edenartlab/sdxl-lora-trainer:0a5ef03f – Run with an API on Replicate

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edenartlab /sdxl-lora-trainer:0a5ef03f

Playground API

Input

name

string

Shift + Return to add a new line

Name of new LORA concept

lora_training_urls

string

Shift + Return to add a new line

Training images for new LORA concept (can be image urls or a .zip file of images)

concept_mode

string

Shift + Return to add a new line

'face' / 'style' / 'object' (default)

Default: "object"

checkpoint

string

Which Stable Diffusion checkpoint to use

Default: "sdxl-v1.0"

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 recommended [768-1024]

Default: 960

train_batch_size

integer

Batch size (per device) for training

Default: 2

num_train_epochs

integer

Number of epochs to loop through your training dataset

Default: 10000

max_train_steps

integer

Number of individual training steps. Takes precedence over num_train_epochs

Default: 1200

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: 10000

is_lora

boolean

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

Default: true

ti_lr

number

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

Default: 0.002

ti_weight_decay

number

weight decay for textual inversion embeddings. Don't alter unless you know what you're doing.

Default: 0.0001

lora_rank

integer

Rank of LoRA embeddings. For faces 5 is good, for complex concepts / styles you can try 8 or 12

Default: 5

lr_scheduler

string

Learning rate scheduler to use for training

Default: "constant"

lr_warmup_steps

integer

Number of warmup steps for lr schedulers with warmups.

Default: 50

caption_prefix

string

Shift + Return to add a new line

Prefix text prepended to automatic captioning. Must contain the 'TOK'. Example is 'a photo of TOK, '. If empty, chatgpt will take care of this automatically

Default: ""

left_right_flip_augmentation

boolean

Add left-right flipped version of each img to the training data, recommended for most cases. If you are learning a face, you prob want to disable this

Default: true

mask_target_prompts

string

Shift + Return to add a new line

Prompt that describes most important part of the image, will be used for CLIP-segmentation. For example, if you are learning a person 'face' would be a good segmentation prompt

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

run_name

string

Shift + Return to add a new line

Subdirectory where all files will be saved

Default: "1700615902"

debug

boolean

for debugging locally only (dont activate this on replicate)

Default: false

hard_pivot

boolean

Use hard freeze for ti_lr. If set to False, will use soft transition of learning rates

Default: false

off_ratio_power

number

How strongly to correct the embedding std vs the avg-std (0=off, 0.05=weak, 0.1=standard)

Default: 0.1

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

npx create-replicate --model=edenartlab/sdxl-lora-trainer

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";

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

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

const output = await replicate.run(
  "edenartlab/sdxl-lora-trainer:0a5ef03f2a50b5258d4d1b2d77c09b7c957dc4a00a3647bffc4d6b47dae7b9de",
  {
    input: {
      debug: false,
      ti_lr: 0.002,
      is_lora: true,
      verbose: true,
      run_name: "1700615902",
      lora_rank: 5,
      checkpoint: "sdxl-v1.0",
      hard_pivot: false,
      resolution: 960,
      concept_mode: "object",
      lr_scheduler: "constant",
      caption_prefix: "",
      lr_warmup_steps: 50,
      max_train_steps: 1200,
      off_ratio_power: 0.1,
      ti_weight_decay: 0.0001,
      num_train_epochs: 10000,
      train_batch_size: 2,
      checkpointing_steps: 10000,
      clipseg_temperature: 1,
      crop_based_on_salience: true,
      use_face_detection_instead: false,
      left_right_flip_augmentation: true
    }
  }
);
console.log(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 edenartlab/sdxl-lora-trainer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "edenartlab/sdxl-lora-trainer:0a5ef03f2a50b5258d4d1b2d77c09b7c957dc4a00a3647bffc4d6b47dae7b9de",
    input={
        "debug": False,
        "ti_lr": 0.002,
        "is_lora": True,
        "verbose": True,
        "run_name": "1700615902",
        "lora_rank": 5,
        "checkpoint": "sdxl-v1.0",
        "hard_pivot": False,
        "resolution": 960,
        "concept_mode": "object",
        "lr_scheduler": "constant",
        "caption_prefix": "",
        "lr_warmup_steps": 50,
        "max_train_steps": 1200,
        "off_ratio_power": 0.1,
        "ti_weight_decay": 0.0001,
        "num_train_epochs": 10000,
        "train_batch_size": 2,
        "checkpointing_steps": 10000,
        "clipseg_temperature": 1,
        "crop_based_on_salience": True,
        "use_face_detection_instead": False,
        "left_right_flip_augmentation": True
    }
)

# The edenartlab/sdxl-lora-trainer model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/edenartlab/sdxl-lora-trainer/api#output-schema
    print(item)

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 edenartlab/sdxl-lora-trainer 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": "0a5ef03f2a50b5258d4d1b2d77c09b7c957dc4a00a3647bffc4d6b47dae7b9de",
    "input": {
      "debug": false,
      "ti_lr": 0.002,
      "is_lora": true,
      "verbose": true,
      "run_name": "1700615902",
      "lora_rank": 5,
      "checkpoint": "sdxl-v1.0",
      "hard_pivot": false,
      "resolution": 960,
      "concept_mode": "object",
      "lr_scheduler": "constant",
      "caption_prefix": "",
      "lr_warmup_steps": 50,
      "max_train_steps": 1200,
      "off_ratio_power": 0.1,
      "ti_weight_decay": 0.0001,
      "num_train_epochs": 10000,
      "train_batch_size": 2,
      "checkpointing_steps": 10000,
      "clipseg_temperature": 1,
      "crop_based_on_salience": true,
      "use_face_detection_instead": false,
      "left_right_flip_augmentation": true
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.