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# *************************************************************************
# Copyright (2023) Bytedance Inc.
#
# Copyright (2023) DragDiffusion Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# *************************************************************************
import os
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
import PIL
from PIL import Image
from copy import deepcopy
from einops import rearrange
from types import SimpleNamespace
import tqdm
import sys
sys.path.insert(0, '../')
from utils.lora_utils import train_lora
if __name__ == '__main__':
all_category = [
'art_work',
'land_scape',
'building_city_view',
'building_countryside_view',
'animals',
'human_head',
'human_upper_body',
'human_full_body',
'interior_design',
'other_objects',
]
# assume root_dir and lora_dir are valid directory
root_dir = 'drag_bench_data'
lora_dir = 'drag_bench_lora'
# mkdir if necessary
if not os.path.isdir(lora_dir):
os.mkdir(lora_dir)
for cat in all_category:
os.mkdir(os.path.join(lora_dir,cat))
for cat in all_category:
file_dir = os.path.join(root_dir, cat)
for sample_name in os.listdir(file_dir):
if sample_name == '.DS_Store':
continue
sample_path = os.path.join(file_dir, sample_name)
# read image file
source_image = Image.open(os.path.join(sample_path, 'original_image.png'))
source_image = np.array(source_image)
# load meta data
with open(os.path.join(sample_path, 'meta_data.pkl'), 'rb') as f:
meta_data = pickle.load(f)
prompt = meta_data['prompt']
# train and save lora
save_lora_path = os.path.join(lora_dir, cat, sample_name)
if not os.path.isdir(save_lora_path):
os.mkdir(save_lora_path)
# you may also increase the number of lora_step here to train longer
train_lora(source_image, prompt,
model_path="runwayml/stable-diffusion-v1-5",
vae_path="default", save_lora_path=save_lora_path,
lora_step=80, lora_lr=0.0005, lora_batch_size=4, lora_rank=16, progress=tqdm, save_interval=10)
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