# 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, hf_hub_download 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): """ Carrega os modelos de forma inteligente: 1. Tenta resolver o caminho do cache local (rápido, sem rede). 2. Se o arquivo não for encontrado localmente, baixa como fallback. Garante que o serviço possa iniciar mesmo que o setup.py não tenha sido executado. """ t0 = time.perf_counter() LTX_REPO = "Lightricks/LTX-Video" print("[DEBUG] Resolvendo caminhos dos modelos de forma inteligente...") # --- Função Auxiliar para Carregamento Inteligente --- def get_or_download_model(repo_id, filename, description): try: # hf_hub_download é a ferramenta certa aqui. Ela verifica o cache PRIMEIRO. # Se o arquivo estiver no cache, retorna o caminho instantaneamente (após uma verificação rápida de metadados). # Se não estiver no cache, ela o baixa. print(f"[DEBUG] Verificando {description}: {filename}...") model_path = hf_hub_download( repo_id=repo_id, filename=filename, # Forçar o uso de um cache específico se necessário cache_dir=os.getenv("HF_HOME_CACHE"), token=os.getenv("HF_TOKEN") ) print(f"[DEBUG] Caminho do {description} resolvido com sucesso.") return model_path except Exception as e: print("\n" + "="*80) print(f"[ERRO CRÍTICO] Falha ao obter o modelo '{filename}'.") print(f"Detalhe do erro: {e}") print("Verifique sua conexão com a internet ou o estado do cache do Hugging Face.") print("="*80 + "\n") sys.exit(1) # --- Checkpoint Principal --- checkpoint_filename = self.config["checkpoint_path"] distilled_model_path = get_or_download_model( LTX_REPO, checkpoint_filename, "checkpoint principal" ) self.config["checkpoint_path"] = distilled_model_path # --- Upscaler Espacial --- upscaler_filename = self.config["spatial_upscaler_model_path"] spatial_upscaler_path = get_or_download_model( LTX_REPO, upscaler_filename, "upscaler espacial" ) self.config["spatial_upscaler_model_path"] = spatial_upscaler_path # --- Construção dos Pipelines --- print("\n[DEBUG] Construindo pipeline a partir dos caminhos resolvidos...") 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 _load_models_old(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[start:stop-1]==========") 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: if i>0: dow=0 else: dow=0 start = (num_latente_por_chunk*i) end = (start+num_latente_por_chunk+(overlap+1)) if i+1 < n_chunks: chunk = latents_brutos[:, :, start-(dow):end, :, :].clone().detach() print(f"[DEBUG] chunk{i+1}[:, :, {start-dow}:{end}, :, :] = {chunk.shape[2]}") else: chunk = latents_brutos[:, :, start-(dow):, :, :].clone().detach() print(f"[DEBUG] chunk{i+1}[:, :, {start-(dow)}:, :, :] = {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("\n\n================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 = [] if crossfade_frames == 0: print("\n\n[DEBUG] CROSSFADE_FRAMES=0 Ship concatenation causal") return video_paths print("\n\n===========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 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, } 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.") first_pass_kwargs = call_kwargs.copy() # --- 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 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})") first_pass_kwargs.update({ **first_pass_config }) first_pass_kwargs.update({ "output_type": "latent", "width": downscaled_width, "height": downscaled_height, "guidance_scale": float(guidance_scale), }) 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,40,0) print("\n\n--- INICIANDO ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---") cc = 1 for latents in latents_parts_up: t_pass2 = time.perf_counter() print("\n\n#########################################") # # --- ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) --- print(f"\n--- INICIANDO ETAPA 3/{cc} ") second_pass_kwargs = first_pass_config.copy() second_pass_config = self.config.get("second_pass", {}).copy() 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})") num_latent_frames_part = latents.shape[2] log_tensor_info(latents, "Latentes input (Pre-Pós-Second Pass)") vae_temporal_scale = self.pipeline.video_scale_factor # Geralmente 4 ou 8 num_pixel_frames_part = ((num_latent_frames_part - 1) * vae_temporal_scale) + 1 print(f"[DEBUG] Parte: {num_latent_frames_part - 1} latentes -> {num_pixel_frames_part} frames de pixel (alvo)") second_pass_kwargs.update({ **second_pass_config }) second_pass_kwargs.update({ "output_type": "latent", "width": second_pass_width, "height": second_pass_height, "num_frames": num_pixel_frames_part, "latents": latents, # O tensor upscaled "guidance_scale": float(guidance_scale), }) 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) cc+=1 print("#########################################") print("\n\n--- FIM ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---") 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" # Remove keys that might conflict or are not used in single pass / handled by above single_pass_kwargs.pop("num_inference_steps", None) single_pass_kwargs.pop("first_pass", None) single_pass_kwargs.pop("second_pass", None) single_pass_kwargs.pop("downscale_factor", None) 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 ---") 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_vae in latents_list: latents_cpu_vae = latents_vae.detach().to("cpu", non_blocking=True) torch.cuda.empty_cache() try: torch.cuda.ipc_collect() except Exception: pass latents_parts_vae = self._dividir_latentes_por_tamanho(latents_cpu_vae,4,1) for latents in latents_parts_vae: 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=0) 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()