alexgenovese/train-sdxl-lora:533e1248 | Run with an API on Replicate

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alexgenovese /train-sdxl-lora:533e1248

Playground API

Input

input_images

*file

A .zip or .tar file containing the image files that will be used for fine-tuning

seed

integer

Random seed for reproducible training. Leave empty to use a random seed

resolution

integer

Square pixel resolution which your images will be resized to for training

Default: 1024

train_batch_size

integer

Batch size (per device) for training

Default: 3

num_train_epochs

integer

Number of epochs to loop through your training dataset

Default: 20

max_train_steps

integer

Number of individual training steps. Takes precedence over num_train_epochs

class_token

string

Token class to pass to ClipSeg for identify the object

Default: "bag"

is_lora

boolean

Whether to use LoRA training. If set to False, will use Full fine tuning

Default: true

unet_learning_rate

number

Learning rate for the U-Net. We recommend this value to be somewhere between `1e-6` to `1e-5`.

Default: 0.0001

ti_lr

number

Scaling of learning rate for training textual inversion embeddings. Don't alter unless you know what you're doing.

Default: 0.0001

lora_lr

number

Scaling of learning rate for training LoRA embeddings. Don't alter unless you know what you're doing.

Default: 0.0004

lora_rank

integer

Rank of LoRA embeddings. Don't alter unless you know what you're doing.

Default: 32

lora_rank_alpha

integer

Rank of LoRA Alpha.

Default: 16

lr_scheduler

string

Learning rate scheduler to use for training

Default: "constant"

optimization

string

TODO: now using

Default: "AdamW"

lr_warmup_steps

integer

Number of warmup steps for lr schedulers with warmups.

Default: 0

token_string

string

Shift + Return to add a new line

A unique string that will be trained to refer to the concept in the input images. Can be anything, but TOK works well

Default: "siduhc"

caption_prefix

string

Shift + Return to add a new line

Text which will be used as prefix during automatic captioning. Must contain the `token_string`. For example, if caption text is 'a photo of TOK', automatic captioning will expand to 'a photo of TOK under a bridge', 'a photo of TOK holding a cup', etc.

Default: "a photo of siduhc "

mask_target_prompts

string

Shift + Return to add a new line

Prompt that describes part of the image that you will find important. For example, if you are fine-tuning your pet, `photo of a dog` will be a good prompt. Prompt-based masking is used to focus the fine-tuning process on the important/salient parts of the image

crop_based_on_salience

boolean

If you want to crop the image to `target_size` based on the important parts of the image, set this to True. If you want to crop the image based on face detection, set this to False

Default: true

use_face_detection_instead

boolean

If you want to use face detection instead of CLIPSeg for masking. For face applications, we recommend using this option.

Default: false

clipseg_temperature

number

How blurry you want the CLIPSeg mask to be. We recommend this value be something between `0.5` to `1.0`. If you want to have more sharp mask (but thus more errorful), you can decrease this value.

Default: 1

verbose

boolean

verbose output

Default: true

checkpointing_steps

integer

Number of steps between saving checkpoints. Set to very very high number to disable checkpointing, because you don't need one.

Default: 999999

input_images_filetype

string

Filetype of the input images. Can be either `zip` or `tar`. By default its `infer`, and it will be inferred from the ext of input file.

Default: "infer"

Run this model in Node.js with one line of code:

npx create-replicate --model=alexgenovese/train-sdxl-lora

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

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 alexgenovese/train-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11",
  {
    input: {
      ti_lr: 0.0001,
      is_lora: true,
      lora_lr: 0.0004,
      verbose: true,
      lora_rank: 32,
      resolution: 1024,
      class_token: "bag",
      lr_scheduler: "constant",
      optimization: "AdamW",
      token_string: "siduhc",
      caption_prefix: "a photo of siduhc ",
      lora_rank_alpha: 16,
      lr_warmup_steps: 0,
      num_train_epochs: 20,
      train_batch_size: 3,
      unet_learning_rate: 0.0001,
      checkpointing_steps: 999999,
      clipseg_temperature: 1,
      input_images_filetype: "infer",
      crop_based_on_salience: true,
      use_face_detection_instead: false
    }
  }
);

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

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

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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run alexgenovese/train-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11",
    input={
        "ti_lr": 0.0001,
        "is_lora": True,
        "lora_lr": 0.0004,
        "verbose": True,
        "lora_rank": 32,
        "resolution": 1024,
        "class_token": "bag",
        "lr_scheduler": "constant",
        "optimization": "AdamW",
        "token_string": "siduhc",
        "caption_prefix": "a photo of siduhc ",
        "lora_rank_alpha": 16,
        "lr_warmup_steps": 0,
        "num_train_epochs": 20,
        "train_batch_size": 3,
        "unet_learning_rate": 0.0001,
        "checkpointing_steps": 999999,
        "clipseg_temperature": 1,
        "input_images_filetype": "infer",
        "crop_based_on_salience": True,
        "use_face_detection_instead": False
    }
)
print(output)

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=<paste-your-token-here>

Find your API token in your account settings.

Run alexgenovese/train-sdxl-lora 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": "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11",
    "input": {
      "ti_lr": 0.0001,
      "is_lora": true,
      "lora_lr": 0.0004,
      "verbose": true,
      "lora_rank": 32,
      "resolution": 1024,
      "class_token": "bag",
      "lr_scheduler": "constant",
      "optimization": "AdamW",
      "token_string": "siduhc",
      "caption_prefix": "a photo of siduhc ",
      "lora_rank_alpha": 16,
      "lr_warmup_steps": 0,
      "num_train_epochs": 20,
      "train_batch_size": 3,
      "unet_learning_rate": 0.0001,
      "checkpointing_steps": 999999,
      "clipseg_temperature": 1,
      "input_images_filetype": "infer",
      "crop_based_on_salience": true,
      "use_face_detection_instead": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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