File size: 5,027 Bytes
e096196 8f29722 e096196 8f29722 e096196 700bc3e e096196 8f29722 e096196 700bc3e 8f29722 e096196 8f29722 e096196 8f29722 e096196 8f29722 e096196 8f29722 700bc3e 8f29722 e096196 8f29722 e096196 8f29722 e096196 8f29722 31c6a52 8f29722 e096196 8f29722 e096196 8f29722 e096196 8f29722 e096196 8f29722 700bc3e 8f29722 700bc3e 8f29722 e096196 8f29722 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
from __future__ import annotations
import datetime
import os
import pathlib
import shlex
import shutil
import subprocess
import gradio as gr
import PIL.Image
import slugify
import torch
from huggingface_hub import HfApi
from constants import UploadTarget
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, hf_token: str | None = None):
self.hf_token = hf_token
self.api = HfApi(token=hf_token)
def prepare_dataset(self, instance_images: list, resolution: int,
instance_data_dir: pathlib.Path) -> None:
shutil.rmtree(instance_data_dir, ignore_errors=True)
instance_data_dir.mkdir(parents=True)
for i, temp_path in enumerate(instance_images):
image = PIL.Image.open(temp_path.name)
image = pad_image(image)
image = image.resize((resolution, resolution))
image = image.convert('RGB')
out_path = instance_data_dir / f'{i:03d}.jpg'
image.save(out_path, format='JPEG', quality=100)
def run(
self,
instance_images: list | None,
instance_prompt: str,
output_model_name: str,
overwrite_existing_model: bool,
validation_prompt: str,
base_model: str,
resolution_s: str,
n_steps: int,
learning_rate: float,
gradient_accumulation: int,
seed: int,
fp16: bool,
use_8bit_adam: bool,
checkpointing_steps: int,
use_wandb: bool,
validation_epochs: int,
upload_to_hub: bool,
use_private_repo: bool,
delete_existing_repo: bool,
upload_to: str,
remove_gpu_after_training: bool,
) -> str:
if not torch.cuda.is_available():
raise gr.Error('CUDA is not available.')
if instance_images is None:
raise gr.Error('You need to upload images.')
if not instance_prompt:
raise gr.Error('The instance prompt is missing.')
if not validation_prompt:
raise gr.Error('The validation prompt is missing.')
resolution = int(resolution_s)
if not output_model_name:
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
output_model_name = f'lora-dreambooth-{timestamp}'
output_model_name = slugify.slugify(output_model_name)
repo_dir = pathlib.Path(__file__).parent
output_dir = repo_dir / 'experiments' / output_model_name
if overwrite_existing_model or upload_to_hub:
shutil.rmtree(output_dir, ignore_errors=True)
if not upload_to_hub:
output_dir.mkdir(parents=True)
instance_data_dir = repo_dir / 'training_data' / output_model_name
self.prepare_dataset(instance_images, resolution, instance_data_dir)
command = f'''
accelerate launch train_dreambooth_lora.py \
--pretrained_model_name_or_path={base_model} \
--instance_data_dir={instance_data_dir} \
--output_dir={output_dir} \
--instance_prompt="{instance_prompt}" \
--resolution={resolution} \
--train_batch_size=1 \
--gradient_accumulation_steps={gradient_accumulation} \
--learning_rate={learning_rate} \
--lr_scheduler=constant \
--lr_warmup_steps=0 \
--max_train_steps={n_steps} \
--checkpointing_steps={checkpointing_steps} \
--validation_prompt="{validation_prompt}" \
--validation_epochs={validation_epochs} \
--seed={seed}
'''
if fp16:
command += ' --mixed_precision fp16'
if use_8bit_adam:
command += ' --use_8bit_adam'
if use_wandb:
command += ' --report_to wandb'
if upload_to_hub:
command += f' --push_to_hub --hub_token {self.hf_token}'
if use_private_repo:
command += ' --private_repo'
if delete_existing_repo:
command += ' --delete_existing_repo'
if upload_to == UploadTarget.LORA_LIBRARY.value:
command += ' --upload_to_lora_library'
subprocess.run(shlex.split(command))
if remove_gpu_after_training:
space_id = os.getenv('SPACE_ID')
if space_id:
self.api.request_space_hardware(repo_id=space_id,
hardware='cpu-basic')
with open(output_dir / 'train.sh', 'w') as f:
command_s = ' '.join(command.split())
f.write(command_s)
return 'Training completed!'
|