2601

train-Copy1.py

@@ -1,897 +0,0 @@
-#from comet_ml import Experiment
-import os
-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,AutoencoderKLFlux2,AsymmetricAutoencoderKL,FlowMatchEulerDiscreteScheduler
-from accelerate import Accelerator
-from datasets import load_from_disk
-from tqdm import tqdm
-from PIL import Image, ImageOps
-import wandb
-import random
-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 AutoTokenizer, AutoModel
-
-# --------------------------- Параметры ---------------------------
-ds_path = "/workspace/sdxs-08b/datasets/d123_640_sd15"
-project = "unet"
-batch_size = 15
-base_learning_rate = 4e-5
-min_learning_rate = 2e-5
-num_epochs = 10
-sample_interval_share = 5
-cfg_dropout = 0.10
-max_length = 248
-use_wandb = False
-use_comet_ml = True
-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
-torch.backends.cuda.enable_mem_efficient_sdp(False)
-dtype = torch.float32
-save_barrier = 1.05
-warmup_percent = 0.03
-percentile_clipping = 95
-betta2 = 0.995
-eps = 1e-7
-clip_grad_norm = 1.0
-limit = 0
-checkpoints_folder = ""
-mixed_precision = "no"
-gradient_accumulation_steps = 1
-
-accelerator = Accelerator(
-    mixed_precision=mixed_precision,
-    gradient_accumulation_steps=gradient_accumulation_steps
-)
-device = accelerator.device
-
-# 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"))
-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)
-
-# --------------------------- Параметры LoRA ---------------------------
-lora_name = "" 
-lora_rank = 32
-lora_alpha = 64
-
-print("init")
-
-loss_ratios = {
-    "mse":   1.25,
-    "mae":   0.25,
-}
-median_coeff_steps = 256 
-
-# Нормализация лоссов по медианам: считаем КОЭФФИЦИЕНТЫ
-class MedianLossNormalizer:
-    def __init__(self, desired_ratios: dict, window_steps: int):
-        # нормируем доли на случай, если сумма != 1
-        #s = sum(desired_ratios.values())
-        #self.ratios = {k: (v / s) for k, v in desired_ratios.items()}
-        self.ratios = {k: float(v) for k, v in desired_ratios.items()}
-        self.buffers = {k: deque(maxlen=window_steps) for k in self.ratios.keys()}
-        self.window = window_steps
-
-    def update_and_total(self, losses: dict):
-        """
-        losses: dict ключ->тензор (значения лоссов)
-        Поведение:
-          - буферим ABS(l) только для активных (ratio>0) лоссов
-          - coeff = ratio / median(abs(loss))
-          - total = sum(coeff * loss) по активным лоссам
-        CHANGED: буферим abs() — чтобы медиана была положительной и не ломала деление.
-        """
-        # буферим только активные лоссы
-        for k, v in losses.items():
-            if k in self.buffers and self.ratios.get(k, 0) > 0:
-                val = v.detach().abs().mean().cpu().item() # .item() лучше float() для тензоров
-                self.buffers[k].append(val)
-                #self.buffers[k].append(float(v.detach().abs().cpu()))
-
-        meds = {k: (np.median(self.buffers[k]) if len(self.buffers[k]) > 0 else 1.0) for k in self.buffers}
-        coeffs = {k: (self.ratios[k] / max(meds[k], 1e-12)) for k in self.ratios}
-
-        # суммируем только по активным (ratio>0)
-        total = sum(coeffs[k] * losses[k] for k in coeffs if self.ratios.get(k, 0) > 0)
-        return total, coeffs, meds
-
-# создаём normalizer после определения loss_ratios
-normalizer = MedianLossNormalizer(loss_ratios, median_coeff_steps)
-
-# --------------------------- Инициализация WandB ---------------------------
-if accelerator.is_main_process:
-    if use_wandb:
-        wandb.init(project=project+lora_name, 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)
-
-# Включение Flash Attention 2/SDPA
-torch.backends.cuda.enable_flash_sdp(True)
-
-# --------------------------- Загрузка моделей ---------------------------
-#vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to("cpu").eval()
-#vae = AutoencoderKLFlux2.from_pretrained("black-forest-labs/FLUX.2-dev",subfolder="vae",torch_dtype=dtype).to(device).eval()
-vae = AsymmetricAutoencoderKL.from_pretrained("vae",torch_dtype=dtype).to(device).eval()
-tokenizer = AutoTokenizer.from_pretrained("tokenizer")
-text_model = AutoModel.from_pretrained("text_encoder").to(device).eval()
-scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("scheduler")
-
-def encode_texts(texts, max_length=77): # Для SD 1.5 лучше жестко 77
-    if texts is None:
-        texts = [""]
-    
-    if isinstance(texts, str):
-        texts = [texts]
-
-    with torch.no_grad():
-        # 1. Основная токенизация
-        toks = tokenizer(
-            texts,
-            padding="max_length",
-            max_length=max_length,
-            truncation=True,
-            return_tensors="pt"
-        ).to(device)
-
-        text_input_ids = toks.input_ids
-        
-        # 2. Проверка на обрезку текста (логика для вывода предупреждений)
-        # Получаем айдишники без обрезки по длине
-        # untruncated_ids = tokenizer(texts, padding="longest", return_tensors="pt").input_ids.to(device)
-
-        # Исправляем проверку: сравниваем тензоры, а не объект BatchEncoding
-        #if untruncated_ids.shape[-1] > text_input_ids.shape[-1] and not torch.equal(
-        #    text_input_ids, untruncated_ids[:, :max_length]
-        #):
-            # Заменяем self.tokenizer на tokenizer
-        #    removed_text = tokenizer.batch_decode(
-        #        untruncated_ids[:, max_length - 1 : -1]
-        #    )
-            #print(f"Warning: Text truncated. Removed part: {removed_text}")
-
-        # 3. Маска внимания
-        #if hasattr(text_model.config, "use_attention_mask") and text_model.config.use_attention_mask:
-        #    attention_mask = toks.attention_mask
-        #else:
-            #attention_mask = None
-        attention_mask = toks.attention_mask
-
-        # 4. Прогон через модель
-        # Правильный вызов: передаем конкретные тензоры или распаковываем словарь **toks
-        outputs = text_model(
-            input_ids=text_input_ids, 
-            attention_mask=attention_mask,
-            output_hidden_states=True # Часто нужно для SD 1.5 (слой -2)
-        )
-        
-        layer_index = -2
-        prompt_embeds = outputs.hidden_states[layer_index]
-
-        # 2. ДОБАВЛЯЕМ ФИНАЛЬНУЮ НОРМАЛИЗАЦИЮ
-        # В CLIP после всех блоков стоит слой LayerNorm. 
-        final_layer_norm = text_model.text_model.final_layer_norm
-        prompt_embeds = final_layer_norm(prompt_embeds)
-
-        return prompt_embeds, attention_mask
-        
-# --- [UPDATED] Функция кодирования текста (с маской и пулингом) ---
-def encode_texts22(texts, max_length=max_length):
-    # Если тексты пустые (для unconditional), создаем заглушки
-    if texts is None: 
-        # В случае None возвращаем нули (логика для get_negative_embedding)
-        # Но здесь мы обычно ожидаем список строк.
-        pass
-
-    with torch.no_grad():
-        if isinstance(texts, str):
-            texts = [texts]
-
-        #for i, prompt_item in enumerate(texts):
-        #    messages = [
-        #        {"role": "user", "content": prompt_item},
-        #    ]
-        #    prompt_item = tokenizer.apply_chat_template(
-        #        messages,
-        #        tokenize=False,
-        #        add_generation_prompt=True,
-        #        enable_thinking=True,
-        #    )
-            #print(prompt_item+"\n")
-        #    texts[i] = prompt_item
-            
-        toks = tokenizer(
-            texts,
-            return_tensors="pt",
-            padding="max_length",
-            truncation=True,
-            max_length=max_length
-        ).to(device)
-
-        text_input_ids = toks.input_ids
-        untruncated_ids = tokenizer(texts, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= toks.shape[-1] and not torch.equal(
-            toks, untruncated_ids
-        ):
-            removed_text = tokenizer.batch_decode(
-                untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-            )
-            print(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                )
-
-        attention_mask = toks.attention_mask.to(device)
-
-        prompt_embeds = text_model(toks.to(device), attention_mask=attention_mask)
-        prompt_embeds = prompt_embeds[-1]
-        return prompt_embeds, attention_mask
-        #outs = text_model(**toks, output_hidden_states=True, return_dict=True)
-        
-        # Используем last_hidden_state или hidden_states[-1] (если Qwen, лучше last_hidden_state - прим человека: ХУЙ)
-        #hidden = outs.hidden_states[-2] 
-        
-        # 2. Маска внимания
-        #attention_mask = toks["attention_mask"] 
-        
-        # 3. Пулинг-эмбеддинг (Последний токен)
-        #sequence_lengths = attention_mask.sum(dim=1) - 1
-        #batch_size = hidden.shape[0]
-        #pooled = hidden[torch.arange(batch_size, device=hidden.device), sequence_lengths]
-
-        #return hidden, attention_mask
-        # --- НОВАЯ ЛОГИКА: ОБЪЕДИНЕНИЕ ДЛЯ КРОСС-ВНИМАНИЯ ---
-        # 1. Расширяем пулинг-вектор до последовательности [B, 1, emb]
-        #pooled_expanded = pooled.unsqueeze(1) 
-                    
-        # 2. Объединяем последовательность токенов и пулинг-вектор
-        # !!! ИЗМЕНЕНИЕ ЗДЕСЬ !!!: Пулинг идет ПЕРВЫМ
-        # Теперь: [B, 1 + L, emb]. Пулинг стал токеном в НАЧАЛЕ.
-        #new_encoder_hidden_states = torch.cat([pooled_expanded, 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((batch_size, 1), device=device), attention_mask], dim=1)
-        
-        #return new_encoder_hidden_states, 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
-scaling_factor = 1.0
-
-class DistributedResolutionBatchSampler(Sampler):
-    def __init__(self, dataset, batch_size, num_replicas, rank, shuffle=True, drop_last=True):
-        self.dataset = dataset
-        self.batch_size = max(1, batch_size // num_replicas)
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.shuffle = shuffle
-        self.drop_last = drop_last
-        self.epoch = 0
-        
-        try:
-            widths = np.array(dataset["width"])
-            heights = np.array(dataset["height"])
-        except KeyError:
-            widths = np.zeros(len(dataset))
-            heights = np.zeros(len(dataset))
-        
-        self.size_keys = np.unique(np.stack([widths, heights], axis=1), axis=0)
-        self.size_groups = {}
-        for w, h in self.size_keys:
-            mask = (widths == w) & (heights == h)
-            self.size_groups[(w, h)] = np.where(mask)[0]
-            
-        self.group_num_batches = {}
-        total_batches = 0
-        for size, indices in self.size_groups.items():
-            num_full_batches = len(indices) // (self.batch_size * self.num_replicas)
-            self.group_num_batches[size] = num_full_batches
-            total_batches += num_full_batches
-            
-        self.num_batches = (total_batches // self.num_replicas) * self.num_replicas
-        
-    def __iter__(self):
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        all_batches = []
-        rng = np.random.RandomState(self.epoch)
-        
-        for size, indices in self.size_groups.items():
-            indices = indices.copy()
-            if self.shuffle:
-                rng.shuffle(indices)
-            num_full_batches = self.group_num_batches[size]
-            if num_full_batches == 0:
-                continue
-            valid_indices = indices[:num_full_batches * self.batch_size * self.num_replicas]
-            batches = valid_indices.reshape(-1, self.batch_size * self.num_replicas)
-            start_idx = self.rank * self.batch_size
-            end_idx = start_idx + self.batch_size
-            gpu_batches = batches[:, start_idx:end_idx]
-            all_batches.extend(gpu_batches)
-        
-        if self.shuffle:
-            rng.shuffle(all_batches)
-        accelerator.wait_for_everyone()
-        return iter(all_batches)
-
-    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/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)}")
-
-# --- [UPDATED] 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)
-    
-    # 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}")
-
-if lora_name:
-    # ... (Код LoRA без изменений, опущен для краткости, если не используется, иначе раскомментируйте оригинальный блок) ...
-    pass 
-
-# Оптимизатор
-if lora_name:
-    trainable_params = [p for p in unet.parameters() if p.requires_grad]
-else:
-    if fbp:
-        trainable_params = list(unet.parameters())
-        
-
-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
-        )
-    elif name == "muon":
-        from muon import MuonWithAuxAdam
-        trainable_params = [p for p in params if p.requires_grad]
-        hidden_weights = [p for p in trainable_params if p.ndim >= 2]
-        hidden_gains_biases = [p for p in trainable_params if p.ndim < 2]
-    
-        param_groups = [ 
-            dict(params=hidden_weights, use_muon=True,
-                lr=1e-3, weight_decay=1e-4),
-            dict(params=hidden_gains_biases, use_muon=False,
-                lr=1e-4, betas=(0.9, 0.95), weight_decay=1e-4),
-        ]
-        optimizer = MuonWithAuxAdam(param_groups)
-        from snooc import SnooC
-        return SnooC(optimizer)
-    else:
-        raise ValueError(f"Unknown optimizer: {name}")
-
-if fbp:
-    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}")
-    
-    # 3. Передаем в оптимизатор ТОЛЬКО обучаемые параметры
-    #optimizer = create_optimizer(optimizer_type, train_params)
-
-    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)
-                
-                    # Подготовка батчей для CFG (Negative + Positive)
-                    # 1. Embeddings
-                    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)
-                    
-                    # 2. Masks
-                    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
-
-                # Предсказание с передачей всех условий
-                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
-
-            latent_for_vae = current_latents.detach() * scaling_factor + shift_factor
-            decoded = vae.decode(latent_for_vae.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=96)
-        
-        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")
-        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:
-        if lora_name:
-            save_lora_checkpoint(unet)
-        else:
-            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}"))
-
-            unet = unet.to(dtype=dtype)
-
-# --------------------------- 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.
-
-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())
-            mae_loss = F.l1_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
-            losses_dict["mae"] = mae_loss
-
-            # === Нормализация всех лоссов ===
-            abs_for_norm = {k: losses_dict.get(k, torch.tensor(0.0, device=device)) for k in normalizer.ratios.keys()}
-            total_loss, coeffs, meds = normalizer.update_and_total(abs_for_norm)
-
-            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
-                accelerator.wait_for_everyone()
-
-            if loss_normalize:    
-                accelerator.backward(total_loss)
-            else:
-                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:
-                    #with torch.amp.autocast('cuda', enabled=False):
-                    grad_val = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
-                    grad = 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["loss_mae"] = mae_loss.detach().item()
-                    log_data["lr"] = current_lr
-                    log_data["grad"] = grad
-                    log_data["loss_norm"] = float(total_loss.item())
-                    for k, c in coeffs.items():
-                        log_data[f"coeff_{k}"] = float(c)
-                    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)
-
-                    if global_step % sample_interval == 0:
-                        # 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)
-
-    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!")