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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
#
# 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 sys
import time
from tqdm import tqdm
import argparse
import math
import random
import torch
import torch.distributed as dist
from src.flux.generate import generate, generate_from_test_sample, seed_everything
from src.flux.pipeline_tools import CustomFluxPipeline, load_modulation_adapter, load_dit_lora
from src.utils.data_utils import get_train_config, get_rank_and_worldsize
from src.utils.data_utils import pad_to_square, pad_to_target, json_dump, json_load, image_grid
import shutil
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config_name", type=str, default="")
parser.add_argument("--model_path", type=str, default="")
parser.add_argument("--target_size", type=int, default=512)
parser.add_argument("--condition_size", type=int, default=128)
parser.add_argument("--save_name", type=str, default="../examples")
parser.add_argument("--test_list_name", type=str, default="base_test_list_200")
args = parser.parse_args()
return args
def main():
args = parse_args()
print(args)
local_rank, global_rank, world_size = get_rank_and_worldsize()
print(f"local_rank={local_rank}, global_rank={global_rank}, world_size={world_size}")
is_local_main_process = local_rank == 0
is_main_process = global_rank == 0
torch.cuda.set_device(local_rank)
dtype = torch.bfloat16
device = "cuda"
config_path = args.config_name
config = get_train_config(config_path)
config["train"]["dataset"]["val_condition_size"] = args.condition_size
config["train"]["dataset"]["val_target_size"] = args.target_size
config["model"]["layer_control"] = False
run_name = time.strftime("%m%d")
num_images = 4
ckpt_root = args.model_path
save_dir = args.save_name
model = CustomFluxPipeline(config, device, ckpt_root=ckpt_root, torch_dtype=dtype)
model.pipe.set_progress_bar_config(leave=False)
model.config = config
if "py" in args.test_list_name:
test_list = globals()[args.test_list_name.split("_py")[0]]
test_list = test_list[5:11] + test_list[17:23] # TODO only for debug
else:
test_list = json_load(f"eval/tools/{args.test_list_name}.json", 'utf-8')
num_samples = len(test_list)
num_ranks = world_size
assert local_rank == global_rank
if world_size > 1:
num_per_rank = math.ceil(num_samples / num_ranks)
test_list_indices = list(range(num_samples))
random.seed(0)
random.shuffle(test_list_indices)
local_test_list_indices = test_list_indices[local_rank*num_per_rank:(local_rank+1)*num_per_rank]
print(f"[worker {local_rank}] got {len(local_test_list_indices)} local samples")
model.clear_modulation_adapters()
model.pipe.transformer.unload_lora()
modulation_adapter = load_modulation_adapter(model, config, dtype, device, f"{ckpt_root}/modulation_adapter", is_training=False)
model.add_modulation_adapter(modulation_adapter)
if config["model"]["use_dit_lora"]:
load_dit_lora(model, model.pipe, config, dtype, device, f"{ckpt_root}", is_training=False)
os.makedirs(save_dir, exist_ok=True)
# 复制配置文件到 save_dir
import shutil
config_dest_path = os.path.join(save_dir, os.path.basename(config_path))
shutil.copy(config_path, config_dest_path)
print(f"已复制配置文件到 {config_dest_path}")
for i in tqdm(local_test_list_indices):
test_sample = test_list[i]
prompt_name = test_sample['prompt'][:40].replace(" ","_")
save_path = f"{save_dir}/{i}_{prompt_name}.png"
if os.path.exists(save_path):
print(f"文件 {save_path} 已存在，跳过保存")
continue
image = generate_from_test_sample(test_sample, model.pipe, model.config, num_images=num_images, store_attn_map=False, use_idip=True)
if isinstance(image, list):
image = image_grid(image, len(image) // 2, 2)
# print(f"{test_sample['prompt']}")
image.save(save_path)
print(f"save results {i} to: {save_path}")
del image
del model
if __name__ == "__main__":
main()
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