train.py

from comet_ml import Experiment
import os
os.environ["NCCL_P2P_DISABLE"] = "1"
os.environ["NCCL_IB_DISABLE"] = "1" # test it
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
import math
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, Sampler
from torch.utils.data.distributed import DistributedSampler
from torch.optim.lr_scheduler import LambdaLR
from collections import defaultdict
from diffusers import UNet2DConditionModel,AutoencoderKL,FlowMatchEulerDiscreteScheduler
from accelerate import Accelerator, DeepSpeedPlugin
from datasets import load_from_disk
from tqdm import tqdm
from PIL import Image, ImageOps
import wandb
import random,time
import gc
from accelerate.state import DistributedType
from torch.distributed import broadcast_object_list
from torch.utils.checkpoint import checkpoint
from diffusers.models.attention_processor import AttnProcessor2_0
from datetime import datetime
import bitsandbytes as bnb
import torch.nn.functional as F
from collections import deque
from transformers import Qwen3_5Tokenizer, Qwen3_5ForConditionalGeneration
import argparse

# --------------------------- Параметры ---------------------------
ds_path = "datasets/ds1234_1280"
project = "unet"
# 1. Считаем локальный батч для ОДНОЙ карты (3 на каждые 32 Гб)
gpu_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
local_bs = max(1, int((gpu_mem_gb / 32) * 2))
# 2. Умножаем на количество ГПУ, чтобы получить ГЛОБАЛЬНЫЙ батч
num_gpus = torch.cuda.device_count()
## total batch (split // num `GPU)
batch_size = local_bs * num_gpus
print(f"GPUs: {num_gpus}, Local BS: {local_bs}, Global BS: {local_bs * num_gpus}")
base_learning_rate = 2e-5
min_learning_rate = 3e-6
num_epochs = num_gpus #8 * max(1, int(num_gpus / 2))
sample_interval_share = 20
cfg_dropout = 0.10
max_length = 248
use_wandb = True
use_comet_ml = False
save_model = True
use_decay = True
fbp = False
optimizer_type = "adam8bit"
torch_compile = False
unet_gradient = True
loss_normalize = False
fixed_seed = False
shuffle = True
comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r" 
comet_ml_workspace = "recoilme" 
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
# Включение Flash Attention 2/SDPA #MAX_JOBS=4 pip install flash-attn --no-build-isolation
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(False) # Отключаем медленный вариант
save_barrier = 1.25
warmup_percent = 0.01
#percentile_clipping = 95
betta2 = 0.995
eps = 1e-7
clip_grad_norm = 1.0
limit = 0
checkpoints_folder = ""
gradient_accumulation_steps = 1
dtype = torch.float32
mixed_precision = "no"

# Параметры для диффузии
n_diffusion_steps = 40
samples_to_generate = 12
guidance_scale = 4

# Папки для сохранения результатов
generated_folder = "samples"
os.makedirs(generated_folder, exist_ok=True)

# Настройка seed
current_date = datetime.now()
seed = int(current_date.strftime("%Y%m%d")) + 42
if fixed_seed:
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

accelerator = Accelerator(
    mixed_precision=mixed_precision,
    gradient_accumulation_steps=gradient_accumulation_steps
)
device = accelerator.device

print("init")
# Создаём объект ArgumentParser с рассчитанными значениями по умолчанию
parser = argparse.ArgumentParser(description='Train a model on a dataset.')
parser.add_argument('--ds-path', type=str, default=ds_path, help='Path to the dataset')
parser.add_argument('--ep', type=int, default=num_epochs, help='Number of epochs to train the model')
parser.add_argument('--batch', type=int, default=batch_size, help='Total batch size')
parser.add_argument('--min-lr', type=float, default=min_learning_rate, help='Minimum learning rate')
parser.add_argument('--max-lr', type=float, default=base_learning_rate, help='Maximum learning rate')
parser.add_argument('--dry-run', action='store_true',default=False, help='Run configuration without saving/sampling')

# Парсим аргументы командной строки
args = parser.parse_args()

# Используем значения из аргументов
ds_path = args.ds_path
base_learning_rate = args.max_lr
min_learning_rate = args.min_lr
num_epochs = args.ep
if args.dry_run:
    save_model = False

# --------------------------- Инициализация WandB ---------------------------
if accelerator.is_main_process:
    if use_wandb:
        wandb.init(project=project, config={
            "batch_size": batch_size,
            "base_learning_rate": base_learning_rate,
            "num_epochs": num_epochs,
            "optimizer_type": optimizer_type,
        })
    if use_comet_ml:
        from comet_ml import Experiment
        comet_experiment = Experiment(
            api_key=comet_ml_api_key,
            project_name=project,
            workspace=comet_ml_workspace
        )
        hyper_params = {
            "batch_size": batch_size,
            "base_learning_rate": base_learning_rate,
            "num_epochs": num_epochs,
        }
        comet_experiment.log_parameters(hyper_params)

# --------------------------- Загрузка моделей ---------------------------
vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to(device).eval()
tokenizer = Qwen3_5Tokenizer.from_pretrained("tokenizer")
text_encoder = Qwen3_5ForConditionalGeneration.from_pretrained("text_encoder", torch_dtype=torch.float16).to(device).eval()
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("scheduler")

def encode_texts(texts, max_length=max_length):
    if texts is None:
        texts = [""]
    if isinstance(texts, str):
        texts = [texts]

    with torch.no_grad():
        
        # --- 2. QWEN Энкодер (через Chat Template) ---
        # 1. Собираем текстовые промпты оборачивая их в Chat Template
        formatted_prompts = []
        for t in texts:
            messages = [{"role": "user", "content": [{"type": "text", "text": t}]}]
            res_text = tokenizer.apply_chat_template(
                messages, 
                add_generation_prompt=True, 
                tokenize=False 
            )
            formatted_prompts.append(res_text)

        # 2. Токенизируем, режем и добавляем паддинг за один раз
        toks = tokenizer(
            formatted_prompts,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="pt"
        ).to(device)

        # 3. Прогоняем через модель
        outputs = text_encoder(
            input_ids=toks.input_ids, 
            attention_mask=toks.attention_mask,
            output_hidden_states=True
        )

        layer_index = -2
        last_hidden = outputs.hidden_states[layer_index]
        seq_len = toks.attention_mask.sum(dim=1) - 1
        pooled = last_hidden[torch.arange(len(last_hidden)), seq_len.clamp(min=0)]
        # --- НОВАЯ ЛОГИКА: ОБЪЕДИНЕНИЕ ДЛЯ КРОСС-ВНИМАНИЯ ---
        # 1. Расширяем пулинг-вектор до последовательности [B, 1, 1024]
        pooled_expanded = pooled.unsqueeze(1) 
                        
        # 2. Объединяем последовательность токенов и пулинг-вектор
        # !!! ИЗМЕНЕНИЕ ЗДЕСЬ !!!: Пулинг идет ПЕРВЫМ
        # Теперь: [B, 1 + L, 1024]. Пулинг стал токеном в НАЧАЛЕ.
        new_encoder_hidden_states = torch.cat([pooled_expanded, last_hidden], dim=1) 
                        
        # 3. Обновляем маску внимания для нового токена
        # Маска внимания: [B, 1 + L]. Добавляем 1 в НАЧАЛО.
        # torch.ones((batch_size, 1), device=device) создает маску [B, 1] со значениями 1.
        new_attention_mask = torch.cat([torch.ones((last_hidden.shape[0], 1), device=device), toks.attention_mask], dim=1)
        return new_encoder_hidden_states.to(dtype), new_attention_mask
        
shift_factor = getattr(vae.config, "shift_factor", 0.0)
if shift_factor is None:
    shift_factor = 0.0

scaling_factor = getattr(vae.config, "scaling_factor", 1.0)
if scaling_factor is None:
    scaling_factor = 1.0
    
mean = getattr(vae.config, "latents_mean", None)
std = getattr(vae.config, "latents_std", None)
if mean is not None and std is not None:
    latents_std = torch.tensor(std, device=device, dtype=dtype).view(1, len(std), 1, 1)
    latents_mean = torch.tensor(mean, device=device, dtype=dtype).view(1, len(mean), 1, 1)

import numpy as np
from torch.utils.data import Sampler


class DistributedResolutionBatchSampler(Sampler):
    def __init__(self, dataset, batch_size, num_replicas, rank, drop_last=True, shuffle=True):
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.shuffle = shuffle
        self.drop_last = drop_last
        self.epoch = 0

        # batch на одну GPU
        self.batch_size = max(1, batch_size // num_replicas)
        self.global_batch = self.batch_size * num_replicas

        try:
            widths = np.asarray(dataset["width"])
            heights = np.asarray(dataset["height"])
        except KeyError:
            widths = np.zeros(len(dataset))
            heights = np.zeros(len(dataset))

        # --- группировка индексов ---
        groups = {}
        for i, (w, h) in enumerate(zip(widths, heights)):
            groups.setdefault((w, h), []).append(i)

        # --- создаём список всех глобальных батчей ---
        all_batches = []

        for indices in groups.values():

            idx = np.asarray(indices, dtype=np.int64)

            num_batches = len(idx) // self.global_batch
            if num_batches == 0:
                continue

            idx = idx[: num_batches * self.global_batch]

            batches = idx.reshape(num_batches, self.global_batch)

            all_batches.append(batches)

        if len(all_batches) > 0:
            self.global_batches = np.concatenate(all_batches, axis=0)
        else:
            self.global_batches = np.empty((0, self.global_batch), dtype=np.int64)

        self.num_batches = len(self.global_batches)

    def __iter__(self):

        rng = np.random.RandomState(self.epoch)

        order = np.arange(self.num_batches)

        if self.shuffle:
            rng.shuffle(order)

        start = self.rank * self.batch_size
        end = start + self.batch_size

        for i in order:
            yield self.global_batches[i][start:end]

    def __len__(self):
        return self.num_batches

    def set_epoch(self, epoch):
        self.epoch = epoch
        
class DistributedResolutionBatchSamplerOld(Sampler):
    def __init__(self, dataset, batch_size, num_replicas, rank, drop_last=True, shuffle=False):
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.shuffle = shuffle
        self.drop_last = drop_last
        self.epoch = 0

        # batch на одну GPU
        self.batch_size = max(1, batch_size // num_replicas)
        self.global_batch = self.batch_size * num_replicas

        try:
            widths = np.asarray(dataset["width"])
            heights = np.asarray(dataset["height"])
        except KeyError:
            widths = np.zeros(len(dataset))
            heights = np.zeros(len(dataset))

        # --- группировка индексов ---
        groups = {}
        for i, (w, h) in enumerate(zip(widths, heights)):
            groups.setdefault((w, h), []).append(i)

        # --- строим батчи один раз (кеш) ---
        self.group_batches = []

        for indices in groups.values():

            idx = np.asarray(indices, dtype=np.int64)

            num_batches = len(idx) // self.global_batch
            if num_batches == 0:
                continue

            idx = idx[: num_batches * self.global_batch]

            batches = idx.reshape(num_batches, self.global_batch)

            self.group_batches.append(batches)

        # число батчей
        self.num_batches = sum(len(g) for g in self.group_batches)

    def __iter__(self):

        rng = np.random.RandomState(self.epoch)

        groups = []

        # shuffle внутри групп
        for g in self.group_batches:

            order = np.arange(len(g))

            if self.shuffle:
                rng.shuffle(order)

            groups.append(g[order])

        # shuffle порядок групп
        if self.shuffle:
            rng.shuffle(groups)

        # --- round robin сборка ---
        group_pos = [0] * len(groups)

        start = self.rank * self.batch_size
        end = start + self.batch_size

        remaining = True

        while remaining:

            remaining = False

            for gi, g in enumerate(groups):

                pos = group_pos[gi]

                if pos < len(g):

                    batch = g[pos]

                    group_pos[gi] += 1
                    remaining = True

                    yield batch[start:end]

    def __len__(self):
        return self.num_batches

    def set_epoch(self, epoch):
        self.epoch = epoch


# --- [UPDATED] Функция для фиксированных семплов ---
def get_fixed_samples_by_resolution(dataset, samples_per_group=1):
    size_groups = defaultdict(list)
    try:
        widths = dataset["width"]
        heights = dataset["height"]
    except KeyError:
        widths = [0] * len(dataset)
        heights = [0] * len(dataset)
    for i, (w, h) in enumerate(zip(widths, heights)):
        size = (w, h)
        size_groups[size].append(i)
    
    fixed_samples = {}
    for size, indices in size_groups.items():
        n_samples = min(samples_per_group, len(indices))
        if len(size_groups)==1:
            n_samples = samples_to_generate
        if n_samples == 0:
            continue
        sample_indices = random.sample(indices, n_samples)
        samples_data = [dataset[idx] for idx in sample_indices]
        
        latents = torch.tensor(np.array([item["vae"] for item in samples_data])).to(device=device, dtype=dtype)
        texts = [item["text"] for item in samples_data]
        
        # Кодируем тексты на лету, чтобы получить маски и пулинг
        embeddings, masks = encode_texts(texts)
        
        fixed_samples[size] = (latents, embeddings, masks, texts)
    
    print(f"Создано {len(fixed_samples)} групп фиксированных семплов по разрешениям")
    return fixed_samples

if limit > 0:
    dataset = load_from_disk(ds_path).select(range(limit))
else:
    dataset = load_from_disk(ds_path)

dataset = dataset.filter(
    lambda x: [not (path.startswith("//workspace/ds/animesfw") or path.startswith("//workspace/animesfw") or path.startswith("//workspace/ds/d4/animesfw"))  for path in x["image_path"]],

    batched=True,
    batch_size=10000, # обрабатываем по 10к строк за раз
    num_proc=8
)
print(f"Осталось примеров после фильтрации: {len(dataset)}")

# --- Collate Function ---
def collate_fn_simple(batch):
    # 1. Латенты (VAE)
    #latents = torch.tensor(np.array([item["vae"] for item in batch])).to(device, dtype=dtype)
    latents = torch.from_numpy(np.array([item["vae"] for item in batch], dtype=np.float16)).to(device, dtype=dtype)
    
    # 2. Текст берем сырой из датасета
    raw_texts = [item["text"] for item in batch]
    texts = [
        "" if t.lower().startswith("zero") 
        else "" if random.random() < cfg_dropout
        else t[1:].lstrip() if t.startswith(".")
        else t.replace("The image shows ", "").replace("The image is ", "").replace("This image captures ","").strip()
        for t in raw_texts
    ]
    # 3. Кодируем на лету
    # Возвращает: hidden (B, L, D), mask (B, L)
    embeddings, attention_mask = encode_texts(texts)
    
    # attention_mask от токенизатора уже имеет нужный формат, но на всякий случай приведем к long
    attention_mask = attention_mask.to(dtype=torch.int64)

    return latents, embeddings, attention_mask

batch_sampler = DistributedResolutionBatchSampler(
        dataset=dataset,
        batch_size=batch_size,
        num_replicas=accelerator.num_processes,
        rank=accelerator.process_index,
        shuffle = shuffle
    )

dataloader = DataLoader(dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_simple)

if accelerator.is_main_process:
    print("Total samples", len(dataloader))
dataloader = accelerator.prepare(dataloader)

start_epoch = 0
global_step = 0
total_training_steps = (len(dataloader) * num_epochs)
world_size = accelerator.state.num_processes

# Загрузка UNet
latest_checkpoint = os.path.join(checkpoints_folder, project)
if os.path.isdir(latest_checkpoint):
    print("Загружаем UNet из чекпоинта:", latest_checkpoint)
    unet = UNet2DConditionModel.from_pretrained(latest_checkpoint).to(device=device, dtype=dtype)
    if unet_gradient:
        unet.enable_gradient_checkpointing()
    unet.set_use_memory_efficient_attention_xformers(False)
    try:
        unet.set_attn_processor(AttnProcessor2_0())
    except Exception as e:
        print(f"Ошибка при включении SDPA: {e}")
        unet.set_use_memory_efficient_attention_xformers(True)
else:
    raise FileNotFoundError(f"UNet checkpoint not found at {latest_checkpoint}")
        

def create_optimizer(name, params):
    if name == "adam8bit":
        return bnb.optim.AdamW8bit(
            params, lr=base_learning_rate, betas=(0.9, betta2), eps=eps, weight_decay=0.01,
            #percentile_clipping=percentile_clipping
        )
    elif name == "adam":
        return torch.optim.AdamW(
            params, lr=base_learning_rate, betas=(0.9, betta2), eps=1e-8, weight_decay=0.01
        )
    else:
        raise ValueError(f"Unknown optimizer: {name}")

if fbp:
    trainable_params = list(unet.parameters())
    optimizer_dict = {p: create_optimizer(optimizer_type, [p]) for p in trainable_params}
    def optimizer_hook(param):
        optimizer_dict[param].step()
        optimizer_dict[param].zero_grad(set_to_none=True)
    for param in trainable_params:
        param.register_post_accumulate_grad_hook(optimizer_hook)
    unet, optimizer = accelerator.prepare(unet, optimizer_dict)
else:
    # 1. Сначала замораживаем ВСЕ параметры UNet
    #unet.requires_grad_(False)
    
    # 2. Размораживаем только нужные
    #trainable_params_names = ["conv_in.weight", "conv_in.bias", "conv_out.weight", "conv_out.bias"]
    #train_params = []
    
    #for name, param in unet.named_parameters():
    #    if any(target in name for target in trainable_params_names):
    #        param.requires_grad = True
    #        train_params.append(param)
    #        print(f"Обучаемый слой: {name}")

    unet.requires_grad_(True)
    optimizer = create_optimizer(optimizer_type, unet.parameters())
    
    def lr_schedule(step):
        x = step / (total_training_steps * world_size)
        warmup = warmup_percent
        if not use_decay:
            return base_learning_rate
        if x < warmup:
            return min_learning_rate + (base_learning_rate - min_learning_rate) * (x / warmup)
        decay_ratio = (x - warmup) / (1 - warmup)
        return min_learning_rate + 0.5 * (base_learning_rate - min_learning_rate) * \
               (1 + math.cos(math.pi * decay_ratio))
    lr_scheduler = LambdaLR(optimizer, lambda step: lr_schedule(step) / base_learning_rate)
    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)

if torch_compile:
    print("compiling")
    unet = torch.compile(unet)
    print("compiling - ok")

# Фиксированные семплы
fixed_samples = get_fixed_samples_by_resolution(dataset)

# --- [UPDATED] Функция для негативного эмбеддинга (возвращает 3 элемента) ---
def get_negative_embedding(neg_prompt="", batch_size=1):
    if not neg_prompt:
        hidden_dim = 2048 
        seq_len = max_length
        empty_emb = torch.zeros((batch_size, seq_len, hidden_dim), dtype=dtype, device=device)
        empty_mask = torch.ones((batch_size, seq_len), dtype=torch.int64, device=device)
        return empty_emb, empty_mask

    uncond_emb, uncond_mask  = encode_texts([neg_prompt])
    uncond_emb = uncond_emb.to(dtype=dtype, device=device).repeat(batch_size, 1, 1)
    uncond_mask = uncond_mask.to(device=device).repeat(batch_size, 1)

    return uncond_emb, uncond_mask
    
# Получаем негативные (пустые) условия для валидации
uncond_emb, uncond_mask = get_negative_embedding("low quality")
    
# --- Функция генерации семплов  ---
@torch.compiler.disable()
@torch.no_grad()
def generate_and_save_samples(fixed_samples_cpu, uncond_data, step):
    uncond_emb, uncond_mask = uncond_data
    
    original_model = None
    try:
        if not torch_compile:
            original_model = accelerator.unwrap_model(unet, keep_torch_compile=True).eval()
        else:
            original_model = unet.eval()

        vae.to(device=device).eval() 
        
        all_generated_images = []
        all_captions = [] 
        
        # Распаковываем 5 элементов (добавились mask)
        for size, (sample_latents, sample_text_embeddings, sample_mask, sample_text) in fixed_samples_cpu.items():
            width, height = size
            sample_latents = sample_latents.to(dtype=dtype, device=device)
            sample_text_embeddings = sample_text_embeddings.to(dtype=dtype, device=device)
            sample_mask = sample_mask.to(device=device)
        
            latents = torch.randn(
                sample_latents.shape,
                device=device,
                dtype=sample_latents.dtype,
                generator=torch.Generator(device=device).manual_seed(seed)
            )
        
            scheduler.set_timesteps(n_diffusion_steps, device=device)

            for t in scheduler.timesteps:
                if guidance_scale != 1:
                    latent_model_input = torch.cat([latents, latents], dim=0)
                    
                    curr_batch_size = sample_text_embeddings.shape[0]
                    seq_len = sample_text_embeddings.shape[1]
                    hidden_dim = sample_text_embeddings.shape[2]
                    
                    neg_emb_batch = uncond_emb[0:1].expand(curr_batch_size, -1, -1)
                    text_embeddings_batch = torch.cat([neg_emb_batch, sample_text_embeddings], dim=0)
                    
                    neg_mask_batch = uncond_mask[0:1].expand(curr_batch_size, -1)
                    attention_mask_batch = torch.cat([neg_mask_batch, sample_mask], dim=0)
                    
                else:
                    latent_model_input = latents
                    text_embeddings_batch = sample_text_embeddings
                    attention_mask_batch = sample_mask
            
                # Теперь всё имеет одинаковый batch size
                model_out = original_model(
                    latent_model_input,
                    t,
                    encoder_hidden_states=text_embeddings_batch,
                    encoder_attention_mask=attention_mask_batch,
                )

                flow = getattr(model_out, "sample", model_out)
        
                if guidance_scale != 1:
                    flow_uncond, flow_cond = flow.chunk(2)
                    flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
        
                latents = scheduler.step(flow, t, latents).prev_sample
        
            current_latents = latents
            if step==0:
                current_latents = sample_latents

            if latents_mean is not None and latents_std is not None:
                latents = current_latents * latents_std + latents_mean
                
            decoded = vae.decode(latents.to(torch.float32)).sample
            decoded_fp32 = decoded.to(torch.float32)
            
            for img_idx, img_tensor in enumerate(decoded_fp32):
                img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy()
                img = img.transpose(1, 2, 0)
    
                if np.isnan(img).any():
                    print("NaNs found, saving stopped! Step:", step)
                pil_img = Image.fromarray((img * 255).astype("uint8"))
                
                max_w_overall = max(s[0] for s in fixed_samples_cpu.keys())
                max_h_overall = max(s[1] for s in fixed_samples_cpu.keys())
                max_w_overall = max(255, max_w_overall)
                max_h_overall = max(255, max_h_overall)
            
                padded_img = ImageOps.pad(pil_img, (max_w_overall, max_h_overall), color='white')
                all_generated_images.append(padded_img)

                caption_text = sample_text[img_idx][:300] if img_idx < len(sample_text) else ""
                all_captions.append(caption_text)
                
                sample_path = f"{generated_folder}/{project}_{width}x{height}_{img_idx}.jpg"
                pil_img.save(sample_path, "JPEG", quality=95)
        
        if use_wandb and accelerator.is_main_process:
            wandb_images = [
                wandb.Image(img, caption=f"{all_captions[i]}")
                for i, img in enumerate(all_generated_images)
            ]
            wandb.log({"generated_images": wandb_images})
        if use_comet_ml and accelerator.is_main_process:
            for i, img in enumerate(all_generated_images):
                comet_experiment.log_image(
                    image_data=img,
                    name=f"step_{step}_img_{i}",
                    step=step,
                    metadata={"caption": all_captions[i]}
                )
    finally:
        vae.to("cpu")
        try:
            all_generated_images.clear()
            all_captions.clear()
            del all_generated_images, all_captions
            del latents, current_latents, latent_model_input, flow
            del decoded, decoded_fp32
            del sample_latents, sample_text_embeddings, sample_mask # Копии на GPU
            del model_out
        except UnboundLocalError:
            pass
            
        # 3. Синхронизируем CUDA перед очисткой
        torch.cuda.synchronize()
        # 4. Теперь чистим кэш аллокатора и вызываем GC
        torch.cuda.empty_cache()
        gc.collect()

# --------------------------- Генерация сэмплов перед обучением ---------------------------
if accelerator.is_main_process:
    if save_model:
        print("Генерация сэмплов до старта обучения...")
        generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), 0)
accelerator.wait_for_everyone()

def save_checkpoint(unet, variant=""):
    if accelerator.is_main_process:
        model_to_save = None
        if not torch_compile:
            model_to_save = accelerator.unwrap_model(unet)
        else:
            model_to_save = unet

        if variant != "":
            model_to_save.to(dtype=torch.float16).save_pretrained(
                os.path.join(checkpoints_folder, f"{project}"), variant=variant
            )
        else:
            model_to_save.save_pretrained(os.path.join(checkpoints_folder, f"{project}"))

        torch.cuda.synchronize()
        torch.cuda.empty_cache()
        gc.collect()
        #unet = unet.to(dtype=dtype) #TODO: wtf???

# --------------------------- Тренировочный цикл ---------------------------
if accelerator.is_main_process:
    print(f"Total steps per GPU: {total_training_steps}")

epoch_loss_points = []
progress_bar = tqdm(total=total_training_steps, disable=not accelerator.is_local_main_process, desc="Training", unit="step")

steps_per_epoch = len(dataloader)
sample_interval = max(1, steps_per_epoch // sample_interval_share)
min_loss = 4.
last_sample_time = time.time() 
sample_interval_seconds = 60 * 60  # 60 минут

for epoch in range(start_epoch, start_epoch + num_epochs):
    batch_losses = []
    batch_grads = []
    batch_sampler.set_epoch(epoch)
    accelerator.wait_for_everyone()
    unet.train()
    
    for step, (latents, embeddings, attention_mask) in enumerate(dataloader):
        with accelerator.accumulate(unet):
            if save_model == False and epoch == 0 and step == 5 :
                used_gb = torch.cuda.max_memory_allocated() / 1024**3
                print(f"Шаг {step}: {used_gb:.2f} GB")
                
            # шум
            noise = torch.randn_like(latents, dtype=latents.dtype)

            # 3. Время t (сэмплим, как и раньше, но чуть сжимаем края)
            u = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
            t = u * (1 - 2 * 1e-5) + 1e-5  # Теперь t строго в (0.00001 ... 0.99999)
            # интерполяция между x0 и шумом
            noisy_latents = (1.0 - t.view(-1, 1, 1, 1)) * latents + t.view(-1, 1, 1, 1) * noise
            # делаем integer timesteps для UNet
            timesteps = t.to(torch.float32).mul(999.0)
            timesteps = timesteps.clamp(0, scheduler.config.num_train_timesteps - 1)

            # --- Вызов UNet с маской  ---
            model_pred = unet(
                noisy_latents, 
                timesteps, 
                encoder_hidden_states=embeddings,
                encoder_attention_mask=attention_mask,
            ).sample
            
            target = noise - latents

            mse_loss = F.mse_loss(model_pred.float(), target.float())                
            batch_losses.append(mse_loss.detach().item())

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()

            losses_dict = {}
            losses_dict["mse"] = mse_loss

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()

            accelerator.backward(mse_loss)

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()
                
            grad = 0.0
            if not fbp:
                if accelerator.sync_gradients:
                    grad_val = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
                    grad = grad_val.float().item() if torch.is_tensor(grad_val) else float(grad_val)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)

            if accelerator.sync_gradients:
                global_step += 1
                progress_bar.update(1)
                if accelerator.is_main_process:
                    if fbp:
                        current_lr = base_learning_rate
                    else:
                        current_lr = lr_scheduler.get_last_lr()[0]
                    batch_grads.append(grad)
    
                    log_data = {}
                    log_data["loss_mse"] = mse_loss.detach().item()
                    log_data["lr"] = current_lr
                    log_data["grad"] = grad
                    if accelerator.sync_gradients:
                        if use_wandb:
                            wandb.log(log_data, step=global_step)
                        if use_comet_ml:
                            comet_experiment.log_metrics(log_data, step=global_step)

                    current_time = time.time()
                    is_time_to_sample = (current_time - last_sample_time) >= sample_interval_seconds
                    if is_time_to_sample or global_step == 50:
                        # Передаем tuple (emb, mask) для негатива
                        if save_model:
                            generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
                        elif epoch % 10 == 0:
                            generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
                        last_n = sample_interval
                        
                        if save_model:
                            has_losses = len(batch_losses) > 0
                            avg_sample_loss = np.mean(batch_losses[-sample_interval:]) if has_losses else 0.0
                            last_loss = batch_losses[-1] if has_losses else 0.0
                            max_loss = max(avg_sample_loss, last_loss)    
                            should_save = max_loss < min_loss * save_barrier
                            print(
                                f"Saving: {should_save} | Max: {max_loss:.4f} | "
                                f"Last: {last_loss:.4f} | Avg: {avg_sample_loss:.4f}"
                            )
                            # 6. Сохранение и обновление
                            if should_save:
                                min_loss = max_loss
                                save_checkpoint(unet)
                        last_sample_time = current_time
                        unet.train()

    if accelerator.is_main_process:
        avg_epoch_loss = np.mean(batch_losses) if len(batch_losses) > 0 else 0.0
        avg_epoch_grad = np.mean(batch_grads) if len(batch_grads) > 0 else 0.0

        print(f"\nЭпоха {epoch} завершена. Средний лосс: {avg_epoch_loss:.6f}")
        log_data_ep = {
                        "epoch_loss": avg_epoch_loss,
                        "epoch_grad": avg_epoch_grad,
                        "epoch": epoch + 1,
                    }
        if use_wandb:
            wandb.log(log_data_ep)
        if use_comet_ml:
            comet_experiment.log_metrics(log_data_ep)

if accelerator.is_main_process:
    print("Обучение завершено! Сохраняем финальную модель...")
    #if save_model:
    save_checkpoint(unet,"fp16")
    if use_comet_ml:
        comet_experiment.end()
accelerator.free_memory()
if torch.distributed.is_initialized():
    torch.distributed.destroy_process_group()
    
print("Готово!")