Upload ai_studio_code (60).py

ai_studio_code (60).py

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+# Euia-AducSdr: Uma implementação aberta e funcional da arquitetura ADUC-SDR para geração de vídeo coerente.
+# Copyright (C) 4 de Agosto de 2025  Carlos Rodrigues dos Santos
+#
+# Contato:
+# Carlos Rodrigues dos Santos
+# carlex22@gmail.com
+# Rua Eduardo Carlos Pereira, 4125, B1 Ap32, Curitiba, PR, Brazil, CEP 8102025
+#
+# Repositórios e Projetos Relacionados:
+# GitHub: https://github.com/carlex22/Aduc-sdr
+# Hugging Face: https://huggingface.co/spaces/Carlexx/Ltx-SuperTime-60Secondos/
+# Hugging Face: https://huggingface.co/spaces/Carlexxx/Novinho/
+#
+# Este programa é software livre: você pode redistribuí-lo e/ou modificá-lo
+# sob os termos da Licença Pública Geral Affero da GNU como publicada pela
+# Free Software Foundation, seja a versão 3 da Licença, ou
+# (a seu critério) qualquer versão posterior.
+#
+# Este programa é distribuído na esperança de que seja útil,
+# mas SEM QUALQUER GARANTIA; sem mesmo a garantia implícita de
+# COMERCIALIZAÇÃO ou ADEQUAÇÃO A UM DETERMINADO FIM. Consulte a
+# Licença Pública Geral Affero da GNU para mais detalhes.
+#
+# Você deve ter recebido uma cópia da Licença Pública Geral Affero da GNU
+# junto com este programa. Se não, veja <https://www.gnu.org/licenses/>.
+
+# --- app.py (NOVINHO-4.2: Versão Final - Arquitetura "Memória, Caminho, Destino") ---
+
+# --- Ato 1: A Convocação da Orquestra (Importações) ---
+import gradio as gr
+import torch
+import os
+import yaml
+from PIL import Image, ImageOps
+import shutil
+import gc
+import subprocess
+import google.generativeai as genai
+import numpy as np
+import imageio
+from pathlib import Path
+import huggingface_hub
+import json
+import time
+
+from inference import create_ltx_video_pipeline, load_image_to_tensor_with_resize_and_crop, ConditioningItem, calculate_padding
+from dreamo_helpers import dreamo_generator_singleton
+
+# --- Ato 2: A Preparação do Palco (Configurações) ---
+config_file_path = "configs/ltxv-13b-0.9.8-distilled.yaml"
+with open(config_file_path, "r") as file: PIPELINE_CONFIG_YAML = yaml.safe_load(file)
+
+LTX_REPO = "Lightricks/LTX-Video"
+models_dir = "downloaded_models_gradio"
+Path(models_dir).mkdir(parents=True, exist_ok=True)
+WORKSPACE_DIR = "aduc_workspace"
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+
+VIDEO_FPS = 36
+VIDEO_DURATION_SECONDS = 4
+VIDEO_TOTAL_FRAMES = VIDEO_DURATION_SECONDS * VIDEO_FPS
+CONVERGENCE_FRAMES = 8
+TARGET_RESOLUTION = 720
+
+print("Criando pipelines LTX na CPU (estado de repouso)...")
+distilled_model_actual_path = huggingface_hub.hf_hub_download(repo_id=LTX_REPO, filename=PIPELINE_CONFIG_YAML["checkpoint_path"], local_dir=models_dir, local_dir_use_symlinks=False)
+pipeline_instance = create_ltx_video_pipeline(
+    ckpt_path=distilled_model_actual_path,
+    precision=PIPELINE_CONFIG_YAML["precision"],
+    text_encoder_model_name_or_path=PIPELINE_CONFIG_YAML["text_encoder_model_name_or_path"],
+    sampler=PIPELINE_CONFIG_YAML["sampler"],
+    device='cpu'
+)
+print("Modelos LTX prontos (na CPU).")
+
+
+# --- Ato 3: As Partituras dos Músicos (Funções Corrigidas, Otimizadas e Documentadas) ---
+
+def load_conditioning_tensor(media_path: str, height: int, width: int) -> torch.Tensor:
+    if not media_path: raise ValueError("Caminho da mídia de condicionamento não pode ser nulo.")
+    # A lógica agora só precisa lidar com imagens, simplificando o processo
+    return load_image_to_tensor_with_resize_and_crop(media_path, height, width)
+
+def run_ltx_animation(current_fragment_index, motion_prompt, conditioning_items_data, width, height, seed, cfg, progress=gr.Progress()):
+    progress(0, desc=f"[TECPIX 5000] Filmando Cena {current_fragment_index}...");
+    output_path = os.path.join(WORKSPACE_DIR, f"fragment_{current_fragment_index}_full.mp4");
+    target_device = pipeline_instance.device
+    try:
+        conditioning_items = []
+        for (path, start_frame, strength) in conditioning_items_data:
+            tensor = load_conditioning_tensor(path, height, width)
+            conditioning_items.append(ConditioningItem(tensor.to(target_device), start_frame, strength))
+        
+        n_val = round((float(VIDEO_TOTAL_FRAMES) - 1.0) / 8.0); actual_num_frames = int(n_val * 8 + 1)
+        padded_h, padded_w = ((height - 1) // 32 + 1) * 32, ((width - 1) // 32 + 1) * 32
+        padding_vals = calculate_padding(height, width, padded_h, padded_w)
+        for cond_item in conditioning_items: cond_item.media_item = torch.nn.functional.pad(cond_item.media_item, padding_vals)
+        
+        decode_every_val = 4
+        kwargs = { "prompt": motion_prompt, "negative_prompt": "blurry, distorted, bad quality, artifacts", "height": padded_h, "width": padded_w, "num_frames": actual_num_frames, "frame_rate": VIDEO_FPS, "generator": torch.Generator(device=target_device).manual_seed(int(seed) + current_fragment_index), "output_type": "pt", "guidance_scale": float(cfg), "timesteps": PIPELINE_CONFIG_YAML.get("first_pass", {}).get("timesteps"), "conditioning_items": conditioning_items, "decode_timestep": PIPELINE_CONFIG_YAML.get("decode_timestep"), "decode_noise_scale": PIPELINE_CONFIG_YAML.get("decode_noise_scale"), "stochastic_sampling": PIPELINE_CONFIG_YAML.get("stochastic_sampling"), "image_cond_noise_scale": 0.15, "is_video": True, "vae_per_channel_normalize": True, "mixed_precision": (PIPELINE_CONFIG_YAML.get("precision") == "mixed_precision"), "enhance_prompt": False, "decode_every": decode_every_val }
+        
+        result_tensor = pipeline_instance(**kwargs).images
+        
+        pad_l, pad_r, pad_t, pad_b = map(int, padding_vals); slice_h = -pad_b if pad_b > 0 else None; slice_w = -pad_r if pad_r > 0 else None
+        cropped_tensor = result_tensor[:, :, :VIDEO_TOTAL_FRAMES, pad_t:slice_h, pad_l:slice_w]; video_np = (cropped_tensor[0].permute(1, 2, 3, 0).cpu().float().numpy() * 255).astype(np.uint8)
+        
+        with imageio.get_writer(output_path, fps=VIDEO_FPS, codec='libx264', quality=8) as writer:
+            for i, frame in enumerate(video_np): 
+                progress(i / len(video_np), desc=f"Renderizando frame {i+1}/{len(video_np)}..."); 
+                writer.append_data(frame)
+        
+        return output_path, actual_num_frames
+    except Exception as e:
+        raise e
+
+def trim_video_to_frames(input_path: str, output_path: str, frames_to_keep: int) -> str:
+    if not os.path.exists(input_path):
+        raise gr.Error(f"Erro Interno: Vídeo de entrada para corte não encontrado: {input_path}")
+    try:
+        trim_cmd = (f"ffmpeg -y -v error -i \"{input_path}\" -vf \"select='lt(n,{frames_to_keep})'\" -an \"{output_path}\"")
+        subprocess.run(trim_cmd, shell=True, check=True, capture_output=True, text=True)
+        return output_path
+    except subprocess.CalledProcessError as e:
+        error_message = f"Editor Mágico (FFmpeg) falhou ao cortar o vídeo para {frames_to_keep} frames: {e}"
+        if hasattr(e, 'stderr'): error_message += f"\nDetalhes: {e.stderr}"
+        raise gr.Error(error_message)
+
+def extract_last_frame_as_image(video_path: str, output_image_path: str) -> str:
+    if not os.path.exists(video_path):
+        raise gr.Error(f"Erro Interno: Vídeo de entrada para extração de frame não encontrado: {video_path}")
+    try:
+        command = (f"ffmpeg -y -v error -sseof -1 -i \"{video_path}\" -update 1 -q:v 1 \"{output_image_path}\"")
+        subprocess.run(command, shell=True, check=True, capture_output=True, text=True)
+        return output_image_path
+    except subprocess.CalledProcessError as e:
+        error_message = f"Editor Mágico (FFmpeg) falhou ao extrair o último frame: {e}"
+        if hasattr(e, 'stderr'): error_message += f"\nDetalhes: {e.stderr}"
+        raise gr.Error(error_message)
+
+def process_image_to_square(image_path: str, size: int = TARGET_RESOLUTION) -> str:
+    if not image_path or not os.path.exists(image_path): return None
+    try:
+        img = Image.open(image_path).convert("RGB")
+        img_square = ImageOps.fit(img, (size, size), Image.Resampling.LANCZOS)
+        output_filename = f"initial_ref_{size}x{size}.png"
+        output_path = os.path.join(WORKSPACE_DIR, output_filename)
+        img_square.save(output_path)
+        return output_path
+    except Exception as e: raise gr.Error(f"Falha ao processar a imagem de referência: {e}")
+
+def get_static_scenes_storyboard(num_fragments: int, prompt: str, initial_image_path: str):
+    if not initial_image_path: raise gr.Error("Por favor, forneça uma imagem de referência inicial.")
+    if not GEMINI_API_KEY: raise gr.Error("Chave da API Gemini não configurada!")
+    genai.configure(api_key=GEMINI_API_KEY)
+    prompt_file = "prompts/photographer_prompt.txt"
+    with open(os.path.join(os.path.dirname(__file__), prompt_file), "r", encoding="utf-8") as f: template = f.read()
+    director_prompt = template.format(user_prompt=prompt, num_fragments=int(num_fragments))
+    model = genai.GenerativeModel('gemini-1.5-flash'); img = Image.open(initial_image_path)
+    response = model.generate_content([director_prompt, img])
+    try:
+        cleaned_response = response.text.strip().replace("```json", "").replace("```", "")
+        storyboard_data = json.loads(cleaned_response)
+        return storyboard_data.get("scene_storyboard", [])
+    except Exception as e: raise gr.Error(f"O Sonhador (Gemini) falhou ao criar o roteiro: {e}. Resposta: {response.text}")
+
+def run_keyframe_generation(storyboard, initial_ref_image_path, sequential_ref_task):
+    if not storyboard: raise gr.Error("Nenhum roteiro para gerar imagens-chave.")
+    if not initial_ref_image_path or not os.path.exists(initial_ref_image_path): raise gr.Error("A imagem de referência principal é obrigatória.")
+    log_history = ""
+    try:
+        print("Pintor (DreamO): Movendo a Câmera (LTX) para a CPU para liberar VRAM...")
+        pipeline_instance.to('cpu')
+        gc.collect()
+        if torch.cuda.is_available(): torch.cuda.empty_cache()
+        print("Pintor (DreamO): VRAM liberada. Movendo o Pintor para a GPU...")
+        dreamo_generator_singleton.to_gpu()
+        with Image.open(initial_ref_image_path) as img:
+            width, height = img.size
+            width, height = (width // 32) * 32, (height // 32) * 32
+        keyframe_paths, current_ref_image_path = [], initial_ref_image_path
+        for i, prompt in enumerate(storyboard):
+            log_history += f"\nPintando Cena {i+1}/{len(storyboard)}...\n"
+            yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=keyframe_paths)}
+            reference_items_for_dreamo = [{'image_np': np.array(Image.open(current_ref_image_path).convert("RGB")), 'task': sequential_ref_task}]
+            log_history += f"  - Usando referência: {os.path.basename(current_ref_image_path)} (Tarefa: {sequential_ref_task})\n"
+            output_path = os.path.join(WORKSPACE_DIR, f"keyframe_{i+1}.png")
+            image = dreamo_generator_singleton.generate_image_with_gpu_management(reference_items=reference_items_for_dreamo, prompt=prompt, width=width, height=height)
+            image.save(output_path)
+            keyframe_paths.append(output_path)
+            current_ref_image_path = output_path
+            yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=keyframe_paths)}
+    except Exception as e:
+        raise gr.Error(f"O Pintor (DreamO) encontrou um erro: {e}")
+    finally:
+        print("Pintor (DreamO): Trabalho concluído. Movendo o Pintor de volta para a CPU.")
+        dreamo_generator_singleton.to_cpu()
+        gc.collect()
+        if torch.cuda.is_available(): torch.cuda.empty_cache()
+    log_history += "\nPintura de todos os keyframes concluída.\n"
+    yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=keyframe_paths), keyframe_images_state: keyframe_paths}
+
+def get_single_motion_prompt(user_prompt: str, story_history: str, start_image_path: str, middle_image_path: str, end_image_path: str):
+    if not GEMINI_API_KEY: raise gr.Error("Chave da API Gemini não configurada!")
+    try:
+        genai.configure(api_key=GEMINI_API_KEY)
+        model = genai.GenerativeModel('gemini-1.5-flash')
+        start_img, middle_img, end_img = Image.open(start_image_path), Image.open(middle_image_path), Image.open(end_image_path)
+        prompt_file_path = os.path.join(os.path.dirname(__file__), "prompts", "director_motion_prompt_three_act.txt")
+        with open(prompt_file_path, "r", encoding="utf-8") as f:
+            template = f.read()
+        director_prompt = template.format(user_prompt=user_prompt, story_history=story_history)
+        model_contents = [director_prompt, "INÍCIO:", start_img, "MEIO:", middle_img, "FIM:", end_img]
+        response = model.generate_content(model_contents)
+        cleaned_text = response.text.strip()
+        if cleaned_text.startswith("```json"): cleaned_text = cleaned_text[len("```json"):].strip()
+        if cleaned_text.endswith("```"): cleaned_text = cleaned_text[:-len("```")].strip()
+        try:
+            motion_data = json.loads(cleaned_text)
+            final_prompt = motion_data.get("motion_prompt", "")
+            if not final_prompt: raise ValueError("Prompt de movimento vazio no JSON.")
+            return final_prompt
+        except (json.JSONDecodeError, ValueError):
+            return cleaned_text.replace("\"", "").replace("{", "").replace("}", "").replace("motion_prompt:", "").strip()
+    except Exception as e:
+        response_text = getattr(e, 'text', 'Nenhuma resposta de texto disponível.')
+        raise gr.Error(f"O Cineasta (Gemini) falhou ao criar o prompt de movimento de 3 atos: {e}. Resposta: {response_text}")
+
+def run_video_production(prompt_geral, keyframe_images_state, scene_storyboard, seed, cfg, cut_frames_value, progress=gr.Progress()):
+    if not keyframe_images_state or len(keyframe_images_state) < 2:
+        raise gr.Error("Pinte pelo menos 2 keyframes na Etapa 2 para produzir as transições.")
+    log_history = "\n--- FASE 3/4: A Câmera e o Cineasta estão filmando em sequência just-in-time...\n"
+    yield {production_log_output: log_history, video_gallery_glitch: []}
+    target_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    try:
+        print(f"Câmera (LTX): Movendo para a {target_device} para a produção em lote.")
+        pipeline_instance.to(target_device)
+        if target_device == 'cuda':
+            if hasattr(pipeline_instance, 'disable_model_cpu_offload'): pipeline_instance.disable_model_cpu_offload()
+            if hasattr(pipeline_instance, 'disable_attention_slicing'): pipeline_instance.disable_attention_slicing()
+            if hasattr(pipeline_instance.vae, 'disable_slicing'): pipeline_instance.vae.disable_slicing()
+            if hasattr(pipeline_instance.vae, 'disable_z_tiling'): pipeline_instance.vae.disable_z_tiling()
+        video_fragments, story_history = [], ""
+        previous_fragment_last_frame_path = keyframe_images_state[0]
+        with Image.open(keyframe_images_state[0]) as img: width, height = img.size
+        num_transitions = len(keyframe_images_state) - 1
+        for i in range(num_transitions):
+            start_image_path = previous_fragment_last_frame_path
+            middle_image_path = keyframe_images_state[i]
+            end_image_path = keyframe_images_state[i+1]
+            fragment_num = i + 1
+            is_last_fragment = (i == num_transitions - 1)
+            progress(i / num_transitions, desc=f"Planejando e Filmando Fragmento {fragment_num}/{num_transitions}")
+            log_history += f"\n--- FRAGMENTO {fragment_num} ---\n"
+            story_history += f"\n- Transição de '{scene_storyboard[i]}' para '{scene_storyboard[i+1]}'."
+            current_motion_prompt = get_single_motion_prompt(prompt_geral, story_history, start_image_path, middle_image_path, end_image_path)
+            log_history += f"Instrução do Cineasta (3 Atos): '{current_motion_prompt}'\n"
+            yield {production_log_output: log_history}
+            foreshadow_frame, foreshadow_strength = 54, 0.3
+            end_frame_index = VIDEO_TOTAL_FRAMES - CONVERGENCE_FRAMES
+            conditioning_items_data = [(start_image_path, 0, 1.0), (end_image_path, foreshadow_frame, foreshadow_strength), (end_image_path, end_frame_index, 1.0)]
+            full_fragment_path, frames_gerados = run_ltx_animation(fragment_num, current_motion_prompt, conditioning_items_data, width, height, seed, cfg, progress)
+            log_history += f"  - Gerado: {frames_gerados} frames\n"
+            if not is_last_fragment:
+                cut_frames = int(cut_frames_value)
+                final_fragment_path = os.path.join(WORKSPACE_DIR, f"fragment_{fragment_num}_final_{cut_frames}f.mp4")
+                trim_video_to_frames(full_fragment_path, final_fragment_path, cut_frames)
+                output_frame_path = os.path.join(WORKSPACE_DIR, f"last_frame_of_frag_{fragment_num}.png")
+                previous_fragment_last_frame_path = extract_last_frame_as_image(final_fragment_path, output_frame_path)
+                log_history += f"  - Cortado para: {cut_frames} frames\n"
+                log_history += f"  - Memória para próxima cena: Último frame extraído\n"
+            else:
+                final_fragment_path = full_fragment_path
+                log_history += f"  - Último fragmento, mantendo duração total: {frames_gerados} frames\n"
+            video_fragments.append(final_fragment_path)
+            yield {production_log_output: log_history, video_gallery_glitch: video_fragments}
+        log_history += "\nFilmagem de todos os fragmentos de transição concluída.\n"
+        progress(1.0, desc="Produção Concluída.")
+        yield {production_log_output: log_history, video_gallery_glitch: video_fragments, fragment_list_state: video_fragments}
+    finally:
+        print(f"Câmera (LTX): Produção em lote concluída. Movendo para a CPU para liberar VRAM.")
+        pipeline_instance.to('cpu')
+        gc.collect()
+        if torch.cuda.is_available(): torch.cuda.empty_cache()
+
+def concatenate_and_trim_masterpiece(fragment_paths: list, progress=gr.Progress()):
+    if not fragment_paths: raise gr.Error("Nenhum fragmento de vídeo para concatenar.")
+    progress(0.5, desc="Montando a obra-prima final...")
+    try:
+        list_file_path, final_output_path = os.path.join(WORKSPACE_DIR, "concat_list.txt"), os.path.join(WORKSPACE_DIR, "obra_prima_final.mp4")
+        with open(list_file_path, "w") as f:
+            for p in fragment_paths: f.write(f"file '{os.path.abspath(p)}'\n")
+        concat_cmd = f"ffmpeg -y -v error -f concat -safe 0 -i \"{list_file_path}\" -c copy \"{final_output_path}\""
+        subprocess.run(concat_cmd, shell=True, check=True, capture_output=True, text=True)
+        progress(1.0, desc="Montagem concluída!")
+        return final_output_path
+    except (subprocess.CalledProcessError, ValueError) as e:
+        error_message = f"FFmpeg falhou durante a concatenação final: {e}"
+        if hasattr(e, 'stderr'): error_message += f"\nDetalhes do erro do FFmpeg: {e.stderr}"
+        raise gr.Error(error_message)
+
+# --- Ato 5: A Interface com o Mundo (A UI Restaurada e Aprimorada) ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# NOVINHO-4.2 (Piloto de Testes - Arquitetura 'Memória, Caminho, Destino')\n*By Carlex & Gemini & DreamO*")
+    
+    if os.path.exists(WORKSPACE_DIR): shutil.rmtree(WORKSPACE_DIR)
+    os.makedirs(WORKSPACE_DIR)
+    Path("examples").mkdir(exist_ok=True)
+    
+    scene_storyboard_state, keyframe_images_state, fragment_list_state = gr.State([]), gr.State([]), gr.State([])
+    prompt_geral_state, processed_ref_path_state = gr.State(""), gr.State("")
+
+    gr.Markdown("--- \n ## ETAPA 1: O ROTEIRO (Sonhador)")
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt_input = gr.Textbox(label="Ideia Geral (Prompt)")
+            num_fragments_input = gr.Slider(2, 10, 4, step=1, label="Número de Cenas")
+            image_input = gr.Image(type="filepath", label=f"Imagem de Referência Principal (será {TARGET_RESOLUTION}x{TARGET_RESOLUTION})")
+            director_button = gr.Button("▶️ 1. Gerar Roteiro de Cenas", variant="primary")
+        with gr.Column(scale=2): storyboard_to_show = gr.JSON(label="Roteiro de Cenas Gerado")
+            
+    gr.Markdown("--- \n ## ETAPA 2: OS KEYFRAMES (Pintor)")
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("### Controles do Pintor (DreamO)\n**Tarefas:** `style` (estilo), `ip` (conteúdo), `id` (identidade).")
+            ref_image_inputs, ref_task_inputs = [], []
+            with gr.Group():
+                with gr.Row():
+                    ref_image_inputs.append(gr.Image(label="Referência Inicial / Sequencial (Automática)", type="filepath", interactive=False))
+                    ref_task_inputs.append(gr.Dropdown(choices=["ip", "id", "style"], value="ip", label="Tarefa da Referência"))
+            photographer_button = gr.Button("▶️ 2. Pintar Imagens-Chave em Cadeia", variant="primary")
+        with gr.Column(scale=1):
+            keyframe_log_output = gr.Textbox(label="Diário de Bordo do Pintor", lines=15, interactive=False)
+            keyframe_gallery_output = gr.Gallery(label="Imagens-Chave Pintadas", object_fit="contain", height="auto", type="filepath")
+
+    gr.Markdown("--- \n ## ETAPA 3: A PRODUÇÃO (Cineasta e Câmera)")
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Row():
+                seed_number = gr.Number(42, label="Seed")
+                cfg_slider = gr.Slider(1.0, 10.0, 2.5, step=0.1, label="CFG")
+            cut_frames_slider = gr.Slider(label="Duração do Fragmento (Frames)", minimum=36, maximum=VIDEO_TOTAL_FRAMES, value=90, step=1)
+            animator_button = gr.Button("▶️ 3. Produzir Cenas em Vídeo", variant="primary")
+            production_log_output = gr.Textbox(label="Diário de Bordo da Produção", lines=15, interactive=False)
+        with gr.Column(scale=1): video_gallery_glitch = gr.Gallery(label="Fragmentos Gerados", object_fit="contain", height="auto", type="video")
+    
+    gr.Markdown(f"--- \n ## ETAPA 4: PÓS-PRODUÇÃO (Editor)")
+    editor_button = gr.Button("▶️ 4. Montar Vídeo Final", variant="primary")
+    final_video_output = gr.Video(label="A Obra-Prima Final", width=TARGET_RESOLUTION)
+
+    gr.Markdown(
+        """
+        ---
+        ### A Arquitetura "Memória, Caminho, Destino"
+        Nossa geração de vídeo é governada por uma orquestração de IAs, onde cada fragmento (`V_i`) é criado com base em três pilares:
+
+        *   **Memória (`M_(i-1)`):** O último frame do fragmento anterior. Garante a **continuidade** visual.
+        *   **Caminho (`Γ_i`):** Uma instrução de movimento gerada pelo "Cineasta" (Gemini) ao analisar a Memória, o Keyframe atual e o Destino. Define a **narrativa** da transição.
+        *   **Destino (`K_(i+1)`):** O próximo keyframe a ser alcançado. Define o **objetivo** da animação.
+
+        A Câmera (LTX) recebe esses três elementos para construir cada cena, resultando em um vídeo coeso e com propósito.
+        """
+    )
+
+    # --- Ato 6: A Regência (Lógica de Conexão dos Botões) ---
+    director_button.click(
+        fn=get_static_scenes_storyboard, 
+        inputs=[num_fragments_input, prompt_input, image_input], 
+        outputs=[scene_storyboard_state]
+    ).success(
+        fn=lambda s, p: (s, p), 
+        inputs=[scene_storyboard_state, prompt_input], 
+        outputs=[storyboard_to_show, prompt_geral_state]
+    ).success(
+        fn=process_image_to_square, 
+        inputs=[image_input], 
+        outputs=[processed_ref_path_state]
+    ).success(
+        fn=lambda p: p, 
+        inputs=[processed_ref_path_state], 
+        outputs=[ref_image_inputs[0]]
+    )
+    
+    photographer_button.click(
+        fn=run_keyframe_generation, 
+        inputs=[scene_storyboard_state, processed_ref_path_state, ref_task_inputs[0]], 
+        outputs=[keyframe_log_output, keyframe_gallery_output, keyframe_images_state]
+    )
+    
+    animator_button.click(
+        fn=run_video_production,
+        inputs=[prompt_geral_state, keyframe_images_state, scene_storyboard_state, seed_number, cfg_slider, cut_frames_slider],
+        outputs=[production_log_output, video_gallery_glitch, fragment_list_state]
+    )
+    
+    editor_button.click(
+        fn=concatenate_and_trim_masterpiece, 
+        inputs=[fragment_list_state], 
+        outputs=[final_video_output]
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", share=True)