edenartlab/sdxl-lora-trainer:2d65dc0d – Run with an API on Replicate

Version

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edenartlab /sdxl-lora-trainer:2d65dc0d

Playground API

Input

lora_training_urls

*string

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

concept_mode

string

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

Default: "style"

sd_model_version

string

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'sdxl' / 'sd15'

Default: "sdxl"

name

string

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Name of new LORA concept

Default: "unnamed"

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: 512 or 640

Default: 512

train_batch_size

integer

Batch size (per device) for training

Default: 4

max_train_steps

integer

Number of training steps.

Default: 400

token_warmup_steps

integer

Number of steps for token (textual_inversion) warmup.

Default: 0

checkpointing_steps

integer

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

Default: 10000

unet_lr

number

final learning rate of unet (after warmup)

Default: 0.001

ti_lr

number

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

Default: 0.001

freeze_ti_after_completion_f

number

Fraction of training steps after which to freeze textual inversion embeddings

Default: 1

lora_rank

integer

Rank of LoRA embeddings for the unet.

Default: 16

caption_model

string

Shift + Return to add a new line

Which captioning model to use. ['gpt4-v', 'blip'] are supported right now

Default: "blip"

n_tokens

integer

How many new tokens to inject per concept

Default: 2

verbose

boolean

verbose output

Default: true

debug

boolean

For debugging locally only (dont activate this on replicate)

Default: false

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:2d65dc0d1cf49e679a443c1da0f46933bf11043681b18a57c0c57636b2418b34",
  {
    input: {
      name: "unnamed",
      debug: false,
      ti_lr: 0.001,
      unet_lr: 0.001,
      verbose: true,
      n_tokens: 2,
      lora_rank: 16,
      resolution: 512,
      concept_mode: "style",
      caption_model: "blip",
      max_train_steps: 400,
      sd_model_version: "sdxl",
      train_batch_size: 4,
      token_warmup_steps: 0,
      checkpointing_steps: 10000,
      freeze_ti_after_completion_f: 1
    }
  }
);

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:2d65dc0d1cf49e679a443c1da0f46933bf11043681b18a57c0c57636b2418b34",
    input={
        "name": "unnamed",
        "debug": False,
        "ti_lr": 0.001,
        "unet_lr": 0.001,
        "verbose": True,
        "n_tokens": 2,
        "lora_rank": 16,
        "resolution": 512,
        "concept_mode": "style",
        "caption_model": "blip",
        "max_train_steps": 400,
        "sd_model_version": "sdxl",
        "train_batch_size": 4,
        "token_warmup_steps": 0,
        "checkpointing_steps": 10000,
        "freeze_ti_after_completion_f": 1
    }
)

# 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": "edenartlab/sdxl-lora-trainer:2d65dc0d1cf49e679a443c1da0f46933bf11043681b18a57c0c57636b2418b34",
    "input": {
      "name": "unnamed",
      "debug": false,
      "ti_lr": 0.001,
      "unet_lr": 0.001,
      "verbose": true,
      "n_tokens": 2,
      "lora_rank": 16,
      "resolution": 512,
      "concept_mode": "style",
      "caption_model": "blip",
      "max_train_steps": 400,
      "sd_model_version": "sdxl",
      "train_batch_size": 4,
      "token_warmup_steps": 0,
      "checkpointing_steps": 10000,
      "freeze_ti_after_completion_f": 1
    }
  }' \
  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.