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from __future__ import annotations
import os
import pathlib
import shlex
import shutil
import subprocess
import gradio as gr
import PIL.Image
import torch
def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
w, h = image.size
if w == h:
return image
elif w > h:
new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
new_image.paste(image, (0, (w - h) // 2))
return new_image
else:
new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
new_image.paste(image, ((h - w) // 2, 0))
return new_image
class Trainer:
def __init__(self):
self.is_running = False
self.is_running_message = "Another training is in progress."
self.output_dir = pathlib.Path("results")
self.class_dir = self.output_dir / "class_data"
self.instance_data_dir = self.output_dir / "training_data"
def check_if_running(self) -> dict:
if self.is_running:
return gr.update(value=self.is_running_message)
else:
return gr.update(value="No training is running.")
def cleanup_dirs(self) -> None:
shutil.rmtree(self.output_dir, ignore_errors=True)
def prepare_dataset(self, concept_images: list, resolution: int) -> None:
self.instance_data_dir.mkdir(parents=True)
for i, temp_path in enumerate(concept_images):
image = PIL.Image.open(temp_path.name)
image = pad_image(image)
image = image.resize((resolution, resolution))
image = image.convert("RGB")
out_path = self.instance_data_dir / f"{i:03d}.jpg"
image.save(out_path, format="JPEG", quality=100)
def copy_class_data(self, class_images: list) -> None:
self.class_dir.mkdir(parents=True)
for i, temp_path in enumerate(class_images):
image = PIL.Image.open(temp_path.name)
out_path = self.class_dir / f"{i:03d}.jpg"
image.save(out_path, format="JPEG", quality=100)
def run(
self,
base_model: str,
resolution_s: str,
n_steps: int,
concept_images: list | None,
concept_prompt: str,
class_images: list | None,
learning_rate: float,
gradient_accumulation: int,
fp16: bool,
use_8bit_adam: bool,
gradient_checkpointing: bool,
train_text_encoder: bool,
with_prior_preservation: bool,
prior_loss_weight: float,
class_prompt: str,
num_class_images: int,
lora_r: int,
lora_alpha: int,
lora_bias: str,
lora_dropout: float,
lora_text_encoder_r: int,
lora_text_encoder_alpha: int,
lora_text_encoder_bias: str,
lora_text_encoder_dropout: float,
) -> tuple[dict, list[pathlib.Path]]:
if not torch.cuda.is_available():
raise gr.Error("CUDA is not available.")
if self.is_running:
return gr.update(value=self.is_running_message), []
if concept_images is None:
raise gr.Error("You need to upload images.")
if not concept_prompt:
raise gr.Error("The concept prompt is missing.")
resolution = int(resolution_s)
self.cleanup_dirs()
self.prepare_dataset(concept_images, resolution)
self.copy_class_data(class_images)
command = f"""
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path={base_model} \
--instance_data_dir={self.instance_data_dir} \
--output_dir={self.output_dir} \
--train_text_encoder \
--instance_prompt="{concept_prompt}" \
--resolution={resolution} \
--gradient_accumulation_steps={gradient_accumulation} \
--learning_rate={learning_rate} \
--max_train_steps={n_steps} \
--enable_xformers_memory_efficient_attention \
--train_batch_size=1 \
--lr_scheduler=constant \
--lr_warmup_steps=0 \
--num_class_images={num_class_images} \
"""
if train_text_encoder:
command += f" --train_text_encoder"
if with_prior_preservation:
command += f""" --with_prior_preservation \
--prior_loss_weight={prior_loss_weight} \
--class_prompt="{class_prompt}" \
--class_data_dir={self.class_dir}
"""
command += f""" --use_lora \
--lora_r={lora_r} \
--lora_alpha={lora_alpha} \
--lora_bias={lora_bias} \
--lora_dropout={lora_dropout}
"""
if train_text_encoder:
command += f""" --lora_text_encoder_r={lora_text_encoder_r} \
--lora_text_encoder_alpha={lora_text_encoder_alpha} \
--lora_text_encoder_bias={lora_text_encoder_bias} \
--lora_text_encoder_dropout={lora_text_encoder_dropout}
"""
if fp16:
command += " --mixed_precision fp16"
if use_8bit_adam:
command += " --use_8bit_adam"
if gradient_checkpointing:
command += " --gradient_checkpointing"
with open(self.output_dir / "train.sh", "w") as f:
command_s = " ".join(command.split())
f.write(command_s)
self.is_running = True
res = subprocess.run(shlex.split(command))
self.is_running = False
if res.returncode == 0:
result_message = "Training Completed!"
else:
result_message = "Training Failed!"
weight_paths = sorted(self.output_dir.glob("*.pt"))
config_paths = sorted(self.output_dir.glob("*.json"))
return gr.update(value=result_message), weight_paths + config_paths
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