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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.data_dir = pathlib.Path("data")
self.ref_data_dir = self.data_dir / "ref"
self.target_data_dir = self.data_dir / "target"
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,
ref_images: list,
target_image: PIL.Image,
target_mask: PIL.Image,
resolution: int
) -> None:
self.ref_data_dir.mkdir(parents=True)
self.target_data_dir.mkdir(parents=True)
for i, temp_path in enumerate(ref_images):
image = PIL.Image.open(temp_path.name)
image = pad_image(image)
image = image.resize((resolution, resolution))
image = image.convert("RGB")
out_path = self.ref_data_dir / f"{i:03d}.jpg"
image.save(out_path, format="JPEG", quality=100)
target_image.save(self.target_data_dir / "target.jpg", format="JPEG", quality=100)
target_mask.save(self.target_data_dir / "mask.jpg", format="JPEG", quality=100)
def run(
self,
base_model: str,
resolution_s: str,
n_steps: int,
ref_images: list | None,
target_image: PIL.Image,
target_mask: PIL.Image,
unet_learning_rate: float,
text_encoder_learning_rate: float,
gradient_accumulation: int,
fp16: bool,
use_8bit_adam: bool,
gradient_checkpointing: bool,
lora_rank: int,
lora_alpha: int,
lora_bias: str,
lora_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 ref_images is None:
raise gr.Error("You need to upload reference images.")
if target_image is None:
raise gr.Error("You need to upload target image.")
if target_mask is None:
raise gr.Error("You need to upload target mask.")
resolution = int(resolution_s)
self.cleanup_dirs()
self.prepare_dataset(ref_images, target_image, target_mask, resolution)
command = f"""
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path={base_model} \
--train_data_dir={self.data_dir} \
--output_dir={self.output_dir} \
--resolution={resolution} \
--gradient_accumulation_steps={gradient_accumulation} \
--unet_learning_rate={unet_learning_rate} \
--text_encoder_learning_rate={text_encoder_learning_rate} \
--max_train_steps={n_steps} \
--train_batch_size=16 \
--lr_scheduler=constant \
--lr_warmup_steps=100 \
--lora_r={lora_rank} \
--lora_alpha={lora_alpha} \
--lora_bias={lora_bias} \
--lora_dropout={lora_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!"
model_paths = sorted(self.output_dir.glob("*"))
return gr.update(value=result_message), model_paths
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