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api/ltx_server.py

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+# ltx_server.py — VideoService (beta 1.1)
+# Sempre output_type="latent"; no final: VAE (bloco inteiro) → pixels → MP4.
+# Ignora UserWarning/FutureWarning e injeta VAE no manager com dtype/device corretos.
+
+# --- 0. WARNINGS E AMBIENTE ---
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", message=".*")
+
+from huggingface_hub import logging
+
+logging.set_verbosity_error()
+logging.set_verbosity_warning()
+logging.set_verbosity_info()
+logging.set_verbosity_debug()
+
+
+LTXV_DEBUG=1
+LTXV_FRAME_LOG_EVERY=8
+
+
+
+# --- 1. IMPORTAÇÕES ---
+import os, subprocess, shlex, tempfile
+import torch
+import json
+import numpy as np
+import random
+import os
+import shlex
+import yaml
+from typing import List, Dict
+from pathlib import Path
+import imageio
+import tempfile
+from huggingface_hub import hf_hub_download
+import sys
+import subprocess
+import gc
+import shutil
+import contextlib
+import time
+import traceback
+from einops import rearrange
+import torch.nn.functional as F
+
+# Singletons (versões simples)
+from managers.vae_manager import vae_manager_singleton
+from tools.video_encode_tool import video_encode_tool_singleton
+
+# --- 2. GERENCIAMENTO DE DEPENDÊNCIAS E SETUP ---
+def _query_gpu_processes_via_nvml(device_index: int) -> List[Dict]:
+    try:
+        import psutil
+        import pynvml as nvml
+        nvml.nvmlInit()
+        handle = nvml.nvmlDeviceGetHandleByIndex(device_index)
+        try:
+            procs = nvml.nvmlDeviceGetComputeRunningProcesses_v3(handle)
+        except Exception:
+            procs = nvml.nvmlDeviceGetComputeRunningProcesses(handle)
+        results = []
+        for p in procs:
+            pid = int(p.pid)
+            used_mb = None
+            try:
+                if getattr(p, "usedGpuMemory", None) is not None and p.usedGpuMemory not in (0,):
+                    used_mb = max(0, int(p.usedGpuMemory) // (1024 * 1024))
+            except Exception:
+                used_mb = None
+            name = "unknown"
+            user = "unknown"
+            try:
+                import psutil
+                pr = psutil.Process(pid)
+                name = pr.name()
+                user = pr.username()
+            except Exception:
+                pass
+            results.append({"pid": pid, "name": name, "user": user, "used_mb": used_mb})
+        nvml.nvmlShutdown()
+        return results
+    except Exception:
+        return []
+
+def _query_gpu_processes_via_nvidiasmi(device_index: int) -> List[Dict]:
+    cmd = f"nvidia-smi -i {device_index} --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits"
+    try:
+        out = subprocess.check_output(shlex.split(cmd), stderr=subprocess.STDOUT, text=True, timeout=2.0)
+    except Exception:
+        return []
+    results = []
+    for line in out.strip().splitlines():
+        parts = [p.strip() for p in line.split(",")]
+        if len(parts) >= 3:
+            try:
+                pid = int(parts[0]); name = parts[1]; used_mb = int(parts[2])
+                user = "unknown"
+                try:
+                    import psutil
+                    pr = psutil.Process(pid)
+                    user = pr.username()
+                except Exception:
+                    pass
+                results.append({"pid": pid, "name": name, "user": user, "used_mb": used_mb})
+            except Exception:
+                continue
+    return results
+
+
+
+def calculate_new_dimensions(orig_w, orig_h, divisor=8):
+    """
+    Calcula novas dimensões mantendo a proporção, garantindo que ambos os
+    lados sejam divisíveis pelo divisor especificado (padrão 8).
+    """
+    if orig_w == 0 or orig_h == 0:
+        # Retorna um valor padrão seguro
+        return 512, 512
+
+    # Preserva a orientação (paisagem vs. retrato)
+    if orig_w >= orig_h:
+        # Paisagem ou quadrado
+        aspect_ratio = orig_w / orig_h
+        # Começa com uma altura base e calcula a largura
+        new_h = 512 # Altura base para paisagem
+        new_w = new_h * aspect_ratio
+    else:
+        # Retrato
+        aspect_ratio = orig_h / orig_w
+        # Começa com uma largura base e calcula a altura
+        new_w = 512 # Largura base para retrato
+        new_h = new_w * aspect_ratio
+        
+    # Arredonda AMBOS os valores para o múltiplo mais próximo do divisor
+    final_w = int(round(new_w / divisor)) * divisor
+    final_h = int(round(new_h / divisor)) * divisor
+
+    # Garante que as dimensões não sejam zero após o arredondamento
+    final_w = max(divisor, final_w)
+    final_h = max(divisor, final_h)
+
+    print(f"[Dimension Calc] Original: {orig_w}x{orig_h} -> Calculado: {new_w:.0f}x{new_h:.0f} -> Final (divisível por {divisor}): {final_w}x{final_h}")
+    return final_h, final_w # Retorna (altura, largura)
+
+
+def handle_media_upload_for_dims(filepath, current_h, current_w):
+    """
+    Esta função agora usará o novo cálculo robusto.
+    (O corpo desta função não precisa de alterações, pois ela já chama a função de cálculo)
+    """
+    if not filepath or not os.path.exists(str(filepath)):
+        return gr.update(value=current_h), gr.update(value=current_w)
+    try:
+        if str(filepath).lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
+            with Image.open(filepath) as img:
+                orig_w, orig_h = img.size
+        else: # Assumir que é um vídeo
+            with imageio.get_reader(filepath) as reader:
+                meta = reader.get_meta_data()
+                orig_w, orig_h = meta.get('size', (current_w, current_h))
+        
+        # Chama a nova função corrigida
+        new_h, new_w = calculate_new_dimensions(orig_w, orig_h)
+        
+        return gr.update(value=new_h), gr.update(value=new_w)
+    except Exception as e:
+        print(f"Erro ao processar mídia para dimensões: {e}")
+        return gr.update(value=current_h), gr.update(value=current_w)
+
+
+def _gpu_process_table(processes: List[Dict], current_pid: int) -> str:
+    if not processes:
+        return "  - Processos ativos: (nenhum)\n"
+    processes = sorted(processes, key=lambda x: (x.get("used_mb") or 0), reverse=True)
+    lines = ["  - Processos ativos (PID | USER | NAME | VRAM MB):"]
+    for p in processes:
+        star = "*" if p["pid"] == current_pid else " "
+        used_str = str(p["used_mb"]) if p.get("used_mb") is not None else "N/A"
+        lines.append(f"    {star} {p['pid']} | {p['user']} | {p['name']} | {used_str}")
+    return "\n".join(lines) + "\n"
+
+def run_setup():
+    setup_script_path = "setup.py"
+    if not os.path.exists(setup_script_path):
+        print("[DEBUG] 'setup.py' não encontrado. Pulando clonagem de dependências.")
+        return
+    try:
+        print("[DEBUG] Executando setup.py para dependências...")
+        subprocess.run([sys.executable, setup_script_path], check=True)
+        print("[DEBUG] Setup concluído com sucesso.")
+    except subprocess.CalledProcessError as e:
+        print(f"[DEBUG] ERRO no setup.py (code {e.returncode}). Abortando.")
+        sys.exit(1)
+
+from api.ltx.inference import (
+    create_ltx_video_pipeline,
+    create_latent_upsampler,
+    load_image_to_tensor_with_resize_and_crop,
+    seed_everething,
+    calculate_padding,
+    load_media_file,
+)
+
+DEPS_DIR = Path("/data")
+LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
+if not LTX_VIDEO_REPO_DIR.exists():
+    print(f"[DEBUG] Repositório não encontrado em {LTX_VIDEO_REPO_DIR}. Rodando setup...")
+    run_setup()
+
+def add_deps_to_path():
+    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
+    if str(LTX_VIDEO_REPO_DIR.resolve()) not in sys.path:
+        sys.path.insert(0, repo_path)
+        print(f"[DEBUG] Repo adicionado ao sys.path: {repo_path}")
+
+add_deps_to_path()
+
+# --- 3. IMPORTAÇÕES ESPECÍFICAS DO MODELO ---
+
+from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXMultiScalePipeline
+from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
+from ltx_video.models.autoencoders.vae_encode import un_normalize_latents, normalize_latents
+from ltx_video.pipelines.pipeline_ltx_video import adain_filter_latent
+
+
+# --- 4. FUNÇÕES HELPER DE LOG ---
+def log_tensor_info(tensor, name="Tensor"):
+    if not isinstance(tensor, torch.Tensor):
+        print(f"\n[INFO] '{name}' não é tensor.")
+        return
+    print(f"\n--- Tensor: {name} ---")
+    print(f"  - Shape: {tuple(tensor.shape)}")
+    print(f"  - Dtype: {tensor.dtype}")
+    print(f"  - Device: {tensor.device}")
+    if tensor.numel() > 0:
+        try:
+            print(f"  - Min: {tensor.min().item():.4f}  Max: {tensor.max().item():.4f}  Mean: {tensor.mean().item():.4f}")
+        except Exception:
+            pass
+    print("------------------------------------------\n")
+
+
+
+
+
+# --- 5. CLASSE PRINCIPAL DO SERVIÇO ---
+class VideoService:
+    def __init__(self):
+        t0 = time.perf_counter()
+        print("[DEBUG] Inicializando VideoService...")
+        self.debug = os.getenv("LTXV_DEBUG", "1") == "1"
+        self.frame_log_every = int(os.getenv("LTXV_FRAME_LOG_EVERY", "8"))
+        self.config = self._load_config()
+        print(f"[DEBUG] Config carregada (precision={self.config.get('precision')}, sampler={self.config.get('sampler')})")
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"[DEBUG] Device selecionado: {self.device}")
+        self.last_memory_reserved_mb = 0.0
+        self._tmp_dirs = set(); self._tmp_files = set(); self._last_outputs = []
+
+        self.pipeline, self.latent_upsampler = self._load_models()
+        print(f"[DEBUG] Pipeline e Upsampler carregados. Upsampler ativo? {bool(self.latent_upsampler)}")
+
+        print(f"[DEBUG] Movendo modelos para {self.device}...")
+        self.pipeline.to(self.device)
+        if self.latent_upsampler:
+            self.latent_upsampler.to(self.device)
+
+        self._apply_precision_policy()
+        print(f"[DEBUG] runtime_autocast_dtype = {getattr(self, 'runtime_autocast_dtype', None)}")
+
+        # Injeta pipeline/vae no manager (impede vae=None)
+        vae_manager_singleton.attach_pipeline(
+            self.pipeline,
+            device=self.device,
+            autocast_dtype=self.runtime_autocast_dtype
+        )
+        print(f"[DEBUG] VAE manager conectado: has_vae={hasattr(self.pipeline, 'vae')} device={self.device}")
+
+        if self.device == "cuda":
+            torch.cuda.empty_cache()
+            self._log_gpu_memory("Após carregar modelos")
+
+        print(f"[DEBUG] VideoService pronto. boot_time={time.perf_counter()-t0:.3f}s")
+
+    def _log_gpu_memory(self, stage_name: str):
+        if self.device != "cuda":
+            return
+        device_index = torch.cuda.current_device() if torch.cuda.is_available() else 0
+        current_reserved_b = torch.cuda.memory_reserved(device_index)
+        current_reserved_mb = current_reserved_b / (1024 ** 2)
+        total_memory_b = torch.cuda.get_device_properties(device_index).total_memory
+        total_memory_mb = total_memory_b / (1024 ** 2)
+        peak_reserved_mb = torch.cuda.max_memory_reserved(device_index) / (1024 ** 2)
+        delta_mb = current_reserved_mb - getattr(self, "last_memory_reserved_mb", 0.0)
+        processes = _query_gpu_processes_via_nvml(device_index) or _query_gpu_processes_via_nvidiasmi(device_index)
+        print(f"\n--- [LOG GPU] {stage_name} (cuda:{device_index}) ---")
+        print(f"  - Reservado: {current_reserved_mb:.2f} MB / {total_memory_mb:.2f} MB  (Δ={delta_mb:+.2f} MB)")
+        if peak_reserved_mb > getattr(self, "last_memory_reserved_mb", 0.0):
+            print(f"  - Pico reservado (nesta fase): {peak_reserved_mb:.2f} MB")
+        print(_gpu_process_table(processes, os.getpid()), end="")
+        print("--------------------------------------------------\n")
+        self.last_memory_reserved_mb = current_reserved_mb
+
+    def _register_tmp_dir(self, d: str):
+        if d and os.path.isdir(d):
+            self._tmp_dirs.add(d); print(f"[DEBUG] Registrado tmp dir: {d}")
+
+    def _register_tmp_file(self, f: str):
+        if f and os.path.exists(f):
+            self._tmp_files.add(f); print(f"[DEBUG] Registrado tmp file: {f}")
+
+    def finalize(self, keep_paths=None, extra_paths=None, clear_gpu=True):
+        print("[DEBUG] Finalize: iniciando limpeza...")
+        keep = set(keep_paths or []); extras = set(extra_paths or [])
+        removed_files = 0
+        for f in list(self._tmp_files | extras):
+            try:
+                if f not in keep and os.path.isfile(f):
+                    os.remove(f); removed_files += 1; print(f"[DEBUG] Removido arquivo tmp: {f}")
+            except Exception as e:
+                print(f"[DEBUG] Falha removendo arquivo {f}: {e}")
+            finally:
+                self._tmp_files.discard(f)
+        removed_dirs = 0
+        for d in list(self._tmp_dirs):
+            try:
+                if d not in keep and os.path.isdir(d):
+                    shutil.rmtree(d, ignore_errors=True); removed_dirs += 1; print(f"[DEBUG] Removido diretório tmp: {d}")
+            except Exception as e:
+                print(f"[DEBUG] Falha removendo diretório {d}: {e}")
+            finally:
+                self._tmp_dirs.discard(d)
+        print(f"[DEBUG] Finalize: arquivos removidos={removed_files}, dirs removidos={removed_dirs}")
+        gc.collect()
+        try:
+            if clear_gpu and torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                try:
+                    torch.cuda.ipc_collect()
+                except Exception:
+                    pass
+        except Exception as e:
+            print(f"[DEBUG] Finalize: limpeza GPU falhou: {e}")
+        try:
+            self._log_gpu_memory("Após finalize")
+        except Exception as e:
+            print(f"[DEBUG] Log GPU pós-finalize falhou: {e}")
+
+    def _load_config(self):
+        base = LTX_VIDEO_REPO_DIR / "configs"
+        candidates = [
+            base / "ltxv-13b-0.9.8-dev-fp8.yaml",
+            base / "ltxv-13b-0.9.8-distilled-fp8.yaml",
+            base / "ltxv-13b-0.9.8-distilled.yaml",
+        ]
+        for cfg in candidates:
+            if cfg.exists():
+                print(f"[DEBUG] Config selecionada: {cfg}")
+                with open(cfg, "r") as file:
+                    return yaml.safe_load(file)
+        cfg = base / "ltxv-13b-0.9.8-distilled-fp8.yaml"
+        print(f"[DEBUG] Config fallback: {cfg}")
+        with open(cfg, "r") as file:
+            return yaml.safe_load(file)
+
+    def _load_models(self):
+        t0 = time.perf_counter()
+        LTX_REPO = "Lightricks/LTX-Video"
+        print("[DEBUG] Baixando checkpoint principal...")
+        distilled_model_path = hf_hub_download(
+            repo_id=LTX_REPO,
+            filename=self.config["checkpoint_path"],
+            local_dir=os.getenv("HF_HOME"),
+            cache_dir=os.getenv("HF_HOME_CACHE"),
+            token=os.getenv("HF_TOKEN"),
+        )
+        self.config["checkpoint_path"] = distilled_model_path
+        print(f"[DEBUG] Checkpoint em: {distilled_model_path}")
+
+        print("[DEBUG] Baixando upscaler espacial...")
+        spatial_upscaler_path = hf_hub_download(
+            repo_id=LTX_REPO,
+            filename=self.config["spatial_upscaler_model_path"],
+            local_dir=os.getenv("HF_HOME"),
+            cache_dir=os.getenv("HF_HOME_CACHE"),
+            token=os.getenv("HF_TOKEN")
+        )
+        self.config["spatial_upscaler_model_path"] = spatial_upscaler_path
+        print(f"[DEBUG] Upscaler em: {spatial_upscaler_path}")
+
+        print("[DEBUG] Construindo pipeline...")
+        pipeline = create_ltx_video_pipeline(
+            ckpt_path=self.config["checkpoint_path"],
+            precision=self.config["precision"],
+            text_encoder_model_name_or_path=self.config["text_encoder_model_name_or_path"],
+            sampler=self.config["sampler"],
+            device="cpu",
+            enhance_prompt=False,
+            prompt_enhancer_image_caption_model_name_or_path=self.config["prompt_enhancer_image_caption_model_name_or_path"],
+            prompt_enhancer_llm_model_name_or_path=self.config["prompt_enhancer_llm_model_name_or_path"],
+        )
+        print("[DEBUG] Pipeline pronto.")
+
+        latent_upsampler = None
+        if self.config.get("spatial_upscaler_model_path"):
+            print("[DEBUG] Construindo latent_upsampler...")
+            latent_upsampler = create_latent_upsampler(self.config["spatial_upscaler_model_path"], device="cpu")
+            print("[DEBUG] Upsampler pronto.")
+        print(f"[DEBUG] _load_models() tempo total={time.perf_counter()-t0:.3f}s")
+        return pipeline, latent_upsampler
+
+    def _promote_fp8_weights_to_bf16(self, module):
+        if not isinstance(module, torch.nn.Module):
+            print("[DEBUG] Promoção FP8→BF16 ignorada: alvo não é nn.Module.")
+            return
+        f8 = getattr(torch, "float8_e4m3fn", None)
+        if f8 is None:
+            print("[DEBUG] torch.float8_e4m3fn indisponível.")
+            return
+        p_cnt = b_cnt = 0
+        for _, p in module.named_parameters(recurse=True):
+            try:
+                if p.dtype == f8:
+                    with torch.no_grad():
+                        p.data = p.data.to(torch.bfloat16); p_cnt += 1
+            except Exception:
+                pass
+        for _, b in module.named_buffers(recurse=True):
+            try:
+                if hasattr(b, "dtype") and b.dtype == f8:
+                    b.data = b.data.to(torch.bfloat16); b_cnt += 1
+            except Exception:
+                pass
+        print(f"[DEBUG] FP8→BF16: params_promoted={p_cnt}, buffers_promoted={b_cnt}")
+
+
+
+    @torch.no_grad()
+    def _upsample_latents_internal(self, latents: torch.Tensor) -> torch.Tensor:
+        """
+        Lógica extraída diretamente da LTXMultiScalePipeline para upscale de latentes.
+        """
+        if not self.latent_upsampler:
+            raise ValueError("Latent Upsampler não está carregado.")
+
+        # Garante que os modelos estejam no dispositivo correto
+        self.latent_upsampler.to(self.device)
+        self.pipeline.vae.to(self.device)
+        print(f"[DEBUG-UPSAMPLE] Shape de entrada: {tuple(latents.shape)}")
+        latents = un_normalize_latents(latents, self.pipeline.vae, vae_per_channel_normalize=True)
+        upsampled_latents = self.latent_upsampler(latents)
+        upsampled_latents = normalize_latents(upsampled_latents, self.pipeline.vae, vae_per_channel_normalize=True)
+        print(f"[DEBUG-UPSAMPLE] Shape de saída: {tuple(upsampled_latents.shape)}")
+        
+        return upsampled_latents
+        
+
+    
+    def _apply_precision_policy(self):
+        prec = str(self.config.get("precision", "")).lower()
+        self.runtime_autocast_dtype = torch.float32
+        print(f"[DEBUG] Aplicando política de precisão: {prec}")
+        if prec == "float8_e4m3fn":
+            self.runtime_autocast_dtype = torch.bfloat16
+            force_promote = os.getenv("LTXV_FORCE_BF16_ON_FP8", "0") == "1"
+            print(f"[DEBUG] FP8 detectado. force_promote={force_promote}")
+            if force_promote and hasattr(torch, "float8_e4m3fn"):
+                try:
+                    self._promote_fp8_weights_to_bf16(self.pipeline)
+                except Exception as e:
+                    print(f"[DEBUG] Promoção FP8→BF16 na pipeline falhou: {e}")
+                try:
+                    if self.latent_upsampler:
+                        self._promote_fp8_weights_to_bf16(self.latent_upsampler)
+                except Exception as e:
+                    print(f"[DEBUG] Promoção FP8→BF16 no upsampler falhou: {e}")
+        elif prec == "bfloat16":
+            self.runtime_autocast_dtype = torch.bfloat16
+        elif prec == "mixed_precision":
+            self.runtime_autocast_dtype = torch.float16
+        else:
+            self.runtime_autocast_dtype = torch.float32
+
+    def _prepare_conditioning_tensor(self, filepath, height, width, padding_values):
+        print(f"[DEBUG] Carregando condicionamento: {filepath}")
+        tensor = load_image_to_tensor_with_resize_and_crop(filepath, height, width)
+        tensor = torch.nn.functional.pad(tensor, padding_values)
+        out = tensor.to(self.device, dtype=self.runtime_autocast_dtype) if self.device == "cuda" else tensor.to(self.device)
+        print(f"[DEBUG] Cond shape={tuple(out.shape)} dtype={out.dtype} device={out.device}")
+        return out
+
+
+    def _dividir_latentes_por_tamanho(self, latents_brutos, num_latente_por_chunk: int, overlap: int = 1):
+        """
+        Divide o tensor de latentes em chunks com tamanho definido em número de latentes.
+        
+        Args:
+            latents_brutos: tensor [B, C, T, H, W]
+            num_latente_por_chunk: número de latentes por chunk
+            overlap: número de frames que se sobrepõem entre chunks
+        
+        Returns:
+            List[tensor]: lista de chunks cloneados
+        """
+        sum_latent = latents_brutos.shape[2]
+        chunks = []
+    
+        if num_latente_por_chunk >= sum_latent:
+            return [latents_brutos]
+    
+        n_chunks = (sum_latent) // num_latente_por_chunk 
+        steps = sum_latent//n_chunks
+        print("================PODA CAUSAL=================")
+        print(f"[DEBUG] TOTAL LATENTES = {sum_latent}")
+        print(f"[DEBUG] LATENTES min por chunk = {num_latente_por_chunk}")
+        print(f"[DEBUG] Número de chunks = {n_chunks}")
+        if n_chunks > 1:
+            i=0
+            while i < n_chunks:
+                start = (num_latente_por_chunk*i)
+                end = (start+num_latente_por_chunk+overlap)
+                if i+1 < n_chunks:
+                    chunk = latents_brutos[:, :, start:end, :, :].clone().detach()
+                    print(f"[DEBUG] chunk{i+1}[:, :, {start}:{end}, :, :] = {chunk.shape[2]}")
+                else:
+                    chunk = latents_brutos[:, :, start:, :, :].clone().detach()
+                    print(f"[DEBUG] chunk{i+1}[:, :, {start}:, :, :] = {chunk.shape[2]}")
+                chunks.append(chunk)
+                i+=1
+        else:
+            print(f"[DEBUG] numero chunks minimo ")
+            print(f"[DEBUG] latents_brutos[:, :, :, :, :] = {latents_brutos.shape[2]}")
+            chunks.append(latents_brutos)
+        print("================PODA CAUSAL=================")
+        return chunks
+   
+    def _get_total_frames(self, video_path: str) -> int:
+        cmd = [
+            "ffprobe",
+            "-v", "error",
+            "-select_streams", "v:0",
+            "-count_frames",
+            "-show_entries", "stream=nb_read_frames",
+            "-of", "default=nokey=1:noprint_wrappers=1",
+            video_path
+        ]
+        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
+        return int(result.stdout.strip())    
+
+    def _gerar_lista_com_transicoes(self, pasta: str, video_paths: list[str], crossfade_frames: int = 8) -> list[str]:
+        """
+        Gera uma nova lista de vídeos aplicando transições suaves (blend frame a frame)
+        seguindo exatamente a lógica linear de Carlos.
+        """
+        import os, subprocess, shutil
+    
+        poda = crossfade_frames
+        total_partes = len(video_paths)
+        video_fade_fim = None
+        video_fade_ini = None
+        nova_lista = []
+
+        print("===========CONCATECAO CAUSAL=============")
+     
+        print(f"[DEBUG] Iniciando pipeline com {total_partes} vídeos e {poda} frames de crossfade")
+    
+        for i in range(total_partes):
+            base = video_paths[i]
+        
+            # --- PODA ---
+            video_podado = os.path.join(pasta, f"{base}_podado_{i}.mp4")
+            
+
+            if i<total_partes-1:
+                end_frame = self._get_total_frames(base) - poda
+            else:
+                end_frame = self._get_total_frames(base)
+            
+            if i>0:
+                start_frame = poda
+            else:
+                start_frame = 0
+                
+            cmd_fim = (
+               f'ffmpeg -y -hide_banner -loglevel error -i "{base}" '
+               f'-vf "trim=start_frame={start_frame}:end_frame={end_frame},setpts=PTS-STARTPTS" '
+               f'-an "{video_podado}"'
+            )
+            subprocess.run(cmd_fim, shell=True, check=True)
+            
+        
+            # --- FADE_INI --- 
+            if i > 0:
+                video_fade_ini = os.path.join(pasta, f"{base}_fade_ini_{i}.mp4")
+                cmd_ini = (
+                    f'ffmpeg -y -hide_banner -loglevel error -i "{base}" '
+                    f'-vf "trim=end_frame={poda},setpts=PTS-STARTPTS" -an "{video_fade_ini}"'
+                )
+                subprocess.run(cmd_ini, shell=True, check=True)
+                
+            # --- TRANSIÇÃO ---
+            if video_fade_fim and video_fade_ini:
+                video_fade  = os.path.join(pasta, f"transicao_{i}_{i+1}.mp4")
+                cmd_blend = (
+                    f'ffmpeg -y -hide_banner -loglevel error '
+                    f'-i "{video_fade_fim}" -i "{video_fade_ini}" '
+                    f'-filter_complex "[0:v][1:v]blend=all_expr=\'A*(1-T/{poda})+B*(T/{poda})\',format=yuv420p" '
+                    f'-frames:v {poda} "{video_fade}"'
+                )
+                subprocess.run(cmd_blend, shell=True, check=True)
+                print(f"[DEBUG] transicao adicionada {i}/{i+1} {self._get_total_frames(video_fade)} frames ✅")
+                nova_lista.append(video_fade)
+                
+            # --- FADE_FIM ---
+            if i<=total_partes-1:
+                video_fade_fim = os.path.join(pasta, f"{base}_fade_fim_{i}.mp4")
+                cmd_fim = (
+                    f'ffmpeg -y -hide_banner -loglevel error -i "{base}" '
+                    f'-vf "trim=start_frame={end_frame-poda},setpts=PTS-STARTPTS" -an "{video_fade_fim}"'
+                )
+                subprocess.run(cmd_fim, shell=True, check=True)
+           
+            nova_lista.append(video_podado)
+            print(f"[DEBUG] Video podado {i+1} adicionado {self._get_total_frames(video_podado)} frames ✅")
+               
+
+
+        print("===========CONCATECAO CAUSAL=============")
+        print(f"[DEBUG] {nova_lista}")
+        return nova_lista
+        
+    def _concat_mp4s_no_reencode(self, mp4_list: List[str], out_path: str):
+        """
+        Concatena múltiplos MP4s sem reencode usando o demuxer do ffmpeg.
+        ATENÇÃO: todos os arquivos precisam ter mesmo codec, fps, resolução etc.
+        """
+        if not mp4_list or len(mp4_list) < 2:
+            raise ValueError("Forneça pelo menos dois arquivos MP4 para concatenar.")
+
+        
+        # Cria lista temporária para o ffmpeg
+        with tempfile.NamedTemporaryFile("w", delete=False, suffix=".txt") as f:
+            for mp4 in mp4_list:
+                f.write(f"file '{os.path.abspath(mp4)}'\n")
+            list_path = f.name
+    
+        cmd = f"ffmpeg -y -f concat -safe 0 -i {list_path} -c copy {out_path}"
+        print(f"[DEBUG] Concat: {cmd}")
+    
+        try:
+            subprocess.check_call(shlex.split(cmd))
+        finally:
+            try:
+                os.remove(list_path)
+            except Exception:
+                pass   
+   
+
+    # ==============================================================================
+    # --- FUNÇÃO GENERATE COMPLETA E ATUALIZADA ---
+    # ==============================================================================
+    def generate(
+        self,
+        prompt,
+        negative_prompt,
+        mode="text-to-video",
+        start_image_filepath=None,
+        middle_image_filepath=None,
+        middle_frame_number=None,
+        middle_image_weight=1.0,
+        end_image_filepath=None,
+        end_image_weight=1.0,
+        input_video_filepath=None,
+        height=512,
+        width=704,
+        duration=2.0,
+        frames_to_use=9,
+        seed=42,
+        randomize_seed=True,
+        guidance_scale=3.0,
+        improve_texture=True,
+        progress_callback=None,
+        external_decode=True,
+    ):
+        t_all = time.perf_counter()
+        print(f"[DEBUG] generate() begin mode={mode} external_decode={external_decode} improve_texture={improve_texture}")
+        if self.device == "cuda":
+            torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats()
+        self._log_gpu_memory("Início da Geração")
+
+        # --- Setup Inicial (como antes) ---
+        if mode == "image-to-video" and not start_image_filepath:
+            raise ValueError("A imagem de início é obrigatória para o modo image-to-video")
+        used_seed = random.randint(0, 2**32 - 1) if randomize_seed else int(seed)
+        seed_everething(used_seed); print(f"[DEBUG] Seed usado: {used_seed}")
+        FPS = 24.0; MAX_NUM_FRAMES = 2570
+        target_frames_rounded = round(duration * FPS)
+        n_val = round((float(target_frames_rounded) - 1.0) / 8.0)
+        actual_num_frames = max(9, min(MAX_NUM_FRAMES, int(n_val * 8 + 1)))
+        height_padded = ((height - 1) // 8 + 1) * 8
+        width_padded = ((width - 1) // 8 + 1) * 8
+        padding_values = calculate_padding(height, width, height_padded, width_padded)
+        generator = torch.Generator(device=self.device).manual_seed(used_seed)
+        
+        conditioning_items = []
+        if mode == "image-to-video":
+            start_tensor = self._prepare_conditioning_tensor(start_image_filepath, height, width, padding_values)
+            conditioning_items.append(ConditioningItem(start_tensor, 0, 1.0))
+            if middle_image_filepath and middle_frame_number is not None:
+                middle_tensor = self._prepare_conditioning_tensor(middle_image_filepath, height, width, padding_values)
+                safe_middle_frame = max(0, min(int(middle_frame_number), actual_num_frames - 1))
+                conditioning_items.append(ConditioningItem(middle_tensor, safe_middle_frame, float(middle_image_weight)))
+            if end_image_filepath:
+                end_tensor = self._prepare_conditioning_tensor(end_image_filepath, height, width, padding_values)
+                last_frame_index = actual_num_frames - 1
+                conditioning_items.append(ConditioningItem(end_tensor, last_frame_index, float(end_image_weight)))
+            print(f"[DEBUG] Conditioning items: {len(conditioning_items)}")
+
+        call_kwargs = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "height": height_padded,
+            "width": width_padded,
+            "num_frames": actual_num_frames,
+            "frame_rate": int(FPS),
+            "generator": generator,
+            "output_type": "latent",
+            "conditioning_items": conditioning_items if conditioning_items else None,
+            "media_items": None,
+            "decode_timestep": self.config["decode_timestep"],
+            "decode_noise_scale": self.config["decode_noise_scale"],
+            "stochastic_sampling": self.config["stochastic_sampling"],
+            "image_cond_noise_scale": 0.01,
+            "is_video": True,
+            "vae_per_channel_normalize": True,
+            "mixed_precision": (self.config["precision"] == "mixed_precision"),
+            "offload_to_cpu": False,
+            "enhance_prompt": False,
+            "skip_layer_strategy": SkipLayerStrategy.AttentionValues,
+        }
+        print(f"[DEBUG] output_type={call_kwargs['output_type']} skip_layer_strategy={call_kwargs['skip_layer_strategy']}")
+
+        latents = None
+        latents_list = []
+        results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
+            
+
+        try:
+            ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext()
+            with ctx:
+                if improve_texture:
+                    if not self.latent_upsampler:
+                        raise ValueError("Upscaler espacial não carregado, mas 'improve_texture' está ativo.")
+                    
+                    # --- ETAPA 1: GERAÇÃO BASE (FIRST PASS) ---
+                    print("\n--- INICIANDO ETAPA 1: GERAÇÃO BASE (FIRST PASS) ---")
+                    t_pass1 = time.perf_counter()
+
+                    first_pass_config = self.config.get("first_pass", {}).copy()
+                    downscale_factor = self.config.get("downscale_factor", 0.6666666)
+                    vae_scale_factor = self.pipeline.vae_scale_factor # Geralmente 8
+                    
+                    # --- <INÍCIO DA LÓGICA DE CÁLCULO EXATA> ---
+                    # Replica a fórmula da LTXMultiScalePipeline
+                    x_width = int(width_padded * downscale_factor)
+                    downscaled_width = x_width - (x_width % vae_scale_factor)
+                    x_height = int(height_padded * downscale_factor)
+                    downscaled_height = x_height - (x_height % vae_scale_factor)
+                    print(f"[DEBUG] First Pass Dims: Original Pad ({width_padded}x{height_padded}) -> Downscaled ({downscaled_width}x{downscaled_height})")
+                    # --- <FIM DA LÓGICA DE CÁLCULO EXATA> ---
+
+                    first_pass_kwargs = call_kwargs.copy()
+    
+                    first_pass_kwargs.update({
+                        "output_type": "latent",
+                        "width": downscaled_width,
+                        "height": downscaled_height,
+                        "guidance_scale": float(guidance_scale),
+                        **first_pass_config
+                    })
+                    
+                    print(f"[DEBUG] First Pass: Gerando em {downscaled_width}x{downscaled_height}...")
+                    base_latents = self.pipeline(**first_pass_kwargs).images
+                    log_tensor_info(base_latents, "Latentes Base (First Pass)")
+                    print(f"[DEBUG] First Pass concluída em {time.perf_counter() - t_pass1:.2f}s")
+                    
+                    # --- ETAPA 2: UPSCALE DOS LATENTES ---
+                    print("\n--- INICIANDO ETAPA 2: UPSCALE DOS LATENTES ---")
+                    t_upscale = time.perf_counter()
+                    
+                    upsampled_latents = self._upsample_latents_internal(base_latents)
+                    upsampled_latents = adain_filter_latent(latents=upsampled_latents, reference_latents=base_latents)
+                    log_tensor_info(upsampled_latents, "Latentes Pós-Upscale")
+                    print(f"[DEBUG] Upscale de Latentes concluído em {time.perf_counter() - t_upscale:.2f}s")
+                    del base_latents; gc.collect(); torch.cuda.empty_cache()
+
+                    par = 0
+                    latents_cpu_up = upsampled_latents.detach().to("cpu", non_blocking=True)
+                    torch.cuda.empty_cache()
+                    try:
+                         torch.cuda.ipc_collect()
+                    except Exception:
+                         pass
+                
+                    latents_parts_up = self._dividir_latentes_por_tamanho(latents_cpu_up,4,1)
+                    
+                    for latents in latents_parts_up:
+               
+                        # # --- ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---
+                        print("\n--- INICIANDO ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---")
+                
+                        second_pass_config = self.config.get("second_pass", {}).copy()
+                        # --- <INÍCIO DA LÓGICA DE CÁLCULO EXATA PARA SECOND PASS> ---
+                        # Usa as dimensões da primeira passagem dobradas, como na pipeline original
+                        second_pass_width = downscaled_width * 2
+                        second_pass_height = downscaled_height * 2
+                        print(f"[DEBUG] Second Pass Dims: Target ({second_pass_width}x{second_pass_height})")
+                        # --- <FIM DA LÓGICA DE CÁLCULO EXATA> --- 
+                        t_pass2 = time.perf_counter()
+
+                        vae_temporal_scale = self.pipeline.video_scale_factor # Geralmente 4 ou 8
+                        num_pixel_frames_part = ((latents.shape[2] - 1) * vae_temporal_scale) + 1
+                        print(f"[DEBUG] Parte {i+1}: {latents.shape[2] - 1} latentes -> {num_pixel_frames_part} frames de pixel (alvo)")
+                       
+                        second_pass_kwargs = call_kwargs.copy()
+                        second_pass_kwargs.update({
+                           "output_type": "latent",
+                           "width": second_pass_width,
+                           "height": second_pass_height,
+                           "num_frames": num_pixel_frames_part,
+                           "latents": upsampled_latents, # O tensor upscaled
+                           "guidance_scale": float(guidance_scale),
+                           **second_pass_config
+                        })
+                    
+                        print(f"[DEBUG] Second Pass: Refinando em {width_padded}x{height_padded}...")
+                        final_latents = self.pipeline(**second_pass_kwargs).images
+                        log_tensor_info(final_latents, "Latentes Finais (Pós-Second Pass)")
+                        print(f"[DEBUG] Second part Pass concluída em {time.perf_counter() - t_pass2:.2f}s")
+                        
+                        latents_list.append(final_latents)
+
+                else: # Geração de etapa única
+                    print("\n--- INICIANDO GERAÇÃO DE ETAPA ÚNICA ---")
+                    t_single = time.perf_counter()
+                    single_pass_kwargs = call_kwargs.copy()
+                    single_pass_kwargs.update(self.config.get("first_pass", {}))
+                    single_pass_kwargs["guidance_scale"] = float(guidance_scale)
+                    single_pass_kwargs["output_type"] = "latent"
+                    
+                    latents = self.pipeline(**single_pass_kwargs).images
+                    log_tensor_info(latents, "Latentes Finais (Etapa Única)")
+                    print(f"[DEBUG] Etapa única concluída em {time.perf_counter() - t_single:.2f}s")
+
+                    latents_list.append(latents)
+
+            # --- ETAPA FINAL: DECODIFICAÇÃO E CODIFICAÇÃO MP4 ---
+            print("\n--- INICIANDO ETAPA FINAL: DECODIFICAÇÃO E MONTAGEM ---")
+            
+            #latents_cpu = latents.detach().to("cpu", non_blocking=True)
+            #torch.cuda.empty_cache()
+            #try:
+            #    torch.cuda.ipc_collect()
+            #except Exception:
+            #    pass
+
+            latents_parts = []
+            for latents in latents_list:
+                latents_parts.append(self._dividir_latentes_por_tamanho(latents,4,1))
+                
+           
+            partes_mp4 = []
+            par = 0
+            for latents in latents_parts:
+                
+                par = par + 1
+                output_video_path = os.path.join(results_dir, f"output_{used_seed}_{par}.mp4")
+                final_output_path = None
+    
+                print("[DEBUG] Decodificando bloco de latentes com VAE {par} → tensor de pixels...")
+                # Usar manager com timestep por item; previne target_shape e rota NoneType.decode
+                pixel_tensor = vae_manager_singleton.decode(
+                    latents.to(self.device, non_blocking=True),
+                    decode_timestep=float(self.config.get("decode_timestep", 0.05))
+                )
+                log_tensor_info(pixel_tensor, "Pixel tensor (VAE saída)")
+    
+                print("[DEBUG] Codificando MP4 a partir do tensor de pixels (bloco inteiro)...")
+                video_encode_tool_singleton.save_video_from_tensor(
+                    pixel_tensor,
+                    output_video_path,
+                    fps=call_kwargs["frame_rate"],
+                    progress_callback=progress_callback
+                )
+    
+                candidate = os.path.join(results_dir, f"output_par_{par}.mp4")
+                try:
+                    shutil.move(output_video_path, candidate)
+                    final_output_path = candidate
+                    print(f"[DEBUG] MP4 parte {par} movido para {final_output_path}")
+                    partes_mp4.append(final_output_path)
+                    
+                except Exception as e:
+                    final_output_path = output_video_path
+                    print(f"[DEBUG] Falha no move; usando tmp como final: {e}")
+
+            total_partes = len(partes_mp4)
+            if (total_partes>1):
+                final_vid = os.path.join(results_dir, f"concat_fim_{used_seed}.mp4")
+                partes_mp4_fade = self._gerar_lista_com_transicoes(pasta=results_dir, video_paths=partes_mp4, crossfade_frames=8)
+                self._concat_mp4s_no_reencode(partes_mp4_fade, final_vid)
+            else:
+                final_vid = partes_mp4[0]
+            
+            
+            self._log_gpu_memory("Fim da Geração")
+            return final_vid, used_seed
+            
+
+        except Exception as e:
+            print("[DEBUG] EXCEÇÃO NA GERAÇÃO:")
+            print("".join(traceback.format_exception(type(e), e, e.__traceback__)))
+            raise
+        finally:
+            try:
+                del latents
+            except Exception:
+                pass
+            try:
+                del multi_scale_pipeline
+            except Exception:
+                pass
+
+            gc.collect()
+            try:
+                if self.device == "cuda":
+                    torch.cuda.empty_cache()
+                    try:
+                        torch.cuda.ipc_collect()
+                    except Exception:
+                        pass
+            except Exception as e:
+                print(f"[DEBUG] Limpeza GPU no finally falhou: {e}")
+
+            try:
+                self.finalize(keep_paths=[])
+            except Exception as e:
+                print(f"[DEBUG] finalize() no finally falhou: {e}")
+
+print("Criando instância do VideoService. O carregamento do modelo começará agora...")
+video_generation_service = VideoService()

api/seedvr_server.py

@@ -0,0 +1,111 @@
+import os
+import shutil
+import subprocess
+import sys
+import time
+import mimetypes
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+from huggingface_hub import hf_hub_download
+
+class SeedVRServer:
+    def __init__(self, **kwargs):
+        self.SEEDVR_ROOT = Path(os.getenv("SEEDVR_ROOT", "/data/SeedVR"))
+        # Apontamos para o nosso diretório de checkpoints customizado
+        self.CKPTS_ROOT = Path("/data/seedvr_models_fp16") 
+        self.OUTPUT_ROOT = Path(os.getenv("OUTPUT_ROOT", "/app/outputs"))
+        self.INPUT_ROOT = Path(os.getenv("INPUT_ROOT", "/app/inputs"))
+        self.HF_HOME_CACHE = Path(os.getenv("HF_HOME", "/data/.cache/huggingface"))
+        self.REPO_URL = os.getenv("SEEDVR_GIT_URL", "https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler")
+        self.NUM_GPUS_TOTAL = int(os.getenv("NUM_GPUS", "4"))
+        
+        print("🚀 SeedVRServer (FP16) inicializando e preparando o ambiente...")
+        for p in [self.SEEDVR_ROOT.parent, self.CKPTS_ROOT, self.OUTPUT_ROOT, self.INPUT_ROOT, self.HF_HOME_CACHE]:
+            p.mkdir(parents=True, exist_ok=True)
+        
+        self.setup_dependencies()
+        print("✅ SeedVRServer (FP16) pronto.")
+
+    def setup_dependencies(self):
+        self._ensure_repo()
+        # O monkey patch agora é feito pelo start_seedvr.sh, não mais aqui.
+        self._ensure_model()
+
+    def _ensure_repo(self) -> None:
+        if not (self.SEEDVR_ROOT / ".git").exists():
+            print(f"[SeedVRServer] Clonando repositório para {self.SEEDVR_ROOT}...")
+            subprocess.run(["git", "clone", "--depth", "1", self.REPO_URL, str(self.SEEDVR_ROOT)], check=True)
+        else:
+            print("[SeedVRServer] Repositório SeedVR já existe.")
+
+    def _ensure_model(self) -> None:
+        """Baixa os arquivos de modelo FP16 otimizados e suas dependências."""
+        print(f"[SeedVRServer] Verificando checkpoints (FP16) em {self.CKPTS_ROOT}...")
+        
+        model_files = {
+            "seedvr2_ema_3b_fp16.safetensors": "MonsterMMORPG/SeedVR2_SECourses", "ema_vae_fp16.safetensors": "MonsterMMORPG/SeedVR2_SECourses",
+            "pos_emb.pt": "ByteDance-Seed/SeedVR2-3B", "neg_emb.pt": "ByteDance-Seed/SeedVR2-3B"
+        }
+        
+        for filename, repo_id in model_files.items():
+            if not (self.CKPTS_ROOT / filename).exists():
+                print(f"Baixando {filename} de {repo_id}...")
+                hf_hub_download(repo_id=repo_id, filename=filename, local_dir=str(self.CKPTS_ROOT), cache_dir=str(self.HF_HOME_CACHE), token=os.getenv("HF_TOKEN"))
+        print("[SeedVRServer] Checkpoints (FP16) estão no local correto.")
+    
+    def _prepare_job(self, input_file: str) -> Tuple[Path, Path]:
+        ts = f"{int(time.time())}_{os.urandom(4).hex()}"
+        job_input_dir = self.INPUT_ROOT / f"job_{ts}"
+        out_dir = self.OUTPUT_ROOT / f"run_{ts}"
+        job_input_dir.mkdir(parents=True, exist_ok=True)
+        out_dir.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(input_file, job_input_dir / Path(input_file).name)
+        return job_input_dir, out_dir
+        
+    def run_inference(self, filepath: str, *, seed: int, resh: int, resw: int, spsize: int, fps: Optional[float] = None):
+        script = self.SEEDVR_ROOT / "inference_cli.py"
+        job_input_dir, outdir = self._prepare_job(filepath)
+        mediatype, _ = mimetypes.guess_type(filepath)
+        is_image = mediatype and mediatype.startswith("image")
+    
+        effective_nproc = 1 if is_image else self.NUM_GPUS_TOTAL
+        effective_spsize = 1 if is_image else spsize
+    
+        output_filename = f"result_{Path(filepath).stem}.mp4" if not is_image else f"{Path(filepath).stem}_upscaled"
+        output_filepath = outdir / output_filename
+
+
+        
+        cmd = [
+            "torchrun", "--standalone", "--nnodes=1",
+            f"--nproc-per-node={effective_nproc}",
+            str(script),
+            "--video_path", str(filepath),
+            "--output", str(output_filepath),
+            "--model_dir", str(self.CKPTS_ROOT),
+            "--seed", str(seed),
+            "--cuda_device", "0",
+            "--resolution", str(resh),
+            "--batch_size", str(effective_spsize),
+            "--model", "seedvr2_ema_3b_fp16.safetensors",
+            "--preserve_vram",
+            "--debug",
+            "--output_format", "video" if not is_image else "png",
+        ]
+    
+    
+        print("SeedVRServer Comando:", " ".join(cmd))
+        try:
+            subprocess.run(cmd, cwd=str(self.SEEDVR_ROOT), check=True, env=os.environ.copy(), stdout=sys.stdout, stderr=sys.stderr)
+            # Constrói a tupla de retorno de forma determinística
+            if is_image:
+                # CLI salva PNGs em diretório args.output (tratado como diretório quando outputformat=png)
+                image_dir = output_filepath if output_filepath.suffix == "" else output_filepath.with_suffix("")
+                return str(image_dir), None, outdir
+            else:
+                # CLI salva vídeo exatamente em output_filepath
+                return None, str(output_filepath), outdir
+        except Exception as e:
+            print(f"[UI ERROR] A inferência falhou: {e}")
+            return None, None, None