13/28 [00:03<00:03,  3.77it/s]
 50%|█████     | 14/28 [00:03<00:03,  3.76it/s]
 54%|█████▎    | 15/28 [00:03<00:03,  3.77it/s]
 57%|█████▋    | 16/28 [00:04<00:03,  3.77it/s]
 61%|██████    | 17/28 [00:04<00:02,  3.77it/s]
 64%|██████▍   | 18/28 [00:04<00:02,  3.76it/s]
 68%|██████▊   | 19/28 [00:04<00:02,  3.77it/s]
 71%|███████▏  | 20/28 [00:05<00:02,  3.77it/s]
 75%|███████▌  | 21/28 [00:05<00:01,  3.77it/s]
 79%|███████▊  | 22/28 [00:05<00:01,  3.77it/s]
 82%|████████▏ | 23/28 [00:06<00:01,  3.77it/s]
 86%|████████▌ | 24/28 [00:06<00:01,  3.77it/s]
 89%|████████▉ | 25/28 [00:06<00:00,  3.77it/s]
 93%|█████████▎| 26/28 [00:06<00:00,  3.77it/s]
 96%|█████████▋| 27/28 [00:07<00:00,  3.77it/s]
100%|██████████| 28/28 [00:07<00:00,  3.78it/s]
100%|██████████| 28/28 [00:07<00:00,  3.80it/s]

Want to make some of these yourself?

Run this model