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test.ipynb

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+oid sha256:39f108e7891549639260ddfe82c52a689652efa7287e7d1bf5be6ac9453d34b1
+size 8665517

train-Copy2.py

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+#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
+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/datasets/768"
+project = "unet"
+batch_size = 36
+base_learning_rate = 2.7e-5 #4e-5
+min_learning_rate = 1e-5 #2.7e-5
+num_epochs = 80
+sample_interval_share = 5
+max_length = 192
+use_wandb = True
+use_comet_ml = False
+save_model = True
+use_decay = True
+fbp = False
+optimizer_type = "adam8bit"
+torch_compile = False
+unet_gradient = True
+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 = 1.01
+warmup_percent = 0.01
+percentile_clipping = 96 #97
+betta2 = 0.999
+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.,
+}
+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.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:
+                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("vae1x", torch_dtype=dtype).to("cpu").eval()
+tokenizer = AutoTokenizer.from_pretrained("tokenizer")
+text_model = AutoModel.from_pretrained("text_encoder").to(device).eval()
+
+# --- [UPDATED] Функция кодирования текста (с маской и пулингом) ---
+def encode_texts(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)
+        
+        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
+
+from diffusers import FlowMatchEulerDiscreteScheduler
+num_train_timesteps = 1000
+scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=num_train_timesteps)
+
+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)
+
+# --- [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() < 0.05
+        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)
+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:
+    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
+
+            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 = 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()
+    
+    for step, (latents, embeddings, attention_mask) 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()
+            
+            # --- 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
+
+            # === 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()
+                
+            accelerator.backward(total_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_loss.detach().item()
+                    log_data["lr"] = current_lr
+                    log_data["grad"] = grad
+                    log_data["loss_total"] = 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
+                        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!")

train.py

@@ -8,7 +8,7 @@ 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
+from diffusers import UNet2DConditionModel, AutoencoderKL,AutoencoderKLFlux2
 from accelerate import Accelerator
 from datasets import load_from_disk
 from tqdm import tqdm
@@ -30,7 +30,7 @@ from transformers import AutoTokenizer, AutoModel
 ds_path = "/workspace/sdxs/datasets/768"
 project = "unet"
 batch_size = 36
-base_learning_rate = 2.7e-5 #4e-5
+base_learning_rate = 2.7e-5
 min_learning_rate = 1e-5 #2.7e-5
 num_epochs = 80
 sample_interval_share = 5
@@ -49,7 +49,7 @@ 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)
+torch.backends.cuda.enable_mem_efficient_sdp(False)
 dtype = torch.float32
 save_barrier = 1.01
 warmup_percent = 0.01
@@ -71,7 +71,7 @@ device = accelerator.device
 # Параметры для диффузии
 n_diffusion_steps = 40
 samples_to_generate = 12
-guidance_scale = 4
+guidance_scale = 2
 
 # Папки для сохранения результатов
 generated_folder = "samples"
@@ -95,9 +95,10 @@ lora_alpha = 64
 print("init")
 
 loss_ratios = {
-    "mse":   1.,
+    "mse":   0.8,
+    "mae":   0.2,
 }
-median_coeff_steps = 128 
+median_coeff_steps = 256 
 
 # Нормализация лоссов по медианам: считаем КОЭФФИЦИЕНТЫ
 class MedianLossNormalizer:
@@ -120,7 +121,9 @@ class MedianLossNormalizer:
         # буферим только активные лоссы
         for k, v in losses.items():
             if k in self.buffers and self.ratios.get(k, 0) > 0:
-                self.buffers[k].append(float(v.detach().abs().cpu()))
+                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}
@@ -160,6 +163,7 @@ torch.backends.cuda.enable_flash_sdp(True)
 
 # --------------------------- Загрузка моделей ---------------------------
 vae = AutoencoderKL.from_pretrained("vae1x", torch_dtype=dtype).to("cpu").eval()
+#vae = AutoencoderKLFlux2.from_pretrained("black-forest-labs/FLUX.2-dev",subfolder="vae",torch_dtype=dtype).to(device).eval()
 tokenizer = AutoTokenizer.from_pretrained("tokenizer")
 text_model = AutoModel.from_pretrained("text_encoder").to(device).eval()
 
@@ -346,7 +350,7 @@ def collate_fn_simple(batch):
     raw_texts = [item["text"] for item in batch]
     texts = [
         "" if t.lower().startswith("zero") 
-        else "" if random.random() < 0.05
+        else "" if random.random() < 0.1
         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
@@ -654,7 +658,10 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
             # 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)
+            #t = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
+            u = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
+            t = torch.sigmoid(torch.randn_like(u))
+            
             # 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
@@ -668,8 +675,10 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
                 encoder_attention_mask=attention_mask
             ).sample
             
-            target = noise - latents 
+            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):
@@ -677,6 +686,7 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
 
             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()}
@@ -711,10 +721,11 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
                     batch_grads.append(grad)
     
                     log_data = {}
-                    log_data["loss"] = mse_loss.detach().item()
+                    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_total"] = float(total_loss.item())
+                    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:
@@ -725,7 +736,8 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
     
                     if global_step % sample_interval == 0:
                         # Передаем tuple (emb, mask) для негатива
-                        generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
+                        if epoch % 10 == 0:
+                            generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
                         last_n = sample_interval
                         
                         if save_model:

unet/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:18ba499f21e7872f47137bc29a4ae8f8d8d48d7b7023fc9bd68b9fb80408be81
+oid sha256:6c9cc2753742ec7627ccddab26e135a2016ac587d40a6c699ee4c7058e5e5107
 size 7444321360