zelenioncode/dreambooth_sdxl:e76c3569 | Run with an API on Replicate

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zelenioncode /dreambooth_sdxl:e76c3569

Playground API

Input

gender

string

Gender of person in training photo ( woman or man )

Default: "woman"

name_model

string

Shift + Return to add a new line

Give name for your .safetensors model

Default: "Dreambooth_sdxl"

send_to_huggingface

boolean

Send folder have .safetensors model direct to your huggingface account

Default: false

token_huggingface

string

Shift + Return to add a new line

If you use huggingface, enter your API TOKEN

Default: "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"

repo_id_huggingface

string

Shift + Return to add a new line

If you use huggingface, enter repo_id to one of your project

Default: "WGlint/SafetensorsFromReplicate"

folder_huggingface

string

Shift + Return to add a new line

Enter a path to download your .safetensors model. Default = ./

Default: ""

input

string

Shift + Return to add a new line

https://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.ziphttps://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.zip

Direct link download with training picture (Only .zip file and picture in 1024px/1024px !)

Default: "https://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.zip"

repeat_input

integer

(minimum: 1, maximum: 1000)

Repeat of time GPU look training data ( e.g. 15 pictures * 100 repeat = 1500 steps )

Default: 100

use_class_reg

boolean

Use regulat classification picture

Default: false

repeat_class_reg

integer

(minimum: 1, maximum: 1000)

Repeat of time GPU look class reg picture ( e.g. 5000 pictures * 2 repeat = 10000 steps cache latents )

Default: 1

class_reg

string

Shift + Return to add a new line

Direct link download for regular classification picture ( Default = class image of gender you use )

Default: ""

model_sdxl

string

Choice a model pretrained can run for SDXL training with dreambooth

Default: "Stable Diffusion XL"

num_cpu_threads_per_process

integer

(minimum: 1, maximum: 10)

Number CPU thread use with accelerate module

Default: 4

resolution

string

Shift + Return to add a new line

Resolution of your training picture data. WARNING ! Write in this formet : width,height ( e.g. 1024,1024 )

Default: "1024,1024"

vae

string

Shift + Return to add a new line

VAE use for create model training

Default: "stabilityai/vae"

lr_scheduler_num_cycles

integer

(minimum: 1, maximum: 1000)

Num learning rate cycles for your training

Default: 1

max_data_loader_n_workers

integer

(minimum: 0, maximum: 100)

Maximun data loader for n workers you set

Default: 0

learning_rate_te1

number

Value for learning_rate te1

Default: 0.00001

learning_rate_te2

number

Value for learning_rate te2

Default: 0.00001

learning_rate

number

Value for learning_rate

Default: 0.00001

lr_scheduler

string

Method use for learning rate scheduler

Default: "constant"

train_batch_size

integer

(minimum: 1, maximum: 64)

Select value for device max train step and speed the generation, WARINING ! High value = High value to have CUDA Memory

Default: 1

max_train_steps

integer

(minimum: 100, maximum: 25000)

Number of step you want for your training, and in average 1000 steps = 10 minutes

Default: 2600

save_every_n_epochs

integer

(minimum: 1, maximum: 64)

Number of epochs model you want

Default: 1

mixed_precision

string

Select if you want to use miwed precision

Default: "fp16"

save_precision

string

Select if you want to use save precision

Default: "fp16"

optimizer_type

string

Select a optimiser type

Default: "AdaFactor"

scale_parameter

boolean

Use scale parameter

Default: false

relative_step

boolean

Use relative step

Default: false

warmup_init

boolean

Use warmup init

Default: false

weight_decay

number

Give a float value for weight decay

Default: 0.01

bucket_reso_steps

integer

(minimum: 1, maximum: 1000)

Give a int value for bucket reso steps

Default: 64

save_every_n_steps

integer

(minimum: 1, maximum: 5)

Number of .safetensors model you want, if you select 2 with 2000 max train steps, you well get 2 .safetensors. 1 with 1000 steps and 1 with 2000 steps

Default: 1

noise_offset

number

Give a float value for noise offset

Default: 0

max_grad_norm

number

Give a float value for max grad norm

Default: 0

cache_latents_to_disk

boolean

Default: true

cache_latents

boolean

Default: true

mem_eff_attn

boolean

Default: true

gradient_checkpointing

boolean

Default: true

full_fp16

boolean

Default: true

xformers

boolean

Default: true

bucket_no_upscale

boolean

Default: true

no_half_vae

boolean

Default: true

train_text_encoder

boolean

Default: true

learning_rate_te1_bool

number

value for learning rate te1 bool

Default: 0.000003

learning_rate_te2_bool

number

value for learning rate te2 bool

Default: 0

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

npx create-replicate --model=zelenioncode/dreambooth_sdxl

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 zelenioncode/dreambooth_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zelenioncode/dreambooth_sdxl:e76c3569c5a59fd8146daef1cc90a3e493b0e1fe4a9f5c82abb5f727d6fcb8c4",
  {
    input: {
      vae: "stabilityai/vae",
      input: "https://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.zip",
      gender: "woman",
      xformers: true,
      class_reg: "",
      full_fp16: true,
      model_sdxl: "Stable Diffusion XL",
      name_model: "Dreambooth_sdxl",
      resolution: "1024,1024",
      no_half_vae: true,
      warmup_init: false,
      lr_scheduler: "constant",
      mem_eff_attn: true,
      noise_offset: 0,
      repeat_input: 100,
      weight_decay: 0.01,
      cache_latents: true,
      learning_rate: 0.00001,
      max_grad_norm: 0,
      relative_step: false,
      use_class_reg: false,
      optimizer_type: "AdaFactor",
      save_precision: "fp16",
      max_train_steps: 2600,
      mixed_precision: "fp16",
      scale_parameter: false,
      repeat_class_reg: 1,
      train_batch_size: 1,
      bucket_no_upscale: true,
      bucket_reso_steps: 64,
      learning_rate_te1: 0.00001,
      learning_rate_te2: 0.00001,
      token_huggingface: "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",
      folder_huggingface: "",
      save_every_n_steps: 1,
      train_text_encoder: true,
      repo_id_huggingface: "WGlint/SafetensorsFromReplicate",
      save_every_n_epochs: 1,
      send_to_huggingface: false,
      cache_latents_to_disk: true,
      gradient_checkpointing: true,
      learning_rate_te1_bool: 0.000003,
      learning_rate_te2_bool: 0,
      lr_scheduler_num_cycles: 1,
      max_data_loader_n_workers: 0,
      num_cpu_threads_per_process: 4
    }
  }
);

// 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 zelenioncode/dreambooth_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zelenioncode/dreambooth_sdxl:e76c3569c5a59fd8146daef1cc90a3e493b0e1fe4a9f5c82abb5f727d6fcb8c4",
    input={
        "vae": "stabilityai/vae",
        "input": "https://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.zip",
        "gender": "woman",
        "xformers": True,
        "class_reg": "",
        "full_fp16": True,
        "model_sdxl": "Stable Diffusion XL",
        "name_model": "Dreambooth_sdxl",
        "resolution": "1024,1024",
        "no_half_vae": True,
        "warmup_init": False,
        "lr_scheduler": "constant",
        "mem_eff_attn": True,
        "noise_offset": 0,
        "repeat_input": 100,
        "weight_decay": 0.01,
        "cache_latents": True,
        "learning_rate": 0.00001,
        "max_grad_norm": 0,
        "relative_step": False,
        "use_class_reg": False,
        "optimizer_type": "AdaFactor",
        "save_precision": "fp16",
        "max_train_steps": 2600,
        "mixed_precision": "fp16",
        "scale_parameter": False,
        "repeat_class_reg": 1,
        "train_batch_size": 1,
        "bucket_no_upscale": True,
        "bucket_reso_steps": 64,
        "learning_rate_te1": 0.00001,
        "learning_rate_te2": 0.00001,
        "token_huggingface": "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",
        "folder_huggingface": "",
        "save_every_n_steps": 1,
        "train_text_encoder": True,
        "repo_id_huggingface": "WGlint/SafetensorsFromReplicate",
        "save_every_n_epochs": 1,
        "send_to_huggingface": False,
        "cache_latents_to_disk": True,
        "gradient_checkpointing": True,
        "learning_rate_te1_bool": 0.000003,
        "learning_rate_te2_bool": 0,
        "lr_scheduler_num_cycles": 1,
        "max_data_loader_n_workers": 0,
        "num_cpu_threads_per_process": 4
    }
)
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 zelenioncode/dreambooth_sdxl 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": "zelenioncode/dreambooth_sdxl:e76c3569c5a59fd8146daef1cc90a3e493b0e1fe4a9f5c82abb5f727d6fcb8c4",
    "input": {
      "vae": "stabilityai/vae",
      "input": "https://huggingface.co/WGlint/SafetensorsFromReplicate/resolve/main/input.zip",
      "gender": "woman",
      "xformers": true,
      "class_reg": "",
      "full_fp16": true,
      "model_sdxl": "Stable Diffusion XL",
      "name_model": "Dreambooth_sdxl",
      "resolution": "1024,1024",
      "no_half_vae": true,
      "warmup_init": false,
      "lr_scheduler": "constant",
      "mem_eff_attn": true,
      "noise_offset": 0,
      "repeat_input": 100,
      "weight_decay": 0.01,
      "cache_latents": true,
      "learning_rate": 0.00001,
      "max_grad_norm": 0,
      "relative_step": false,
      "use_class_reg": false,
      "optimizer_type": "AdaFactor",
      "save_precision": "fp16",
      "max_train_steps": 2600,
      "mixed_precision": "fp16",
      "scale_parameter": false,
      "repeat_class_reg": 1,
      "train_batch_size": 1,
      "bucket_no_upscale": true,
      "bucket_reso_steps": 64,
      "learning_rate_te1": 0.00001,
      "learning_rate_te2": 0.00001,
      "token_huggingface": "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",
      "folder_huggingface": "",
      "save_every_n_steps": 1,
      "train_text_encoder": true,
      "repo_id_huggingface": "WGlint/SafetensorsFromReplicate",
      "save_every_n_epochs": 1,
      "send_to_huggingface": false,
      "cache_latents_to_disk": true,
      "gradient_checkpointing": true,
      "learning_rate_te1_bool": 0.000003,
      "learning_rate_te2_bool": 0,
      "lr_scheduler_num_cycles": 1,
      "max_data_loader_n_workers": 0,
      "num_cpu_threads_per_process": 4
    }
  }' \
  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.