import os from einops import rearrange import torch import torch.nn as nn from xfuser.core.distributed import ( get_sequence_parallel_rank, get_sequence_parallel_world_size, get_sp_group, ) from einops import rearrange, repeat from functools import lru_cache import imageio import uuid from tqdm import tqdm import numpy as np import subprocess import soundfile as sf VID_EXTENSIONS = (".mp4", ".avi", ".mov", ".mkv") ASPECT_RATIO_627 = { '0.26': ([320, 1216], 1), '0.38': ([384, 1024], 1), '0.50': ([448, 896], 1), '0.67': ([512, 768], 1), '0.82': ([576, 704], 1), '1.00': ([640, 640], 1), '1.22': ([704, 576], 1), '1.50': ([768, 512], 1), '1.86': ([832, 448], 1), '2.00': ([896, 448], 1), '2.50': ([960, 384], 1), '2.83': ([1088, 384], 1), '3.60': ([1152, 320], 1), '3.80': ([1216, 320], 1), '4.00': ([1280, 320], 1)} ASPECT_RATIO_960 = { '0.22': ([448, 2048], 1), '0.29': ([512, 1792], 1), '0.36': ([576, 1600], 1), '0.45': ([640, 1408], 1), '0.55': ([704, 1280], 1), '0.63': ([768, 1216], 1), '0.76': ([832, 1088], 1), '0.88': ([896, 1024], 1), '1.00': ([960, 960], 1), '1.14': ([1024, 896], 1), '1.31': ([1088, 832], 1), '1.50': ([1152, 768], 1), '1.58': ([1216, 768], 1), '1.82': ([1280, 704], 1), '1.91': ([1344, 704], 1), '2.20': ([1408, 640], 1), '2.30': ([1472, 640], 1), '2.67': ([1536, 576], 1), '2.89': ([1664, 576], 1), '3.62': ([1856, 512], 1), '3.75': ([1920, 512], 1)} def torch_gc(): torch.cuda.empty_cache() torch.cuda.ipc_collect() # 确保所有必要的库都已导入 import torch import numpy as np import imageio from PIL import Image, ImageDraw, ImageFont import textwrap from tqdm import tqdm import subprocess import os # 1. 确保所有必要的库都已导入 import torch import numpy as np import imageio from PIL import Image, ImageDraw, ImageFont import textwrap from tqdm import tqdm import subprocess import os def merge_audio_and_video(video_path, audio_path, output_path): """ 使用 FFmpeg 将指定的音频和视频合并。 参数: video_path (str): 原始视频文件路径。 audio_path (str): 要合并的音频文件路径。 output_path (str): 合成后视频的保存路径。 """ # 构建 FFmpeg 命令 # -i video.mp4: 输入视频文件 # -i audio.wav: 输入音频文件 # -c:v copy: 直接复制视频流，不重新编码，速度快且无损画质 # -c:a aac: 将音频编码为 AAC 格式，这是 mp4 容器的常用格式 # -map 0:v:0: 映射第一个输入文件（视频）的视频流 # -map 1:a:0: 映射第二个输入文件（音频）的音频流 # -shortest: 当最短的输入流结束时，完成编码。这确保了如果音频比视频长，音频会被截断以匹配视频长度。 # -y: 如果输出文件已存在，则自动覆盖 new_path = '/apdcephfs_cq10/share_1367250/raylanzhang/ffmpeg2/ffmpeg/ffmpeg-7.0.2-i686-static' # 获取当前的 PATH 环境变量 # 使用 os.environ.get('PATH', '') 来避免在 PATH 不存在时出错 current_path = os.environ.get('PATH', '') # 构建新的 PATH，将新路径添加到最前面 # os.pathsep 是系统特定的路径分隔符 (在 Linux 和 macOS 上是 ':'，在 Windows 上是 ';') new_path_value = new_path + os.pathsep + current_path # 设置新的 PATH 环境变量 os.environ['PATH'] = new_path_value command = [ 'ffmpeg', '-i', video_path, '-i', audio_path, '-c:v', 'copy', '-c:a', 'aac', '-map', '0:v:0', '-map', '1:a:0', '-shortest', # <--- 关键参数 '-y', output_path ] try: print(f"正在处理: {os.path.basename(video_path)} 和 {audio_path}") # 执行命令，并隐藏 FFmpeg 的输出信息 subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) print(f"成功 -> {os.path.basename(output_path)}") except subprocess.CalledProcessError as e: print(f"处理失败: {os.path.basename(video_path)}") # 打印错误信息以便调试 print("FFmpeg 错误信息:", e.stderr.decode()) except FileNotFoundError: print("错误：找不到 'ffmpeg' 命令。请确保 FFmpeg 已正确安装并已添加到系统 PATH 环境变量中。") return def save_composite_video_with_audio( main_video_tensor, save_path, motion_video_tensor=None, vocal_audio_path=None, text=None, fps=25, quality=7, # 默认质量稍作提高 font_path=None, bg_color='black', text_color='white' ): """ 将一或两个视频与可选的文本和音频合并，并保存为一个最终的 MP4 文件。 该函数整合了视觉合成和音频添加的功能，并开启了调试模式： 1. 它首先将主视频、可选的第二个视频和可选的文本面板横向拼接， 生成一个无声的视觉合成视频。 2. 如果提供了音频文件，它会使用 FFmpeg 将音频剪辑到与视频等长， 然后将其混入视频中。FFmpeg 的所有输出都会被打印，以便调试。 3. 最后清理所有临时文件，只留下最终的带音频的视频。 参数: main_video_tensor (torch.Tensor): 形状为 (C, T, H, W) 的主视频张量，像素值范围 [-1, 1]。 save_path (str): 最终视频的保存路径（.mp4 后缀会自动处理）。 motion_video_tensor (torch.Tensor, optional): 形状与主视频相同的第二个视频张量。 vocal_audio_path (str or list, optional): 输入的音频文件路径。如果为 None，则只输出无声视频。 text (str, optional): 要显示在视频右侧的文字。 fps (int): 视频的帧率。 quality (int): 视频质量，数值越高质量越好。 font_path (str, optional): .ttf 或 .otf 字体文件的路径。 bg_color (str): 文字区域的背景颜色。 text_color (str): 文字的颜色。 """ # ------------------ 设置文件路径 ------------------ base_save_path = os.path.splitext(save_path)[0] final_video_path = base_save_path + ".mp4" temp_video_path = base_save_path + "_temp_video.mp4" temp_audio_path = base_save_path + "_temp_audio.wav" # ========================================================================= # PART 1: 视觉合成 (生成无声视频) # ========================================================================= print("步骤 1/3: 正在生成视觉合成视频...") # 内部辅助函数 def _save_video_from_frames(frames, path, fps, quality): # 使用 imageio V3 API writer = imageio.get_writer(path, fps=fps, quality=quality, macro_block_size=1) for frame in tqdm(frames, desc=f"正在保存临时视频到 {path}"): writer.append_data(np.array(frame)) writer.close() def _process_video_tensor(video_tensor): video_frames = (video_tensor + 1) / 2 video_frames = video_frames.permute(1, 2, 3, 0).cpu().numpy() return np.clip(video_frames * 255, 0, 255).astype(np.uint8) # 1.1 转换视频张量 video_frames_uint8 = _process_video_tensor(main_video_tensor) _, T, H, W = main_video_tensor.shape motion_frames_uint8 = None if motion_video_tensor is not None: motion_frames_uint8 = _process_video_tensor(motion_video_tensor) # 1.2 创建文本面板 text_panel = None if text: margin = int(W * 0.1) max_text_width, max_text_height = W - margin, H - margin font_size = max(15, int(H / 15)) font = None wrapped_text = text while font_size > 8: try: font = ImageFont.truetype(font_path, font_size) except (IOError, TypeError): if font is None: font = ImageFont.load_default() avg_char_width = font_size * 0.6 wrap_width = max(10, int(W / avg_char_width)) wrapped_text = textwrap.fill(text, width=wrap_width) temp_draw = ImageDraw.Draw(Image.new('RGB', (W, H))) bbox = temp_draw.multiline_textbbox((0, 0), wrapped_text, font=font) text_width, text_height = bbox[2] - bbox[0], bbox[3] - bbox[1] if text_width < max_text_width and text_height < max_text_height: break font_size -= 1 text_panel = Image.new('RGB', (W, H), color=bg_color) draw = ImageDraw.Draw(text_panel) text_x, text_y = (W - text_width) / 2, (H - text_height) / 2 draw.multiline_text((text_x, text_y), wrapped_text, font=font, fill=text_color, align='center') # 1.3 合成每一帧 num_columns = 1 + (motion_frames_uint8 is not None) + (text_panel is not None) final_width = W * num_columns composite_frames = [] for i in range(len(video_frames_uint8)): composite_frame = Image.new('RGB', (final_width, H)) current_x = 0 composite_frame.paste(Image.fromarray(video_frames_uint8[i]), (current_x, 0)) current_x += W if motion_frames_uint8 is not None: composite_frame.paste(Image.fromarray(motion_frames_uint8[i]), (current_x, 0)) current_x += W if text_panel is not None: composite_frame.paste(text_panel, (current_x, 0)) composite_frames.append(composite_frame) # 1.4 保存无声视频 _save_video_from_frames(composite_frames, temp_video_path, fps, quality) print("无声视频已成功生成。") # ========================================================================= # PART 2: 添加音频 (使用 FFmpeg) # ========================================================================= if vocal_audio_path is None: print("未提供音频文件，将无声视频重命名为最终文件。") os.rename(temp_video_path, final_video_path) print(f"最终视频已保存到: {final_video_path}") return print("步骤 2/3: 正在处理并添加音频...") # 2.1 智能处理音频路径输入 (str 或 list) actual_audio_file = None if isinstance(vocal_audio_path, list): if len(vocal_audio_path) > 0: actual_audio_file = vocal_audio_path[0] if len(vocal_audio_path) > 1: print(f"警告: 提供了 {len(vocal_audio_path)} 个音频文件，只会使用第一个: {actual_audio_file}") elif isinstance(vocal_audio_path, str): actual_audio_file = vocal_audio_path if not actual_audio_file: print("警告: 提供的音频路径为空或格式不正确，将生成无声视频。") os.rename(temp_video_path, final_video_path) return # 2.2 剪辑音频 duration = T / fps try: crop_command = [ "ffmpeg", "-y", "-i", actual_audio_file, "-t", f'{duration}', "-acodec", "pcm_s16le", # 使用标准的WAV编码，兼容性好 temp_audio_path, ] print("\n[调试信息] 将要执行音频剪辑命令:") print(" ".join(crop_command)) # 执行命令并显示所有输出 subprocess.run(crop_command, check=True) print(f"音频已成功剪辑到 {duration:.2f} 秒。") except FileNotFoundError: print("\n错误: 'ffmpeg' 命令未找到。请确保 FFmpeg 已正确安装并已添加到系统的 PATH 环境变量中。") if os.path.exists(temp_video_path): os.remove(temp_video_path) return except subprocess.CalledProcessError as e: print(f"\n错误：剪辑音频失败。FFmpeg 返回了非零退出码。请检查上面的 FFmpeg 输出信息以了解详情。") print(f"Python 错误详情: {e}") if os.path.exists(temp_video_path): os.remove(temp_video_path) return # 2.3 合并视频和音频 try: merge_command = [ "ffmpeg", "-y", "-i", temp_video_path, "-i", temp_audio_path, "-c:v", "copy", # 直接复制视频流，速度快 "-c:a", "aac", # 编码音频流 "-shortest", # 以最短的流为准结束 final_video_path, ] print("\n[调试信息] 将要执行音视频合并命令:") print(" ".join(merge_command)) # 执行命令并显示所有输出 subprocess.run(merge_command, check=True) print("视频和音频合并成功。") except FileNotFoundError: print("\n错误: 'ffmpeg' 命令未找到。请确保 FFmpeg 已正确安装并已添加到系统的 PATH 环境变量中。") except subprocess.CalledProcessError as e: print(f"\n错误：合并视频和音频失败。FFmpeg 返回了非零退出码。请检查上面的 FFmpeg 输出信息以了解详情。") print(f"Python 错误详情: {e}") finally: # ========================================================================= # PART 3: 清理临时文件 # ========================================================================= print("步骤 3/3: 正在清理临时文件...") if os.path.exists(temp_video_path): os.remove(temp_video_path) if os.path.exists(temp_audio_path): os.remove(temp_audio_path) if os.path.exists(final_video_path): print(f"任务完成！最终视频已成功保存到: {final_video_path}") else: print("任务失败，未生成最终文件。请检查上面的日志输出。") def split_token_counts_and_frame_ids(T, token_frame, world_size, rank): S = T * token_frame split_sizes = [S // world_size + (1 if i < S % world_size else 0) for i in range(world_size)] start = sum(split_sizes[:rank]) end = start + split_sizes[rank] counts = [0] * T for idx in range(start, end): t = idx // token_frame counts[t] += 1 counts_filtered = [] frame_ids = [] for t, c in enumerate(counts): if c > 0: counts_filtered.append(c) frame_ids.append(t) return counts_filtered, frame_ids def normalize_and_scale(column, source_range, target_range, epsilon=1e-8): source_min, source_max = source_range new_min, new_max = target_range normalized = (column - source_min) / (source_max - source_min + epsilon) scaled = normalized * (new_max - new_min) + new_min return scaled @torch.compile def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, mode='mean', attn_bias=None): ref_k = ref_k.to(visual_q.dtype).to(visual_q.device) scale = 1.0 / visual_q.shape[-1] ** 0.5 visual_q = visual_q * scale visual_q = visual_q.transpose(1, 2) ref_k = ref_k.transpose(1, 2) attn = visual_q @ ref_k.transpose(-2, -1) if attn_bias is not None: attn = attn + attn_bias x_ref_attn_map_source = attn.softmax(-1) # B, H, x_seqlens, ref_seqlens x_ref_attn_maps = [] ref_target_masks = ref_target_masks.to(visual_q.dtype) x_ref_attn_map_source = x_ref_attn_map_source.to(visual_q.dtype) for class_idx, ref_target_mask in enumerate(ref_target_masks): torch_gc() ref_target_mask = ref_target_mask[None, None, None, ...] x_ref_attnmap = x_ref_attn_map_source * ref_target_mask x_ref_attnmap = x_ref_attnmap.sum(-1) / ref_target_mask.sum() # B, H, x_seqlens, ref_seqlens --> B, H, x_seqlens x_ref_attnmap = x_ref_attnmap.permute(0, 2, 1) # B, x_seqlens, H if mode == 'mean': x_ref_attnmap = x_ref_attnmap.mean(-1) # B, x_seqlens elif mode == 'max': x_ref_attnmap = x_ref_attnmap.max(-1) # B, x_seqlens x_ref_attn_maps.append(x_ref_attnmap) del attn del x_ref_attn_map_source torch_gc() return torch.concat(x_ref_attn_maps, dim=0) def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2, enable_sp=False): """Args: query (torch.tensor): B M H K key (torch.tensor): B M H K shape (tuple): (N_t, N_h, N_w) ref_target_masks: [B, N_h * N_w] """ N_t, N_h, N_w = shape if enable_sp: ref_k = get_sp_group().all_gather(ref_k, dim=1) x_seqlens = N_h * N_w ref_k = ref_k[:, :x_seqlens] _, seq_lens, heads, _ = visual_q.shape class_num, _ = ref_target_masks.shape x_ref_attn_maps = torch.zeros(class_num, seq_lens).to(visual_q.device).to(visual_q.dtype) split_chunk = heads // split_num for i in range(split_num): x_ref_attn_maps_perhead = calculate_x_ref_attn_map( visual_q[:, :, i*split_chunk:(i+1)*split_chunk, :], ref_k[:, :, i*split_chunk:(i+1)*split_chunk, :], ref_target_masks ) x_ref_attn_maps += x_ref_attn_maps_perhead return x_ref_attn_maps / split_num def rotate_half(x): x = rearrange(x, "... (d r) -> ... d r", r=2) x1, x2 = x.unbind(dim=-1) x = torch.stack((-x2, x1), dim=-1) return rearrange(x, "... d r -> ... (d r)") class RotaryPositionalEmbedding1D(nn.Module): def __init__(self, head_dim, ): super().__init__() self.head_dim = head_dim self.base = 10000 @lru_cache(maxsize=32) def precompute_freqs_cis_1d(self, pos_indices): freqs = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2)[: (self.head_dim // 2)].float() / self.head_dim)) freqs = freqs.to(pos_indices.device) freqs = torch.einsum("..., f -> ... f", pos_indices.float(), freqs) freqs = repeat(freqs, "... n -> ... (n r)", r=2) return freqs def forward(self, x, pos_indices): """1D RoPE. Args: query (torch.tensor): [B, head, seq, head_dim] pos_indices (torch.tensor): [seq,] Returns: query with the same shape as input. """ freqs_cis = self.precompute_freqs_cis_1d(pos_indices) x_ = x.float() freqs_cis = freqs_cis.float().to(x.device) cos, sin = freqs_cis.cos(), freqs_cis.sin() cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d') x_ = (x_ * cos) + (rotate_half(x_) * sin) return x_.type_as(x) def save_video_ffmpeg(gen_video_samples, save_path, vocal_audio_list, fps=25, quality=5): def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None): writer = imageio.get_writer( save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params ) for frame in tqdm(frames, desc="Saving video"): frame = np.array(frame) writer.append_data(frame) writer.close() save_path_tmp = save_path + "-temp.mp4" video_audio = (gen_video_samples+1)/2 # C T H W video_audio = video_audio.permute(1, 2, 3, 0).cpu().numpy() video_audio = np.clip(video_audio * 255, 0, 255).astype(np.uint8) save_video(video_audio, save_path_tmp, fps=fps, quality=quality) # crop audio according to video length _, T, _, _ = gen_video_samples.shape duration = T / fps save_path_crop_audio = save_path + "-cropaudio.wav" final_command = [ "ffmpeg", "-i", vocal_audio_list[0], "-t", f'{duration}', save_path_crop_audio, ] subprocess.run(final_command, check=True) # generate video with audio save_path = save_path + ".mp4" final_command = [ "ffmpeg", "-y", "-i", save_path_tmp, "-i", save_path_crop_audio, "-c:v", "libx264", "-c:a", "aac", "-shortest", save_path, ] subprocess.run(final_command, check=True) os.remove(save_path_tmp) os.remove(save_path_crop_audio) class MomentumBuffer: def __init__(self, momentum: float): self.momentum = momentum self.running_average = 0 def update(self, update_value: torch.Tensor): new_average = self.momentum * self.running_average self.running_average = update_value + new_average def project( v0: torch.Tensor, # [B, C, T, H, W] v1: torch.Tensor, # [B, C, T, H, W] ): dtype = v0.dtype v0, v1 = v0.double(), v1.double() v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3, -4]) v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3, -4], keepdim=True) * v1 v0_orthogonal = v0 - v0_parallel return v0_parallel.to(dtype), v0_orthogonal.to(dtype) def adaptive_projected_guidance( diff: torch.Tensor, # [B, C, T, H, W] pred_cond: torch.Tensor, # [B, C, T, H, W] momentum_buffer: MomentumBuffer = None, eta: float = 0.0, norm_threshold: float = 55, ): if momentum_buffer is not None: momentum_buffer.update(diff) diff = momentum_buffer.running_average if norm_threshold > 0: ones = torch.ones_like(diff) diff_norm = diff.norm(p=2, dim=[-1, -2, -3, -4], keepdim=True) print(f"diff_norm: {diff_norm}") scale_factor = torch.minimum(ones, norm_threshold / diff_norm) diff = diff * scale_factor diff_parallel, diff_orthogonal = project(diff, pred_cond) normalized_update = diff_orthogonal + eta * diff_parallel return normalized_update