Guide: 1to2: Training Multiple-Subject Models using only Single-Subject Data (Experimental)版本v1.0 (ID: 35902)

Updates will be mirrored on both Hugging Face and Civitai.

Introduction

It has been shown that multiple characters can be trained into the same model. A harder task is to create a model that can generate multiple characters simultaneously without modifying the generation pipeline. This document describes a simple technique that has been shown to help generating multiple characters in the same image.

Method

Requirement: Sets of single-character images

Steps:

1. Train a multi-concept model using the original dataset

2. Create an augmentation dataset of joined image pairs (composites) from the original dataset

3. Train on the augmentation dataset

Experiment

Setup

3 characters from the game Cinderella Girls are chosen for the experiment. The base model is anime-final-pruned. It has been checked that the base model has minimal knowledge of the trained characters. Images with multiple characters were removed (edit: after training it was found that some unrelated background characters were not removed).

For the captions of the joined images, the template format CharLeft/CharRight/COMPOSITE, TagsLeft, TagsRight is used.

A LoRA (Hadamard product) is trained using the config file below:

[model_arguments]

v2 = false

v_parameterization = false

pretrained_model_name_or_path = "Animefull-final-pruned.ckpt"



[additional_network_arguments]

no_metadata = false

unet_lr = 0.0005

text_encoder_lr = 0.0005

network_module = "lycoris.kohya"

network_dim = 8

network_alpha = 1

network_args = [ "conv_dim=0", "conv_alpha=16", "algo=loha",]

network_train_unet_only = false

network_train_text_encoder_only = false



[optimizer_arguments]

optimizer_type = "AdamW8bit"

learning_rate = 0.0005

max_grad_norm = 1.0

lr_scheduler = "cosine"

lr_warmup_steps = 0



[dataset_arguments]

debug_dataset = false

# keep token 1

# resolution 640*640



[training_arguments]

output_name = "cg3comp"

save_precision = "fp16"

save_every_n_epochs = 1

train_batch_size = 4

max_token_length = 225

mem_eff_attn = false

xformers = true

max_train_epochs = 40

max_data_loader_n_workers = 8

persistent_data_loader_workers = true

gradient_checkpointing = false

gradient_accumulation_steps = 1

mixed_precision = "fp16"

clip_skip = 2

lowram = true



[sample_prompt_arguments]

sample_every_n_epochs = 1

sample_sampler = "k_euler_a"



[saving_arguments]

save_model_as = "safetensors"

For the second stage of training, the batch size was reduced to 2 and the resolution was set to 768 * 768 while keeping other settings identical. The training took less than 2 hours on a T4 GPU.

Results

(see preview images)

Limitations

• This technique doubles the memory/compute requirement

• Composites can still be generated despite negative prompting

• Cloned characters seem to become the primary failure mode in place of blended characters

• Can generate 2 characters but not 3 for some reason

Related Works

Models been trained on datasets based on anime shows have demonstrated multi-subject capability. Simply using concepts distant enough such as 1girl, 1boy has also been shown to be effective.

Future work

Below is a list of ideas yet to be explored

• Synthetic datasets

• Unequal aspect ratios

• Regularization

• Joint training instaed of sequential

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