train_vae_fdl.py

# -*- 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
# QWEN: импорт класса
from diffusers import AutoencoderKLQwenImage
from diffusers import AutoencoderKLWan

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 FDL_pytorch import FDL_loss # pip install fdl-pytorch
from collections import deque

# --------------------------- Параметры ---------------------------
ds_path            = "/workspace/d23"
project            = "vae4"
batch_size         = 2
base_learning_rate = 5e-5
min_learning_rate  = 1e-5
num_epochs         = 25
sample_interval_share = 5
use_wandb          = True
save_model         = True
use_decay          = True
optimizer_type     = "adam8bit"
dtype              = torch.float32

model_resolution   = 288
high_resolution    = 576 
limit              = 0
save_barrier       = 1.3
warmup_percent     = 0.005
percentile_clipping = 99
beta2              = 0.997
eps                = 1e-8
clip_grad_norm     = 1.0
mixed_precision    = "no"
gradient_accumulation_steps = 4
generated_folder   = "samples"
save_as            = "vae5"
num_workers        = 0
device = None

# --- Режимы обучения ---
# QWEN: учим только декодер
train_decoder_only = True
train_up_only      = False
full_training      = False  # если True — учим весь VAE и добавляем KL (ниже)
kl_ratio           = 0.00

# Доли лоссов
loss_ratios = {
    "lpips": 0.70,#0.50,
    "fdl" :  0.10,#0.25,   
    "edge":  0.05,
    "mse":   0.10,
    "mae":   0.05,
    "kl":    0.00,  # активируем при full_training=True
}
median_coeff_steps = 1000

resize_long_side = 1280  # ресайз длинной стороны исходных картинок

# QWEN: конфиг загрузки модели
vae_kind      = "kl"  # "qwen" или "kl" (обычный)

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,
        "train_decoder_only": train_decoder_only,
        "full_training": full_training,
        "kl_ratio": kl_ratio,
        "vae_kind": vae_kind,
    })

# --------------------------- VAE ---------------------------
def get_core_model(model):
    m = model
    # если модель уже обёрнута torch.compile
    if hasattr(m, "_orig_mod"):
        m = m._orig_mod
    return m

def is_video_vae(model) -> bool:
    # WAN/Qwen — это видео-VAEs
    if vae_kind in ("wan", "qwen"):
        return True
    # fallback по структуре (если понадобится)
    try:
        core = get_core_model(model)
        enc = getattr(core, "encoder", None)
        conv_in = getattr(enc, "conv_in", None)
        w = getattr(conv_in, "weight", None)
        if isinstance(w, torch.nn.Parameter):
            return w.ndim == 5
    except Exception:
        pass
    return False

# загрузка
if vae_kind == "qwen":
    vae = AutoencoderKLQwenImage.from_pretrained("Qwen/Qwen-Image", subfolder="vae")
else:
    if vae_kind == "wan":
        vae = AutoencoderKLWan.from_pretrained(project)
    else:
        # старое поведение (пример)
        if model_resolution==high_resolution:
            vae = AutoencoderKL.from_pretrained(project)
        else:
            vae = AsymmetricAutoencoderKL.from_pretrained(project)

vae = vae.to(dtype)

# torch.compile (опционально)
if hasattr(torch, "compile"):
    try:
        vae = torch.compile(vae)
    except Exception as e:
        print(f"[WARN] torch.compile failed: {e}")

# --------------------------- Freeze/Unfreeze ---------------------------
core = get_core_model(vae)

for p in core.parameters():
    p.requires_grad = False

unfrozen_param_names = []

if full_training and not train_decoder_only:
    for name, p in core.named_parameters():
        p.requires_grad = True
        unfrozen_param_names.append(name)
        loss_ratios["kl"] = float(kl_ratio)
        trainable_module = core
else:
    # учим только 0-й блок декодера + post_quant_conv
    if hasattr(core, "decoder"):
        if train_up_only:#hasattr(core.decoder, "up_blocks") and len(core.decoder.up_blocks) > 0:
            # --- только 0-й up_block ---
            for name, p in core.decoder.up_blocks[0].named_parameters():
                p.requires_grad = True
                unfrozen_param_names.append(f"{name}")
        else:
            print("Decoder — fallback to full decoder")
            for name, p in core.decoder.named_parameters():
                p.requires_grad = True
                unfrozen_param_names.append(f"decoder.{name}")
    if hasattr(core, "post_quant_conv"):
        for name, p in core.post_quant_conv.named_parameters():
            p.requires_grad = True
            unfrozen_param_names.append(f"post_quant_conv.{name}")
    trainable_module = core.decoder if hasattr(core, "decoder") else core


print(f"[INFO] Разморожено параметров: {len(unfrozen_param_names)}. Первые 200 имён:")
for nm in unfrozen_param_names[:200]:
    print(" ", nm)

# --------------------------- Датасет ---------------------------
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 = []
        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))
        if limit:
            self.paths = self.paths[:limit]
        valid = []
        for p in self.paths:
            try:
                with Image.open(p) as im:
                    im.verify()
                valid.append(p)
            except (OSError, UnidentifiedImageError):
                continue
        self.paths = valid
        if len(self.paths) == 0:
            raise RuntimeError(f"No valid PNG images found under {root_dir}")
        random.shuffle(self.paths)

    def __len__(self):
        return len(self.paths)

    def __getitem__(self, idx):
        p = self.paths[idx % len(self.paths)]
        with Image.open(p) as img:
            img = img.convert("RGB")
            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.BICUBIC)

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.BICUBIC)
    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])
])

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}")

def collate_fn(batch):
    imgs = []
    for img in batch:
        img = random_crop(img, high_resolution)
        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 vae.named_parameters():  # глобально по vae, с фильтром requires_grad
        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 get_param_groups(module, weight_decay=0.001):
    no_decay_tokens = ("bias", "norm", "rms", "layernorm")
    decay_params, no_decay_params = [], []
    for n, p in module.named_parameters():
        if not p.requires_grad:
            continue
        n_l = n.lower()
        if any(t in n_l for t in no_decay_tokens):
            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(get_core_model(vae), weight_decay=0.001)
optimizer = create_optimizer(optimizer_type, param_groups)

# --------------------------- LR schedule ---------------------------
batches_per_epoch = len(dataloader)
steps_per_epoch = int(math.ceil(batches_per_epoch / float(gradient_accumulation_steps)))
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.parameters() if p.requires_grad]

# fdl
fdl_loss = FDL_loss()
fdl_loss = fdl_loss.to(accelerator.device)

# --------------------------- LPIPS и вспомогательные ---------------------------
_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

_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:
    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)

class MedianLossNormalizer:
    def __init__(self, desired_ratios: dict, window_steps: int):
        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):
        for k, v in abs_losses.items():
            if k in self.buffers:
                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}
        total = sum(coeffs[k] * abs_losses[k] for k in abs_losses if k in coeffs)
        return total, coeffs, meds

if full_training and not train_decoder_only:
    loss_ratios["kl"] = float(kl_ratio)
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]
    tensors = []
    for img in pil_imgs:
        img = random_crop(img, high_resolution)
        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:
    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():
            orig_high = fixed_samples
            orig_low = F.interpolate(
                orig_high,
                size=(model_resolution, model_resolution),
                mode="bilinear",
                align_corners=False
            )
            model_dtype = next(temp_vae.parameters()).dtype
            orig_low = orig_low.to(dtype=model_dtype)

            # Encode/decode с учётом видео-режима
            if is_video_vae(temp_vae):
                x_in = orig_low.unsqueeze(2)  # [B,3,1,H,W]
                enc = temp_vae.encode(x_in)
                latents_mean = enc.latent_dist.mean
                dec = temp_vae.decode(latents_mean).sample  # [B,3,1,H,W]
                rec = dec.squeeze(2)  # [B,3,H,W]
            else:
                enc = temp_vae.encode(orig_low)
                latents_mean = enc.latent_dist.mean
                rec = temp_vae.decode(latents_mean).sample

        # Подгон размеров, если надо
        #if rec.shape[-2:] != orig_high.shape[-2:]:
        #    rec = F.interpolate(rec, size=orig_high.shape[-2:], mode="bilinear", align_corners=False)

        # Сохраняем все real/decoded
        for i in range(rec.shape[0]):
            real_img = _to_pil_uint8(orig_high[i])
            dec_img  = _to_pil_uint8(rec[i])
            real_img.save(f"{generated_folder}/sample_real_{i}.jpg", quality=95)
            dec_img.save(f"{generated_folder}/sample_decoded_{i}.jpg", quality=95)

        # LPIPS
        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))

        # W&B логирование
        if use_wandb and accelerator.is_main_process:
            log_data = {"lpips_mean": avg_lpips}
            for i in range(rec.shape[0]):
                log_data[f"sample/real_{i}"] = wandb.Image(f"{generated_folder}/sample_real_{i}.jpg", caption=f"real_{i}")
                log_data[f"sample/decoded_{i}"] = wandb.Image(f"{generated_folder}/sample_decoded_{i}.jpg", caption=f"decoded_{i}")
            wandb.log(log_data, 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 = [], []
    track_losses = {k: [] for k in loss_ratios.keys()}

    for imgs in dataloader:
        with accelerator.accumulate(vae):
            imgs = imgs.to(accelerator.device)

            if high_resolution != model_resolution:
                imgs_low = F.interpolate(imgs, size=(model_resolution, model_resolution), mode="bilinear", align_corners=False)
            else:
                imgs_low = imgs

            model_dtype = next(vae.parameters()).dtype
            imgs_low_model = imgs_low.to(dtype=model_dtype) if imgs_low.dtype != model_dtype else imgs_low

            # QWEN: encode/decode с T=1
            if is_video_vae(vae):
                x_in = imgs_low_model.unsqueeze(2)             # [B,3,1,H,W]
                enc = vae.encode(x_in)
                latents = enc.latent_dist.mean if train_decoder_only else enc.latent_dist.sample()
                dec = vae.decode(latents).sample               # [B,3,1,H,W]
                rec = dec.squeeze(2)                           # [B,3,H,W]
            else:
                enc = vae.encode(imgs_low_model)
                latents = enc.latent_dist.mean if train_decoder_only else enc.latent_dist.sample()
                rec = vae.decode(latents).sample

            #if rec.shape[-2:] != imgs.shape[-2:]:
            #    rec = F.interpolate(rec, size=imgs.shape[-2:], mode="bilinear", align_corners=False)

            rec_f32 = rec.to(torch.float32)
            imgs_f32 = imgs.to(torch.float32)

            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(),
                "fdl":   fdl_loss(rec_f32, imgs_f32),
                "edge":  F.l1_loss(sobel_edges(rec_f32), sobel_edges(imgs_f32)),
            }

            if full_training and not train_decoder_only:
                mean   = enc.latent_dist.mean
                logvar = enc.latent_dist.logvar
                kl = -0.5 * torch.mean(1 + logvar - mean.pow(2) - logvar.exp())
                abs_losses["kl"] = kl
            else:
                abs_losses["kl"] = torch.tensor(0.0, device=accelerator.device, dtype=torch.float32)

            total_loss, coeffs, meds = normalizer.update_and_total(abs_losses)

            if torch.isnan(total_loss) or torch.isinf(total_loss):
                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)

            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.detach().cpu().item()) if isinstance(grad_norm, torch.Tensor) else 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],
                    }
                    for k, v in abs_losses.items():
                        log_dict[f"loss_{k}"] = float(v.detach().item())
                    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)

            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()

                n_micro = sample_interval * gradient_accumulation_steps
                avg_loss = float(np.mean(batch_losses[-n_micro:])) if len(batch_losses) >= n_micro else 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!")