# 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 # 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 _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 # --- 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}") 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.clone()] 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] Num LATENTES por chunk = {num_latente_por_chunk}") print(f"[DEBUG] Número de chunks = {n_chunks}") if n_chunks > 1: i = 0 end=1 while i < n_chunks: start = end-1 end += num_latente_por_chunk if end+2>=sum_latent: end = sum_latent-1 i = n_chunks else: i += 1 chunk = latents_brutos[:, :, start:end, :, :].clone() chunks.append(chunk) print(f"[DEBUG] chunk{i}[:, :, {start}:{end}, :, :] = {chunk.shape[2]}") 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 i0: start_frame = poda else: start_frame = 0 print(f"[DEBUG] primeira iteracao") 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}"' ) print(f"[DEBUG] transicao adicionada {i}/{i+1} {self._get_total_frames(video_fade)} frames ") subprocess.run(cmd_blend, shell=True, check=True) 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") end_frame = self._get_total_frames(base)-poda cmd_fim = ( f'ffmpeg -y -hide_banner -loglevel error -i "{base}" ' f'-vf "trim=start_frame={end_frame},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 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, # Sempre latent → VAE → MP4 (simples) 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") if mode == "image-to-video" and not start_image_filepath: raise ValueError("A imagem de início é obrigatória para o modo image-to-video") if mode == "video-to-video" and not input_video_filepath: raise ValueError("O vídeo de entrada é obrigatório para o modo video-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))) print(f"[DEBUG] Frames alvo: {actual_num_frames} (dur={duration}s @ {FPS}fps)") height_padded = ((height - 1) // 32 + 1) * 32 width_padded = ((width - 1) // 32 + 1) * 32 padding_values = calculate_padding(height, width, height_padded, width_padded) print(f"[DEBUG] Dimensões: ({height},{width}) -> pad ({height_padded},{width_padded}); padding={padding_values}") 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)}") # Sempre pedimos latentes (simples) 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']}") if mode == "video-to-video": media = load_media_file( media_path=input_video_filepath, height=height, width=width, max_frames=int(frames_to_use), padding=padding_values, ).to(self.device) call_kwargs["media_items"] = media print(f"[DEBUG] media_items shape={tuple(media.shape)}") latents = None multi_scale_pipeline = None try: if improve_texture: if not self.latent_upsampler: raise ValueError("Upscaler espacial não carregado.") print("[DEBUG] Multi-escala: construindo pipeline...") multi_scale_pipeline = LTXMultiScalePipeline(self.pipeline, self.latent_upsampler) first_pass_args = self.config.get("first_pass", {}).copy() first_pass_args["guidance_scale"] = float(guidance_scale) second_pass_args = self.config.get("second_pass", {}).copy() second_pass_args["guidance_scale"] = float(guidance_scale) multi_scale_call_kwargs = call_kwargs.copy() multi_scale_call_kwargs.update( { "downscale_factor": self.config["downscale_factor"], "first_pass": first_pass_args, "second_pass": second_pass_args, } ) print("[DEBUG] Chamando multi_scale_pipeline...") t_ms = time.perf_counter() ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext() with ctx: result = multi_scale_pipeline(**multi_scale_call_kwargs) print(f"[DEBUG] multi_scale_pipeline tempo={time.perf_counter()-t_ms:.3f}s") if hasattr(result, "latents"): latents = result.latents elif hasattr(result, "images") and isinstance(result.images, torch.Tensor): latents = result.images else: latents = result print(f"[DEBUG] Latentes (multi-escala): shape={tuple(latents.shape)}") else: single_pass_kwargs = call_kwargs.copy() first_pass_config = self.config.get("first_pass", {}) single_pass_kwargs.update( { "guidance_scale": float(guidance_scale), "stg_scale": first_pass_config.get("stg_scale"), "rescaling_scale": first_pass_config.get("rescaling_scale"), "skip_block_list": first_pass_config.get("skip_block_list"), } ) schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps") if mode == "video-to-video": schedule = [0.7]; print("[INFO] Modo video-to-video (etapa única): timesteps=[0.7]") if isinstance(schedule, (list, tuple)) and len(schedule) > 0: single_pass_kwargs["timesteps"] = schedule single_pass_kwargs["guidance_timesteps"] = schedule print(f"[DEBUG] Single-pass: timesteps_len={len(schedule) if schedule else 0}") print("\n[INFO] Executando pipeline de etapa única...") t_sp = time.perf_counter() ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext() with ctx: result = self.pipeline(**single_pass_kwargs) print(f"[DEBUG] single-pass tempo={time.perf_counter()-t_sp:.3f}s") if hasattr(result, "latents"): latents = result.latents elif hasattr(result, "images") and isinstance(result.images, torch.Tensor): latents = result.images else: latents = result print(f"[DEBUG] Latentes (single-pass): shape={tuple(latents.shape)}") # Staging e escrita MP4 (simples: VAE → pixels → MP4) latents_cpu = latents.detach().to("cpu", non_blocking=True) torch.cuda.empty_cache() try: torch.cuda.ipc_collect() except Exception: pass latents_parts = self._dividir_latentes_por_tamanho(latents_cpu,4,1) temp_dir = tempfile.mkdtemp(prefix="ltxv_"); self._register_tmp_dir(temp_dir) results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True) partes_mp4 = [] par = 0 for latents in latents_parts: print(f"[DEBUG] Partição {par}: {tuple(latents.shape)}") par = par + 1 output_video_path = os.path.join(temp_dir, f"output_{used_seed}_{par}.mp4") final_output_path = None print("[DEBUG] Decodificando bloco de latentes com VAE → 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_concat, 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()