v001

src/model_create.ipynb

@@ -569,7 +569,7 @@
     "\n",
     "\n",
     "if 1:\n",
-    "    checkpoint_path = \"/workspace/sdxs3d/unet\"#\"sdxs\"\n",
+    "    checkpoint_path = \"/workspace/sdxs3d/butterfly\"#\"sdxs\"\n",
     "    import torch\n",
     "    from diffusers import UNet2DConditionModel\n",
     "    print(\"test unet\")\n",

train.py

@@ -31,7 +31,7 @@ project = "unet"
 batch_size = 128
 base_learning_rate = 8e-5
 min_learning_rate = 2e-5
-num_epochs = 100
+num_epochs = 50
 # samples/save per epoch
 sample_interval_share = 5
 use_wandb = True
@@ -71,7 +71,7 @@ device = accelerator.device
 # Параметры для диффузии
 n_diffusion_steps = 50
 samples_to_generate = 12
-guidance_scale = 1
+guidance_scale = 4
 
 # Папки для сохранения результатов
 generated_folder = "samples"
@@ -184,7 +184,16 @@ if scaling_factor is None:
 latents_mean = getattr(vae.config, "latents_mean", None)
 latents_std = getattr(vae.config, "latents_std", None)
 
+from diffusers import FlowMatchEulerDiscreteScheduler
 
+# Подстрой под свои параметры
+num_train_timesteps = 1000
+
+scheduler = FlowMatchEulerDiscreteScheduler(
+    num_train_timesteps=num_train_timesteps,
+    #shift=3.0,               # пример; подбирается при необходимости
+    #use_dynamic_shifting=True
+)
 
 
 class DistributedResolutionBatchSampler(Sampler):
@@ -420,9 +429,73 @@ else:
 # --------------------------- Фиксированные семплы для генерации ---------------------------
 fixed_samples = get_fixed_samples_by_resolution(dataset)
 
+def get_negative_embedding(neg_prompt="", batch_size=1):
+    """
+    Возвращает эмбеддинг негативного промпта с батчем.
+    Загружает модели, вычисляет эмбеддинг, выгружает модели на CPU.
+    """
+    import torch
+    from transformers import AutoTokenizer, AutoModel
+
+    # Настройки
+    dtype = torch.float16
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # Загрузка моделей (если ещё не загружены)
+    if not hasattr(get_negative_embedding, "tokenizer"):
+        get_negative_embedding.tokenizer = AutoTokenizer.from_pretrained(
+            "Qwen/Qwen3-Embedding-0.6B", padding_side="left"
+        )
+        get_negative_embedding.text_model = AutoModel.from_pretrained(
+            "Qwen/Qwen3-Embedding-0.6B"
+        ).to(device).eval()
+
+    # Вспомогательная функция для пулинга
+    def last_token_pool(last_hidden_states, attention_mask):
+        left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+        if left_padding:
+            return last_hidden_states[:, -1]
+        else:
+            sequence_lengths = attention_mask.sum(dim=1) - 1
+            batch_size = last_hidden_states.shape[0]
+            return last_hidden_states[
+                torch.arange(batch_size, device=last_hidden_states.device),
+                sequence_lengths
+            ]
+
+    # Вычисление эмбеддинга
+    def encode_texts(texts, max_length=512):
+        with torch.inference_mode():
+            toks = get_negative_embedding.tokenizer(
+                texts, return_tensors="pt", padding="max_length", truncation=True, max_length=max_length
+            ).to(device)
+            outs = get_negative_embedding.text_model(**toks)
+            emb = last_token_pool(outs.last_hidden_state, toks["attention_mask"])
+            emb = emb.unsqueeze(1)  # Добавляем размерность sequence_length
+            return emb
+
+    # Возвращаем эмбеддинг
+    if not neg_prompt:
+        hidden_dim = 1024  # Размерность эмбеддинга Qwen3-Embedding-0.6B
+        return torch.zeros((batch_size, 1, hidden_dim), dtype=dtype, device=device)
+
+    uncond_emb = encode_texts([neg_prompt]).to(dtype=dtype, device=device)
+    uncond_emb = uncond_emb.repeat(batch_size, 1, 1)  # Добавляем батч
+
+    # Выгружаем модели
+    if hasattr(get_negative_embedding, "text_model"):
+        get_negative_embedding.text_model = get_negative_embedding.text_model.to("cpu")
+    if hasattr(get_negative_embedding, "tokenizer"):
+        del get_negative_embedding.tokenizer  # Освобождаем память
+    torch.cuda.empty_cache()
+
+    return uncond_emb
+    
+uncond_emb = get_negative_embedding("low quality")
+
 @torch.compiler.disable()
 @torch.no_grad()
-def generate_and_save_samples(fixed_samples_cpu, step):
+def generate_and_save_samples(fixed_samples_cpu,empty_embeddings, step):
     original_model = None
     try:
         original_model = accelerator.unwrap_model(unet, keep_torch_compile=True).eval()
@@ -436,39 +509,41 @@ def generate_and_save_samples(fixed_samples_cpu, step):
             width, height = size
             sample_latents = sample_latents.to(dtype=dtype, device=device)
             sample_text_embeddings = sample_text_embeddings.to(dtype=dtype, device=device)
-
-            noise = torch.randn(
+        
+            # начальный шум
+            latents = torch.randn(
                 sample_latents.shape,
-                generator=gen,
                 device=device,
-                dtype=sample_latents.dtype
+                dtype=sample_latents.dtype,
+                generator=torch.Generator(device=device).manual_seed(seed)
             )
-            current_latents = noise.clone()
-            
-            if guidance_scale != 1:
-                empty_embeddings = torch.zeros_like(sample_text_embeddings, dtype=sample_text_embeddings.dtype, device=device)
-                text_embeddings_batch = torch.cat([empty_embeddings, sample_text_embeddings], dim=0)
-            else:
-                text_embeddings_batch = sample_text_embeddings
-            
-            timesteps = torch.linspace(0, 1, n_diffusion_steps+1, device=device, dtype=sample_latents.dtype)
-            for i in range(0, n_diffusion_steps):
-                t_cur = timesteps[i].unsqueeze(0)
-                t_next = timesteps[i+1]
-                dt = t_next - t_cur
+        
+            # подготовим timesteps через шедулер
+            scheduler.set_timesteps(n_diffusion_steps, device=device)
+        
+            for t in scheduler.timesteps:
+                # guidance: удваиваем батч
                 if guidance_scale != 1:
-                    latent_model_input = torch.cat((current_latents, current_latents))
+                    latent_model_input = torch.cat([latents, latents], dim=0)
+                    text_embeddings_batch = torch.cat([empty_embeddings, sample_text_embeddings], dim=0)
                 else:
-                    latent_model_input = current_latents
-                t_batch = t_cur.repeat(latent_model_input.shape[0]).to(device)
-                t_batch = (t_batch * 1000).long().view(-1)
-                flow = original_model(latent_model_input, t_batch, text_embeddings_batch).sample               
-                
+                    latent_model_input = latents
+                    text_embeddings_batch = sample_text_embeddings
+        
+                # предсказание потока (velocity)
+                model_out = original_model(latent_model_input, t, encoder_hidden_states=text_embeddings_batch)
+                flow = getattr(model_out, "sample", model_out)
+        
+                # guidance объединение
                 if guidance_scale != 1:
                     flow_uncond, flow_cond = flow.chunk(2)
                     flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
+        
+                # шаг через scheduler
+                latents = scheduler.step(flow, t, latents).prev_sample
+        
+            current_latents = latents
 
-                current_latents = current_latents + flow * dt.to(device)
 
             # Параметры нормализации
             latent_for_vae = current_latents.detach() / scaling_factor + shift_factor
@@ -535,7 +610,7 @@ def generate_and_save_samples(fixed_samples_cpu, step):
 if accelerator.is_main_process:
     if save_model:
         print("Генерация сэмплов до старта обучения...")
-        generate_and_save_samples(fixed_samples,0)
+        generate_and_save_samples(fixed_samples,uncond_emb,0)
 accelerator.wait_for_everyone()
 
 # Модифицируем функцию сохранения модели для поддержки LoRA
@@ -574,15 +649,26 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
                 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)
-            t = logit_normal_samples((latents.shape[0], 1, 1, 1), mu=0.0, sigma=1.0, device=latents.device, dtype=latents.dtype)
-            noisy_latents = (1 - t) * noise + t * latents
-
-            t_for_unet = (t * 1000).long().view(-1)
-            model_pred = unet(noisy_latents, t_for_unet, embeddings).sample
-            target_pred = latents - noise
-
-            mse_loss = F.mse_loss(model_pred.float(), target_pred.float())
+    
+            # берём t из [0, 1]
+            t = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
+    
+            # интерполяция между x0 и шумом
+            noisy_latents = (1.0 - t.view(-1, 1, 1, 1)) * latents + t.view(-1, 1, 1, 1) * noise
+    
+            # делаем integer timesteps для UNet
+            timesteps = (t * scheduler.config.num_train_timesteps).long()
+    
+            # предсказание потока (Flow)
+            model_pred = unet(noisy_latents, timesteps, embeddings).sample
+    
+            # таргет — векторное поле (= разность между конечными точками)
+            target = noise - latents   # или latents - noise?
+    
+            # MSE лосс
+            mse_loss = F.mse_loss(model_pred.float(), target.float())
 
             # Сохраняем для логов (мы сохраняем MSE отдельно — как показатель)
             batch_losses.append(mse_loss.detach().item())
@@ -629,7 +715,7 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
     
                     # Генерируем сэмплы с заданным интервалом
                     if global_step % sample_interval == 0:
-                        generate_and_save_samples(fixed_samples,global_step)
+                        generate_and_save_samples(fixed_samples,uncond_emb, global_step)
                         last_n = sample_interval
     
                         if save_model:

train_chatgpt.py

@@ -1,678 +0,0 @@
-import os
-import math
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-from torch.utils.data import DataLoader, Sampler
-from torch.utils.data.distributed import DistributedSampler
-from torch.optim.lr_scheduler import LambdaLR
-from collections import defaultdict
-from diffusers import UNet2DConditionModel, AutoencoderKL
-from accelerate import Accelerator
-from datasets import load_from_disk
-from tqdm import tqdm
-from PIL import Image, ImageOps
-import wandb
-import random
-import gc
-from accelerate.state import DistributedType
-from torch.distributed import broadcast_object_list
-from torch.utils.checkpoint import checkpoint
-from diffusers.models.attention_processor import AttnProcessor2_0
-from datetime import datetime
-import bitsandbytes as bnb
-import torch.nn.functional as F
-from collections import deque
-
-# --------------------------- Параметры ---------------------------
-ds_path = "/workspace/sdxs3d/datasets/butterfly"
-project = "unet"
-batch_size = 128
-base_learning_rate = 9e-5
-min_learning_rate = 1e-5
-num_epochs = 100
-# samples/save per epoch
-sample_interval_share = 1
-use_wandb = True
-use_comet_ml = False
-save_model = False
-use_decay = True
-fbp = False # fused backward pass
-optimizer_type = "adam8bit"
-torch_compile = False
-unet_gradient = True
-clip_sample = False #Scheduler
-fixed_seed = True
-shuffle = True
-comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r"
-comet_ml_workspace = "recoilme"
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.backends.cuda.enable_mem_efficient_sdp(False)
-dtype = torch.float32
-save_barrier = 1.01
-warmup_percent = 0.01
-percentile_clipping = 99 # 8bit optim
-betta2 = 0.995
-eps = 1e-8
-clip_grad_norm = 1.0
-steps_offset = 0 # Scheduler
-limit = 0
-checkpoints_folder = ""
-mixed_precision = "no" # "fp16"
-gradient_accumulation_steps = 1
-accelerator = Accelerator(
-    mixed_precision=mixed_precision,
-    gradient_accumulation_steps=gradient_accumulation_steps
-)
-device = accelerator.device
-
-# Параметры для диффузии
-n_diffusion_steps = 50
-samples_to_generate = 12
-guidance_scale = 1
-
-# Папки для сохранения результатов
-generated_folder = "samples"
-os.makedirs(generated_folder, exist_ok=True)
-
-# Настройка seed для воспроизводимости
-current_date = datetime.now()
-seed = int(current_date.strftime("%Y%m%d"))
-if fixed_seed:
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-# --------------------------- Параметры LoRA ---------------------------
-lora_name = ""
-lora_rank = 32
-lora_alpha = 64
-
-print("init")
-
-# ---------------------------  вспомогательные функции ---------------------------
-def sample_timesteps_bias(
-    batch_size: int,
-    progress: float,           # [0..1]
-    num_train_timesteps: int,  # обычно 1000
-    steps_offset: int = 0,
-    device=None,
-    mode: str = "beta",        # "beta", "uniform"
-) -> torch.Tensor:
-    max_idx = num_train_timesteps - 1 - steps_offset
-
-    if mode == "beta":
-        alpha = 1.0 + .5 * (1.0 - progress)
-        beta  = 1.0 + .5 * progress
-        samples = torch.distributions.Beta(alpha, beta).sample((batch_size,))
-
-    elif mode == "uniform":
-        samples = torch.rand(batch_size)
-
-    else:
-        raise ValueError(f"Unknown mode: {mode}")
-
-    timesteps = steps_offset + (samples * max_idx).long().to(device)
-    return timesteps
-
-
-def logit_normal_samples(shape, mu=0.0, sigma=1.0, device=None, dtype=None):
-    normal_samples = torch.normal(mean=mu, std=sigma, size=shape, device=device, dtype=dtype)
-    logit_normal_samples = torch.sigmoid(normal_samples)
-    return logit_normal_samples
-
-# --------------------------- Инициализация WandB ---------------------------
-if accelerator.is_main_process:
-    if use_wandb:
-        wandb.init(project=project + lora_name, config={
-            "batch_size": batch_size,
-            "base_learning_rate": base_learning_rate,
-            "num_epochs": num_epochs,
-            "fbp": fbp,
-            "optimizer_type": optimizer_type,
-        })
-    if use_comet_ml:
-        from comet_ml import Experiment
-        comet_experiment = Experiment(
-            api_key=comet_ml_api_key,
-            project_name=project,
-            workspace=comet_ml_workspace
-        )
-        hyper_params = {
-            "batch_size": batch_size,
-            "base_learning_rate": base_learning_rate,
-            "min_learning_rate": min_learning_rate,
-            "num_epochs": num_epochs,
-            "n_diffusion_steps": n_diffusion_steps,
-            "guidance_scale": guidance_scale,
-            "optimizer_type": optimizer_type,
-            "mixed_precision": mixed_precision,
-        }
-        comet_experiment.log_parameters(hyper_params)
-
-# Включение Flash Attention 2/SDPA
-torch.backends.cuda.enable_flash_sdp(True)
-# --------------------------- Инициализация Accelerator --------------------
-gen = torch.Generator(device=device)
-gen.manual_seed(seed)
-
-# --------------------------- Загрузка моделей ---------------------------
-vae = AutoencoderKL.from_pretrained("AiArtLab/simplevae", subfolder="vae", torch_dtype=dtype).to("cpu").eval()
-
-shift_factor = getattr(vae.config, "shift_factor", 0.0)
-if shift_factor is None:
-    shift_factor = 0.0
-
-scaling_factor = getattr(vae.config, "scaling_factor", 1.0)
-if scaling_factor is None:
-    scaling_factor = 1.0
-    
-latents_mean = getattr(vae.config, "latents_mean", None)
-latents_std = getattr(vae.config, "latents_std", None)
-
-
-class DistributedResolutionBatchSampler(Sampler):
-    def __init__(self, dataset, batch_size, num_replicas, rank, shuffle=True, drop_last=True):
-        self.dataset = dataset
-        self.batch_size = max(1, batch_size // num_replicas)
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.shuffle = shuffle
-        self.drop_last = drop_last
-        self.epoch = 0
-        
-        try:
-            widths = np.array(dataset["width"])
-            heights = np.array(dataset["height"])
-        except KeyError:
-            widths = np.zeros(len(dataset))
-            heights = np.zeros(len(dataset))
-        
-        self.size_keys = np.unique(np.stack([widths, heights], axis=1), axis=0)
-        self.size_groups = {}
-        for w, h in self.size_keys:
-            mask = (widths == w) & (heights == h)
-            self.size_groups[(w, h)] = np.where(mask)[0]
-            
-        self.group_num_batches = {}
-        total_batches = 0
-        for size, indices in self.size_groups.items():
-            num_full_batches = len(indices) // (self.batch_size * self.num_replicas)
-            self.group_num_batches[size] = num_full_batches
-            total_batches += num_full_batches
-            
-        self.num_batches = (total_batches // self.num_replicas) * self.num_replicas
-        
-    def __iter__(self):
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        all_batches = []
-        rng = np.random.RandomState(self.epoch)
-        
-        for size, indices in self.size_groups.items():
-            indices = indices.copy()
-            if self.shuffle:
-                rng.shuffle(indices)
-            num_full_batches = self.group_num_batches[size]
-            if num_full_batches == 0:
-                continue
-            valid_indices = indices[:num_full_batches * self.batch_size * self.num_replicas]
-            batches = valid_indices.reshape(-1, self.batch_size * self.num_replicas)
-            start_idx = self.rank * self.batch_size
-            end_idx = start_idx + self.batch_size
-            gpu_batches = batches[:, start_idx:end_idx]
-            all_batches.extend(gpu_batches)
-        
-        if self.shuffle:
-            rng.shuffle(all_batches)
-        accelerator.wait_for_everyone()
-        return iter(all_batches)
-
-    def __len__(self):
-        return self.num_batches 
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
-
-# Функция для выборки фиксированных семплов по размерам
-def get_fixed_samples_by_resolution(dataset, samples_per_group=1):
-    size_groups = defaultdict(list)
-    try:
-        widths = dataset["width"]
-        heights = dataset["height"]
-    except KeyError:
-        widths = [0] * len(dataset)
-        heights = [0] * len(dataset)
-    for i, (w, h) in enumerate(zip(widths, heights)):
-        size = (w, h)
-        size_groups[size].append(i)
-    
-    fixed_samples = {}
-    for size, indices in size_groups.items():
-        n_samples = min(samples_per_group, len(indices))
-        if len(size_groups) == 1:
-            n_samples = samples_to_generate
-        if n_samples == 0:
-            continue
-        sample_indices = random.sample(indices, n_samples)
-        samples_data = [dataset[idx] for idx in sample_indices]
-
-        # FIXED: keep fixed samples on CPU to avoid device/device-transfer issues when creating dataset
-        latents = torch.tensor(np.array([item["vae"] for item in samples_data]), dtype=dtype).cpu()
-        embeddings = torch.tensor(np.array([item["embeddings"] for item in samples_data]), dtype=dtype).cpu()
-        texts = [item["text"] for item in samples_data]
-        fixed_samples[size] = (latents, embeddings, texts)
-    
-    print(f"Создано {len(fixed_samples)} групп фиксированных семплов по разрешениям")
-    return fixed_samples
-
-if limit > 0:
-    dataset = load_from_disk(ds_path).select(range(limit))
-else:
-    dataset = load_from_disk(ds_path)
-
-
-def collate_fn_simple(batch):
-    latents = torch.tensor(np.array([item["vae"] for item in batch]), dtype=dtype).to(device)
-    embeddings = torch.tensor(np.array([item["embeddings"] for item in batch]), dtype=dtype).to(device)
-    return latents, embeddings
-
-batch_sampler = DistributedResolutionBatchSampler(
-        dataset=dataset,
-        batch_size=batch_size,
-        num_replicas=accelerator.num_processes,
-        rank=accelerator.process_index,
-        shuffle=shuffle
-    )
-
-# NOTE: we create dataloader first, then prepare it with accelerator. We'll create optimizer/lr_scheduler after
-# we know len(dataloader) (which is per-process after prepare) so that scheduling is consistent.
-
-dataloader = DataLoader(dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_simple)
-print("Total batches (pre-prepare):", len(dataloader))
-dataloader = accelerator.prepare(dataloader)
-
-# --------------------------- Теперь безопасно --- вычисляем шаги и создаём оптимизатор/шедулер ---------------------------
-steps_per_epoch = len(dataloader)  # this is per-process (after prepare)
-total_training_steps = steps_per_epoch * num_epochs
-print(f"[rank {accelerator.process_index}] steps_per_epoch={steps_per_epoch}, total_training_steps={total_training_steps}")
-
-# --------------------------- Создание/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 torch_compile:
-        print("compiling")
-        torch.set_float32_matmul_precision('high')
-        unet = torch.compile(unet)
-        print("compiling - ok")
-    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}. Положи UNet чекпоинт в {latest_checkpoint} или укажи другой путь.")
-
-# --------------------------- LoRA (если нужно) ---------------------------
-if lora_name:
-    print(f"--- Настройка LoRA через PEFT (Rank={lora_rank}, Alpha={lora_alpha}) ---")
-    from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
-    from peft.tuners.lora import LoraModel
-    unet.requires_grad_(False)
-    print("Параметры базового UNet заморожены.")
-
-    lora_config = LoraConfig(
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        target_modules=["to_q", "to_k", "to_v", "to_out.0"],
-    )
-    unet.add_adapter(lora_config)
-
-    from peft import get_peft_model
-    peft_unet = get_peft_model(unet, lora_config)
-    params_to_optimize = list(p for p in peft_unet.parameters() if p.requires_grad)
-
-    if accelerator.is_main_process:
-        lora_params_count = sum(p.numel() for p in params_to_optimize)
-        total_params_count = sum(p.numel() for p in unet.parameters())
-        print(f"Количество обучаемых параметров (LoRA): {lora_params_count:,}")
-        print(f"Общее количество параметров UNet: {total_params_count:,}")
-
-    lora_save_path = os.path.join("lora", lora_name)
-    os.makedirs(lora_save_path, exist_ok=True)
-
-    def save_lora_checkpoint(model):
-        if accelerator.is_main_process:
-            print(f"Сохраняем LoRA адаптеры  в {lora_save_path}")
-            from peft.utils.save_and_load import get_peft_model_state_dict
-            lora_state_dict = get_peft_model_state_dict(model)
-            torch.save(lora_state_dict, os.path.join(lora_save_path, "adapter_model.bin"))
-            model.peft_config["default"].save_pretrained(lora_save_path)
-            from diffusers import StableDiffusionXLPipeline
-            StableDiffusionXLPipeline.save_lora_weights(lora_save_path, lora_state_dict)
-
-# --------------------------- Оптимизатор ---------------------------
-if lora_name:
-    trainable_params = [p for p in unet.parameters() if p.requires_grad]
-else:
-    trainable_params = list(unet.parameters()) if fbp else [p for p in unet.parameters() if p.requires_grad]
-
-
-def create_optimizer(name, params):
-    if name == "adam8bit":
-        return bnb.optim.AdamW8bit(
-            params, lr=base_learning_rate, betas=(0.9, betta2), eps=eps, weight_decay=0.01,
-            percentile_clipping=percentile_clipping
-        )
-    elif name == "adam":
-        return torch.optim.AdamW(
-            params, lr=base_learning_rate, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.01
-        )
-    elif name == "lion8bit":
-        return bnb.optim.Lion8bit(
-            params, lr=base_learning_rate, betas=(0.9, 0.97), weight_decay=0.01,
-            percentile_clipping=percentile_clipping
-        )
-    elif name == "adafactor":
-        from transformers import Adafactor
-        return Adafactor(
-            params, lr=base_learning_rate, scale_parameter=True, relative_step=False,
-            warmup_init=False, eps=(1e-30, 1e-3), clip_threshold=1.0,
-            beta1=0.9, weight_decay=0.01
-        )
-    else:
-        raise ValueError(f"Unknown optimizer: {name}")
-
-if fbp:
-    optimizer_dict = {p: create_optimizer(optimizer_type, [p]) for p in trainable_params}
-    def optimizer_hook(param):
-        optimizer_dict[param].step()
-        optimizer_dict[param].zero_grad(set_to_none=True)
-    for param in trainable_params:
-        param.register_post_accumulate_grad_hook(optimizer_hook)
-    # FIXED: prepare fbp variant (keeps original logic)
-    unet, optimizer = accelerator.prepare(unet, optimizer_dict)
-else:
-    optimizer = create_optimizer(optimizer_type, trainable_params)
-
-    # FIXED: LR schedule should be based on total_training_steps (per-process steps * epochs)
-    def lr_schedule(step):
-        # step is current scheduler step (0..total_training_steps)
-        x = step / max(1, total_training_steps)
-        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))
-
-    # LambdaLR expects a multiplier, so divide by base_learning_rate
-    lr_scheduler = LambdaLR(optimizer, lambda step: lr_schedule(step) / base_learning_rate)
-
-    num_params = sum(p.numel() for p in unet.parameters())
-    print(f"[rank {accelerator.process_index}] total params: {num_params}")
-    for name, param in unet.named_parameters():
-        if torch.isnan(param).any() or torch.isinf(param).any():
-            print(f"[rank {accelerator.process_index}] NaN/Inf in {name}")
-
-    # FIXED: prepare model, optimizer, scheduler AFTER creating them and after dataloader.prepare
-    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)
-
-# --------------------------- Фиксированные семплы для генерации ---------------------------
-fixed_samples = get_fixed_samples_by_resolution(dataset)
-
-@torch.no_grad()
-def generate_and_save_samples(fixed_samples_cpu, step):
-    original_model = None
-    try:
-        original_model = accelerator.unwrap_model(unet, keep_torch_compile=True).eval()
-        vae.to(device=device).eval()  # временно подгружаем VAE на GPU для декодинга
-
-        all_generated_images = []
-        all_captions = [] 
-        
-        for size, (sample_latents, sample_text_embeddings, sample_text) in fixed_samples_cpu.items():
-            width, height = size
-            # move CPU tensors to device here (they were kept on CPU in get_fixed_samples_by_resolution)
-            sample_latents = sample_latents.to(dtype=dtype, device=device)
-            sample_text_embeddings = sample_text_embeddings.to(dtype=dtype, device=device)
-
-            noise = torch.randn(
-                sample_latents.shape,
-                generator=gen,
-                device=device,
-                dtype=sample_latents.dtype
-            )
-            current_latents = noise.clone()
-            
-            if guidance_scale != 1:
-                empty_embeddings = torch.zeros_like(sample_text_embeddings, dtype=sample_text_embeddings.dtype, device=device)
-                text_embeddings_batch = torch.cat([empty_embeddings, sample_text_embeddings], dim=0)
-            else:
-                text_embeddings_batch = sample_text_embeddings
-            
-            timesteps = torch.linspace(0, 1, n_diffusion_steps+1, device=device, dtype=sample_latents.dtype)
-            for i in range(0, n_diffusion_steps):
-                t_cur = timesteps[i].unsqueeze(0)
-                t_next = timesteps[i+1]
-                dt = t_next - t_cur
-                if guidance_scale != 1:
-                    latent_model_input = torch.cat((current_latents, current_latents))
-                else:
-                    latent_model_input = current_latents
-                t_batch = t_cur.repeat(latent_model_input.shape[0]).to(device)
-                t_batch = (t_batch * 1000).long().view(-1)
-                flow = original_model(latent_model_input, t_batch, text_embeddings_batch).sample               
-                
-                if guidance_scale != 1:
-                    flow_uncond, flow_cond = flow.chunk(2)
-                    flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
-
-                current_latents = current_latents + flow * dt.to(device)
-
-            # Параметры нормализации
-            latent_for_vae = current_latents.detach() / scaling_factor + shift_factor
-            decoded = vae.decode(latent_for_vae.to(torch.float32)).sample
-            decoded_fp32 = decoded.to(torch.float32)
-            for img_idx, img_tensor in enumerate(decoded_fp32):
-                img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy()
-                img = img.transpose(1, 2, 0)
-                if np.isnan(img).any():
-                    print("NaNs found, saving stopped! Step:", step)
-                pil_img = Image.fromarray((img * 255).astype("uint8"))
-                
-                max_w_overall = max(s[0] for s in fixed_samples_cpu.keys())
-                max_h_overall = max(s[1] for s in fixed_samples_cpu.keys())
-                max_w_overall = max(255, max_w_overall)
-                max_h_overall = max(255, max_h_overall)
-            
-                padded_img = ImageOps.pad(pil_img, (max_w_overall, max_h_overall), color='white')
-                all_generated_images.append(padded_img)
-
-                caption_text = sample_text[img_idx][:200] if img_idx < len(sample_text) else ""
-                all_captions.append(caption_text)
-                
-                sample_path = f"{generated_folder}/{project}_{width}x{height}_{img_idx}.jpg"
-                pil_img.save(sample_path, "JPEG", quality=96)
-        
-        if use_wandb and accelerator.is_main_process:
-            wandb_images = [
-                wandb.Image(img, caption=f"{all_captions[i]}")
-                for i, img in enumerate(all_generated_images)
-            ]
-            wandb.log({"generated_images": wandb_images}, step=step)
-        if use_comet_ml and accelerator.is_main_process:
-            for i, img in enumerate(all_generated_images):
-                comet_experiment.log_image(
-                    image_data=img,
-                    name=f"step_{step}_img_{i}",
-                    step=step,
-                    metadata={
-                        "caption": all_captions[i],
-                        "width": img.width,
-                        "height": img.height,
-                        "global_step": step
-                    }
-                )
-    finally:
-        vae.to("cpu")
-        torch.cuda.empty_cache()
-        gc.collect()
-
-# --------------------------- Генерация сэмплов перед обучением ---------------------------
-if accelerator.is_main_process:
-    if save_model:
-        print("Генерация сэмплов до старта обучения...")
-        generate_and_save_samples(fixed_samples, 0)
-accelerator.wait_for_everyone()
-
-# Модифицируем функцию сохранения модели для поддержки LoRA
-def save_checkpoint(unet, variant=""):
-    if accelerator.is_main_process:
-        if lora_name:
-            save_lora_checkpoint(unet)
-        else:
-            model_to_save = accelerator.unwrap_model(unet)
-            dest = os.path.join(checkpoints_folder, f"{project}")
-            if variant != "":
-                model_to_save.to(dtype=torch.float16).save_pretrained(dest, variant=variant)
-            else:
-                model_to_save.save_pretrained(dest)
-
-
-# --------------------------- 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")
-
-sample_interval = max(1, steps_per_epoch // sample_interval_share)
-min_loss = 2.
-global_step = 0
-
-for epoch in range(0, num_epochs):
-    # FIXED: set epoch on the dataloader's batch_sampler if available (accelerator may have wrapped it)
-    if hasattr(dataloader, "batch_sampler") and hasattr(dataloader.batch_sampler, "set_epoch"):
-        dataloader.batch_sampler.set_epoch(epoch)
-    elif hasattr(batch_sampler, "set_epoch"):
-        batch_sampler.set_epoch(epoch)
-
-    accelerator.wait_for_everyone()
-    unet.train()
-
-    batch_losses = []
-    batch_grads = []
-
-    for step, (latents, embeddings) in enumerate(dataloader):
-        with accelerator.accumulate(unet):
-            if save_model == False and step == 5:
-                used_gb = torch.cuda.max_memory_allocated() / 1024**3
-                print(f"Шаг {step}: {used_gb:.2f} GB")
-                
-            noise = torch.randn_like(latents, dtype=latents.dtype)
-            t = logit_normal_samples((latents.shape[0], 1, 1, 1), mu=0.0, sigma=1.0, device=latents.device, dtype=latents.dtype)
-            noisy_latents = (1 - t) * noise + t * latents
-
-            t_for_unet = (t * 1000).long().view(-1)
-            model_pred = unet(noisy_latents, t_for_unet, embeddings).sample
-            target_pred = latents - noise
-
-            mse_loss = F.mse_loss(model_pred.float(), target_pred.float())
-
-            # Сохраняем для локальных логов
-            batch_losses.append(mse_loss.detach().cpu().item())
-
-            # Backward
-            accelerator.backward(mse_loss)
-
-            grad_norm_val = 0.0
-            if not fbp:
-                if accelerator.sync_gradients:
-                    # Clip gradients and step only when gradients are synchronized (i.e. actual optimizer step)
-                    grad_norm = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
-                    grad_norm_val = float(grad_norm)
-                    optimizer.step()
-                    lr_scheduler.step()
-                    optimizer.zero_grad(set_to_none=True)
-
-            # increment global_step only when we have synchronized gradients (i.e. on optimizer step)
-            # FIXED: ensure global_step reflects optimizer updates, not micro-batches
-            if accelerator.sync_gradients:
-                global_step += 1
-                progress_bar.update(1)
-
-                # Логируем метрики только на главном процессе
-                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_norm_val)
-
-                    log_data = {
-                        "loss": mse_loss.detach().item(),
-                        "lr": current_lr,
-                        "grad": grad_norm_val,
-                        "epoch": epoch + 1,
-                        "global_step": global_step,
-                    }
-
-                    if use_wandb:
-                        wandb.log(log_data, step=global_step)
-                    if use_comet_ml:
-                        comet_experiment.log_metrics(log_data, step=global_step)
-
-                    # Генерируем сэмплы с заданным интервалом (только на главном процессе)
-                    if global_step % sample_interval == 0:
-                        generate_and_save_samples(fixed_samples, global_step)
-
-                        if save_model:
-                            avg_sample_loss = np.mean(batch_losses[-sample_interval:]) if len(batch_losses) > 0 else 0.0
-                            if use_wandb:
-                                wandb.log({"sample_loss": avg_sample_loss})
-                            print("saving:", avg_sample_loss is not None and avg_sample_loss < min_loss * save_barrier, "Avg:", avg_sample_loss)
-                            if avg_sample_loss is not None and avg_sample_loss < min_loss * save_barrier:
-                                min_loss = avg_sample_loss
-                                save_checkpoint(unet)
-
-    # Эпоха окончена — агрегируем и логируем средние значения
-    if accelerator.is_main_process:
-        # local averages
-        avg_epoch_loss = np.mean(batch_losses) if len(batch_losses) > 0 else 0.0
-        avg_epoch_grad = np.mean(batch_grads) if len(batch_grads) > 0 else 0.0
-
-        print(f"\nЭпоха {epoch} завершена. Средний лосс: {avg_epoch_loss:.6f}")
-        log_data_ep = {
-                        "epoch_loss": avg_epoch_loss,
-                        "epoch_grad": avg_epoch_grad,
-                        "epoch": epoch + 1,
-                    }
-        if use_wandb:
-            wandb.log(log_data_ep)
-        if use_comet_ml:
-            comet_experiment.log_metrics(log_data_ep)
-
-# Завершение обучения - сохраняем финальную модель
-if accelerator.is_main_process:
-    print("Обучение завершено! Сохраняем финальную модель...")
-    if save_model:
-        save_checkpoint(unet, "fp16")
-    if use_comet_ml:
-        comet_experiment.end()
-accelerator.free_memory()
-if torch.distributed.is_initialized():
-    torch.distributed.destroy_process_group()
-    
-print("Готово!")

train_velocity.py

@@ -1,825 +0,0 @@
-import os
-import math
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-from torch.utils.data import DataLoader, Sampler
-from torch.utils.data.distributed import DistributedSampler
-from torch.optim.lr_scheduler import LambdaLR
-from collections import defaultdict
-from torch.optim.lr_scheduler import LambdaLR
-from diffusers import UNet2DConditionModel, AutoencoderKLWan,AutoencoderKL, DDPMScheduler
-from accelerate import Accelerator
-from datasets import load_from_disk
-from tqdm import tqdm
-from PIL import Image,ImageOps
-import wandb
-import random
-import gc
-from accelerate.state import DistributedType
-from torch.distributed import broadcast_object_list
-from torch.utils.checkpoint import checkpoint
-from diffusers.models.attention_processor import AttnProcessor2_0
-from datetime import datetime
-import bitsandbytes as bnb
-import torch.nn.functional as F
-from collections import deque
-
-# --------------------------- Параметры ---------------------------
-ds_path = "/workspace/sdxs3d/datasets/butterfly"
-project = "unet"
-batch_size = 16
-base_learning_rate = 9e-5
-min_learning_rate = 1e-5
-num_epochs = 300
-# samples/save per epoch
-sample_interval_share = 1
-use_wandb = True
-save_model = True
-use_decay = True
-fbp = False # fused backward pass
-optimizer_type = "adam8bit"
-torch_compile = False
-unet_gradient = True
-clip_sample = False #Scheduler
-fixed_seed = True
-shuffle = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.backends.cuda.enable_mem_efficient_sdp(False)
-dtype = torch.float32
-save_barrier = 2.03 # TODO
-warmup_percent = 0.01
-dispersive_temperature=0.5
-dispersive_weight= 0.05
-percentile_clipping = 99 # 8bit optim
-betta2 = 0.995
-eps = 1e-8
-clip_grad_norm = 1.0
-steps_offset = 0 # Scheduler
-limit = 0
-checkpoints_folder = ""
-mixed_precision = "no" #"fp16"
-gradient_accumulation_steps = 1
-accelerator = Accelerator(
-    mixed_precision=mixed_precision,
-    gradient_accumulation_steps=gradient_accumulation_steps
-)
-device = accelerator.device
-
-# Параметры для диффузии
-n_diffusion_steps = 50
-samples_to_generate = 12
-guidance_scale = 5
-
-# Папки для сохранения результатов
-generated_folder = "samples"
-os.makedirs(generated_folder, exist_ok=True)
-
-# Настройка seed для воспроизводимости
-current_date = datetime.now()
-seed = int(current_date.strftime("%Y%m%d"))
-if fixed_seed:
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-# --- Пропорции лоссов и окно медианного нормирования (КОЭФ., не значения) ---
-# CHANGED: добавлен huber и dispersive в пропорции, суммы = 1.0
-loss_ratios = {
-    "mse":   1.0,
-    "mae":   0.0,
-    "huber": 0.0,
-    "dispersive": 0.0,
-}
-median_coeff_steps = 128  # за сколько шагов считать медианные коэффициенты
-
-# --------------------------- Параметры LoRA ---------------------------
-lora_name = "" 
-lora_rank = 32
-lora_alpha = 64
-
-print("init")
-
-# ---------------------------  вспомогательные функции ---------------------------
-def sample_timesteps_bias(
-    batch_size: int,
-    progress: float,           # [0..1]
-    num_train_timesteps: int,  # обычно 1000
-    steps_offset: int = 0,
-    device=None,
-    mode: str = "uniform",        # "beta", "uniform"
-) -> torch.Tensor:
-    """
-    Возвращает timesteps с разным bias:
-    - beta   : как раньше (сдвиг в начало или конец в зависимости от progress)
-    - normal : около середины (гауссовое распределение)
-    - uniform: равномерно по всем timestep’ам
-    """
-
-    max_idx = num_train_timesteps - 1 - steps_offset
-
-    if mode == "beta":
-        alpha = 1.0 + .5 * (1.0 - progress)
-        beta  = 1.0 + .5 * progress
-        samples = torch.distributions.Beta(alpha, beta).sample((batch_size,))
-
-    elif mode == "uniform":
-        samples = torch.rand(batch_size)
-
-    else:
-        raise ValueError(f"Unknown mode: {mode}")
-
-    timesteps = steps_offset + (samples * max_idx).long().to(device)
-    return timesteps
-
-
-# Нормализация лоссов по медианам: считаем КОЭФФИЦИЕНТЫ
-class MedianLossNormalizer:
-    def __init__(self, desired_ratios: dict, window_steps: int):
-        # нормируем доли на случай, если сумма != 1
-        s = sum(desired_ratios.values())
-        self.ratios = {k: (v / s) for k, v in desired_ratios.items()}
-        self.buffers = {k: deque(maxlen=window_steps) for k in self.ratios.keys()}
-        self.window = window_steps
-
-    def update_and_total(self, losses: dict):
-        """
-        losses: dict ключ->тензор (значения лоссов)
-        Поведение:
-          - буферим ABS(l) только для активных (ratio>0) лоссов
-          - coeff = ratio / median(abs(loss))
-          - total = sum(coeff * loss) по активным лоссам
-        CHANGED: буферим abs() — чтобы медиана была положительной и не ломала деление.
-        """
-        # буферим только активные лоссы
-        for k, v in losses.items():
-            if k in self.buffers and self.ratios.get(k, 0) > 0:
-                self.buffers[k].append(float(v.detach().abs().cpu()))
-
-        meds = {k: (np.median(self.buffers[k]) if len(self.buffers[k]) > 0 else 1.0) for k in self.buffers}
-        coeffs = {k: (self.ratios[k] / max(meds[k], 1e-12)) for k in self.ratios}
-
-        # суммируем только по активным (ratio>0)
-        total = sum(coeffs[k] * losses[k] for k in coeffs if self.ratios.get(k, 0) > 0)
-        return total, coeffs, meds
-
-# создаём normalizer после определения loss_ratios
-normalizer = MedianLossNormalizer(loss_ratios, median_coeff_steps)
-
-class AccelerateDispersiveLoss:
-    def __init__(self, accelerator, temperature=0.5, weight=0.5):
-        self.accelerator = accelerator
-        self.temperature = temperature
-        self.weight = weight
-        self.activations = []
-        self.hooks = []
-    
-    def register_hooks(self, model, target_layer="down_blocks.0"):
-        unwrapped_model = self.accelerator.unwrap_model(model)
-        print("=== Поиск слоев в unwrapped модели ===")
-        for name, module in unwrapped_model.named_modules():
-            if target_layer in name:
-                hook = module.register_forward_hook(self.hook_fn)
-                self.hooks.append(hook)
-                print(f"✅ Хук зарегистрирован на: {name}")
-                break
-    
-    def hook_fn(self, module, input, output):
-        if isinstance(output, tuple):
-            activation = output[0]
-        else:
-            activation = output
-        if len(activation.shape) > 2:
-            activation = activation.view(activation.shape[0], -1)
-        self.activations.append(activation.detach().clone())
-        
-    def compute_dispersive_loss(self):
-        if not self.activations:
-            return torch.tensor(0.0, requires_grad=True, device=device)
-        local_activations = self.activations[-1].float()
-        batch_size = local_activations.shape[0]
-        if batch_size < 2:
-            return torch.tensor(0.0, requires_grad=True, device=device)
-        sf = local_activations / torch.norm(local_activations, dim=1, keepdim=True)
-        distance = torch.nn.functional.pdist(sf.float(), p=2) ** 2
-        exp_neg_dist = torch.exp(-distance / self.temperature) + 1e-5
-        dispersive_loss = torch.log(torch.mean(exp_neg_dist))
-        return dispersive_loss
-    
-    def clear_activations(self):
-        self.activations.clear()
-    
-    def remove_hooks(self):
-        for hook in self.hooks:
-            hook.remove()
-        self.hooks.clear()
-
-
-# --------------------------- Инициализация WandB ---------------------------
-if use_wandb and accelerator.is_main_process:
-    wandb.init(project=project+lora_name, config={
-        "batch_size": batch_size,
-        "base_learning_rate": base_learning_rate,
-        "num_epochs": num_epochs,
-        "fbp": fbp,
-        "optimizer_type": optimizer_type,
-    })
-
-# Включение Flash Attention 2/SDPA
-torch.backends.cuda.enable_flash_sdp(True)
-# --------------------------- Инициализация Accelerator --------------------
-gen = torch.Generator(device=device)
-gen.manual_seed(seed)
-
-# --------------------------- Загрузка моделей ---------------------------
-# VAE загружается на CPU для экономии GPU-памяти (как в твоём оригинальном коде)
-#vae = AutoencoderKLWan.from_pretrained("vae", variant="fp16").to(device="cpu", dtype=torch.float16).eval()
-#vae = AutoencoderKLWan.from_pretrained(
-#        "AiArtLab/simplevae", subfolder="wan16x_vae_nightly",
-#        torch_dtype=dtype
-#    ).to(device="cpu").eval()
-vae = AutoencoderKL.from_pretrained("AiArtLab/simplevae",subfolder="simple_vae_nightly",torch_dtype=dtype).to(device).eval()
-
-shift_factor = getattr(vae.config, "shift_factor", 0.0)
-if shift_factor is None:
-    shift_factor = 0.0
-
-scaling_factor = getattr(vae.config, "scaling_factor", 1.0)
-if scaling_factor is None:
-    scaling_factor = 1.0
-    
-latents_mean = getattr(vae.config, "latents_mean", None)
-latents_std = getattr(vae.config, "latents_std", None)
-
-# DDPMScheduler с V_Prediction и Zero-SNR
-scheduler = DDPMScheduler(
-    num_train_timesteps=1000,
-    prediction_type="v_prediction",
-    rescale_betas_zero_snr=True,
-    clip_sample = clip_sample,
-    steps_offset = steps_offset
-)
-
-
-class DistributedResolutionBatchSampler(Sampler):
-    def __init__(self, dataset, batch_size, num_replicas, rank, shuffle=True, drop_last=True):
-        self.dataset = dataset
-        self.batch_size = max(1, batch_size // num_replicas)
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.shuffle = shuffle
-        self.drop_last = drop_last
-        self.epoch = 0
-        
-        try:
-            widths = np.array(dataset["width"])
-            heights = np.array(dataset["height"])
-        except KeyError:
-            widths = np.zeros(len(dataset))
-            heights = np.zeros(len(dataset))
-        
-        self.size_keys = np.unique(np.stack([widths, heights], axis=1), axis=0)
-        self.size_groups = {}
-        for w, h in self.size_keys:
-            mask = (widths == w) & (heights == h)
-            self.size_groups[(w, h)] = np.where(mask)[0]
-            
-        self.group_num_batches = {}
-        total_batches = 0
-        for size, indices in self.size_groups.items():
-            num_full_batches = len(indices) // (self.batch_size * self.num_replicas)
-            self.group_num_batches[size] = num_full_batches
-            total_batches += num_full_batches
-            
-        self.num_batches = (total_batches // self.num_replicas) * self.num_replicas
-        
-    def __iter__(self):
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        all_batches = []
-        rng = np.random.RandomState(self.epoch)
-        
-        for size, indices in self.size_groups.items():
-            indices = indices.copy()
-            if self.shuffle:
-                rng.shuffle(indices)
-            num_full_batches = self.group_num_batches[size]
-            if num_full_batches == 0:
-                continue
-            valid_indices = indices[:num_full_batches * self.batch_size * self.num_replicas]
-            batches = valid_indices.reshape(-1, self.batch_size * self.num_replicas)
-            start_idx = self.rank * self.batch_size
-            end_idx = start_idx + self.batch_size
-            gpu_batches = batches[:, start_idx:end_idx]
-            all_batches.extend(gpu_batches)
-        
-        if self.shuffle:
-            rng.shuffle(all_batches)
-        accelerator.wait_for_everyone()
-        return iter(all_batches)
-
-    def __len__(self):
-        return self.num_batches 
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
-
-# Функция для выборки фиксированных семплов по размерам
-def get_fixed_samples_by_resolution(dataset, samples_per_group=1):
-    size_groups = defaultdict(list)
-    try:
-        widths = dataset["width"]
-        heights = dataset["height"]
-    except KeyError:
-        widths = [0] * len(dataset)
-        heights = [0] * len(dataset)
-    for i, (w, h) in enumerate(zip(widths, heights)):
-        size = (w, h)
-        size_groups[size].append(i)
-    
-    fixed_samples = {}
-    for size, indices in size_groups.items():
-        n_samples = min(samples_per_group, len(indices))
-        if len(size_groups)==1:
-            n_samples = samples_to_generate
-        if n_samples == 0:
-            continue
-        sample_indices = random.sample(indices, n_samples)
-        samples_data = [dataset[idx] for idx in sample_indices]
-        latents = torch.tensor(np.array([item["vae"] for item in samples_data])).to(device=device,dtype=dtype)
-        embeddings = torch.tensor(np.array([item["embeddings"] for item in samples_data])).to(device,dtype=dtype)
-        texts = [item["text"] for item in samples_data]
-        fixed_samples[size] = (latents, embeddings, texts)
-    
-    print(f"Создано {len(fixed_samples)} групп фиксированных семплов по разрешениям")
-    return fixed_samples
-
-if limit > 0:
-    dataset = load_from_disk(ds_path).select(range(limit))
-else:
-    dataset = load_from_disk(ds_path)
-
-def collate_fn_simple(batch):
-    latents = torch.tensor(np.array([item["vae"] for item in batch])).to(device,dtype=dtype)
-    embeddings = torch.tensor(np.array([item["embeddings"] for item in batch])).to(device,dtype=dtype)
-    return latents, embeddings
-
-batch_sampler = DistributedResolutionBatchSampler(
-        dataset=dataset,
-        batch_size=batch_size,
-        num_replicas=accelerator.num_processes,
-        rank=accelerator.process_index,
-        shuffle=shuffle
-    )
-
-dataloader = DataLoader(dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_simple)
-print("Total samples",len(dataloader))
-dataloader = accelerator.prepare(dataloader)
-
-start_epoch = 0
-global_step = 0
-total_training_steps = (len(dataloader) * num_epochs)
-world_size = accelerator.state.num_processes
-
-# Опция загрузки модели из последнего чекпоинта (если существует)
-latest_checkpoint = os.path.join(checkpoints_folder, project)
-if os.path.isdir(latest_checkpoint):
-    print("Загружаем UNet из чекпоинта:", latest_checkpoint)
-    unet = UNet2DConditionModel.from_pretrained(latest_checkpoint).to(device=device,dtype=dtype)
-    if torch_compile:
-        print("compiling")
-        torch.set_float32_matmul_precision('high')
-        unet = torch.compile(unet)
-        print("compiling - ok")
-    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)
-
-    # Создаём hook для dispersive только если нужно
-    if loss_ratios.get("dispersive", 0) > 0:
-        dispersive_hook = AccelerateDispersiveLoss(
-            accelerator=accelerator,
-            temperature=dispersive_temperature,
-            weight=dispersive_weight
-        )
-else:
-    # FIX: если чекпоинта нет — прекращаем с понятной ошибкой (лучше, чем неожиданные NameError дальше)
-    raise FileNotFoundError(f"UNet checkpoint not found at {latest_checkpoint}. Положи UNet чекпоинт в {latest_checkpoint} или укажи другой путь.")
-
-if lora_name:
-    print(f"--- Настройка LoRA через PEFT (Rank={lora_rank}, Alpha={lora_alpha}) ---")
-    from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
-    from peft.tuners.lora import LoraModel
-    import os
-    unet.requires_grad_(False)
-    print("Параметры базового UNet заморожены.")
-
-    lora_config = LoraConfig(
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        target_modules=["to_q", "to_k", "to_v", "to_out.0"],
-    )
-    unet.add_adapter(lora_config)
-
-    from peft import get_peft_model
-    peft_unet = get_peft_model(unet, lora_config)
-    params_to_optimize = list(p for p in peft_unet.parameters() if p.requires_grad)
-
-    if accelerator.is_main_process:
-        lora_params_count = sum(p.numel() for p in params_to_optimize)
-        total_params_count = sum(p.numel() for p in unet.parameters())
-        print(f"Количество обучаемых параметров (LoRA): {lora_params_count:,}")
-        print(f"Общее количество параметров UNet: {total_params_count:,}")
-
-    lora_save_path = os.path.join("lora", lora_name)
-    os.makedirs(lora_save_path, exist_ok=True)
-
-    def save_lora_checkpoint(model):
-        if accelerator.is_main_process:
-            print(f"Сохраняем LoRA адаптеры  в {lora_save_path}")
-            from peft.utils.save_and_load import get_peft_model_state_dict
-            lora_state_dict = get_peft_model_state_dict(model)
-            torch.save(lora_state_dict, os.path.join(lora_save_path, "adapter_model.bin"))
-            model.peft_config["default"].save_pretrained(lora_save_path)
-            from diffusers import StableDiffusionXLPipeline
-            StableDiffusionXLPipeline.save_lora_weights(lora_save_path, lora_state_dict)
-
-# --------------------------- Оптимизатор ---------------------------
-if lora_name:
-    trainable_params = [p for p in unet.parameters() if p.requires_grad]
-else:
-    if fbp:
-        trainable_params = list(unet.parameters())
-
-def create_optimizer(name, params):
-    if name == "adam8bit":
-        return bnb.optim.AdamW8bit(
-            params, lr=base_learning_rate, betas=(0.9, betta2), eps=eps, weight_decay=0.01,
-            percentile_clipping=percentile_clipping
-        )
-    elif name == "adam":
-        return torch.optim.AdamW(
-            params, lr=base_learning_rate, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.01
-        )
-    elif name == "lion8bit":
-        return bnb.optim.Lion8bit(
-            params, lr=base_learning_rate, betas=(0.9, 0.97), weight_decay=0.01,
-            percentile_clipping=percentile_clipping
-        )
-    elif name == "adafactor":
-        from transformers import Adafactor
-        return Adafactor(
-            params, lr=base_learning_rate, scale_parameter=True, relative_step=False,
-            warmup_init=False, eps=(1e-30, 1e-3), clip_threshold=1.0,
-            beta1=0.9, weight_decay=0.01
-        )
-    else:
-        raise ValueError(f"Unknown optimizer: {name}")
-
-if fbp:
-    optimizer_dict = {p: create_optimizer(optimizer_type, [p]) for p in trainable_params}
-    def optimizer_hook(param):
-        optimizer_dict[param].step()
-        optimizer_dict[param].zero_grad(set_to_none=True)
-    for param in trainable_params:
-        param.register_post_accumulate_grad_hook(optimizer_hook)
-    unet, optimizer = accelerator.prepare(unet, optimizer_dict)
-else:
-    optimizer = create_optimizer(optimizer_type, unet.parameters())
-    def lr_schedule(step):
-        x = step / (total_training_steps * world_size)
-        warmup = warmup_percent
-        if not use_decay:
-            return base_learning_rate
-        if x < warmup:
-            return min_learning_rate + (base_learning_rate - min_learning_rate) * (x / warmup)
-        decay_ratio = (x - warmup) / (1 - warmup)
-        return min_learning_rate + 0.5 * (base_learning_rate - min_learning_rate) * \
-               (1 + math.cos(math.pi * decay_ratio))
-    lr_scheduler = LambdaLR(optimizer, lambda step: lr_schedule(step) / base_learning_rate)
-
-    num_params = sum(p.numel() for p in unet.parameters())
-    print(f"[rank {accelerator.process_index}] total params: {num_params}")
-    for name, param in unet.named_parameters():
-        if torch.isnan(param).any() or torch.isinf(param).any():
-            print(f"[rank {accelerator.process_index}] NaN/Inf in {name}")
-    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)
-    
-# Регистрация хуков ПОСЛЕ prepare
-if loss_ratios.get("dispersive", 0) > 0:
-    dispersive_hook.register_hooks(unet, "down_blocks.2")
-
-# --------------------------- Фиксированные семплы для генерации ---------------------------
-fixed_samples = get_fixed_samples_by_resolution(dataset)
-
-@torch.compiler.disable()
-@torch.no_grad()
-def generate_and_save_samples(fixed_samples_cpu, step):
-    original_model = None
-    try:
-        original_model = accelerator.unwrap_model(unet, keep_torch_compile=True).eval()
-        vae.to(device=device).eval()  # временно подгружаем VAE на GPU для декодинга
-
-        scheduler.set_timesteps(n_diffusion_steps)
-        
-        all_generated_images = []
-        all_captions = [] 
-        
-        for size, (sample_latents, sample_text_embeddings, sample_text) in fixed_samples_cpu.items():
-            width, height = size
-            sample_latents = sample_latents.to(dtype=dtype, device=device)
-            sample_text_embeddings = sample_text_embeddings.to(dtype=dtype, device=device)
-
-            noise = torch.randn(
-                sample_latents.shape,
-                generator=gen,
-                device=device,
-                dtype=sample_latents.dtype
-            )
-            current_latents = noise.clone()
-            
-            if guidance_scale > 0:
-                empty_embeddings = torch.zeros_like(sample_text_embeddings, dtype=sample_text_embeddings.dtype, device=device)
-                text_embeddings_batch = torch.cat([empty_embeddings, sample_text_embeddings], dim=0)
-            else:
-                text_embeddings_batch = sample_text_embeddings
-            
-            for t in scheduler.timesteps:
-                t_batch = t.repeat(current_latents.shape[0]).to(device)
-                if guidance_scale > 0:
-                    latent_model_input = torch.cat([current_latents] * 2)
-                else:
-                    latent_model_input = current_latents
-                
-                latent_model_input_scaled = scheduler.scale_model_input(latent_model_input, t_batch)
-                noise_pred = original_model(latent_model_input_scaled, t_batch, text_embeddings_batch).sample               
-                
-                if guidance_scale > 0:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                current_latents = scheduler.step(noise_pred, t, current_latents).prev_sample
-
-            #print(current_latents.ndim, current_latents.shape)
-            #if current_latents.ndim == 4:
-            #    current_latents = current_latents.unsqueeze(2)
-            # Латент  в форме [B, C, T, H, W]
-            #print(current_latents.ndim, current_latents.shape)
-
-            # Параметры нормализации
-            latent_for_vae = current_latents.detach() * scaling_factor + shift_factor
-
-            if latents_mean!=None and latents_std!=None:
-                latent_for_vae = latent_for_vae * torch.tensor(latents_std, device=device, dtype=dtype).view(1, -1, 1, 1, 1) + torch.tensor(latents_mean, device=device, dtype=dtype).view(1, -1, 1, 1, 1)
-            
-            decoded = vae.decode(latent_for_vae.to(torch.float32)).sample
-            #decoded = decoded[:, :, 0, :, :]  # [3, H, W]
-            #print(decoded.ndim, decoded.shape)
-            
-            decoded_fp32 = decoded.to(torch.float32)
-            for img_idx, img_tensor in enumerate(decoded_fp32):
-
-                # Форма: [3, H, W] -> преобразуем в [H, W, 3]
-                img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy()
-                img = img.transpose(1, 2, 0)  # Из [3, H, W] в [H, W, 3]
-    
-                #img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy().transpose(1, 2, 0)
-                if np.isnan(img).any():
-                    print("NaNs found, saving stopped! Step:", step)
-                pil_img = Image.fromarray((img * 255).astype("uint8"))
-                
-                max_w_overall = max(s[0] for s in fixed_samples_cpu.keys())
-                max_h_overall = max(s[1] for s in fixed_samples_cpu.keys())
-                max_w_overall = max(255, max_w_overall)
-                max_h_overall = max(255, max_h_overall)
-            
-                padded_img = ImageOps.pad(pil_img, (max_w_overall, max_h_overall), color='white')
-                all_generated_images.append(padded_img)
-
-                caption_text = sample_text[img_idx][:200] if img_idx < len(sample_text) else ""
-                all_captions.append(caption_text)
-                
-                sample_path = f"{generated_folder}/{project}_{width}x{height}_{img_idx}.jpg"
-                pil_img.save(sample_path, "JPEG", quality=96)
-        
-        if use_wandb and accelerator.is_main_process:
-            wandb_images = [
-                wandb.Image(img, caption=f"{all_captions[i]}")
-                for i, img in enumerate(all_generated_images)
-            ]
-            wandb.log({"generated_images": wandb_images, "global_step": step})
-    finally:
-        # вернуть VAE на CPU (как было в твоём коде)
-        vae.to("cpu")
-        for var in list(locals().keys()):
-            if isinstance(locals()[var], torch.Tensor):
-                del locals()[var]
-        torch.cuda.empty_cache()
-        gc.collect()
-
-# --------------------------- Генерация сэмплов перед обучением ---------------------------
-if accelerator.is_main_process:
-    if save_model:
-        print("Генерация сэмплов до старта обучения...")
-        generate_and_save_samples(fixed_samples,0)
-accelerator.wait_for_everyone()
-
-# Модифицируем функцию сохранения модели для поддержки LoRA
-def save_checkpoint(unet,variant=""):
-    if accelerator.is_main_process:
-        if lora_name:
-            save_lora_checkpoint(unet)
-        else:
-            if variant!="":
-                accelerator.unwrap_model(unet.to(dtype=torch.float16)).save_pretrained(os.path.join(checkpoints_folder, f"{project}"),variant=variant)
-            else:
-                accelerator.unwrap_model(unet).save_pretrained(os.path.join(checkpoints_folder, f"{project}"))
-            unet = unet.to(dtype=dtype)
-
-def batch_pred_original_from_step(model_outputs, timesteps_tensor, noisy_latents, scheduler):
-    device = noisy_latents.device
-    dtype = noisy_latents.dtype
-
-    available_ts = scheduler.timesteps
-    if not isinstance(available_ts, torch.Tensor):
-        available_ts = torch.tensor(available_ts, device="cpu")
-    else:
-        available_ts = available_ts.cpu()
-
-    B = model_outputs.shape[0]
-    preds = []
-    for i in range(B):
-        t_i = int(timesteps_tensor[i].item())
-        diffs = torch.abs(available_ts - t_i)
-        idx = int(torch.argmin(diffs).item())
-        t_for_step = int(available_ts[idx].item())
-        model_out_i = model_outputs[i:i+1]
-        noisy_latent_i = noisy_latents[i:i+1]
-        step_out = scheduler.step(model_out_i, t_for_step, noisy_latent_i)
-        preds.append(step_out.pred_original_sample)
-
-    return torch.cat(preds, dim=0).to(device=device, dtype=dtype)
-
-# --------------------------- Тренировочный цикл ---------------------------
-if accelerator.is_main_process:
-    print(f"Total steps per GPU: {total_training_steps}")
-
-epoch_loss_points = []
-progress_bar = tqdm(total=total_training_steps, disable=not accelerator.is_local_main_process, desc="Training", unit="step")
-
-steps_per_epoch = len(dataloader)
-sample_interval = max(1, steps_per_epoch // sample_interval_share)
-min_loss = 1.
-
-for epoch in range(start_epoch, start_epoch + num_epochs):
-    batch_losses = []
-    batch_tlosses = []
-    batch_grads = []
-    batch_sampler.set_epoch(epoch)
-    accelerator.wait_for_everyone()
-    unet.train()
-    print("epoch:",epoch)
-    for step, (latents, embeddings) in enumerate(dataloader):
-        with accelerator.accumulate(unet):
-            if save_model == False and step == 5 :
-                used_gb = torch.cuda.max_memory_allocated() / 1024**3
-                print(f"Шаг {step}: {used_gb:.2f} GB")
-
-            noise = torch.randn_like(latents, dtype=latents.dtype)
-
-            progress = global_step / max(1, total_training_steps - 1)
-            timesteps = sample_timesteps_bias(
-                batch_size=latents.shape[0],
-                progress=progress,
-                num_train_timesteps=scheduler.config.num_train_timesteps,
-                steps_offset=steps_offset,
-                device=device
-            )
-
-            noisy_latents = scheduler.add_noise(latents, noise, timesteps)
-
-            if loss_ratios.get("dispersive", 0) > 0:
-                dispersive_hook.clear_activations()
-
-            #print(latents.shape,embeddings.shape)
-            model_pred = unet(noisy_latents, timesteps, embeddings).sample
-            target_pred = scheduler.get_velocity(latents, noise, timesteps)
-
-            # === Losses ===
-            losses_dict = {}
-
-            mse_loss = F.mse_loss(model_pred.float(), target_pred.float())
-            losses_dict["mse"] = mse_loss
-            losses_dict["mae"] = F.l1_loss(model_pred.float(), target_pred.float())
-
-            # CHANGED: Huber (smooth_l1) loss added
-            losses_dict["huber"] = F.smooth_l1_loss(model_pred.float(), target_pred.float())
-
-            # === Dispersive loss ===
-            if loss_ratios.get("dispersive", 0) > 0:
-                disp_raw = dispersive_hook.compute_dispersive_loss().to(device)  # может быть отрицательным
-                losses_dict["dispersive"] = dispersive_hook.weight * disp_raw
-            else:
-                losses_dict["dispersive"] = torch.tensor(0.0, device=device)
-
-            # === Нормализация всех лоссов ===
-            abs_for_norm = {k: losses_dict.get(k, torch.tensor(0.0, device=device)) for k in normalizer.ratios.keys()}
-            total_loss, coeffs, meds = normalizer.update_and_total(abs_for_norm)
-
-            # Сохраняем для логов (мы сохраняем MSE отдельно — как показатель)
-            batch_losses.append(mse_loss.detach().item())
-
-            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
-                accelerator.wait_for_everyone()
-                
-            # Backward
-            accelerator.backward(total_loss)
-
-            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
-                accelerator.wait_for_everyone()
-                
-            grad = 0.0
-            if not fbp:
-                if accelerator.sync_gradients:
-                    with torch.amp.autocast('cuda', enabled=False):
-                        grad_val = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
-                    grad = float(grad_val)
-                    optimizer.step()
-                    lr_scheduler.step()
-                    optimizer.zero_grad(set_to_none=True)
-
-            global_step += 1
-            progress_bar.update(1)
-
-            # Логируем метрики
-            if accelerator.is_main_process:
-                if fbp:
-                    current_lr = base_learning_rate
-                else:
-                    current_lr = lr_scheduler.get_last_lr()[0]
-                batch_tlosses.append(total_loss.detach().item())
-                batch_grads.append(grad)
-
-                # Логируем только активные лоссы (ratio>0)
-                active_keys = [k for k, v in loss_ratios.items() if v > 0]
-                log_data = {}
-                for k in active_keys:
-                    v = losses_dict.get(k, None)
-                    if v is None:
-                        continue
-                    log_data[f"loss/{k}"] = (v.item() if isinstance(v, torch.Tensor) else float(v))
-
-                log_data["loss/total"] = float(total_loss.item())
-                log_data["loss/lr"] = current_lr
-                for k, c in coeffs.items():
-                    log_data[f"coeff/{k}"] = float(c)
-                if use_wandb and accelerator.sync_gradients:
-                    wandb.log(log_data, step=global_step)
-
-                # Генерируем сэмплы с заданным интервалом
-                if global_step % sample_interval == 0:
-                    generate_and_save_samples(fixed_samples,global_step)
-                    last_n = sample_interval
-                    avg_loss = float(np.mean(batch_losses[-last_n:])) if len(batch_losses) > 0 else 0.0
-                    avg_tloss = float(np.mean(batch_tlosses[-last_n:])) if len(batch_tlosses) > 0 else 0.0
-                    avg_grad = float(np.mean(batch_grads[-last_n:])) if len(batch_grads) > 0 else 0.0
-                    print(f"Эпоха {epoch}, шаг {global_step}, средний лосс: {avg_loss:.6f}, grad: {avg_grad:.6f}")
-
-                    if save_model:
-                        print("saving:",avg_loss < min_loss*save_barrier)
-                        if avg_loss < min_loss*save_barrier:
-                            min_loss = avg_loss
-                            save_checkpoint(unet)
-                    if use_wandb:
-                        avg_data = {}
-                        avg_data["avg/loss"] = avg_loss
-                        avg_data["avg/tloss"] = avg_tloss
-                        avg_data["avg/grad"] = avg_grad
-                        wandb.log(avg_data, step=global_step)
-
-    if accelerator.is_main_process:
-        avg_epoch_loss = np.mean(batch_losses) if len(batch_losses)>0 else 0.0
-        print(f"\nЭпоха {epoch} завершена. Средний лосс: {avg_epoch_loss:.6f}")
-        if use_wandb:
-            wandb.log({"epoch_loss": avg_epoch_loss, "epoch": epoch+1})
-
-# Завершение обучения - сохраняем финальную модель
-if loss_ratios.get("dispersive", 0) > 0:
-    dispersive_hook.remove_hooks()
-if accelerator.is_main_process:
-    print("Обучение завершено! Сохраняем финальную модель...")
-    if save_model:
-        save_checkpoint(unet,"fp16")
-accelerator.free_memory()
-if torch.distributed.is_initialized():
-    torch.distributed.destroy_process_group()
-    
-print("Готово!")

unet/diffusion_pytorch_model.safetensors

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