Delete deformes4D_engine.py

deformes4D_engine.py

@@ -1,258 +0,0 @@
-# deformes4D_engine.py
-# Copyright (C) 4 de Agosto de 2025  Carlos Rodrigues dos Santos
-# (Licenciamento e cabeçalhos permanecem os mesmos)
-
-import os
-import time
-import imageio
-import numpy as np
-import torch
-import logging
-from PIL import Image, ImageOps
-from dataclasses import dataclass
-import gradio as gr
-import subprocess
-import gc
-
-from ltx_manager_helpers import ltx_manager_singleton
-from gemini_helpers import gemini_singleton 
-from upscaler_specialist import upscaler_specialist_singleton
-from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
-from ltx_video.models.autoencoders.vae_encode import vae_encode, vae_decode
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class LatentConditioningItem:
-    latent_tensor: torch.Tensor
-    media_frame_number: int
-    conditioning_strength: float
-
-class Deformes4DEngine:
-    def __init__(self, ltx_manager, workspace_dir="deformes_workspace"):
-        self.ltx_manager = ltx_manager
-        self.workspace_dir = workspace_dir
-        self._vae = None
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        logger.info("Especialista Deformes4D (Executor ADUC-SDR: Câmera Ψ e Destilador Δ) inicializado.")
-
-    @property
-    def vae(self):
-        if self._vae is None:
-            self._vae = self.ltx_manager.workers[0].pipeline.vae
-        self._vae.to(self.device); self._vae.eval()
-        return self._vae
-
-    # MÉTODOS AUXILIARES (IDÊNTICOS AO v35)
-    def save_latent_tensor(self, tensor: torch.Tensor, path: str):
-        torch.save(tensor.cpu(), path)
-
-    def load_latent_tensor(self, path: str) -> torch.Tensor:
-        return torch.load(path, map_location=self.device)
-
-    @torch.no_grad()
-    def pixels_to_latents(self, tensor: torch.Tensor) -> torch.Tensor:
-        tensor = tensor.to(self.device, dtype=self.vae.dtype)
-        return vae_encode(tensor, self.vae, vae_per_channel_normalize=True)
-
-    @torch.no_grad()
-    def latents_to_pixels(self, latent_tensor: torch.Tensor, decode_timestep: float = 0.05) -> torch.Tensor:
-        latent_tensor = latent_tensor.to(self.device, dtype=self.vae.dtype)
-        timestep_tensor = torch.tensor([decode_timestep] * latent_tensor.shape[0], device=self.device, dtype=latent_tensor.dtype)
-        return vae_decode(latent_tensor, self.vae, is_video=True, timestep=timestep_tensor, vae_per_channel_normalize=True)
-
-    def save_video_from_tensor(self, video_tensor: torch.Tensor, path: str, fps: int = 24):
-        if video_tensor is None or video_tensor.ndim != 5 or video_tensor.shape[2] == 0: return
-        video_tensor = video_tensor.squeeze(0).permute(1, 2, 3, 0)
-        video_tensor = (video_tensor.clamp(-1, 1) + 1) / 2.0
-        video_np = (video_tensor.detach().cpu().float().numpy() * 255).astype(np.uint8)
-        with imageio.get_writer(path, fps=fps, codec='libx264', quality=8) as writer:
-            for frame in video_np: writer.append_data(frame)
-
-    def _preprocess_image_for_latent_conversion(self, image: Image.Image, target_resolution: tuple) -> Image.Image:
-        if image.size != target_resolution:
-            return ImageOps.fit(image, target_resolution, Image.Resampling.LANCZOS)
-        return image
-
-    def pil_to_latent(self, pil_image: Image.Image) -> torch.Tensor:
-        image_np = np.array(pil_image).astype(np.float32) / 255.0
-        tensor = torch.from_numpy(image_np).permute(2, 0, 1).unsqueeze(0).unsqueeze(2)
-        tensor = (tensor * 2.0) - 1.0
-        return self.pixels_to_latents(tensor)
-    
-    def _get_video_frame_count(self, video_path: str) -> int | None:
-        if not os.path.exists(video_path): return None
-        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]
-        try:
-            result = subprocess.run(cmd, check=True, capture_output=True, text=True, encoding='utf-8')
-            return int(result.stdout.strip())
-        except Exception: return None
-
-    def _trim_last_frame_ffmpeg(self, input_path: str, output_path: str) -> bool:
-        frame_count = self._get_video_frame_count(input_path)
-        if frame_count is None or frame_count < 2:
-            if os.path.exists(input_path): os.rename(input_path, output_path)
-            return True
-        vf_filter = f"select='lt(n,{frame_count - 1})',setpts=PTS-STARTPTS"
-        cmd_list = ['ffmpeg', '-y', '-i', input_path, '-vf', vf_filter, '-an', output_path]
-        try:
-            subprocess.run(cmd_list, check=True, capture_output=True, text=True, encoding='utf-8')
-            return True
-        except subprocess.CalledProcessError: return False
-            
-    def concatenate_videos_ffmpeg(self, video_paths: list[str], output_path: str) -> str:
-        if not video_paths: raise gr.Error("Nenhum fragmento de vídeo para montar.")
-        list_file_path = os.path.join(self.workspace_dir, "concat_list.txt")
-        with open(list_file_path, 'w', encoding='utf-8') as f:
-            for path in video_paths: f.write(f"file '{os.path.abspath(path)}'\n")
-        cmd_list = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', list_file_path, '-c', 'copy', output_path]
-        try:
-            subprocess.run(cmd_list, check=True, capture_output=True, text=True)
-        except subprocess.CalledProcessError as e:
-            raise gr.Error(f"Falha na montagem final do vídeo. Detalhes: {e.stderr}")
-        return output_path
-
-    # --- PIPELINE DE PÓS-PRODUÇÃO LATENTE ---
-    def _render_and_post_process_latents(self, 
-                                     low_res_latents: torch.Tensor, 
-                                     base_name: str,
-                                     conditioning_items_for_refine: list,
-                                     motion_prompt_for_refine: str
-                                    ) -> str:
-        high_res_latents = upscaler_specialist_singleton.upscale(low_res_latents)
-        
-        _, _, _, refined_h_latent, refined_w_latent = high_res_latents.shape
-        video_h = refined_h_latent * self.ltx_manager.workers[0].pipeline.vae_scale_factor
-        video_w = refined_w_latent * self.ltx_manager.workers[0].pipeline.vae_scale_factor
-        num_latent_frames = high_res_latents.shape[2]
-        num_video_frames = num_latent_frames * self.ltx_manager.workers[0].pipeline.video_scale_factor
-        if isinstance(self.vae, CausalVideoAutoencoder):
-            num_video_frames -= 1
-        
-        refine_kwargs = {
-            'height': video_h, 'width': video_w, 'video_total_frames': num_video_frames, 'video_fps': 24,
-            'current_fragment_index': int(time.time()), 'motion_prompt': motion_prompt_for_refine,
-            'conditioning_items_data': conditioning_items_for_refine,
-            'denoise_strength': 0.4, 'refine_steps': 10
-        }
-
-        final_latents, _ = self.ltx_manager.refine_latents(high_res_latents, **refine_kwargs)
-
-        untrimmed_video_path = os.path.join(self.workspace_dir, f"{base_name}_untrimmed.mp4")
-        trimmed_video_path = os.path.join(self.workspace_dir, f"{base_name}.mp4")
-
-        pixel_tensor = self.latents_to_pixels(final_latents)
-        self.save_video_from_tensor(pixel_tensor, untrimmed_video_path, fps=24)
-        del pixel_tensor, final_latents, high_res_latents
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        success = self._trim_last_frame_ffmpeg(untrimmed_video_path, trimmed_video_path)
-        
-        if os.path.exists(untrimmed_video_path):
-            os.remove(untrimmed_video_path)
-        
-        return trimmed_video_path if success else untrimmed_video_path
-
-    # NÚCLEO DA LÓGICA ADUC-SDR
-    def generate_full_movie(self, keyframes: list, global_prompt: str, storyboard: list, 
-                            seconds_per_fragment: float, trim_percent: int,
-                            handler_strength: float, destination_convergence_strength: float, 
-                            video_resolution: int, use_continuity_director: bool, 
-                            progress: gr.Progress = gr.Progress()):
-        
-        FPS = 24
-        FRAMES_PER_LATENT_CHUNK = 8
-        ECO_LATENT_CHUNKS = 2
-        
-        total_frames_brutos = self._quantize_to_multiple(int(seconds_per_fragment * FPS), FRAMES_PER_LATENT_CHUNK)
-        total_latents_brutos = total_frames_brutos // FRAMES_PER_LATENT_CHUNK
-        frames_a_podar = self._quantize_to_multiple(int(total_frames_brutos * (trim_percent / 100)), FRAMES_PER_LATENT_CHUNK)
-        latents_a_podar = frames_a_podar // FRAMES_PER_LATENT_CHUNK
-
-        if total_latents_brutos <= latents_a_podar + 1:
-            raise gr.Error(f"A combinação de duração e poda é muito agressiva.")
-
-        DEJAVU_FRAME_TARGET = frames_a_podar - 1
-        DESTINATION_FRAME_TARGET = total_frames_brutos - 1
-        
-        base_ltx_params = {"guidance_scale": 2.0, "stg_scale": 0.025, "rescaling_scale": 0.15, "num_inference_steps": 20, "image_cond_noise_scale": 0.00}
-        keyframe_paths = [item[0] if isinstance(item, tuple) else item for item in keyframes]
-        video_clips_paths, story_history = [], ""
-        target_resolution_tuple = (video_resolution, video_resolution) 
-        
-        eco_latent_for_next_loop = None
-        dejavu_latent_for_next_loop = None
-        
-        num_transitions_to_generate = len(keyframe_paths) - 1
-        
-        for i in range(num_transitions_to_generate):
-            fragment_index = i + 1
-            progress(fragment_index / num_transitions_to_generate, desc=f"Produzindo Transição {fragment_index}/{num_transitions_to_generate}")
-            
-            past_keyframe_path = keyframe_paths[i - 1] if i > 0 else keyframe_paths[i]
-            start_keyframe_path = keyframe_paths[i]
-            destination_keyframe_path = keyframe_paths[i + 1]
-            future_story_prompt = storyboard[i + 1] if (i + 1) < len(storyboard) else "A cena final."
-            decision = gemini_singleton.get_cinematic_decision(
-                global_prompt, story_history, past_keyframe_path, start_keyframe_path, destination_keyframe_path,
-                storyboard[i - 1] if i > 0 else "O início.", storyboard[i], future_story_prompt)
-            transition_type, motion_prompt = decision["transition_type"], decision["motion_prompt"]
-            story_history += f"\n- Ato {fragment_index}: {motion_prompt}"
-
-            conditioning_items = []
-            if eco_latent_for_next_loop is None:
-               img_start = self._preprocess_image_for_latent_conversion(Image.open(start_keyframe_path).convert("RGB"), target_resolution_tuple)
-               conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_start), 0, 1.0))
-            else:
-               conditioning_items.append(LatentConditioningItem(eco_latent_for_next_loop, 0, 1.0))
-               conditioning_items.append(LatentConditioningItem(dejavu_latent_for_next_loop, DEJAVU_FRAME_TARGET, handler_strength))
-            img_dest = self._preprocess_image_for_latent_conversion(Image.open(destination_keyframe_path).convert("RGB"), target_resolution_tuple)
-            conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_dest), DESTINATION_FRAME_TARGET, destination_convergence_strength))
-
-            current_ltx_params = {**base_ltx_params, "motion_prompt": motion_prompt}
-            latents_brutos, _ = self._generate_latent_tensor_internal(conditioning_items, current_ltx_params, target_resolution_tuple, total_frames_brutos)
-
-            last_trim = latents_brutos[:, :, -(latents_a_podar+1):, :, :].clone()
-            eco_latent_for_next_loop = last_trim[:, :, :ECO_LATENT_CHUNKS, :, :].clone()   
-            dejavu_latent_for_next_loop = last_trim[:, :, -1:, :, :].clone()
-            latents_video = latents_brutos[:, :, :-(latents_a_podar-1), :, :].clone()
-            latents_video = latents_video[:, :, 1:, :, :]
-
-            if transition_type == "cut":
-                eco_latent_for_next_loop, dejavu_latent_for_next_loop = None, None
-
-            base_name = f"fragment_{fragment_index}_{int(time.time())}"
-            video_path = self._render_and_post_process_latents(
-                low_res_latents=latents_video, base_name=base_name,
-                conditioning_items_for_refine=conditioning_items,
-                motion_prompt_for_refine=motion_prompt)
-            video_clips_paths.append(video_path)
-            yield {"fragment_path": video_path}
-
-            if eco_latent_for_next_loop is not None:
-                eco_base_name = f"fragment_{fragment_index}_eco_diagnostic"
-                eco_pixel_tensor = self.latents_to_pixels(eco_latent_for_next_loop)
-                eco_video_path = os.path.join(self.workspace_dir, f"{eco_base_name}.mp4")
-                self.save_video_from_tensor(eco_pixel_tensor, eco_video_path)
-                video_clips_paths.append(eco_video_path)
-                yield {"fragment_path": eco_video_path}
-                 
-        final_movie_path = os.path.join(self.workspace_dir, f"final_movie_silent_{int(time.time())}.mp4")
-        self.concatenate_videos_ffmpeg(video_clips_paths, final_movie_path)
-        
-        yield {"final_path": final_movie_path}
-
-    def _generate_latent_tensor_internal(self, conditioning_items, ltx_params, target_resolution, total_frames_to_generate):
-        kwargs = {
-            **ltx_params, 'width': target_resolution[0], 'height': target_resolution[1],
-            'video_total_frames': total_frames_to_generate, 'video_fps': 24,
-            'current_fragment_index': int(time.time()), 'conditioning_items_data': conditioning_items
-        }
-        return self.ltx_manager.generate_latent_fragment(**kwargs)
-
-    def _quantize_to_multiple(self, n, m):
-        if m == 0: return n
-        quantized = int(round(n / m) * m)
-        return m if n > 0 and quantized == 0 else quantized