train_sdxl_vae_full

eval_alchemist.py

@@ -6,7 +6,7 @@ from PIL import Image, UnidentifiedImageError
 from tqdm import tqdm
 from torch.utils.data import Dataset, DataLoader
 from torchvision.transforms import Compose, Resize, ToTensor, CenterCrop,ToPILImage 
-from diffusers import AutoencoderKL, AsymmetricAutoencoderKL
+from diffusers import AutoencoderKL, AsymmetricAutoencoderKL, AutoencoderKLWan,AutoencoderKLLTXVideo
 import random
 
 # --------------------------- Параметры ---------------------------
@@ -15,24 +15,28 @@ DTYPE = torch.float16
 IMAGE_FOLDER = "/workspace/alchemist" #wget https://huggingface.co/datasets/AiArtLab/alchemist/resolve/main/alchemist.zip
 MIN_SIZE = 1280
 CROP_SIZE = 512
-BATCH_SIZE = 10
-MAX_IMAGES = 0
+BATCH_SIZE = 1
+MAX_IMAGES = 100
 NUM_WORKERS = 4
-NUM_SAMPLES_TO_SAVE = 10  # Сколько примеров сохранить (0 - не сохранять)
+NUM_SAMPLES_TO_SAVE = 2  # Сколько примеров сохранить (0 - не сохранять)
 SAMPLES_FOLDER = "vaetest"
 
 # Список VAE для тестирования
 VAE_LIST = [
-
 #    ("stable-diffusion-v1-5/stable-diffusion-v1-5", AutoencoderKL, "stable-diffusion-v1-5/stable-diffusion-v1-5", "vae"),
 #    ("cross-attention/asymmetric-autoencoder-kl-x-1-5", AsymmetricAutoencoderKL, "cross-attention/asymmetric-autoencoder-kl-x-1-5", None),
-    ("madebyollin/sdxl-vae-fp16", AutoencoderKL, "madebyollin/sdxl-vae-fp16-fix", None),
-#    ("vae", AutoencoderKL, "/workspace/sdxl_vae/vae", None),
-    ("KBlueLeaf/EQ-SDXL-VAE", AutoencoderKL, "KBlueLeaf/EQ-SDXL-VAE", None),
-    ("AiArtLab/sdxl_vae", AutoencoderKL, "AiArtLab/sdxl_vae", None),
-    ("AiArtLab/sdxlvae_nightly", AutoencoderKL, "AiArtLab/sdxl_vae", "vae_nightly"),
+#    ("madebyollin/sdxl-vae-fp16", AutoencoderKL, "madebyollin/sdxl-vae-fp16-fix", None),
+#    ("KBlueLeaf/EQ-SDXL-VAE", AutoencoderKL, "KBlueLeaf/EQ-SDXL-VAE", None),
+#    ("AiArtLab/sdxl_vae", AutoencoderKL, "AiArtLab/sdxl_vae", None),
+#    ("AiArtLab/sdxlvae_nightly", AutoencoderKL, "AiArtLab/sdxl_vae", "vae_nightly"),
+#    ("Lightricks/LTX-Video", AutoencoderKLLTXVideo, "Lightricks/LTX-Video", "vae"),
+#    ("Wan2.2-TI2V-5B-Diffusers", AutoencoderKLWan, "Wan-AI/Wan2.2-TI2V-5B-Diffusers", "vae"),
+#    ("Wan2.2-T2V-A14B-Diffusers", AutoencoderKLWan, "Wan-AI/Wan2.2-T2V-A14B-Diffusers", "vae"),
     ("AiArtLab/sdxs", AutoencoderKL, "AiArtLab/sdxs", "vae"),
-    ("FLUX.1-schnell-vae", AutoencoderKL, "black-forest-labs/FLUX.1-schnell", "vae"),
+#    ("FLUX.1-schnell-vae", AutoencoderKL, "black-forest-labs/FLUX.1-schnell", "vae"),
+#    ("simple_vae", AutoencoderKL, "/workspace/sdxl_vae/simple_vae", None),
+    ("simple_vae_nightly", AutoencoderKL, "/workspace/sdxl_vae/simple_vae_nightly", None),
+    
 ]
 
 # --------------------------- Sobel Edge Detection ---------------------------
@@ -129,6 +133,44 @@ def deprocess(x):
 def _sanitize_name(name: str) -> str:
     return name.replace('/', '_').replace('-', '_')
 
+# --------------------------- Анализ VAE ---------------------------
+@torch.no_grad()
+def tensor_stats(name, x: torch.Tensor):
+    finite = torch.isfinite(x)
+    fin_ratio = finite.float().mean().item()
+    x_f = x[finite]
+    minv = x_f.min().item() if x_f.numel() else float('nan')
+    maxv = x_f.max().item() if x_f.numel() else float('nan')
+    mean = x_f.mean().item() if x_f.numel() else float('nan')
+    std  = x_f.std().item()  if x_f.numel() else float('nan')
+    big  = (x_f.abs() > 20).float().mean().item() if x_f.numel() else float('nan')
+    print(f"[{name}] shape={tuple(x.shape)} dtype={x.dtype} "
+          f"finite={fin_ratio:.6f} min={minv:.4g} max={maxv:.4g} mean={mean:.4g} std={std:.4g} |x|>20={big:.6f}")
+
+@torch.no_grad()
+def analyze_vae_latents(vae, name, images):
+    """
+    images: [B,3,H,W] в [-1,1]
+    """
+    try:
+        enc = vae.encode(images)
+        if hasattr(enc, "latent_dist"):
+            mu, logvar = enc.latent_dist.mean, enc.latent_dist.logvar
+            z = enc.latent_dist.sample()
+        else:
+            mu, logvar = enc[0], enc[1]
+            z = mu
+        tensor_stats(f"{name}.mu", mu)
+        tensor_stats(f"{name}.logvar", logvar)
+        tensor_stats(f"{name}.z_raw", z)
+
+        sf = getattr(vae.config, "scaling_factor", 1.0)
+        z_scaled = z * sf
+        tensor_stats(f"{name}.z_scaled(x{sf})", z_scaled)
+    except Exception as e:
+        print(f"⚠️ Ошибка анализа VAE {name}: {e}")
+
+
 # --------------------------- Основной код ---------------------------
 if __name__ == "__main__":
     if NUM_SAMPLES_TO_SAVE > 0:
@@ -182,13 +224,23 @@ if __name__ == "__main__":
         for batch in tqdm(dataloader, desc="Обработка батчей"):
             batch = batch.to(DEVICE)                                # [B,3,H,W] в [0,1]
             test_inp = process(batch).to(DTYPE)                     # [-1,1] для энкодера
+            # >>> Анализируем латенты каждой VAE на первой итерации
+            if images_saved == 0:   # только для первого батча, чтобы не засорять лог
+                for vae, name in zip(vaes, names):
+                    analyze_vae_latents(vae, name, test_inp)
 
             # 1) считаем реконструкции для всех VAE на весь батч
             recon_list = []
-            for vae in vaes:
-                latent = vae.encode(test_inp).latent_dist.mode()
-                dec = vae.decode(latent).sample.float()             # [-1,1] (как правило)
-                recon = deprocess(dec).clamp(0.0, 1.0)              # -> [0,1], clamp убирает артефакты
+            for vae, name in zip(vaes, names):
+                test_inp_vae = test_inp  # локальная копия
+                #if name == "Wan2.2-T2V-A14B-Diffusers" and test_inp_vae.ndim == 4:
+                if (isinstance(vae, AutoencoderKLWan) or isinstance(vae, AutoencoderKLLTXVideo)) and test_inp_vae.ndim == 4:
+                    test_inp_vae = test_inp_vae.unsqueeze(2)   # только для Wan
+                latent = vae.encode(test_inp_vae).latent_dist.mode()
+                dec = vae.decode(latent).sample.float()
+                if dec.ndim == 5:
+                    dec = dec.squeeze(2)
+                recon = deprocess(dec).clamp(0.0, 1.0)
                 recon_list.append(recon)
 
             # 2) обновляем метрики (по каждой VAE)

simple_vae/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.35.0.dev0",
+  "_name_or_path": "simple_vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 1.0,
+  "shift_factor": 0,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}

samples/sample_0.jpg → simple_vae/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3dce2033b5ea0f9ce2006d9e3b2c8cb123cdfd1a58a72d3562103b8979ebedd0
-size 79093
+oid sha256:010d2cb8824a347425be4e41d662b22492965ffb61393621eb1253be8b7fa0ce
+size 335311892

simple_vae_nightly/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.35.0.dev0",
+  "_name_or_path": "simple_vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 1.0,
+  "shift_factor": 0,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}

samples/sample_1.jpg → simple_vae_nightly/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a530cdc04fe70def63768ec9ca898fae8e100ea07bce8536b2d4a1115685e79e
-size 157701
+oid sha256:ccd57f2cd9455d6c66ed2fee9396dbb53cbeb675fa0c1fbee87a9b0f94c3de79
+size 335311892

train_sdxl_vae.py

@@ -24,11 +24,11 @@ from collections import deque
 
 # --------------------------- Параметры ---------------------------
 ds_path            = "/workspace/png"
-project            = "vae"
+project            = "simple_vae"
 batch_size         = 3
-base_learning_rate = 6e-6
-min_learning_rate  = 1e-6
-num_epochs         = 8
+base_learning_rate = 5e-5
+min_learning_rate  = 9e-7
+num_epochs         = 16
 sample_interval_share = 10
 use_wandb          = True
 save_model         = True
@@ -50,7 +50,7 @@ clip_grad_norm     = 1.0
 mixed_precision    = "no"   # или "fp16"/"bf16" при поддержке
 gradient_accumulation_steps = 5
 generated_folder   = "samples"
-save_as            = "vae_nightly"
+save_as            = "simple_vae_nightly"
 num_workers        = 0
 device = None  # accelerator задаст устройство
 
@@ -81,7 +81,7 @@ torch.manual_seed(seed)
 np.random.seed(seed)
 random.seed(seed)
 
-torch.backends.cudnn.benchmark = True
+torch.backends.cudnn.benchmark = False
 
 # --------------------------- WandB ---------------------------
 if use_wandb and accelerator.is_main_process:

train_sdxl_vae_full.py

@@ -0,0 +1,590 @@
+# -*- coding: utf-8 -*-
+import os
+import math
+import re
+import torch
+import numpy as np
+import random
+import gc
+from datetime import datetime
+from pathlib import Path
+
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+from torch.optim.lr_scheduler import LambdaLR
+from diffusers import AutoencoderKL, AsymmetricAutoencoderKL
+from accelerate import Accelerator
+from PIL import Image, UnidentifiedImageError
+from tqdm import tqdm
+import bitsandbytes as bnb
+import wandb
+import lpips   # pip install lpips
+from collections import deque
+
+# --------------------------- Параметры ---------------------------
+ds_path            = "/workspace/png"
+project            = "simple_vae"
+batch_size         = 3
+base_learning_rate = 5e-5
+min_learning_rate  = 9e-7
+num_epochs         = 16
+sample_interval_share = 10
+use_wandb          = True
+save_model         = True
+use_decay          = True
+asymmetric         = False
+optimizer_type     = "adam8bit"
+dtype              = torch.float32
+# model_resolution — то, что подавается в VAE (низкое разрешение)
+model_resolution   = 512   # бывший `resolution`
+# high_resolution — настоящий «высокий» кроп, на котором считаем метрики и сохраняем сэмплы
+high_resolution    = 512 
+limit              = 0
+save_barrier       = 1.03
+warmup_percent     = 0.01
+percentile_clipping = 95
+beta2              = 0.97
+eps                = 1e-6
+clip_grad_norm     = 1.0
+mixed_precision    = "no"   # или "fp16"/"bf16" при поддержке
+gradient_accumulation_steps = 5
+generated_folder   = "samples"
+save_as            = "simple_vae_nightly"
+num_workers        = 0
+device = None  # accelerator задаст устройство
+
+# --------------------------- Тренировочные режимы ---------------------------
+# CHANGED: добавлен параметр для полного обучения VAE (а не только декодера).
+# Если False — поведение прежнее: учим только decoder.* (up_blocks + mid_block).
+# Если True  — размораживаем ВСЮ модель и добавляем KL-loss для энкодера.
+full_training      = False
+
+# CHANGED: добавлен вес (через долю в нормализаторе) для KL, используется только при full_training=True.
+kl_ratio           = 0.05  # простая доля для KL в общей смеси (KISS). Игнорируется, если full_training=False.
+
+# --- Пропорции лоссов и окно медианного нормирования (КОЭФ., не значения) ---
+# Итоговые доли в total loss (сумма = 1.0 после нормализации).
+loss_ratios = {
+    "lpips": 0.85,
+    "edge":  0.05,
+    "mse":   0.05,
+    "mae":   0.05,
+    # CHANGED: заранее добавлен ключ "kl" (по умолчанию 0.0). Если включаем full_training — активируем ниже.
+    "kl":    0.00,
+}
+median_coeff_steps = 256  # за сколько шагов считать медианные коэффициенты
+
+# --------------------------- параметры препроцессинга ---------------------------
+resize_long_side = 1280  # если None или 0 — ресайза не будет; рекомендовано 1280
+
+Path(generated_folder).mkdir(parents=True, exist_ok=True)
+
+accelerator = Accelerator(
+    mixed_precision=mixed_precision,
+    gradient_accumulation_steps=gradient_accumulation_steps
+)
+device = accelerator.device
+
+# reproducibility
+seed = int(datetime.now().strftime("%Y%m%d"))
+torch.manual_seed(seed)
+np.random.seed(seed)
+random.seed(seed)
+
+torch.backends.cudnn.benchmark = False
+
+# --------------------------- WandB ---------------------------
+if use_wandb and accelerator.is_main_process:
+    wandb.init(project=project, config={
+        "batch_size": batch_size,
+        "base_learning_rate": base_learning_rate,
+        "num_epochs": num_epochs,
+        "optimizer_type": optimizer_type,
+        "model_resolution": model_resolution,
+        "high_resolution": high_resolution,
+        "gradient_accumulation_steps": gradient_accumulation_steps,
+        "full_training": full_training,  # CHANGED: логируем режим
+        "kl_ratio": kl_ratio,            # CHANGED: логируем долю KL
+    })
+
+# --------------------------- VAE ---------------------------
+if model_resolution==high_resolution and not asymmetric:
+    vae = AutoencoderKL.from_pretrained(project).to(dtype)
+else:
+    vae = AsymmetricAutoencoderKL.from_pretrained(project).to(dtype)
+
+# torch.compile (если доступно) — просто и без лишней логики
+if hasattr(torch, "compile"):
+    try:
+        vae = torch.compile(vae)
+    except Exception as e:
+        print(f"[WARN] torch.compile failed: {e}")
+
+# >>> Стратегия заморозки / разморозки
+for p in vae.parameters():
+    p.requires_grad = False
+
+decoder = getattr(vae, "decoder", None)
+if decoder is None:
+    raise RuntimeError("vae.decoder not found — не могу применить стратегию разморозки. Проверь структуру модели.")
+
+unfrozen_param_names = []
+
+if not full_training:
+    # === Прежнее поведение: обучаем только decoder.up_blocks и decoder.mid_block ===
+    if not hasattr(decoder, "up_blocks"):
+        raise RuntimeError("decoder.up_blocks не найдены — ожидается список блоков декодера.")
+
+    n_up = len(decoder.up_blocks)
+    start_idx = 0
+    for idx in range(start_idx, n_up):
+        block = decoder.up_blocks[idx]
+        for name, p in block.named_parameters():
+            p.requires_grad = True
+            unfrozen_param_names.append(f"decoder.up_blocks.{idx}.{name}")
+
+    if hasattr(decoder, "mid_block"):
+        for name, p in decoder.mid_block.named_parameters():
+            p.requires_grad = True
+            unfrozen_param_names.append(f"decoder.mid_block.{name}")
+    else:
+        print("[WARN] decoder.mid_block не найден — mid_block не разморожен.")
+
+    # Обучаем только декодер
+    trainable_module = vae.decoder
+else:
+    # === CHANGED: Полное обучение — размораживаем ВСЕ слои VAE (и энкодер, и декодер, и пост-проекцию) ===
+    for name, p in vae.named_parameters():
+        p.requires_grad = True
+        unfrozen_param_names.append(name)
+    trainable_module = vae  # CHANGED: учим всю модель
+
+    # CHANGED: активируем KL-долю в нормализаторе
+    loss_ratios["kl"] = float(kl_ratio)
+
+print(f"[INFO] Разморожено параметров: {len(unfrozen_param_names)}. Первые 200 имён:")
+for nm in unfrozen_param_names[:200]:
+    print("  ", nm)
+
+# --------------------------- Custom PNG Dataset (only .png, skip corrupted) -----------
+class PngFolderDataset(Dataset):
+    def __init__(self, root_dir, min_exts=('.png',), resolution=1024, limit=0):
+        self.root_dir = root_dir
+        self.resolution = resolution
+        self.paths = []
+        # collect png files recursively
+        for root, _, files in os.walk(root_dir):
+            for fname in files:
+                if fname.lower().endswith(tuple(ext.lower() for ext in min_exts)):
+                    self.paths.append(os.path.join(root, fname))
+        # optional limit
+        if limit:
+            self.paths = self.paths[:limit]
+        # verify images and keep only valid ones
+        valid = []
+        for p in self.paths:
+            try:
+                with Image.open(p) as im:
+                    im.verify()  # fast check for truncated/corrupted images
+                valid.append(p)
+            except (OSError, UnidentifiedImageError):
+                # skip corrupted image
+                continue
+        self.paths = valid
+        if len(self.paths) == 0:
+            raise RuntimeError(f"No valid PNG images found under {root_dir}")
+        # final shuffle for randomness
+        random.shuffle(self.paths)
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, idx):
+        p = self.paths[idx % len(self.paths)]
+        # open and convert to RGB; ensure file is closed promptly
+        with Image.open(p) as img:
+            img = img.convert("RGB")
+            # пережимаем длинную сторону до resize_long_side (Lanczos)
+            if not resize_long_side or resize_long_side <= 0:
+                return img
+            w, h = img.size
+            long = max(w, h)
+            if long <= resize_long_side:
+                return img
+            scale = resize_long_side / float(long)
+            new_w = int(round(w * scale))
+            new_h = int(round(h * scale))
+            return img.resize((new_w, new_h), Image.LANCZOS)
+
+# --------------------------- Датасет и трансформы ---------------------------
+
+def random_crop(img, sz):
+    w, h = img.size
+    if w < sz or h < sz:
+        img = img.resize((max(sz, w), max(sz, h)), Image.LANCZOS)
+    x = random.randint(0, max(1, img.width - sz))
+    y = random.randint(0, max(1, img.height - sz))
+    return img.crop((x, y, x + sz, y + sz))
+
+tfm = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+
+# build dataset using high_resolution crops
+dataset = PngFolderDataset(ds_path, min_exts=('.png',), resolution=high_resolution, limit=limit)
+if len(dataset) < batch_size:
+    raise RuntimeError(f"Not enough valid images ({len(dataset)}) to form a batch of size {batch_size}")
+
+# collate_fn кропит до high_resolution
+def collate_fn(batch):
+    imgs = []
+    for img in batch:  # img is PIL.Image
+        img = random_crop(img, high_resolution)   # кропим high-res
+        imgs.append(tfm(img))
+    return torch.stack(imgs)
+
+dataloader = DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    collate_fn=collate_fn,
+    num_workers=num_workers,
+    pin_memory=True,
+    drop_last=True
+)
+
+# --------------------------- Оптимизатор ---------------------------
+
+def get_param_groups(module, weight_decay=0.001):
+    no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight", "ln_1.weight", "ln_f.weight"]
+    decay_params = []
+    no_decay_params = []
+    for n, p in module.named_parameters():
+        if not p.requires_grad:
+            continue
+        if any(nd in n for nd in no_decay):
+            no_decay_params.append(p)
+        else:
+            decay_params.append(p)
+    return [
+        {"params": decay_params, "weight_decay": weight_decay},
+        {"params": no_decay_params, "weight_decay": 0.0},
+    ]
+
+def create_optimizer(name, param_groups):
+    if name == "adam8bit":
+        return bnb.optim.AdamW8bit(
+            param_groups, lr=base_learning_rate, betas=(0.9, beta2), eps=eps
+        )
+    raise ValueError(name)
+
+param_groups = get_param_groups(trainable_module, weight_decay=0.001)
+optimizer = create_optimizer(optimizer_type, param_groups)
+
+# --------------------------- График LR ---------------------------
+
+batches_per_epoch = len(dataloader)  # число микро-батчей (dataloader steps)
+steps_per_epoch = int(math.ceil(batches_per_epoch / float(gradient_accumulation_steps)))  # число optimizer.step() за эпоху
+total_steps = steps_per_epoch * num_epochs
+
+def lr_lambda(step):
+    if not use_decay:
+        return 1.0
+    x = float(step) / float(max(1, total_steps))
+    warmup = float(warmup_percent)
+    min_ratio = float(min_learning_rate) / float(base_learning_rate)
+    if x < warmup:
+        return min_ratio + (1.0 - min_ratio) * (x / warmup)
+    decay_ratio = (x - warmup) / (1.0 - warmup)
+    return min_ratio + 0.5 * (1.0 - min_ratio) * (1.0 + math.cos(math.pi * decay_ratio))
+
+scheduler = LambdaLR(optimizer, lr_lambda)
+
+# Подготовка
+dataloader, vae, optimizer, scheduler = accelerator.prepare(dataloader, vae, optimizer, scheduler)
+
+# CHANGED: формируем список trainable_params исходя из выбранного trainable_module
+trainable_params = [p for p in (trainable_module.parameters() if hasattr(trainable_module, "parameters") else []) if p.requires_grad]
+
+# --------------------------- LPIPS и вспомогательные функции ---------------------------
+_lpips_net = None
+
+def _get_lpips():
+    global _lpips_net
+    if _lpips_net is None:
+        _lpips_net = lpips.LPIPS(net='vgg', verbose=False).eval().to(accelerator.device).eval()
+    return _lpips_net
+
+# Собель для edge loss
+_sobel_kx = torch.tensor([[[[-1,0,1],[-2,0,2],[-1,0,1]]]], dtype=torch.float32)
+_sobel_ky = torch.tensor([[[[-1,-2,-1],[0,0,0],[1,2,1]]]], dtype=torch.float32)
+
+def sobel_edges(x: torch.Tensor) -> torch.Tensor:
+    # x: [B,C,H,W] в [-1,1]
+    C = x.shape[1]
+    kx = _sobel_kx.to(x.device, x.dtype).repeat(C, 1, 1, 1)
+    ky = _sobel_ky.to(x.device, x.dtype).repeat(C, 1, 1, 1)
+    gx = F.conv2d(x, kx, padding=1, groups=C)
+    gy = F.conv2d(x, ky, padding=1, groups=C)
+    return torch.sqrt(gx * gx + gy * gy + 1e-12)
+
+# 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) if s > 0 else 0.0 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, abs_losses: dict):
+        # Заполняем буферы фактическими АБСОЛЮТНЫМИ значениями лоссов
+        for k, v in abs_losses.items():
+            if k in self.buffers:
+                self.buffers[k].append(float(v.detach().abs().cpu()))
+        # Медианы (устойчивые к выбросам)
+        meds = {k: (np.median(self.buffers[k]) if len(self.buffers[k]) > 0 else 1.0) for k in self.buffers}
+        # Вычисляем КОЭФФИЦИЕНТЫ как ratio_k / median_k — т.е. именно коэффициенты, а не значения
+        coeffs = {k: (self.ratios[k] / max(meds[k], 1e-12)) for k in self.ratios}
+        # Итоговый total — сумма по ключам, присутствующим в abs_losses
+        total = sum(coeffs[k] * abs_losses[k] for k in abs_losses if k in coeffs)
+        return total, coeffs, meds
+
+# CHANGED: создаём нормализатор ПОСЛЕ возможной активации kl_ratio выше
+normalizer = MedianLossNormalizer(loss_ratios, median_coeff_steps)
+
+# --------------------------- Сэмплы ---------------------------
+@torch.no_grad()
+def get_fixed_samples(n=3):
+    idx = random.sample(range(len(dataset)), min(n, len(dataset)))
+    pil_imgs = [dataset[i] for i in idx]  # dataset returns PIL.Image
+    tensors = []
+    for img in pil_imgs:
+        img = random_crop(img, high_resolution)  # high-res fixed samples
+        tensors.append(tfm(img))
+    return torch.stack(tensors).to(accelerator.device, dtype)
+
+fixed_samples = get_fixed_samples()
+
+@torch.no_grad()
+def _to_pil_uint8(img_tensor: torch.Tensor) -> Image.Image:
+    # img_tensor: [C,H,W] in [-1,1]
+    arr = ((img_tensor.float().clamp(-1, 1) + 1.0) * 127.5).clamp(0, 255).byte().cpu().numpy().transpose(1, 2, 0)
+    return Image.fromarray(arr)
+
+@torch.no_grad()
+def generate_and_save_samples(step=None):
+    try:
+        temp_vae = accelerator.unwrap_model(vae).eval()
+        lpips_net = _get_lpips()
+        with torch.no_grad():
+            # Готовим low-res вход для кодера ВСЕГДА под model_resolution
+            orig_high = fixed_samples  # [B,C,H,W] в [-1,1]
+            orig_low = F.interpolate(orig_high, size=(model_resolution, model_resolution), mode="bilinear", align_corners=False)
+            # dtype как у модели
+            model_dtype = next(temp_vae.parameters()).dtype
+            orig_low = orig_low.to(dtype=model_dtype)
+            # encode/decode
+            # CHANGED: при валидации/сэмплах всегда используем mean (стабильно и детерминированно)
+            enc = temp_vae.encode(orig_low)
+            latents_mean = enc.latent_dist.mean
+            rec = temp_vae.decode(latents_mean).sample
+
+        # Приводим spatial размер рекона к high-res (downsample для асимметричных VAE)
+        if rec.shape[-2:] != orig_high.shape[-2:]:
+            rec = F.interpolate(rec, size=orig_high.shape[-2:], mode="bilinear", align_corners=False)
+
+        # Сохраняем ПЕРВЫЙ семпл: real и decoded без номера шага в имени
+        first_real = _to_pil_uint8(orig_high[0])
+        first_dec  = _to_pil_uint8(rec[0])
+        first_real.save(f"{generated_folder}/sample_real.jpg", quality=95)
+        first_dec.save(f"{generated_folder}/sample_decoded.jpg", quality=95)
+
+        # Дополнительно сохраняем текущие реконструкции без номера шага (чтобы не плодить файлы — будут перезаписываться)
+        for i in range(rec.shape[0]):
+            _to_pil_uint8(rec[i]).save(f"{generated_folder}/sample_{i}.jpg", quality=95)
+
+        # LPIPS на полном изображении (high-res) — для лога
+        lpips_scores = []
+        for i in range(rec.shape[0]):
+            orig_full = orig_high[i:i+1].to(torch.float32)
+            rec_full  = rec[i:i+1].to(torch.float32)
+            if rec_full.shape[-2:] != orig_full.shape[-2:]:
+                rec_full = F.interpolate(rec_full, size=orig_full.shape[-2:], mode="bilinear", align_corners=False)
+            lpips_val = lpips_net(orig_full, rec_full).item()
+            lpips_scores.append(lpips_val)
+        avg_lpips = float(np.mean(lpips_scores))
+
+        if use_wandb and accelerator.is_main_process:
+            wandb.log({
+                "lpips_mean": avg_lpips,
+            }, step=step)
+    finally:
+        gc.collect()
+        torch.cuda.empty_cache()
+
+if accelerator.is_main_process and save_model:
+    print("Генерация сэмплов до старта обучения...")
+    generate_and_save_samples(0)
+
+accelerator.wait_for_everyone()
+
+# --------------------------- Тренировка ---------------------------
+
+progress = tqdm(total=total_steps, disable=not accelerator.is_local_main_process)
+global_step = 0
+min_loss = float("inf")
+sample_interval = max(1, total_steps // max(1, sample_interval_share * num_epochs))
+
+for epoch in range(num_epochs):
+    vae.train()
+    batch_losses = []
+    batch_grads = []
+    # Доп. трекинг по отдельным лоссам
+    track_losses = {k: [] for k in loss_ratios.keys()}
+    for imgs in dataloader:
+        with accelerator.accumulate(vae):
+            # imgs: high-res tensor from dataloader ([-1,1]), move to device
+            imgs = imgs.to(accelerator.device)
+
+            # ВСЕГДА даунсемплим вход под model_resolution для кодера
+            if high_resolution != model_resolution:
+                imgs_low = F.interpolate(imgs, size=(model_resolution, model_resolution), mode="bilinear", align_corners=False)
+            else:
+                imgs_low = imgs 
+
+            # ensure dtype matches model params to avoid float/half mismatch
+            model_dtype = next(vae.parameters()).dtype
+            imgs_low_model = imgs_low.to(dtype=model_dtype) if imgs_low.dtype != model_dtype else imgs_low
+
+            # Encode/decode
+            enc = vae.encode(imgs_low_model)
+
+            # CHANGED: если тренируем всю модель — используем reparameterization sample()
+            # это важно для стохастичности и согласованности с KL.
+            latents = enc.latent_dist.sample() if full_training else enc.latent_dist.mean
+
+            rec = vae.decode(latents).sample  # rec может быть увеличенным (асимметричный VAE)
+
+            # Приводим размер к high-res
+            if rec.shape[-2:] != imgs.shape[-2:]:
+                rec = F.interpolate(rec, size=imgs.shape[-2:], mode="bilinear", align_corners=False)
+
+            # Лоссы считаем на high-res
+            rec_f32 = rec.to(torch.float32)
+            imgs_f32 = imgs.to(torch.float32)
+
+            # Отдельные лоссы (абсолютные значения)
+            abs_losses = {
+                "mae":   F.l1_loss(rec_f32, imgs_f32),
+                "mse":   F.mse_loss(rec_f32, imgs_f32),
+                "lpips": _get_lpips()(rec_f32, imgs_f32).mean(),
+                "edge":  F.l1_loss(sobel_edges(rec_f32), sobel_edges(imgs_f32)),
+            }
+
+            # CHANGED: KL-loss добавляется ТОЛЬКО при полном обучении.
+            # KL(q(z|x) || N(0,1)) = -0.5 * sum(1 + logσ^2 - μ^2 - σ^2).
+            if full_training:
+                mean   = enc.latent_dist.mean
+                logvar = enc.latent_dist.logvar
+                # стабильное усреднение по батчу и пространству
+                kl = -0.5 * torch.mean(1 + logvar - mean.pow(2) - logvar.exp())
+                abs_losses["kl"] = kl
+            else:
+                # ключ присутствует в ratios, но при partial-training его доля = 0 и он не влияет
+                abs_losses["kl"] = torch.tensor(0.0, device=accelerator.device, dtype=torch.float32)
+
+            # Total с медианными КОЭФФИЦИЕНТАМИ
+            total_loss, coeffs, meds = normalizer.update_and_total(abs_losses)
+
+            if torch.isnan(total_loss) or torch.isinf(total_loss):
+                print("NaN/Inf loss – stopping")
+                raise RuntimeError("NaN/Inf loss")
+
+            accelerator.backward(total_loss)
+
+            grad_norm = torch.tensor(0.0, device=accelerator.device)
+            if accelerator.sync_gradients:
+                grad_norm = accelerator.clip_grad_norm_(trainable_params, clip_grad_norm)
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+                global_step += 1
+                progress.update(1)
+
+            # --- Логирование ---
+            if accelerator.is_main_process:
+                try:
+                    current_lr = optimizer.param_groups[0]["lr"]
+                except Exception:
+                    current_lr = scheduler.get_last_lr()[0]
+
+                batch_losses.append(total_loss.detach().item())
+                # CHANGED: корректно извлекаем scalar из разн. типов
+                if isinstance(grad_norm, torch.Tensor):
+                    batch_grads.append(float(grad_norm.detach().cpu().item()))
+                else:
+                    batch_grads.append(float(grad_norm))
+
+                for k, v in abs_losses.items():
+                    track_losses[k].append(float(v.detach().item()))
+
+                if use_wandb and accelerator.sync_gradients:
+                    log_dict = {
+                        "total_loss": float(total_loss.detach().item()),
+                        "learning_rate": current_lr,
+                        "epoch": epoch,
+                        "grad_norm": batch_grads[-1],
+                        "mode/full_training": int(full_training),  # CHANGED: для наглядности в логах
+                    }
+                    # добавляем отдельные лоссы
+                    for k, v in abs_losses.items():
+                        log_dict[f"loss_{k}"] = float(v.detach().item())
+                    # логи коэффициентов и медиан
+                    for k in coeffs:
+                        log_dict[f"coeff_{k}"] = float(coeffs[k])
+                        log_dict[f"median_{k}"] = float(meds[k])
+                    wandb.log(log_dict, step=global_step)
+
+            # периодические сэмплы и чекпоинты
+            if global_step > 0 and global_step % sample_interval == 0:
+                if accelerator.is_main_process:
+                    generate_and_save_samples(global_step)
+                accelerator.wait_for_everyone()
+
+                # Средние по последним итерациям
+                n_micro = sample_interval * gradient_accumulation_steps
+                if len(batch_losses) >= n_micro:
+                    avg_loss = float(np.mean(batch_losses[-n_micro:]))
+                else:
+                    avg_loss = float(np.mean(batch_losses)) if batch_losses else float("nan")
+
+                avg_grad = float(np.mean(batch_grads[-n_micro:])) if len(batch_grads) >= 1 else float(np.mean(batch_grads)) if batch_grads else 0.0
+
+                if accelerator.is_main_process:
+                    print(f"Epoch {epoch} step {global_step} loss: {avg_loss:.6f}, grad_norm: {avg_grad:.6f}, lr: {current_lr:.9f}")
+                    if save_model and avg_loss < min_loss * save_barrier:
+                        min_loss = avg_loss
+                        accelerator.unwrap_model(vae).save_pretrained(save_as)
+                    if use_wandb:
+                        wandb.log({"interm_loss": avg_loss, "interm_grad": avg_grad}, step=global_step)
+
+    if accelerator.is_main_process:
+        epoch_avg = float(np.mean(batch_losses)) if batch_losses else float("nan")
+        print(f"Epoch {epoch} done, avg loss {epoch_avg:.6f}")
+        if use_wandb:
+            wandb.log({"epoch_loss": epoch_avg, "epoch": epoch + 1}, step=global_step)
+
+# --------------------------- Финальное сохранение ---------------------------
+if accelerator.is_main_process:
+    print("Training finished – saving final model")
+    if save_model:
+        accelerator.unwrap_model(vae).save_pretrained(save_as)
+
+accelerator.free_memory()
+if torch.distributed.is_initialized():
+    torch.distributed.destroy_process_group()
+print("Готово!")

train_sdxl_vae_simple.py

@@ -0,0 +1,547 @@
+# -*- coding: utf-8 -*-
+import os
+import math
+import re
+import torch
+import numpy as np
+import random
+import gc
+from datetime import datetime
+from pathlib import Path
+
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset
+from torch.optim.lr_scheduler import LambdaLR
+from diffusers import AutoencoderKL, AsymmetricAutoencoderKL
+from accelerate import Accelerator
+from PIL import Image, UnidentifiedImageError
+from tqdm import tqdm
+import bitsandbytes as bnb
+import wandb
+import lpips   # pip install lpips
+from collections import deque
+
+# --------------------------- Параметры ---------------------------
+ds_path            = "/workspace/png"
+project            = "simple_vae"
+batch_size         = 3
+base_learning_rate = 5e-5
+min_learning_rate  = 9e-7
+num_epochs         = 16
+sample_interval_share = 10
+use_wandb          = True
+save_model         = True
+use_decay          = True
+asymmetric         = False
+optimizer_type     = "adam8bit"
+dtype              = torch.float32
+# model_resolution — то, что подавается в VAE (низкое разрешение)
+model_resolution   = 512   # бывший `resolution`
+# high_resolution — настоящий «высокий» кроп, на котором считаем метрики и сохраняем сэмплы
+high_resolution    = 512 
+limit              = 0
+save_barrier       = 1.03
+warmup_percent     = 0.01
+percentile_clipping = 95
+beta2              = 0.97
+eps                = 1e-6
+clip_grad_norm     = 1.0
+mixed_precision    = "no"   # или "fp16"/"bf16" при поддержке
+gradient_accumulation_steps = 5
+generated_folder   = "samples"
+save_as            = "simple_vae_nightly"
+num_workers        = 0
+device = None  # accelerator задаст устройство
+
+# --- Пропорции лоссов и окно медианного нормирования (КОЭФ., не значения) ---
+# Итоговые доли в total loss (сумма = 1.0)
+loss_ratios = {
+    "lpips": 0.85,
+    "edge":  0.05,
+    "mse":   0.05,
+    "mae":   0.05,
+}
+median_coeff_steps = 256  # за сколько шагов считать медианные коэффициенты
+
+# --------------------------- параметры препроцессинга ---------------------------
+resize_long_side = 1280  # если None или 0 — ресайза не будет; рекомендовано 1280
+
+Path(generated_folder).mkdir(parents=True, exist_ok=True)
+
+accelerator = Accelerator(
+    mixed_precision=mixed_precision,
+    gradient_accumulation_steps=gradient_accumulation_steps
+)
+device = accelerator.device
+
+# reproducibility
+seed = int(datetime.now().strftime("%Y%m%d"))
+torch.manual_seed(seed)
+np.random.seed(seed)
+random.seed(seed)
+
+torch.backends.cudnn.benchmark = True
+
+# --------------------------- WandB ---------------------------
+if use_wandb and accelerator.is_main_process:
+    wandb.init(project=project, config={
+        "batch_size": batch_size,
+        "base_learning_rate": base_learning_rate,
+        "num_epochs": num_epochs,
+        "optimizer_type": optimizer_type,
+        "model_resolution": model_resolution,
+        "high_resolution": high_resolution,
+        "gradient_accumulation_steps": gradient_accumulation_steps,
+    })
+
+# --------------------------- VAE ---------------------------
+if model_resolution==high_resolution and not asymmetric:
+    vae = AutoencoderKL.from_pretrained(project).to(dtype)
+else:
+    vae = AsymmetricAutoencoderKL.from_pretrained(project).to(dtype)
+
+# torch.compile (если доступно) — просто и без лишней логики
+if hasattr(torch, "compile"):
+    try:
+        vae = torch.compile(vae)
+    except Exception as e:
+        print(f"[WARN] torch.compile failed: {e}")
+
+# >>> Заморозка всех параметров, затем выборочная разморозка
+for p in vae.parameters():
+    p.requires_grad = False
+
+decoder = getattr(vae, "decoder", None)
+if decoder is None:
+    raise RuntimeError("vae.decoder not found — не могу применить стратегию разморозки. Проверь структуру модели.")
+
+unfrozen_param_names = []
+
+if not hasattr(decoder, "up_blocks"):
+    raise RuntimeError("decoder.up_blocks не найдены — ожидается список блоков декодера.")
+
+# >>> Размораживаем все up_blocks и mid_block (как было в твоём варианте start_idx=0)
+n_up = len(decoder.up_blocks)
+start_idx = 0
+for idx in range(start_idx, n_up):
+    block = decoder.up_blocks[idx]
+    for name, p in block.named_parameters():
+        p.requires_grad = True
+        unfrozen_param_names.append(f"decoder.up_blocks.{idx}.{name}")
+
+if hasattr(decoder, "mid_block"):
+    for name, p in decoder.mid_block.named_parameters():
+        p.requires_grad = True
+        unfrozen_param_names.append(f"decoder.mid_block.{name}")
+else:
+    print("[WARN] decoder.mid_block не найден — mid_block не разморожен.")
+
+print(f"[INFO] Разморожено параметров: {len(unfrozen_param_names)}. Первые 200 имён:")
+for nm in unfrozen_param_names[:200]:
+    print("  ", nm)
+
+# сохраняем trainable_module (get_param_groups будет учитывать p.requires_grad)
+trainable_module = vae.decoder
+
+# --------------------------- Custom PNG Dataset (only .png, skip corrupted) -----------
+class PngFolderDataset(Dataset):
+    def __init__(self, root_dir, min_exts=('.png',), resolution=1024, limit=0):
+        self.root_dir = root_dir
+        self.resolution = resolution
+        self.paths = []
+        # collect png files recursively
+        for root, _, files in os.walk(root_dir):
+            for fname in files:
+                if fname.lower().endswith(tuple(ext.lower() for ext in min_exts)):
+                    self.paths.append(os.path.join(root, fname))
+        # optional limit
+        if limit:
+            self.paths = self.paths[:limit]
+        # verify images and keep only valid ones
+        valid = []
+        for p in self.paths:
+            try:
+                with Image.open(p) as im:
+                    im.verify()  # fast check for truncated/corrupted images
+                valid.append(p)
+            except (OSError, UnidentifiedImageError):
+                # skip corrupted image
+                continue
+        self.paths = valid
+        if len(self.paths) == 0:
+            raise RuntimeError(f"No valid PNG images found under {root_dir}")
+        # final shuffle for randomness
+        random.shuffle(self.paths)
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, idx):
+        p = self.paths[idx % len(self.paths)]
+        # open and convert to RGB; ensure file is closed promptly
+        with Image.open(p) as img:
+            img = img.convert("RGB")
+            # пережимаем длинную сторону до resize_long_side (Lanczos)
+            if not resize_long_side or resize_long_side <= 0:
+                return img
+            w, h = img.size
+            long = max(w, h)
+            if long <= resize_long_side:
+                return img
+            scale = resize_long_side / float(long)
+            new_w = int(round(w * scale))
+            new_h = int(round(h * scale))
+            return img.resize((new_w, new_h), Image.LANCZOS)
+
+# --------------------------- Датасет и трансформы ---------------------------
+
+def random_crop(img, sz):
+    w, h = img.size
+    if w < sz or h < sz:
+        img = img.resize((max(sz, w), max(sz, h)), Image.LANCZOS)
+    x = random.randint(0, max(1, img.width - sz))
+    y = random.randint(0, max(1, img.height - sz))
+    return img.crop((x, y, x + sz, y + sz))
+
+tfm = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+
+# build dataset using high_resolution crops
+dataset = PngFolderDataset(ds_path, min_exts=('.png',), resolution=high_resolution, limit=limit)
+if len(dataset) < batch_size:
+    raise RuntimeError(f"Not enough valid images ({len(dataset)}) to form a batch of size {batch_size}")
+
+# collate_fn кропит до high_resolution
+
+def collate_fn(batch):
+    imgs = []
+    for img in batch:  # img is PIL.Image
+        img = random_crop(img, high_resolution)   # кропим high-res
+        imgs.append(tfm(img))
+    return torch.stack(imgs)
+
+dataloader = DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    collate_fn=collate_fn,
+    num_workers=num_workers,
+    pin_memory=True,
+    drop_last=True
+)
+
+# --------------------------- Оптимизатор ---------------------------
+
+def get_param_groups(module, weight_decay=0.001):
+    no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight", "ln_1.weight", "ln_f.weight"]
+    decay_params = []
+    no_decay_params = []
+    for n, p in module.named_parameters():
+        if not p.requires_grad:
+            continue
+        if any(nd in n for nd in no_decay):
+            no_decay_params.append(p)
+        else:
+            decay_params.append(p)
+    return [
+        {"params": decay_params, "weight_decay": weight_decay},
+        {"params": no_decay_params, "weight_decay": 0.0},
+    ]
+
+def create_optimizer(name, param_groups):
+    if name == "adam8bit":
+        return bnb.optim.AdamW8bit(
+            param_groups, lr=base_learning_rate, betas=(0.9, beta2), eps=eps
+        )
+    raise ValueError(name)
+
+param_groups = get_param_groups(trainable_module, weight_decay=0.001)
+optimizer = create_optimizer(optimizer_type, param_groups)
+
+# --------------------------- Подготовка Accelerate (вместе) ---------------------------
+
+batches_per_epoch = len(dataloader)  # число микро-батчей (dataloader steps)
+steps_per_epoch = int(math.ceil(batches_per_epoch / float(gradient_accumulation_steps)))  # число optimizer.step() за эпоху
+total_steps = steps_per_epoch * num_epochs
+
+
+def lr_lambda(step):
+    if not use_decay:
+        return 1.0
+    x = float(step) / float(max(1, total_steps))
+    warmup = float(warmup_percent)
+    min_ratio = float(min_learning_rate) / float(base_learning_rate)
+    if x < warmup:
+        return min_ratio + (1.0 - min_ratio) * (x / warmup)
+    decay_ratio = (x - warmup) / (1.0 - warmup)
+    return min_ratio + 0.5 * (1.0 - min_ratio) * (1.0 + math.cos(math.pi * decay_ratio))
+
+scheduler = LambdaLR(optimizer, lr_lambda)
+
+# Подготовка
+dataloader, vae, optimizer, scheduler = accelerator.prepare(dataloader, vae, optimizer, scheduler)
+
+trainable_params = [p for p in vae.decoder.parameters() if p.requires_grad]
+
+# --------------------------- LPIPS и вспомогательные функции ---------------------------
+_lpips_net = None
+
+def _get_lpips():
+    global _lpips_net
+    if _lpips_net is None:
+        _lpips_net = lpips.LPIPS(net='vgg', verbose=False).eval().to(accelerator.device).eval()
+    return _lpips_net
+
+# Собель для edge loss
+_sobel_kx = torch.tensor([[[[-1,0,1],[-2,0,2],[-1,0,1]]]], dtype=torch.float32)
+_sobel_ky = torch.tensor([[[[-1,-2,-1],[0,0,0],[1,2,1]]]], dtype=torch.float32)
+
+def sobel_edges(x: torch.Tensor) -> torch.Tensor:
+    # x: [B,C,H,W] в [-1,1]
+    C = x.shape[1]
+    kx = _sobel_kx.to(x.device, x.dtype).repeat(C, 1, 1, 1)
+    ky = _sobel_ky.to(x.device, x.dtype).repeat(C, 1, 1, 1)
+    gx = F.conv2d(x, kx, padding=1, groups=C)
+    gy = F.conv2d(x, ky, padding=1, groups=C)
+    return torch.sqrt(gx * gx + gy * gy + 1e-12)
+
+# 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, abs_losses: dict):
+        # Заполняем буферы фактическими АБСОЛЮТНЫМИ значениями лоссов
+        for k, v in abs_losses.items():
+            if k in self.buffers:
+                self.buffers[k].append(float(v.detach().cpu()))
+        # Медианы (устойчивые к выбросам)
+        meds = {k: (np.median(self.buffers[k]) if len(self.buffers[k]) > 0 else 1.0) for k in self.buffers}
+        # Вычисляем КОЭФФИЦИЕНТЫ как ratio_k / median_k — т.е. именно коэффициенты, а не значения
+        coeffs = {k: (self.ratios[k] / max(meds[k], 1e-12)) for k in self.ratios}
+        # Важно: при таких коэффициентах сумма (coeff_k * median_k) = сумма(ratio_k) = 1, т.е. масштаб стабилен
+        total = sum(coeffs[k] * abs_losses[k] for k in coeffs)
+        return total, coeffs, meds
+
+normalizer = MedianLossNormalizer(loss_ratios, median_coeff_steps)
+
+# --------------------------- Сэмплы ---------------------------
+@torch.no_grad()
+def get_fixed_samples(n=3):
+    idx = random.sample(range(len(dataset)), min(n, len(dataset)))
+    pil_imgs = [dataset[i] for i in idx]  # dataset returns PIL.Image
+    tensors = []
+    for img in pil_imgs:
+        img = random_crop(img, high_resolution)  # high-res fixed samples
+        tensors.append(tfm(img))
+    return torch.stack(tensors).to(accelerator.device, dtype)
+
+fixed_samples = get_fixed_samples()
+
+@torch.no_grad()
+def _to_pil_uint8(img_tensor: torch.Tensor) -> Image.Image:
+    # img_tensor: [C,H,W] in [-1,1]
+    arr = ((img_tensor.float().clamp(-1, 1) + 1.0) * 127.5).clamp(0, 255).byte().cpu().numpy().transpose(1, 2, 0)
+    return Image.fromarray(arr)
+
+@torch.no_grad()
+def generate_and_save_samples(step=None):
+    try:
+        temp_vae = accelerator.unwrap_model(vae).eval()
+        lpips_net = _get_lpips()
+        with torch.no_grad():
+            # Готовим low-res вход для кодера ВСЕГДА под model_resolution
+            orig_high = fixed_samples  # [B,C,H,W] в [-1,1]
+            orig_low = F.interpolate(orig_high, size=(model_resolution, model_resolution), mode="bilinear", align_corners=False)
+            # dtype как у модели
+            model_dtype = next(temp_vae.parameters()).dtype
+            orig_low = orig_low.to(dtype=model_dtype)
+            # encode/decode
+            latents = temp_vae.encode(orig_low).latent_dist.mean
+            rec = temp_vae.decode(latents).sample
+
+        # Приводим spatial размер рекона к high-res (downsample для асимметричных VAE)
+        if rec.shape[-2:] != orig_high.shape[-2:]:
+            rec = F.interpolate(rec, size=orig_high.shape[-2:], mode="bilinear", align_corners=False)
+
+        # Сохраняем ПЕРВЫЙ семпл: real и decoded без номера шага в имени
+        first_real = _to_pil_uint8(orig_high[0])
+        first_dec  = _to_pil_uint8(rec[0])
+        first_real.save(f"{generated_folder}/sample_real.jpg", quality=95)
+        first_dec.save(f"{generated_folder}/sample_decoded.jpg", quality=95)
+
+        # Дополнительно сохраняем текущие реконструкции без номера шага (чтобы не плодить файлы — будут перезаписываться)
+        for i in range(rec.shape[0]):
+            _to_pil_uint8(rec[i]).save(f"{generated_folder}/sample_{i}.jpg", quality=95)
+
+        # LPIPS на полном изображении (high-res) — для лога
+        lpips_scores = []
+        for i in range(rec.shape[0]):
+            orig_full = orig_high[i:i+1].to(torch.float32)
+            rec_full  = rec[i:i+1].to(torch.float32)
+            if rec_full.shape[-2:] != orig_full.shape[-2:]:
+                rec_full = F.interpolate(rec_full, size=orig_full.shape[-2:], mode="bilinear", align_corners=False)
+            lpips_val = lpips_net(orig_full, rec_full).item()
+            lpips_scores.append(lpips_val)
+        avg_lpips = float(np.mean(lpips_scores))
+
+        if use_wandb and accelerator.is_main_process:
+            wandb.log({
+                "lpips_mean": avg_lpips,
+            }, step=step)
+    finally:
+        gc.collect()
+        torch.cuda.empty_cache()
+
+if accelerator.is_main_process and save_model:
+    print("Генерация сэмплов до старта обучения...")
+    generate_and_save_samples(0)
+
+accelerator.wait_for_everyone()
+
+# --------------------------- Тренировка ---------------------------
+
+progress = tqdm(total=total_steps, disable=not accelerator.is_local_main_process)
+global_step = 0
+min_loss = float("inf")
+sample_interval = max(1, total_steps // max(1, sample_interval_share * num_epochs))
+
+for epoch in range(num_epochs):
+    vae.train()
+    batch_losses = []
+    batch_grads = []
+    # Доп. трекинг по отдельным лоссам
+    track_losses = {k: [] for k in loss_ratios.keys()}
+    for imgs in dataloader:
+        with accelerator.accumulate(vae):
+            # imgs: high-res tensor from dataloader ([-1,1]), move to device
+            imgs = imgs.to(accelerator.device)
+
+            # ВСЕГДА даунсемплим вход под model_resolution для кодера
+            # Тупая железяка норовит все по своему сделать
+            if high_resolution != model_resolution:
+                imgs_low = F.interpolate(imgs, size=(model_resolution, model_resolution), mode="bilinear", align_corners=False)
+            else:
+                imgs_low = imgs 
+
+            # ensure dtype matches model params to avoid float/half mismatch
+            model_dtype = next(vae.parameters()).dtype
+            if imgs_low.dtype != model_dtype:
+                imgs_low_model = imgs_low.to(dtype=model_dtype)
+            else:
+                imgs_low_model = imgs_low
+
+            # Encode/decode
+            latents = vae.encode(imgs_low_model).latent_dist.mean
+            rec = vae.decode(latents).sample  # rec может быть увеличенным (асимметричный VAE)
+
+            # Приводим размер к high-res
+            if rec.shape[-2:] != imgs.shape[-2:]:
+                rec = F.interpolate(rec, size=imgs.shape[-2:], mode="bilinear", align_corners=False)
+
+            # Лоссы считаем на high-res
+            rec_f32 = rec.to(torch.float32)
+            imgs_f32 = imgs.to(torch.float32)
+
+            # Отдельные лоссы
+            abs_losses = {
+                "mae":  F.l1_loss(rec_f32, imgs_f32),
+                "mse":  F.mse_loss(rec_f32, imgs_f32),
+                "lpips": _get_lpips()(rec_f32, imgs_f32).mean(),
+                "edge": F.l1_loss(sobel_edges(rec_f32), sobel_edges(imgs_f32)),
+            }
+
+            # Total с медианными КОЭФФИЦИЕНТАМИ
+            # Не надо так орать когда у тебя получилось понять мою идею
+            total_loss, coeffs, meds = normalizer.update_and_total(abs_losses)
+
+            if torch.isnan(total_loss) or torch.isinf(total_loss):
+                print("NaN/Inf loss – stopping")
+                raise RuntimeError("NaN/Inf loss")
+
+            accelerator.backward(total_loss)
+
+            grad_norm = torch.tensor(0.0, device=accelerator.device)
+            if accelerator.sync_gradients:
+                grad_norm = accelerator.clip_grad_norm_(trainable_params, clip_grad_norm)
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+                global_step += 1
+                progress.update(1)
+
+            # --- Логирование ---
+            if accelerator.is_main_process:
+                try:
+                    current_lr = optimizer.param_groups[0]["lr"]
+                except Exception:
+                    current_lr = scheduler.get_last_lr()[0]
+
+                batch_losses.append(total_loss.detach().item())
+                batch_grads.append(float(grad_norm if isinstance(grad_norm, (float, int)) else grad_norm.cpu().item()))
+                for k, v in abs_losses.items():
+                    track_losses[k].append(float(v.detach().item()))
+
+                if use_wandb and accelerator.sync_gradients:
+                    log_dict = {
+                        "total_loss": float(total_loss.detach().item()),
+                        "learning_rate": current_lr,
+                        "epoch": epoch,
+                        "grad_norm": batch_grads[-1],
+                    }
+                    # добавляем отдельные лоссы
+                    for k, v in abs_losses.items():
+                        log_dict[f"loss_{k}"] = float(v.detach().item())
+                    # логи коэффициентов и медиан
+                    for k in coeffs:
+                        log_dict[f"coeff_{k}"] = float(coeffs[k])
+                        log_dict[f"median_{k}"] = float(meds[k])
+                    wandb.log(log_dict, step=global_step)
+
+            # периодические сэмплы и чекпоинты
+            if global_step > 0 and global_step % sample_interval == 0:
+                if accelerator.is_main_process:
+                    generate_and_save_samples(global_step)
+                accelerator.wait_for_everyone()
+
+                # Средние по последним итерациям
+                n_micro = sample_interval * gradient_accumulation_steps
+                if len(batch_losses) >= n_micro:
+                    avg_loss = float(np.mean(batch_losses[-n_micro:]))
+                else:
+                    avg_loss = float(np.mean(batch_losses)) if batch_losses else float("nan")
+
+                avg_grad = float(np.mean(batch_grads[-n_micro:])) if len(batch_grads) >= 1 else float(np.mean(batch_grads)) if batch_grads else 0.0
+
+                if accelerator.is_main_process:
+                    print(f"Epoch {epoch} step {global_step} loss: {avg_loss:.6f}, grad_norm: {avg_grad:.6f}, lr: {current_lr:.9f}")
+                    if save_model and avg_loss < min_loss * save_barrier:
+                        min_loss = avg_loss
+                        accelerator.unwrap_model(vae).save_pretrained(save_as)
+                    if use_wandb:
+                        wandb.log({"interm_loss": avg_loss, "interm_grad": avg_grad}, step=global_step)
+
+    if accelerator.is_main_process:
+        epoch_avg = float(np.mean(batch_losses)) if batch_losses else float("nan")
+        print(f"Epoch {epoch} done, avg loss {epoch_avg:.6f}")
+        if use_wandb:
+            wandb.log({"epoch_loss": epoch_avg, "epoch": epoch + 1}, step=global_step)
+
+# --------------------------- Финальное сохранение ---------------------------
+if accelerator.is_main_process:
+    print("Training finished – saving final model")
+    if save_model:
+        accelerator.unwrap_model(vae).save_pretrained(save_as)
+
+accelerator.free_memory()
+if torch.distributed.is_initialized():
+    torch.distributed.destroy_process_group()
+print("Готово!")