mask

dataset_new.py

@@ -0,0 +1,376 @@
+# pip install flash-attn --no-build-isolation
+import torch
+import os
+import gc
+import numpy as np
+import random
+import json
+import shutil
+import time
+
+from datasets import Dataset, load_from_disk, concatenate_datasets
+from diffusers import AutoencoderKL,AutoencoderKLWan
+from torchvision.transforms import Resize, ToTensor, Normalize, Compose, InterpolationMode, Lambda
+from transformers import AutoModel, AutoImageProcessor, AutoTokenizer, AutoModelForCausalLM
+from typing import Dict, List, Tuple, Optional, Any
+from PIL import Image
+from tqdm import tqdm
+from datetime import timedelta
+
+# ---------------- 1️⃣ Настройки ----------------
+dtype = torch.float16
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+batch_size = 10
+min_size = 320 #192 #256 #192
+max_size = 640 #384 #256 #384
+step = 64 #64
+empty_share = 0.05
+limit = 0
+# Основная процедура обработки
+folder_path = "/workspace/ds/dataset/mjnj" #alchemist"
+save_path = "/workspace/mjnj_640" #"alchemist"
+os.makedirs(save_path, exist_ok=True)
+
+# Функция для очистки CUDA памяти
+def clear_cuda_memory():
+    if torch.cuda.is_available():
+        used_gb = torch.cuda.max_memory_allocated() / 1024**3
+        print(f"used_gb: {used_gb:.2f} GB")
+        torch.cuda.empty_cache()
+        gc.collect()
+
+# ---------------- 2️⃣ Загрузка моделей ----------------
+def load_models():
+    print("Загрузка моделей...")
+    vae = AutoencoderKL.from_pretrained("AiArtLab/sdxs3d",subfolder="vae",torch_dtype=dtype).to(device).eval()
+
+    model_name = "Qwen/Qwen3-0.6B"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=dtype,
+        device_map=device
+    ).eval()
+    return vae, model, tokenizer
+
+vae, model, tokenizer = load_models()
+
+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
+    
+latents_mean = getattr(vae.config, "latents_mean", None)
+latents_std = getattr(vae.config, "latents_std", None)
+    
+# ---------------- 3️⃣ Трансформации ----------------
+def get_image_transform(min_size=256, max_size=512, step=64):
+    def transform(img, dry_run=False):
+        # Сохраняем исходные размеры изображения
+        original_width, original_height = img.size
+
+        # 0. Ресайз: масштабируем изображение, чтобы максимальная сторона была равна max_size
+        if original_width >= original_height:
+            new_width = max_size
+            new_height = int(max_size * original_height / original_width)
+        else:
+            new_height = max_size
+            new_width = int(max_size * original_width / original_height)
+
+        if new_height < min_size or new_width < min_size:
+            # 1. Ресайз: масштабируем изображение, чтобы минимальная сторона была равна min_size
+            if original_width <= original_height:
+                new_width = min_size
+                new_height = int(min_size * original_height / original_width)
+            else:
+                new_height = min_size
+                new_width = int(min_size * original_width / original_height)
+
+        # 2. Проверка: если одна из сторон превышает max_size, готовимся к обрезке
+        crop_width = min(max_size, (new_width // step) * step)
+        crop_height = min(max_size, (new_height // step) * step)
+
+        # Убеждаемся, что размеры обрезки не меньше min_size
+        crop_width = max(min_size, crop_width)
+        crop_height = max(min_size, crop_height)
+        
+        # Если запрошен только предварительный расчёт размеров
+        if dry_run:
+            return crop_width, crop_height
+        
+        # Конвертация в RGB и ресайз
+        img_resized = img.convert("RGB").resize((new_width, new_height), Image.LANCZOS)
+        
+        # Определение координат обрезки (обрезаем с учетом вотермарок - треть сверху)
+        top = (new_height - crop_height) // 3
+        left = 0
+        
+        # Обрезка изображения
+        img_cropped = img_resized.crop((left, top, left + crop_width, top + crop_height))
+        
+        # Сохраняем итоговые размеры после всех преобразований
+        final_width, final_height = img_cropped.size
+        
+        # тензор
+        img_tensor = ToTensor()(img_cropped)
+        img_tensor = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])(img_tensor)
+        return img_tensor, img_cropped, final_width, final_height
+
+    return transform
+
+# ---------------- 4️⃣ Функции обработки ----------------
+def encode_texts_batch(texts, tokenizer, model, device="cuda", max_length=150):
+    with torch.inference_mode():
+        # 1. Tokenization
+        batch = tokenizer(
+            texts,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=max_length,
+        ).to(device)
+
+        # 2. Forward pass
+        outputs = model.model(**batch, output_hidden_states=True, return_dict=True)
+        hidden = outputs.hidden_states[-1]
+        mask = batch["attention_mask"].unsqueeze(-1)  # (B, L, 1)
+
+        # 3. Zero-pad embeddings for pad tokens
+        hidden = hidden * mask                  # (B, L, D)
+
+        return hidden.cpu().numpy()
+
+
+def clean_label(label):
+    label = label.replace("Image 1", "").replace("Image 2", "").replace("Image 3", "").replace("Image 4", "")
+    return label
+
+def process_labels_for_guidance(original_labels, prob_to_make_empty=0.01):
+    """
+    Обрабатывает список меток для classifier-free guidance.
+
+    С вероятностью prob_to_make_empty:
+    - Метка в первом списке заменяется на пустую строку.
+    - К метке во втором списке добавляется префикс "zero:".
+
+    В противном случае метки в обоих списках остаются оригинальными.
+
+    """
+    labels_for_model = []
+    labels_for_logging = []
+
+    for label in original_labels:
+        if random.random() < prob_to_make_empty:
+            labels_for_model.append("")  # Заменяем на пустую строку для модели
+            labels_for_logging.append(f"zero: {label}") # Добавляем префикс для логгирования
+        else:
+            labels_for_model.append(label) # Оставляем оригинальную метку для модели
+            labels_for_logging.append(label) # Оставляем оригинальную метку для логгирования
+
+    return labels_for_model, labels_for_logging
+
+def encode_to_latents(images, texts):
+    transform = get_image_transform(min_size, max_size, step)
+    
+    try:
+        # Обработка изображений (все одинакового размера)
+        transformed_tensors = []
+        pil_images = []
+        widths, heights = [], []
+        
+        # Применяем трансформацию ко всем изображениям
+        for img in images:
+            try:
+                t_img, pil_img, w, h = transform(img)
+                transformed_tensors.append(t_img)
+                pil_images.append(pil_img)
+                widths.append(w)
+                heights.append(h)
+            except Exception as e:
+                print(f"Ошибка трансформации: {e}")
+                continue
+
+        if not transformed_tensors:
+            return None
+
+        # Создаём батч
+        batch_tensor = torch.stack(transformed_tensors).to(device, dtype)
+        if batch_tensor.ndim==5:
+            batch_tensor = batch_tensor.unsqueeze(2)  # [B, C, 1, H, W]
+        
+        # Кодируем батч
+        with torch.no_grad():
+            posteriors = vae.encode(batch_tensor).latent_dist.mode()
+            latents = (posteriors - shift_factor) / scaling_factor
+        
+        latents_np = latents.to(dtype).cpu().numpy()
+
+        # Обрабатываем тексты
+        text_labels = [clean_label(text) for text in texts]
+
+        model_prompts, text_labels = process_labels_for_guidance(text_labels, empty_share)                
+        embeddings = encode_texts_batch(model_prompts, tokenizer, model)
+
+        return {
+            "vae": latents_np,
+            "embeddings": embeddings,
+            "text": text_labels,
+            "width": widths,
+            "height": heights
+        }
+        
+    except Exception as e:
+        print(f"Критическая ошибка в encode_to_latents: {e}")
+        raise
+        
+
+# ---------------- 5️⃣ Обработка папки с изображениями и текстами ----------------
+def process_folder(folder_path, limit=None):
+    """
+    Рекурсивно обходит указанную директорию и все вложенные директории,
+    собирая пути к изображениям и соответствующим текстовым файлам.
+    """
+    image_paths = []
+    text_paths = []
+    width = []
+    height = []
+    transform = get_image_transform(min_size, max_size, step)
+    
+    # Используем os.walk для рекурсивного обхода директорий
+    for root, dirs, files in os.walk(folder_path):
+        for filename in files:
+            # Проверяем, является ли файл изображением
+            if filename.lower().endswith((".jpg", ".jpeg", ".png")):
+                image_path = os.path.join(root, filename)
+                try:
+                    img = Image.open(image_path)
+                except Exception as e:
+                    print(f"Ошибка при открытии {image_path}: {e}")
+                    os.remove(image_path)
+                    text_path = os.path.splitext(image_path)[0] + ".txt"
+                    if os.path.exists(text_path):
+                        os.remove(text_path)
+                    continue
+                # Применяем трансформацию только для получения размеров
+                w, h = transform(img, dry_run=True)
+                # Формируем путь к текстовому файлу
+                text_path = os.path.splitext(image_path)[0] + ".txt"
+                
+                # Добавляем пути, если текстовый файл существует
+                if os.path.exists(text_path) and min(w, h)>0:
+                    image_paths.append(image_path)
+                    text_paths.append(text_path)
+                    width.append(w)  # Добавляем в список
+                    height.append(h)  # Добавляем в список
+                    
+                    # Проверяем ограничение на количество
+                    if limit and limit>0 and len(image_paths) >= limit:
+                        print(f"Достигнут лимит в {limit} изображений")
+                        return image_paths, text_paths, width, height
+    
+    print(f"Найдено {len(image_paths)} изображений с текстовыми описаниями")
+    return image_paths, text_paths, width, height
+    
+def process_in_chunks(image_paths, text_paths, width, height, chunk_size=10000, batch_size=1):
+    total_files = len(image_paths)
+    start_time = time.time()
+    chunks = range(0, total_files, chunk_size)
+    
+    for chunk_idx, start in enumerate(chunks, 1):
+        end = min(start + chunk_size, total_files)
+        chunk_image_paths = image_paths[start:end]
+        chunk_text_paths = text_paths[start:end]
+        chunk_widths = width[start:end] if isinstance(width, list) else [width] * len(chunk_image_paths)
+        chunk_heights = height[start:end] if isinstance(height, list) else [height] * len(chunk_image_paths)
+        
+        # Чтение текстов
+        chunk_texts = []
+        for text_path in chunk_text_paths:
+            try:
+                with open(text_path, 'r', encoding='utf-8') as f:
+                    text = f.read().strip()
+                chunk_texts.append(text)
+            except Exception as e:
+                print(f"Ошибка чтения {text_path}: {e}")
+                chunk_texts.append("")
+        
+        # Группируем изображения по размерам
+        size_groups = {}
+        for i in range(len(chunk_image_paths)):
+            size_key = (chunk_widths[i], chunk_heights[i])
+            if size_key not in size_groups:
+                size_groups[size_key] = {"image_paths": [], "texts": []}
+            size_groups[size_key]["image_paths"].append(chunk_image_paths[i])
+            size_groups[size_key]["texts"].append(chunk_texts[i])
+        
+        # Обрабатываем каждую группу размеров отдельно
+        for size_key, group_data in size_groups.items():
+            print(f"Обработка группы с размером {size_key[0]}x{size_key[1]} - {len(group_data['image_paths'])} изображений")
+            
+            group_dataset = Dataset.from_dict({
+                "image_path": group_data["image_paths"],
+                "text": group_data["texts"]
+            })
+            
+            # Теперь можно использовать указанный batch_size, т.к. все изображения одного размера
+            processed_group = group_dataset.map(
+                lambda examples: encode_to_latents(
+                    [Image.open(path) for path in examples["image_path"]],
+                    examples["text"]
+                ),
+                batched=True,
+                batch_size=batch_size,
+                #remove_columns=["image_path"],
+                desc=f"Обработка группы размера {size_key[0]}x{size_key[1]}"
+            )
+            
+            # Сохраняем результаты группы
+            group_save_path = f"{save_path}_temp/chunk_{chunk_idx}_size_{size_key[0]}x{size_key[1]}"
+            processed_group.save_to_disk(group_save_path)
+            clear_cuda_memory()
+            elapsed = time.time() - start_time
+            processed = (chunk_idx - 1) * chunk_size + sum([len(sg["image_paths"]) for sg in list(size_groups.values())[:list(size_groups.values()).index(group_data) + 1]])
+            if processed > 0:
+                remaining = (elapsed / processed) * (total_files - processed)
+                elapsed_str = str(timedelta(seconds=int(elapsed)))
+                remaining_str = str(timedelta(seconds=int(remaining)))
+                print(f"ETA: Прошло {elapsed_str}, Осталось {remaining_str}, Прогресс {processed}/{total_files} ({processed/total_files:.1%})")
+
+# ---------------- 7️⃣ Объединение чанков ----------------
+def combine_chunks(temp_path, final_path):
+    """Объединение обработанных чанков в финальный датасет"""
+    chunks = sorted([
+        os.path.join(temp_path, d) 
+        for d in os.listdir(temp_path) 
+        if d.startswith("chunk_")
+    ])
+    
+    datasets = [load_from_disk(chunk) for chunk in chunks]
+    combined = concatenate_datasets(datasets)
+    combined.save_to_disk(final_path)
+    
+    print(f"✅ Датасет успешно сохранен в: {final_path}")
+
+# Создаем временную папку для чанков
+temp_path = f"{save_path}_temp"
+os.makedirs(temp_path, exist_ok=True)
+
+# Получаем список файлов
+image_paths, text_paths, width, height = process_folder(folder_path,limit)
+print(f"Всего найдено {len(image_paths)} изображений")
+
+# Обработка с чанкованием
+process_in_chunks(image_paths, text_paths, width, height, chunk_size=25000, batch_size=batch_size)
+
+# Объединение чанков в финальный датасет
+combine_chunks(temp_path, save_path)
+
+# Удаление временной папки
+try:
+    shutil.rmtree(temp_path)
+    print(f"✅ Временная папка {temp_path} успешно удалена")
+except Exception as e:
+    print(f"⚠️ Ошибка при удалении временной папки: {e}")

pipeline_sdxs.py

@@ -49,8 +49,14 @@ class SdxsPipeline(DiffusionPipeline):
                     padding_side = 'right',
                     max_length=max_length
                 ).to(device)
-                outs = self.text_encoder(**toks, output_hidden_states=True)
-                return outs.hidden_states[-1]
+                outs = self.text_encoder(**toks, output_hidden_states=True, return_dict=True)
+                hidden = outs.hidden_states[-1]
+                mask = toks["attention_mask"].unsqueeze(-1)  # (B, L, 1)
+
+                # 3. Zero-pad embeddings for pad tokens
+                hidden = hidden * mask      
+        
+                return hidden
     
         # Кодируем позитивные и негативные промпты
         pos_embeddings = encode_texts(prompt) if prompt is not None else None

src/dataset_mjnj.ipynb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8cf317c438de242a8cc0c7d710c00ceec53e887108b081235a1fb05dae0074b0
-size 23158
+oid sha256:2eb6dcd0ea9aef5fc624bd91abb3058a663d2a66f2843ee9817cd7927b396523
+size 23542

test.ipynb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:19c5220e0f9401dbd9abae23049b050b0f540b55205c46894e1a7e57cf0ab6bf
-size 1076278
+oid sha256:00123f2a11487702da78dff4cca6f78c71a25549eb878fac8412c752d2e45f19
+size 5703503

train-Copy1.py

@@ -0,0 +1,771 @@
+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 torch.optim.lr_scheduler import LambdaLR
+from diffusers import UNet2DConditionModel, AutoencoderKLWan,AutoencoderKL
+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
+
+# --------------------------- Параметры ---------------------------
+ds_path = "/workspace/sdxs/datasets/640"
+project = "unet"
+batch_size = 48
+base_learning_rate = 4e-5
+min_learning_rate = 2e-5
+num_epochs = 50
+# samples/save per epoch
+sample_interval_share = 5
+use_wandb = True
+use_comet_ml = False
+save_model = True
+use_decay = True
+fbp = False # fused backward pass
+optimizer_type = "adam8bit"
+torch_compile = False
+unet_gradient = True
+clip_sample = False #Scheduler
+fixed_seed = True
+shuffle = True
+comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r"  # Добавлен API ключ для Comet ML
+comet_ml_workspace = "recoilme"  # Добавлен workspace для Comet ML
+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 = 1.006
+warmup_percent = 0.005
+percentile_clipping = 99 # 8bit optim
+betta2 = 0.99
+eps = 1e-8
+clip_grad_norm = 1.0
+steps_offset = 0 # Scheduler
+limit = 0
+checkpoints_folder = ""
+mixed_precision = "no" #"fp16"
+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 = 50
+samples_to_generate = 12
+guidance_scale = 4
+
+# Папки для сохранения результатов
+generated_folder = "samples"
+os.makedirs(generated_folder, exist_ok=True)
+
+# Настройка seed для воспроизводимости
+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")
+
+# ---------------------------  вспомогательные функции ---------------------------
+def sample_timesteps_bias(
+    batch_size: int,
+    progress: float,           # [0..1]
+    num_train_timesteps: int,  # обычно 1000
+    steps_offset: int = 0,
+    device=None,
+    mode: str = "beta",        # "beta", "uniform"
+) -> torch.Tensor:
+    """
+    Возвращает timesteps с разным bias:
+    - beta   : как раньше (сдвиг в начало или конец в зависимости от progress)
+    - normal : около середины (гауссовое распределение)
+    - uniform: равномерно по всем timestep’ам
+    """
+
+    max_idx = num_train_timesteps - 1 - steps_offset
+
+    if mode == "beta":
+        alpha = 1.0 + .5 * (1.0 - progress)
+        beta  = 1.0 + .5 * progress
+        samples = torch.distributions.Beta(alpha, beta).sample((batch_size,))
+
+    elif mode == "uniform":
+        samples = torch.rand(batch_size)
+
+    else:
+        raise ValueError(f"Unknown mode: {mode}")
+
+    timesteps = steps_offset + (samples * max_idx).long().to(device)
+    return timesteps
+
+def logit_normal_samples(shape, mu=0.0, sigma=1.0, device=None, dtype=None):
+    normal_samples = torch.normal(mean=mu, std=sigma, size=shape, device=device, dtype=dtype)
+
+    logit_normal_samples = torch.sigmoid(normal_samples)
+
+    return logit_normal_samples
+
+# --------------------------- 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,
+            "fbp": fbp,
+            "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
+        )
+        # Логируем гиперпа��аметры в Comet ML
+        hyper_params = {
+            "batch_size": batch_size,
+            "base_learning_rate": base_learning_rate,
+            "min_learning_rate": min_learning_rate,
+            "num_epochs": num_epochs,
+            "n_diffusion_steps": n_diffusion_steps,
+            "guidance_scale": guidance_scale,
+            "optimizer_type": optimizer_type,
+            "mixed_precision": mixed_precision,
+        }
+        comet_experiment.log_parameters(hyper_params)
+
+# Включение Flash Attention 2/SDPA
+torch.backends.cuda.enable_flash_sdp(True)
+# --------------------------- Инициализация Accelerator --------------------
+gen = torch.Generator(device=device)
+gen.manual_seed(seed)
+
+# --------------------------- Загрузка моделей ---------------------------
+# VAE загружается на CPU для экономии GPU-памяти (как в твоём оригинальном коде)
+vae = AutoencoderKL.from_pretrained("AiArtLab/simplevae", subfolder="vae", torch_dtype=dtype).to("cpu").eval()
+
+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
+    
+latents_mean = getattr(vae.config, "latents_mean", None)
+latents_std = getattr(vae.config, "latents_std", None)
+
+from diffusers import FlowMatchEulerDiscreteScheduler
+
+# Подстрой под свои параметры
+num_train_timesteps = 1000
+
+scheduler = FlowMatchEulerDiscreteScheduler(
+    num_train_timesteps=num_train_timesteps,
+    #shift=3.0,               # пример; подбирается при необходимости
+    #use_dynamic_shifting=True
+)
+
+
+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
+
+# Функция для выборки фиксированных семплов по размерам
+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)
+        embeddings = torch.tensor(np.array([item["embeddings"] for item in samples_data])).to(device,dtype=dtype)
+        texts = [item["text"] for item in samples_data]
+        fixed_samples[size] = (latents, embeddings, 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)
+
+def collate_fn_simple(batch):
+    latents = torch.tensor(np.array([item["vae"] for item in batch])).to(device,dtype=dtype)
+    embeddings = torch.tensor(np.array([item["embeddings"] for item in batch])).to(device,dtype=dtype)
+    return latents, embeddings
+
+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)
+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
+
+# Опция загрузки модели из последнего чекпоинта (если существует)
+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:
+    # FIX: если чекпоинта нет — прекращаем с понятной ошибкой (лучше, чем неожиданные NameError дальше)
+    raise FileNotFoundError(f"UNet checkpoint not found at {latest_checkpoint}. Положи UNet чекпоинт в {latest_checkpoint} или укажи другой путь.")
+
+if lora_name:
+    print(f"--- Настройка LoRA через PEFT (Rank={lora_rank}, Alpha={lora_alpha}) ---")
+    from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+    from peft.tuners.lora import LoraModel
+    import os
+    unet.requires_grad_(False)
+    print("Параметры базового UNet заморожены.")
+
+    lora_config = LoraConfig(
+        r=lora_rank,
+        lora_alpha=lora_alpha,
+        target_modules=["to_q", "to_k", "to_v", "to_out.0"],
+    )
+    unet.add_adapter(lora_config)
+
+    from peft import get_peft_model
+    peft_unet = get_peft_model(unet, lora_config)
+    params_to_optimize = list(p for p in peft_unet.parameters() if p.requires_grad)
+
+    if accelerator.is_main_process:
+        lora_params_count = sum(p.numel() for p in params_to_optimize)
+        total_params_count = sum(p.numel() for p in unet.parameters())
+        print(f"Количество обучаемых параметров (LoRA): {lora_params_count:,}")
+        print(f"Общее количество параметров UNet: {total_params_count:,}")
+
+    lora_save_path = os.path.join("lora", lora_name)
+    os.makedirs(lora_save_path, exist_ok=True)
+
+    def save_lora_checkpoint(model):
+        if accelerator.is_main_process:
+            print(f"Сохраняем LoRA адаптеры  в {lora_save_path}")
+            from peft.utils.save_and_load import get_peft_model_state_dict
+            lora_state_dict = get_peft_model_state_dict(model)
+            torch.save(lora_state_dict, os.path.join(lora_save_path, "adapter_model.bin"))
+            model.peft_config["default"].save_pretrained(lora_save_path)
+            from diffusers import StableDiffusionXLPipeline
+            StableDiffusionXLPipeline.save_lora_weights(lora_save_path, lora_state_dict)
+
+# --------------------------- 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, 0.999), eps=1e-8, weight_decay=0.01
+        )
+    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:
+    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)
+
+    num_params = sum(p.numel() for p in unet.parameters())
+    print(f"[rank {accelerator.process_index}] total params: {num_params}")
+    for name, param in unet.named_parameters():
+        if torch.isnan(param).any() or torch.isinf(param).any():
+            print(f"[rank {accelerator.process_index}] NaN/Inf in {name}")
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)
+
+if torch_compile:
+    print("compiling")
+    torch.set_float32_matmul_precision('high')
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cuda.matmul.allow_tf32 = True
+    unet = torch.compile(unet)#, mode='max-autotune')
+    print("compiling - ok")
+
+# --------------------------- Фиксированные семплы для генерации ---------------------------
+fixed_samples = get_fixed_samples_by_resolution(dataset)
+
+def get_negative_embedding(neg_prompt="", batch_size=1):
+    """
+    Возвращает эмбеддинг негативного промпта с батчем.
+    Загружает модели, вычисляет эмбеддинг, выгружает модели на CPU.
+    """
+    import torch
+    from transformers import AutoTokenizer, AutoModel
+
+    # Настройки
+    dtype = torch.float16
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # Загрузка моделей (если ещё не загружены)
+    if not hasattr(get_negative_embedding, "tokenizer"):
+        get_negative_embedding.tokenizer = AutoTokenizer.from_pretrained(
+            "Qwen/Qwen3-0.6B"
+        )
+        get_negative_embedding.text_model = AutoModel.from_pretrained(
+            "Qwen/Qwen3-0.6B"
+        ).to(device).eval()
+
+    # Вычисление эмбеддинга
+    def encode_texts(texts, max_length=150):
+        with torch.inference_mode():
+            toks = get_negative_embedding.tokenizer(
+                texts, return_tensors="pt", padding="max_length", truncation=True, max_length=max_length
+            ).to(device)
+
+            outs = get_negative_embedding.text_model(**toks, output_hidden_states=True)
+            hidden_states = outs.hidden_states[-1]  # [B, L, D]
+            return hidden_states
+
+    # Возвращаем эмбеддинг
+    if not neg_prompt:
+        hidden_dim = 1024  # Размерность эмбеддинга Qwen3-Embedding-0.6B
+        seq_len = 150
+        return torch.zeros((batch_size, seq_len, hidden_dim), dtype=dtype, device=device)
+
+    uncond_emb = encode_texts([neg_prompt]).to(dtype=dtype, device=device)
+    uncond_emb = uncond_emb.repeat(batch_size, 1, 1)  # Добавляем батч
+
+    # Выгружаем модели
+    if hasattr(get_negative_embedding, "text_model"):
+        get_negative_embedding.text_model = get_negative_embedding.text_model.to("cpu")
+    if hasattr(get_negative_embedding, "tokenizer"):
+        del get_negative_embedding.tokenizer  # Освобождаем память
+    torch.cuda.empty_cache()
+
+    return uncond_emb
+    
+uncond_emb = get_negative_embedding("low quality")
+
+@torch.compiler.disable()
+@torch.no_grad()
+def generate_and_save_samples(fixed_samples_cpu,empty_embeddings, step):
+    original_model = None
+    try:
+        # безопасный unwrap: если компилировано, unwrap не нужен
+        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()  # временно подгружаем VAE на GPU для декодинга
+
+        
+        all_generated_images = []
+        all_captions = [] 
+        
+        for size, (sample_latents, sample_text_embeddings, 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)
+        
+            # начальный шум
+            latents = torch.randn(
+                sample_latents.shape,
+                device=device,
+                dtype=sample_latents.dtype,
+                generator=torch.Generator(device=device).manual_seed(seed)
+            )
+        
+            # подготовим timesteps через шедулер
+            scheduler.set_timesteps(n_diffusion_steps, device=device)
+        
+            for t in scheduler.timesteps:
+                # guidance: удваиваем батч
+                if guidance_scale != 1:
+                    latent_model_input = torch.cat([latents, latents], dim=0)
+                
+                    # empty_embeddings: [1, 1, hidden_dim] → повторяем по seq_len и batch
+                    seq_len = sample_text_embeddings.shape[1]
+                    hidden_dim = sample_text_embeddings.shape[2]
+                    empty_embeddings_exp = empty_embeddings.expand(-1, seq_len, hidden_dim)  # [1, seq_len, hidden_dim]
+                    empty_embeddings_exp = empty_embeddings_exp.repeat(sample_text_embeddings.shape[0], 1, 1)  # [batch, seq_len, hidden_dim]
+                
+                    text_embeddings_batch = torch.cat([empty_embeddings_exp, sample_text_embeddings], dim=0)
+                else:
+                    latent_model_input = latents
+                    text_embeddings_batch = sample_text_embeddings
+
+
+        
+                # предсказание потока (velocity)
+                model_out = original_model(latent_model_input, t, encoder_hidden_states=text_embeddings_batch)
+                flow = getattr(model_out, "sample", model_out)
+        
+                # guidance объединение
+                if guidance_scale != 1:
+                    flow_uncond, flow_cond = flow.chunk(2)
+                    flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
+        
+                # шаг через scheduler
+                latents = scheduler.step(flow, t, latents).prev_sample
+        
+            current_latents = latents
+
+
+            # Параметры нормализации
+            latent_for_vae = current_latents.detach() / scaling_factor + shift_factor
+            
+            decoded = vae.decode(latent_for_vae.to(torch.float32)).sample
+            #decoded = decoded[:, :, 0, :, :]  # [3, H, W]
+            #print(decoded.ndim, decoded.shape)
+            
+            decoded_fp32 = decoded.to(torch.float32)
+            for img_idx, img_tensor in enumerate(decoded_fp32):
+
+                # Форма: [3, H, W] -> преобразуем в [H, W, 3]
+                img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy()
+                img = img.transpose(1, 2, 0)  # Из [3, H, W] в [H, W, 3]
+    
+                #img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy().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][:200] 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],
+                        "width": img.width,
+                        "height": img.height,
+                        "global_step": step
+                    }
+                )
+    finally:
+        # вернуть VAE на CPU (как было в твоём коде)
+        vae.to("cpu")
+        for var in list(locals().keys()):
+            if isinstance(locals()[var], torch.Tensor):
+                del locals()[var]
+        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,0)
+accelerator.wait_for_everyone()
+
+# Модифицируем функцию сохранения модели для поддержки LoRA
+def save_checkpoint(unet, variant=""):
+    if accelerator.is_main_process:
+        if lora_name:
+            save_lora_checkpoint(unet)
+        else:
+            # безопасный unwrap для компилированной модели
+            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 = 2.
+
+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()
+    #print("epoch:",epoch)
+    for step, (latents, embeddings) in enumerate(dataloader):
+        with accelerator.accumulate(unet):
+            if save_model == False 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)
+    
+            # берём t из [0, 1]
+            t = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
+    
+            # 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 * scheduler.config.num_train_timesteps).long()
+    
+            # предсказание потока (Flow)
+            model_pred = unet(noisy_latents, timesteps, embeddings).sample
+    
+            # таргет — векторное поле (= разность между конечными точками)
+            target = noise - latents   # или latents - noise?
+    
+            # MSE лосс
+            mse_loss = F.mse_loss(model_pred.float(), target.float())
+
+            # Сохраняем для логов (мы сохраняем MSE отдельно — как показатель)
+            batch_losses.append(mse_loss.detach().item())
+
+            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
+                accelerator.wait_for_everyone()
+                
+            # Backward
+            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)
+                # Логируем метрики
+                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_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)
+    
+                    # Генерируем сэмплы с заданным интервалом
+                    if global_step % sample_interval == 0:
+                        generate_and_save_samples(fixed_samples,uncond_emb, global_step)
+                        last_n = sample_interval
+    
+                        if save_model:
+                            avg_sample_loss = np.mean(batch_losses[-sample_interval:]) if len(batch_losses) > 0 else 0.0
+                            print("saving:", avg_sample_loss < min_loss * save_barrier, "Avg:", avg_sample_loss)
+                            if avg_sample_loss is not None and avg_sample_loss < min_loss * save_barrier:
+                                min_loss = avg_sample_loss
+                                save_checkpoint(unet)
+
+
+    if accelerator.is_main_process:
+        # local averages
+        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)
+
+# Завершение обучения - сохраняем финальную модель
+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!")

train.py

@@ -26,11 +26,11 @@ import torch.nn.functional as F
 from collections import deque
 
 # --------------------------- Параметры ---------------------------
-ds_path = "/workspace/sdxs/datasets/640"
+ds_path = "/workspace/sdxs/datasets/640_mjnj"
 project = "unet"
 batch_size = 48
-base_learning_rate = 4e-5
-min_learning_rate = 2e-5
+base_learning_rate = 5e-5
+min_learning_rate = 2.5e-5
 num_epochs = 50
 # samples/save per epoch
 sample_interval_share = 5
@@ -52,7 +52,7 @@ torch.backends.cudnn.allow_tf32 = True
 torch.backends.cuda.enable_mem_efficient_sdp(False)
 dtype = torch.float32
 save_barrier = 1.006
-warmup_percent = 0.005
+warmup_percent = 0.01
 percentile_clipping = 99 # 8bit optim
 betta2 = 0.99
 eps = 1e-8
@@ -94,45 +94,6 @@ lora_alpha = 64
 
 print("init")
 
-# ---------------------------  вспомогательные функции ---------------------------
-def sample_timesteps_bias(
-    batch_size: int,
-    progress: float,           # [0..1]
-    num_train_timesteps: int,  # обычно 1000
-    steps_offset: int = 0,
-    device=None,
-    mode: str = "beta",        # "beta", "uniform"
-) -> torch.Tensor:
-    """
-    Возвращает timesteps с разным bias:
-    - beta   : как раньше (сдвиг в начало или конец в зависимости от progress)
-    - normal : около середины (гауссовое распределение)
-    - uniform: равномерно по всем timestep’ам
-    """
-
-    max_idx = num_train_timesteps - 1 - steps_offset
-
-    if mode == "beta":
-        alpha = 1.0 + .5 * (1.0 - progress)
-        beta  = 1.0 + .5 * progress
-        samples = torch.distributions.Beta(alpha, beta).sample((batch_size,))
-
-    elif mode == "uniform":
-        samples = torch.rand(batch_size)
-
-    else:
-        raise ValueError(f"Unknown mode: {mode}")
-
-    timesteps = steps_offset + (samples * max_idx).long().to(device)
-    return timesteps
-
-def logit_normal_samples(shape, mu=0.0, sigma=1.0, device=None, dtype=None):
-    normal_samples = torch.normal(mean=mu, std=sigma, size=shape, device=device, dtype=dtype)
-
-    logit_normal_samples = torch.sigmoid(normal_samples)
-
-    return logit_normal_samples
-
 # --------------------------- Инициализация WandB ---------------------------
 if accelerator.is_main_process:
     if use_wandb:
@@ -460,9 +421,12 @@ def get_negative_embedding(neg_prompt="", batch_size=1):
                 texts, return_tensors="pt", padding="max_length", truncation=True, max_length=max_length
             ).to(device)
 
-            outs = get_negative_embedding.text_model(**toks, output_hidden_states=True)
-            hidden_states = outs.hidden_states[-1]  # [B, L, D]
-            return hidden_states
+            outs = get_negative_embedding.text_model(**toks, output_hidden_states=True, return_dict=True)
+            hidden = outs.hidden_states[-1]  # [B, L, D]
+            mask = toks["attention_mask"].unsqueeze(-1)  # (B, L, 1)
+            hidden = hidden * mask   
+            
+            return hidden
 
     # Возвращаем эмбеддинг
     if not neg_prompt: