edenartlab/sdxl-lora-trainer | Run with an API on Replicate

Cold

LoRa trainer for both SDXL and SD15

Public

7.5K runs

Run with an API

Playground API Examples README Versions

Input

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:4767bababe6048535114863799de828c25ec5b935dab7f879d4fa29495118d22",
  {
    input: {
      name: "unnamed",
      ti_lr: 0.001,
      unet_lr: 0.0003,
      n_tokens: 3,
      lora_rank: 16,
      resolution: 512,
      concept_mode: "style",
      n_sample_imgs: 4,
      max_train_steps: 300,
      sd_model_version: "sdxl",
      train_batch_size: 4,
      checkpointing_steps: 10000,
      validation_img_size: 1024
    }
  }
);

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:4767bababe6048535114863799de828c25ec5b935dab7f879d4fa29495118d22",
    input={
        "name": "unnamed",
        "ti_lr": 0.001,
        "unet_lr": 0.0003,
        "n_tokens": 3,
        "lora_rank": 16,
        "resolution": 512,
        "concept_mode": "style",
        "n_sample_imgs": 4,
        "max_train_steps": 300,
        "sd_model_version": "sdxl",
        "train_batch_size": 4,
        "checkpointing_steps": 10000,
        "validation_img_size": 1024
    }
)

# 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:4767bababe6048535114863799de828c25ec5b935dab7f879d4fa29495118d22",
    "input": {
      "name": "unnamed",
      "ti_lr": 0.001,
      "unet_lr": 0.0003,
      "n_tokens": 3,
      "lora_rank": 16,
      "resolution": 512,
      "concept_mode": "style",
      "n_sample_imgs": 4,
      "max_train_steps": 300,
      "sd_model_version": "sdxl",
      "train_batch_size": 4,
      "checkpointing_steps": 10000,
      "validation_img_size": 1024
    }
  }' \
  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.

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This trainer uses a single training script that is compatible with both SDXL and SD15.

The trainer has the following capabilities: - automatic image captioning using BLIP - automatic segmentation using CLIPseg - textual_inversion training of a new token to represent the concept - 3 training modes: “style” / “face” / “object” - Full finetuning or LoRa or Dora training modes are supported in the code - LoRa modules are possible for both unet and txt-encoders

The generated checkpoint files are compatible with ComfyUI and AUTO111