skytells-research/sdxl-lora-training | Run with an API on Replicate

skytells-research

sdxl-lora-training

Introducing a Lora Instant Training model for crafting stunning 1024x1024 visuals. Train your own Lora Model via zip photos for instant outputs.

Public

35 runs

L40S

Run with an API

Pricing

Playground API Examples README Versions

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: 768

train_batch_size

integer

Batch size (per device) for training

Default: 4

num_train_epochs

integer

Number of epochs to loop through your training dataset

Default: 4000

max_train_steps

integer

Number of individual training steps. Takes precedence over num_train_epochs

Default: 1000

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: 0.000001

ti_lr

number

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

Default: 0.0003

lora_lr

number

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

Default: 0.0001

lora_rank

integer

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

Default: 32

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: 100

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: "TOK"

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 TOK, "

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=skytells-research/sdxl-lora-training

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

const output = await replicate.run(
  "skytells-research/sdxl-lora-training:50b089b04b93f6a3e48b9ae205bbc7aa7f57db50af4dc5dee4557e089af94f69",
  {
    input: {
      ti_lr: 0.0003,
      is_lora: true,
      lora_lr: 0.0001,
      verbose: true,
      lora_rank: 32,
      resolution: 768,
      lr_scheduler: "constant",
      token_string: "TOK",
      caption_prefix: "a photo of TOK, ",
      lr_warmup_steps: 100,
      max_train_steps: 1000,
      num_train_epochs: 4000,
      train_batch_size: 4,
      unet_learning_rate: 0.000001,
      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 skytells-research/sdxl-lora-training using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "skytells-research/sdxl-lora-training:50b089b04b93f6a3e48b9ae205bbc7aa7f57db50af4dc5dee4557e089af94f69",
    input={
        "ti_lr": 0.0003,
        "is_lora": True,
        "lora_lr": 0.0001,
        "verbose": True,
        "lora_rank": 32,
        "resolution": 768,
        "lr_scheduler": "constant",
        "token_string": "TOK",
        "caption_prefix": "a photo of TOK, ",
        "lr_warmup_steps": 100,
        "max_train_steps": 1000,
        "num_train_epochs": 4000,
        "train_batch_size": 4,
        "unet_learning_rate": 0.000001,
        "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:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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 skytells-research/sdxl-lora-training 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": "skytells-research/sdxl-lora-training:50b089b04b93f6a3e48b9ae205bbc7aa7f57db50af4dc5dee4557e089af94f69",
    "input": {
      "ti_lr": 0.0003,
      "is_lora": true,
      "lora_lr": 0.0001,
      "verbose": true,
      "lora_rank": 32,
      "resolution": 768,
      "lr_scheduler": "constant",
      "token_string": "TOK",
      "caption_prefix": "a photo of TOK, ",
      "lr_warmup_steps": 100,
      "max_train_steps": 1000,
      "num_train_epochs": 4000,
      "train_batch_size": 4,
      "unet_learning_rate": 0.000001,
      "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

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

Introducing SDXL LoRa Model: Where Your Creativity Knows No Bounds

Step into the visionary realm of SDXL LoRa Model and witness the fusion of your imagination with cutting-edge AI technology. Elevate your artistic journey by seamlessly blending your creative prowess with personalized LoRa models to craft stunning 1024x1024 visuals.

Unleash Your Creative Potential with Stability AI: Designed by Stability AI, the SDXL LoRa Model empowers you to explore the perfect harmony between artistic expression and customization. This is your opportunity to transcend boundaries and give life to your unique artistic aspirations.