learners-superpumped/dreambooth-tar | Run with an API on Replicate

learners-superpumped / dreambooth-tar

Cold

Public
2.5K runs
L40S

Run with an API

Playground API Examples README Versions

Input

instance_data

*file

A ZIP file containing your training images (JPG, PNG, etc. size not restricted). These images contain your 'subject' that you want the trained model to embed in the output domain for later generating customized scenes beyond the training images. For best results, use images without noise or unrelated objects in the background.

class_data

file

An optional ZIP file containing the training data of class images. This corresponds to `class_prompt` above, also with the purpose of keeping the model generalizable. By default, the pretrained stable-diffusion model will generate N images (determined by the `num_class_images` you set) based on the `class_prompt` provided. But to save time or to have your preferred specific set of `class_data`, you can also provide them in a ZIP file.

resolution

integer

The resolution for input images. All the images in the train/validation dataset will be resized to this resolution.

Default: 512

ckpt_base

string

Shift + Return to add a new line

https://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensorshttps://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensors

The base model for tuning

Default: "https://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensors"

train_batch_size

integer

Batch size (per device) for the training dataloader.

Default: 2

user_name

string

Shift + Return to add a new line

User name

Default: "testuser"

max_train_steps

integer

Total number of training steps to perform. If provided, overrides num_train_epochs.

Default: 1760

learning_rate

number

Initial learning rate (after the potential warmup period) to use.

Default: 0.00002

lr_scheduler

string

The scheduler type to use

Default: "constant_with_warmup"

model_id

string

Shift + Return to add a new line

model save path

Default: "test_model"

lr_warmup_steps

integer

Number of steps for the warmup in the lr scheduler.

Default: 176

lambda_arc

number

Loss weight for arcface loss

Default: 0.075

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

npx create-replicate --model=learners-superpumped/dreambooth-tar

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 learners-superpumped/dreambooth-tar using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "learners-superpumped/dreambooth-tar:d1810add313d309c5c17a7b32a2db6c14ec733fedc852c74ce2a86dfec3451cd",
  {
    input: {
      model_id: "test_model",
      ckpt_base: "https://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensors",
      user_name: "testuser",
      lambda_arc: 0.075,
      resolution: 512,
      lr_scheduler: "constant_with_warmup",
      learning_rate: 0.00002,
      lr_warmup_steps: 176,
      max_train_steps: 1760,
      train_batch_size: 2
    }
  }
);

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 learners-superpumped/dreambooth-tar using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "learners-superpumped/dreambooth-tar:d1810add313d309c5c17a7b32a2db6c14ec733fedc852c74ce2a86dfec3451cd",
    input={
        "model_id": "test_model",
        "ckpt_base": "https://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensors",
        "user_name": "testuser",
        "lambda_arc": 0.075,
        "resolution": 512,
        "lr_scheduler": "constant_with_warmup",
        "learning_rate": 0.00002,
        "lr_warmup_steps": 176,
        "max_train_steps": 1760,
        "train_batch_size": 2
    }
)
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 learners-superpumped/dreambooth-tar 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": "learners-superpumped/dreambooth-tar:d1810add313d309c5c17a7b32a2db6c14ec733fedc852c74ce2a86dfec3451cd",
    "input": {
      "model_id": "test_model",
      "ckpt_base": "https://huggingface.co/BanKaiPls/AsianModel/resolve/main/Brav6.safetensors",
      "user_name": "testuser",
      "lambda_arc": 0.075,
      "resolution": 512,
      "lr_scheduler": "constant_with_warmup",
      "learning_rate": 0.00002,
      "lr_warmup_steps": 176,
      "max_train_steps": 1760,
      "train_batch_size": 2
    }
  }' \
  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 costs approximately $1.29 to run on Replicate, or 0 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 22 minutes. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.