2603

dataset_sample.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76ee79200e6b42a95c9ec06059a7d37061324ed356eac772d2929588eea8f9da
+size 47263813

model_index.json

@@ -1,3 +1,3 @@
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-oid sha256:eff4428f96497a4c7d58ec805016420c0b33523c8a7ee0544e94d53a6cc9c2ab
-size 560
+oid sha256:dba75f85251de1820fc58c9446edc54a1e48523e5a813fe6ea0971df7faace5d
+size 555

pipeline_sdxs.py

@@ -26,7 +26,12 @@ class SdxsPipeline(DiffusionPipeline):
             unet=unet, 
             scheduler=scheduler
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)            
+        mean = getattr(self.vae.config, "latents_mean", None)
+        std = getattr(self.vae.config, "latents_std", None)
+        if mean is not None and std is not None:
+            self.vae_latents_std = torch.tensor(std, device=self.unet.device, dtype=self.unet.dtype).view(1, len(std), 1, 1)
+            self.vae_latents_mean = torch.tensor(mean, device=self.unet.device, dtype=self.unet.dtype).view(1, len(mean), 1, 1)
         
     def preprocess_image(self, image: Image.Image, width: int, height: int):
         """Ресайз и центрированный кроп изображения для асимметричного VAE."""
@@ -52,53 +57,6 @@ class SdxsPipeline(DiffusionPipeline):
         image = torch.from_numpy(image)
         return 2.0 * image - 1.0  # [-1, 1]
 
-    @staticmethod
-    def _patchify_latents(latents):
-        batch_size, num_channels_latents, height, width = latents.shape
-        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-        latents = latents.permute(0, 1, 3, 5, 2, 4)
-        latents = latents.reshape(batch_size, num_channels_latents * 4, height // 2, width // 2)
-        return latents
-    
-    @staticmethod
-    def _unpatchify_latents(latents):
-        batch_size, num_channels_latents, height, width = latents.shape
-        latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), 2, 2, height, width)
-        latents = latents.permute(0, 1, 4, 2, 5, 3)
-        latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), height * 2, width * 2)
-        return latents
-
-    def flux_encode(self, latents):
-        # 1. Patchify
-        image_latents = self._patchify_latents(latents)
-        
-        # 2. Normalization
-        bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-        bn_var = self.vae.bn.running_var.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-        eps = getattr(self.vae.config, "batch_norm_eps", 1e-5)
-        
-        latents_bn_std = torch.sqrt(bn_var + eps)
-        latents = (image_latents - bn_mean) / latents_bn_std
-        
-        # 3. Unpatchify
-        latents = self._unpatchify_latents(latents)
-        return latents
-
-    def flux_decode(self, latents):
-        # 1. Patchify
-        image_latents = self._patchify_latents(latents)
-        
-        # 2. De-normalization
-        bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-        bn_var = self.vae.bn.running_var.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-        eps = getattr(self.vae.config, "batch_norm_eps", 1e-5)
-        
-        latents_bn_std = torch.sqrt(bn_var + eps)
-        latents = image_latents * latents_bn_std + bn_mean
-        
-        # 3. Unpatchify
-        latents = self._unpatchify_latents(latents)
-        return latents
 
     def encode_prompt(self, prompt, negative_prompt, device, dtype):
         def get_single_encode(texts):
@@ -301,9 +259,7 @@ class SdxsPipeline(DiffusionPipeline):
     
             # --- Кодируем в latent ---
             latents_clean = self.vae.encode(image_tensor).latent_dist.sample(generator=generator)
-            vae_scaling_factor = getattr(self.vae.config, "scaling_factor", 1.0)
-            vae_shift_factor = getattr(self.vae.config, "shift_factor", 0.0)
-            latents_clean = (latents_clean - vae_shift_factor) / vae_scaling_factor
+            latents_clean = (latents_clean - self.vae_latents_mean.to(device, self.vae.dtype)) / self.vae_latents_std.to(device, self.vae.dtype)
             latents_clean = latents_clean.to(dtype)
     
             # --- Добавляем шум по Rectified Flow формуле ---
@@ -322,9 +278,7 @@ class SdxsPipeline(DiffusionPipeline):
             timesteps = timesteps[t_start:]
     
         else:
-            # txt2img — оставляем как было
-            vae_scaling_factor = getattr(self.vae.config, "scaling_factor", 1.0)
-            vae_shift_factor = getattr(self.vae.config, "shift_factor", 0.0)
+            # txt2img
             latent_h = height // self.vae_scale_factor
             latent_w = width // self.vae_scale_factor
     
@@ -359,9 +313,7 @@ class SdxsPipeline(DiffusionPipeline):
             if not return_dict: return (latents, prompt)
             return SdxsPipelineOutput(images=latents, prompt=prompt)
     
-        latents = latents * vae_scaling_factor + vae_shift_factor
-        latents = self.flux_decode(latents)
-
+        latents = latents * self.vae_latents_std.to(device, self.vae.dtype) + self.vae_latents_mean.to(device, self.vae.dtype)
         image_output = self.vae.decode(latents.to(self.vae.dtype), return_dict=False)[0]
     
         image_output = (image_output.clamp(-1, 1) + 1) / 2

test.ipynb

@@ -1,3 +1,3 @@
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-oid sha256:1da01a71ae7db2ab06541fc09ddfd80a8b81f804ab2194f4defdabda9c060f60
-size 4347682
+oid sha256:335003bf999bc08859b184e4aa35164dd96b273f10ff3bf5cb6cae18bbd5ac75
+size 4327634

train (1).py

@@ -0,0 +1,827 @@
+#from comet_ml import Experiment
+import os
+os.environ["NCCL_P2P_DISABLE"] = "1"
+# disable this on old GPU?
+#os.environ["NCCL_IB_DISABLE"] = "1"
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+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,FlowMatchEulerDiscreteScheduler
+from accelerate import Accelerator, DeepSpeedPlugin
+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, Qwen2Tokenizer,Qwen3VLForConditionalGeneration
+
+# --------------------------- Параметры ---------------------------
+ds_path = "/workspace/ds1_1280_vaech32"
+project = "unet"
+## total batch (split // num `GPU)
+batch_size = 3
+base_learning_rate = 3e-5
+min_learning_rate = 3e-6
+num_epochs = 1
+sample_interval_share = 50
+cfg_dropout = 0.10
+max_length = 248
+use_wandb = False
+use_comet_ml = True
+save_model = True
+use_decay = True
+fbp = False
+optimizer_type = "adam8bit"
+torch_compile = False
+unet_gradient = True
+loss_normalize = False
+fixed_seed = False
+shuffle = True
+comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r" 
+comet_ml_workspace = "recoilme" 
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+# Включение Flash Attention 2/SDPA #MAX_JOBS=4 pip install flash-attn --no-build-isolation
+torch.backends.cuda.enable_flash_sdp(True)
+torch.backends.cuda.enable_mem_efficient_sdp(True)
+torch.backends.cuda.enable_math_sdp(False) # Отключаем медленный вариант
+save_barrier = 1.25
+warmup_percent = 0.03
+#percentile_clipping = 95
+betta2 = 0.995
+eps = 1e-7
+clip_grad_norm = 1.0
+limit = 0
+checkpoints_folder = ""
+gradient_accumulation_steps = 1
+dtype = torch.float32
+mixed_precision = "no"
+
+# 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")) + 10000001
+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)
+
+accelerator = Accelerator(
+    mixed_precision=mixed_precision,
+    gradient_accumulation_steps=gradient_accumulation_steps
+)
+device = accelerator.device
+
+print("init")
+
+# --------------------------- Инициализация WandB ---------------------------
+if accelerator.is_main_process:
+    if use_wandb:
+        wandb.init(project=project, 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)
+
+# --------------------------- Загрузка моделей ---------------------------
+#vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to("cpu").eval()
+#vae = AutoencoderKLFlux2.from_pretrained("black-forest-labs/FLUX.2-dev",subfolder="vae",torch_dtype=dtype).to(device).eval()
+#vae = AsymmetricAutoencoderKL.from_pretrained("vae",torch_dtype=dtype).to(device).eval()
+vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to(device).eval()
+tokenizer = AutoTokenizer.from_pretrained("tokenizer")
+text_encoder = AutoModel.from_pretrained("text_encoder", torch_dtype=torch.float16).to(device).eval()
+tokenizer2 = Qwen2Tokenizer.from_pretrained("tokenizer2")
+text_encoder2 = Qwen3VLForConditionalGeneration.from_pretrained("text_encoder2", torch_dtype=torch.float16).to(device).eval()
+scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("scheduler")
+
+def encode_texts(texts, max_length=max_length):
+    if texts is None:
+        texts = [""]
+    if isinstance(texts, str):
+        texts = [texts]
+
+    with torch.no_grad():
+        # --- 1. CLIP Энкодер ---
+        toks = tokenizer(
+            texts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt"
+        ).to(device)
+
+        outputs = text_encoder(
+            input_ids=toks.input_ids, 
+            attention_mask=toks.attention_mask,
+            output_hidden_states=True
+        )
+
+        # 1. Берем -2 слой [Batch, Seq, Dim]
+        hidden = outputs.hidden_states[-2]
+
+        # 2. Достаем pooled вектор (последний токен) [Batch, Dim]
+        seq_lens = toks.attention_mask.sum(dim=1) - 1
+        pooled_clip = hidden[torch.arange(hidden.shape[0]), seq_lens.clamp(min=0)]
+
+        # 3. Нормализация
+        norm = text_encoder.text_model.final_layer_norm
+        hidden = norm(hidden)
+        pooled_clip = norm(pooled_clip)
+
+        # 4. Объединяем в матрицу: Пулед (как 1-й токен) + остальные токены
+        # pooled.unsqueeze(1) делает [Batch, 1, Dim]
+        prompt_embeds = torch.cat([pooled_clip.unsqueeze(1), hidden], dim=1)
+
+        # 5. Расширяем маску для нового токена (добавляем единицы спереди)
+        ones = torch.ones((toks.attention_mask.shape[0], 1), dtype=toks.attention_mask.dtype, device=device)
+        mask = torch.cat([ones, toks.attention_mask], dim=1)
+
+        # --- 2. QWEN Энкодер (через Chat Template) ---
+        # 1. Собираем текстовые промпты оборачивая их в Chat Template
+        formatted_prompts = []
+        for t in texts:
+            messages = [{"role": "user", "content": [{"type": "text", "text": t}]}]
+            res_text = tokenizer2.apply_chat_template(
+                messages, 
+                add_generation_prompt=True, 
+                tokenize=False 
+            )
+            formatted_prompts.append(res_text)
+
+        # 2. Токенизируем, режем и добавляем паддинг за один раз
+        toks = tokenizer2(
+            formatted_prompts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt"
+        ).to(device)
+
+        # 3. Прогоняем через модель
+        outputs = text_encoder2(
+            input_ids=toks.input_ids, 
+            attention_mask=toks.attention_mask,
+            output_hidden_states=True
+        )
+
+        layer_index = -2
+        last_hidden = outputs.hidden_states[layer_index]
+        seq_len = toks.attention_mask.sum(dim=1) - 1
+        pooled = last_hidden[torch.arange(len(last_hidden)), seq_len.clamp(min=0)]
+        return prompt_embeds.to(dtype), mask, pooled.to(dtype)
+
+
+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
+    
+mean = getattr(vae.config, "latents_mean", None)
+std = getattr(vae.config, "latents_std", None)
+if mean is not None and std is not None:
+    latents_std = torch.tensor(std, device=device, dtype=dtype).view(1, len(std), 1, 1)
+    latents_mean = torch.tensor(mean, device=device, dtype=dtype).view(1, len(mean), 1, 1)
+
+def _patchify_latents(latents):
+    batch_size, num_channels_latents, height, width = latents.shape
+    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 1, 3, 5, 2, 4)
+    latents = latents.reshape(batch_size, num_channels_latents * 4, height // 2, width // 2)
+    return latents
+
+@staticmethod
+def _unpatchify_latents(latents):
+    batch_size, num_channels_latents, height, width = latents.shape
+    latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), 2, 2, height, width)
+    latents = latents.permute(0, 1, 4, 2, 5, 3)
+    latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), height * 2, width * 2)
+    return latents
+
+def flux_encode(vae,latents):
+    # patch
+    image_latents = _patchify_latents(latents)
+    # norm
+    latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
+    latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps)
+    latents = (image_latents - latents_bn_mean) / latents_bn_std
+    # unpatch
+    latents = _unpatchify_latents(latents)
+    return latents
+    
+def flux_decode(vae,latents):
+    # patch
+    image_latents = _patchify_latents(latents)
+    # norm
+    latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
+    latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps)
+    latents = image_latents * latents_bn_std + latents_bn_mean
+    # unpatch
+    latents = _unpatchify_latents(latents)
+    return latents
+
+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, pooled = encode_texts(texts)
+        
+        fixed_samples[size] = (latents, embeddings, masks, texts, pooled)
+    
+    print(f"Создано {len(fixed_samples)} групп фиксированных семплов по разрешениям")
+    return fixed_samples
+
+if limit > 0:
+    dataset = load_from_disk(ds_path).select(range(limit))
+else:
+    dataset = load_from_disk(ds_path)
+
+dataset = dataset.filter(
+    lambda x: [not (path.startswith("/workspace/dataset/animesfw") or path.startswith("/workspace/dataset/d4/animesfw"))  for path in x["image_path"]],
+    batched=True,
+    batch_size=10000, # обрабатываем по 10к строк за раз
+    num_proc=8
+)
+print(f"Осталось примеров после фильтрации: {len(dataset)}")
+
+# --- Collate Function ---
+def collate_fn_simple(batch):
+    # 1. Латенты (VAE)
+    latents = torch.tensor(np.array([item["vae"] for item in batch])).to(device, dtype=dtype)
+    
+    # 2. Текст берем сырой из датасета
+    raw_texts = [item["text"] for item in batch]
+    texts = [
+        "" if t.lower().startswith("zero") 
+        else "" if random.random() < cfg_dropout
+        else t[1:].lstrip() if t.startswith(".")
+        else t.replace("The image shows ", "").replace("The image is ", "").replace("This image captures ","").strip()
+        for t in raw_texts
+    ]
+    # 3. Кодируем на лету
+    # Возвращает: hidden (B, L, D), mask (B, L)
+    embeddings, attention_mask, pooled = 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, pooled
+
+batch_sampler = DistributedResolutionBatchSampler(
+        dataset=dataset,
+        batch_size=batch_size,
+        num_replicas=accelerator.num_processes,
+        rank=accelerator.process_index,
+        shuffle=shuffle
+    )
+
+dataloader = DataLoader(dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_simple)
+if accelerator.is_main_process:
+    print("Total samples", len(dataloader))
+dataloader = accelerator.prepare(dataloader)
+
+start_epoch = 0
+global_step = 0
+total_training_steps = (len(dataloader) * num_epochs)
+world_size = accelerator.state.num_processes
+
+# Загрузка UNet
+latest_checkpoint = os.path.join(checkpoints_folder, project)
+if os.path.isdir(latest_checkpoint):
+    print("Загружаем UNet из чекпоинта:", latest_checkpoint)
+    unet = UNet2DConditionModel.from_pretrained(latest_checkpoint).to(device=device, dtype=dtype)
+    if unet_gradient:
+        unet.enable_gradient_checkpointing()
+    unet.set_use_memory_efficient_attention_xformers(False)
+    try:
+        unet.set_attn_processor(AttnProcessor2_0())
+    except Exception as e:
+        print(f"Ошибка при включении SDPA: {e}")
+        unet.set_use_memory_efficient_attention_xformers(True)
+else:
+    raise FileNotFoundError(f"UNet checkpoint not found at {latest_checkpoint}")
+        
+
+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
+        )
+    else:
+        raise ValueError(f"Unknown optimizer: {name}")
+
+if fbp:
+    trainable_params = list(unet.parameters())
+    optimizer_dict = {p: create_optimizer(optimizer_type, [p]) for p in trainable_params}
+    def optimizer_hook(param):
+        optimizer_dict[param].step()
+        optimizer_dict[param].zero_grad(set_to_none=True)
+    for param in trainable_params:
+        param.register_post_accumulate_grad_hook(optimizer_hook)
+    unet, optimizer = accelerator.prepare(unet, optimizer_dict)
+else:
+    # 1. Сначала замораживаем ВСЕ параметры UNet
+    #unet.requires_grad_(False)
+    
+    # 2. Размораживаем только нужные
+    #trainable_params_names = ["conv_in.weight", "conv_in.bias", "conv_out.weight", "conv_out.bias"]
+    #train_params = []
+    
+    #for name, param in unet.named_parameters():
+    #    if any(target in name for target in trainable_params_names):
+    #        param.requires_grad = True
+    #        train_params.append(param)
+    #        print(f"Обучаемый слой: {name}")
+
+    unet.requires_grad_(True)
+    optimizer = create_optimizer(optimizer_type, unet.parameters())
+    
+    def lr_schedule(step):
+        x = step / (total_training_steps * world_size)
+        warmup = warmup_percent
+        if not use_decay:
+            return base_learning_rate
+        if x < warmup:
+            return min_learning_rate + (base_learning_rate - min_learning_rate) * (x / warmup)
+        decay_ratio = (x - warmup) / (1 - warmup)
+        return min_learning_rate + 0.5 * (base_learning_rate - min_learning_rate) * \
+               (1 + math.cos(math.pi * decay_ratio))
+    lr_scheduler = LambdaLR(optimizer, lambda step: lr_schedule(step) / base_learning_rate)
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)
+
+if torch_compile:
+    print("compiling")
+    unet = torch.compile(unet)
+    print("compiling - ok")
+
+# Фиксированные семплы
+fixed_samples = get_fixed_samples_by_resolution(dataset)
+
+# --- [UPDATED] Функция для негативного эмбеддинга (возвращает 3 элемента) ---
+def get_negative_embedding(neg_prompt="", batch_size=1):
+    if not neg_prompt:
+        hidden_dim = 2048 
+        seq_len = max_length
+        empty_emb = torch.zeros((batch_size, seq_len, hidden_dim), dtype=dtype, device=device)
+        empty_mask = torch.ones((batch_size, seq_len), dtype=torch.int64, device=device)
+        return empty_emb, empty_mask
+
+    uncond_emb, uncond_mask, uncond_pooled  = 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)
+    uncond_pooled = uncond_pooled.to(device=device).repeat(batch_size, 1)
+
+    return uncond_emb, uncond_mask, uncond_pooled
+    
+# Получаем негативные (пустые) условия для валидации
+uncond_emb, uncond_mask, uncond_pooled = 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_pooled = 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, sample_pooled) 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)
+            sample_pooled = sample_pooled.to(dtype=dtype, 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)
+
+                    neg_pooled_batch = uncond_pooled[0:1].expand(curr_batch_size, -1)
+                    attention_pooled_batch = torch.cat([neg_pooled_batch, sample_pooled], dim=0)
+
+                else:
+                    latent_model_input = latents
+                    text_embeddings_batch = sample_text_embeddings
+                    attention_mask_batch = sample_mask
+                    attention_pooled_batch = sample_pooled
+
+                added_cond_kwargs = {
+                    "text_embeds": attention_pooled_batch,
+                }
+                # 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,
+                    added_cond_kwargs=added_cond_kwargs,
+                )
+                flow = getattr(model_out, "sample", model_out)
+        
+                if guidance_scale != 1:
+                    flow_uncond, flow_cond = flow.chunk(2)
+                    flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
+        
+                latents = scheduler.step(flow, t, latents).prev_sample
+        
+            current_latents = latents
+            if step==0:
+                current_latents = sample_latents
+
+            if latents_mean is not None and latents_std is not None:
+                latents = current_latents * latents_std + latents_mean
+            decoded = vae.decode(latents.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")
+        try:
+            all_generated_images.clear()
+            all_captions.clear()
+            del all_generated_images, all_captions
+            del latents, current_latents, latent_model_input, flow
+            del decoded, decoded_fp32
+            del sample_latents, sample_text_embeddings, sample_mask, sample_pooled # Копии на GPU
+            del model_out
+        except UnboundLocalError:
+            pass
+            
+        # 3. Синхронизируем CUDA перед очисткой
+        torch.cuda.synchronize()
+        # 4. Теперь чистим кэш аллокатора и вызываем GC
+        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,uncond_pooled), 0)
+accelerator.wait_for_everyone()
+
+def save_checkpoint(unet, variant=""):
+    if accelerator.is_main_process:
+        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}"))
+
+        torch.cuda.synchronize()
+        torch.cuda.empty_cache()
+        gc.collect()
+        #unet = unet.to(dtype=dtype) #TODO: wtf???
+
+# --------------------------- Тренировочный цикл ---------------------------
+if accelerator.is_main_process:
+    print(f"Total steps per GPU: {total_training_steps}")
+
+epoch_loss_points = []
+progress_bar = tqdm(total=total_training_steps, disable=not accelerator.is_local_main_process, desc="Training", unit="step")
+
+steps_per_epoch = len(dataloader)
+sample_interval = max(1, steps_per_epoch // sample_interval_share)
+min_loss = 4.
+
+for epoch in range(start_epoch, start_epoch + num_epochs):
+    batch_losses = []
+    batch_grads = []
+    batch_sampler.set_epoch(epoch)
+    accelerator.wait_for_everyone()
+    unet.train()
+    
+    for step, (latents, embeddings, attention_mask, pooled) in enumerate(dataloader):
+        with accelerator.accumulate(unet):
+            if save_model == False and epoch == 0 and step == 5 :
+                used_gb = torch.cuda.max_memory_allocated() / 1024**3
+                print(f"Шаг {step}: {used_gb:.2f} GB")
+                
+            # шум
+            noise = torch.randn_like(latents, dtype=latents.dtype)
+
+            # 3. Время t (сэмплим, как и раньше, но чуть сжимаем края)
+            u = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
+            t = u * (1 - 2 * 1e-5) + 1e-5  # Теперь t строго в (0.00001 ... 0.99999)
+            # интерполяция между x0 и шумом
+            noisy_latents = (1.0 - t.view(-1, 1, 1, 1)) * latents + t.view(-1, 1, 1, 1) * noise
+            # делаем integer timesteps для UNet
+            timesteps = t.to(torch.float32).mul(999.0)
+            timesteps = timesteps.clamp(0, scheduler.config.num_train_timesteps - 1)
+
+            added_cond_kwargs = {
+                "text_embeds": pooled,
+            }
+            # --- Вызов UNet с маской  ---
+            model_pred = unet(
+                noisy_latents, 
+                timesteps, 
+                encoder_hidden_states=embeddings,
+                encoder_attention_mask=attention_mask,
+                added_cond_kwargs=added_cond_kwargs,
+            ).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
+
+            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
+                accelerator.wait_for_everyone()
+
+            accelerator.backward(mse_loss)
+
+            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
+                accelerator.wait_for_everyone()
+                
+            grad = 0.0
+            if not fbp:
+                if accelerator.sync_gradients:
+                    grad_val = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
+                    grad = grad_val.float().item() if torch.is_tensor(grad_val) else float(grad_val)
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad(set_to_none=True)
+
+            if accelerator.sync_gradients:
+                global_step += 1
+                progress_bar.update(1)
+                if accelerator.is_main_process:
+                    if fbp:
+                        current_lr = base_learning_rate
+                    else:
+                        current_lr = lr_scheduler.get_last_lr()[0]
+                    batch_grads.append(grad)
+    
+                    log_data = {}
+                    log_data["loss_mse"] = mse_loss.detach().item()
+                    log_data["lr"] = current_lr
+                    log_data["grad"] = grad
+                    if accelerator.sync_gradients:
+                        if use_wandb:
+                            wandb.log(log_data, step=global_step)
+                        if use_comet_ml:
+                            comet_experiment.log_metrics(log_data, step=global_step)
+
+                    if global_step % sample_interval == 0 or global_step==50:
+                        # Передаем tuple (emb, mask) для негатива
+                        if save_model:
+                            generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask,uncond_pooled), global_step)
+                        elif epoch % 10 == 0:
+                            generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask,uncond_pooled), 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)
+                        unet.train()
+
+    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

@@ -12,7 +12,7 @@ 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,AutoencoderKLFlux2,FlowMatchEulerDiscreteScheduler
+from diffusers import UNet2DConditionModel,AutoencoderKL,FlowMatchEulerDiscreteScheduler
 from accelerate import Accelerator, DeepSpeedPlugin
 from datasets import load_from_disk
 from tqdm import tqdm
@@ -31,14 +31,14 @@ from collections import deque
 from transformers import AutoTokenizer, AutoModel, Qwen2Tokenizer,Qwen3VLForConditionalGeneration
 
 # --------------------------- Параметры ---------------------------
-ds_path = "/workspace/sdxs-1b/datasets/ds1234_flux32"
+ds_path = "/workspace/sdxs-1b/datasets/ds234_1280"
 project = "unet"
 ## total batch (split // num `GPU)
-batch_size = 48
-base_learning_rate = 6e-6
-min_learning_rate = 7e-7
-num_epochs = 8
-sample_interval_share = 10
+batch_size = 12
+base_learning_rate = 3e-5
+min_learning_rate = 3e-6
+num_epochs = 1
+sample_interval_share = 20
 cfg_dropout = 0.10
 max_length = 248
 use_wandb = False
@@ -126,7 +126,8 @@ if accelerator.is_main_process:
 #vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to("cpu").eval()
 #vae = AutoencoderKLFlux2.from_pretrained("black-forest-labs/FLUX.2-dev",subfolder="vae",torch_dtype=dtype).to(device).eval()
 #vae = AsymmetricAutoencoderKL.from_pretrained("vae",torch_dtype=dtype).to(device).eval()
-vae = AutoencoderKLFlux2.from_pretrained("vae", torch_dtype=dtype).to(device).eval()
+#vae = AutoencoderKLFlux2.from_pretrained("vae", torch_dtype=dtype).to(device).eval()
+vae = AutoencoderKL.from_pretrained("vae", torch_dtype=dtype).to(device).eval()
 tokenizer = AutoTokenizer.from_pretrained("tokenizer")
 text_encoder = AutoModel.from_pretrained("text_encoder", torch_dtype=torch.float16).to(device).eval()
 tokenizer2 = Qwen2Tokenizer.from_pretrained("tokenizer2")
@@ -212,46 +213,18 @@ def encode_texts(texts, max_length=max_length):
 
 
 shift_factor = getattr(vae.config, "shift_factor", 0.0)
-if shift_factor is None: 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
-
-def _patchify_latents(latents):
-    batch_size, num_channels_latents, height, width = latents.shape
-    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-    latents = latents.permute(0, 1, 3, 5, 2, 4)
-    latents = latents.reshape(batch_size, num_channels_latents * 4, height // 2, width // 2)
-    return latents
-
-@staticmethod
-def _unpatchify_latents(latents):
-    batch_size, num_channels_latents, height, width = latents.shape
-    latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), 2, 2, height, width)
-    latents = latents.permute(0, 1, 4, 2, 5, 3)
-    latents = latents.reshape(batch_size, num_channels_latents // (2 * 2), height * 2, width * 2)
-    return latents
-
-def flux_encode(vae,latents):
-    # patch
-    image_latents = _patchify_latents(latents)
-    # norm
-    latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-    latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps)
-    latents = (image_latents - latents_bn_mean) / latents_bn_std
-    # unpatch
-    latents = _unpatchify_latents(latents)
-    return latents
+if scaling_factor is None:
+    scaling_factor = 1.0
     
-def flux_decode(vae,latents):
-    # patch
-    image_latents = _patchify_latents(latents)
-    # norm
-    latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(image_latents.device, image_latents.dtype)
-    latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps)
-    latents = image_latents * latents_bn_std + latents_bn_mean
-    # unpatch
-    latents = _unpatchify_latents(latents)
-    return latents
+mean = getattr(vae.config, "latents_mean", None)
+std = getattr(vae.config, "latents_std", None)
+if mean is not None and std is not None:
+    latents_std = torch.tensor(std, device=device, dtype=dtype).view(1, len(std), 1, 1)
+    latents_mean = torch.tensor(mean, device=device, dtype=dtype).view(1, len(mean), 1, 1)
 
 class DistributedResolutionBatchSampler(Sampler):
     def __init__(self, dataset, batch_size, num_replicas, rank, shuffle=True, drop_last=True):
@@ -604,8 +577,9 @@ def generate_and_save_samples(fixed_samples_cpu, uncond_data, step):
             if step==0:
                 current_latents = sample_latents
 
-            latents = current_latents.detach() * scaling_factor + shift_factor
-            latents = flux_decode(vae,latents)
+            if latents_mean is not None and latents_std is not None:
+                latents = current_latents * latents_std + latents_mean
+                
             decoded = vae.decode(latents.to(torch.float32)).sample
             decoded_fp32 = decoded.to(torch.float32)
             
@@ -629,7 +603,7 @@ def generate_and_save_samples(fixed_samples_cpu, uncond_data, step):
                 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)
+                pil_img.save(sample_path, "JPEG", quality=95)
         
         if use_wandb and accelerator.is_main_process:
             wandb_images = [

unet/diffusion_pytorch_model.safetensors

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-size 2980309336
+oid sha256:9f7cfc982ee086865e4d29cb66c1f562f5bad9415079ff34ec0bda8873f5edb0
+size 5960474736

vae/config.json

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-size 769
+oid sha256:a35c34ddc8de51cec98075acd06feb9c55fd79a75d4f7e055adb5f4809e637c3
+size 2331

vae/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
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-size 336213556
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+size 336212308

vae_flux2/config.json

@@ -0,0 +1,3 @@
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+oid sha256:85bca6192575d1432df5fddbadcfe4f5b4143a284ce6e192cd264e18c2126c5a
+size 769

vae_flux2/diffusion_pytorch_model.safetensors

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