mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
354 lines
12 KiB
Python
354 lines
12 KiB
Python
import os
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from einops import rearrange
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import torch
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import torch.nn as nn
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from einops import rearrange, repeat
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from functools import lru_cache
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import imageio
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import uuid
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from tqdm import tqdm
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import numpy as np
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import subprocess
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import soundfile as sf
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import torchvision
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import binascii
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import os.path as osp
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VID_EXTENSIONS = (".mp4", ".avi", ".mov", ".mkv")
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ASPECT_RATIO_627 = {
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'0.26': ([320, 1216], 1), '0.38': ([384, 1024], 1), '0.50': ([448, 896], 1), '0.67': ([512, 768], 1),
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'0.82': ([576, 704], 1), '1.00': ([640, 640], 1), '1.22': ([704, 576], 1), '1.50': ([768, 512], 1),
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'1.86': ([832, 448], 1), '2.00': ([896, 448], 1), '2.50': ([960, 384], 1), '2.83': ([1088, 384], 1),
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'3.60': ([1152, 320], 1), '3.80': ([1216, 320], 1), '4.00': ([1280, 320], 1)}
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ASPECT_RATIO_960 = {
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'0.22': ([448, 2048], 1), '0.29': ([512, 1792], 1), '0.36': ([576, 1600], 1), '0.45': ([640, 1408], 1),
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'0.55': ([704, 1280], 1), '0.63': ([768, 1216], 1), '0.76': ([832, 1088], 1), '0.88': ([896, 1024], 1),
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'1.00': ([960, 960], 1), '1.14': ([1024, 896], 1), '1.31': ([1088, 832], 1), '1.50': ([1152, 768], 1),
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'1.58': ([1216, 768], 1), '1.82': ([1280, 704], 1), '1.91': ([1344, 704], 1), '2.20': ([1408, 640], 1),
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'2.30': ([1472, 640], 1), '2.67': ([1536, 576], 1), '2.89': ([1664, 576], 1), '3.62': ([1856, 512], 1),
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'3.75': ([1920, 512], 1)}
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def torch_gc():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def split_token_counts_and_frame_ids(T, token_frame, world_size, rank):
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S = T * token_frame
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split_sizes = [S // world_size + (1 if i < S % world_size else 0) for i in range(world_size)]
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start = sum(split_sizes[:rank])
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end = start + split_sizes[rank]
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counts = [0] * T
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for idx in range(start, end):
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t = idx // token_frame
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counts[t] += 1
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counts_filtered = []
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frame_ids = []
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for t, c in enumerate(counts):
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if c > 0:
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counts_filtered.append(c)
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frame_ids.append(t)
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return counts_filtered, frame_ids
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def normalize_and_scale(column, source_range, target_range, epsilon=1e-8):
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source_min, source_max = source_range
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new_min, new_max = target_range
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normalized = (column - source_min) / (source_max - source_min + epsilon)
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scaled = normalized * (new_max - new_min) + new_min
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return scaled
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# @torch.compile
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def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, ref_images_count, mode='mean', attn_bias=None):
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ref_k = ref_k.to(visual_q.dtype).to(visual_q.device)
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scale = 1.0 / visual_q.shape[-1] ** 0.5
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visual_q = visual_q * scale
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visual_q = visual_q.transpose(1, 2)
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ref_k = ref_k.transpose(1, 2)
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attn = visual_q @ ref_k.transpose(-2, -1)
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if attn_bias is not None: attn += attn_bias
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x_ref_attn_map_source = attn.softmax(-1) # B, H, x_seqlens, ref_seqlens
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x_ref_attn_maps = []
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ref_target_masks = ref_target_masks.to(visual_q.dtype)
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x_ref_attn_map_source = x_ref_attn_map_source.to(visual_q.dtype)
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for class_idx, ref_target_mask in enumerate(ref_target_masks):
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ref_target_mask = ref_target_mask[None, None, None, ...]
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x_ref_attnmap = x_ref_attn_map_source * ref_target_mask
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x_ref_attnmap = x_ref_attnmap.sum(-1) / ref_target_mask.sum() # B, H, x_seqlens, ref_seqlens --> B, H, x_seqlens
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x_ref_attnmap = x_ref_attnmap.permute(0, 2, 1) # B, x_seqlens, H
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if mode == 'mean':
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x_ref_attnmap = x_ref_attnmap.mean(-1) # B, x_seqlens
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elif mode == 'max':
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x_ref_attnmap = x_ref_attnmap.max(-1) # B, x_seqlens
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x_ref_attn_maps.append(x_ref_attnmap)
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del attn
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del x_ref_attn_map_source
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return torch.concat(x_ref_attn_maps, dim=0)
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def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=10, ref_images_count = 0):
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"""Args:
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query (torch.tensor): B M H K
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key (torch.tensor): B M H K
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shape (tuple): (N_t, N_h, N_w)
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ref_target_masks: [B, N_h * N_w]
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"""
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N_t, N_h, N_w = shape
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x_seqlens = N_h * N_w
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ref_k = ref_k[:, :x_seqlens]
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if ref_images_count > 0 :
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visual_q_shape = visual_q.shape
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visual_q = visual_q.reshape(visual_q_shape[0], N_t, -1)
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visual_q = visual_q[:, ref_images_count:]
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visual_q = visual_q.reshape(visual_q_shape[0], -1, *visual_q_shape[-2:])
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_, seq_lens, heads, _ = visual_q.shape
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class_num, _ = ref_target_masks.shape
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x_ref_attn_maps = torch.zeros(class_num, seq_lens, dtype=visual_q.dtype, device=visual_q.device)
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split_chunk = heads // split_num
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for i in range(split_num):
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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, ref_images_count)
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x_ref_attn_maps += x_ref_attn_maps_perhead
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x_ref_attn_maps /= split_num
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return x_ref_attn_maps
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def rotate_half(x):
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x = rearrange(x, "... (d r) -> ... d r", r=2)
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x1, x2 = x.unbind(dim=-1)
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x = torch.stack((-x2, x1), dim=-1)
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return rearrange(x, "... d r -> ... (d r)")
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class RotaryPositionalEmbedding1D(nn.Module):
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def __init__(self,
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head_dim,
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):
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super().__init__()
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self.head_dim = head_dim
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self.base = 10000
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@lru_cache(maxsize=32)
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def precompute_freqs_cis_1d(self, pos_indices):
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freqs = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2)[: (self.head_dim // 2)].float() / self.head_dim))
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freqs = freqs.to(pos_indices.device)
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freqs = torch.einsum("..., f -> ... f", pos_indices.float(), freqs)
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freqs = repeat(freqs, "... n -> ... (n r)", r=2)
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return freqs
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def forward(self, x, pos_indices):
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"""1D RoPE.
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Args:
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query (torch.tensor): [B, head, seq, head_dim]
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pos_indices (torch.tensor): [seq,]
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Returns:
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query with the same shape as input.
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"""
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freqs_cis = self.precompute_freqs_cis_1d(pos_indices)
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x_ = x.float()
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freqs_cis = freqs_cis.float().to(x.device)
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cos, sin = freqs_cis.cos(), freqs_cis.sin()
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cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d')
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x_ = (x_ * cos) + (rotate_half(x_) * sin)
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return x_.type_as(x)
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def rand_name(length=8, suffix=''):
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name = binascii.b2a_hex(os.urandom(length)).decode('utf-8')
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if suffix:
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if not suffix.startswith('.'):
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suffix = '.' + suffix
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name += suffix
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return name
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def cache_video(tensor,
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save_file=None,
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fps=30,
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suffix='.mp4',
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nrow=8,
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normalize=True,
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value_range=(-1, 1),
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retry=5):
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# cache file
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cache_file = osp.join('/tmp', rand_name(
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suffix=suffix)) if save_file is None else save_file
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# save to cache
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error = None
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for _ in range(retry):
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# preprocess
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tensor = tensor.clamp(min(value_range), max(value_range))
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tensor = torch.stack([
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torchvision.utils.make_grid(
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u, nrow=nrow, normalize=normalize, value_range=value_range)
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for u in tensor.unbind(2)
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],
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dim=1).permute(1, 2, 3, 0)
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tensor = (tensor * 255).type(torch.uint8).cpu()
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# write video
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writer = imageio.get_writer(cache_file, fps=fps, codec='libx264', quality=10, ffmpeg_params=["-crf", "10"])
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for frame in tensor.numpy():
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writer.append_data(frame)
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writer.close()
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return cache_file
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def save_video_ffmpeg(gen_video_samples, save_path, vocal_audio_list, fps=25, quality=5, high_quality_save=False):
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def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None):
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writer = imageio.get_writer(
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save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params
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)
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for frame in tqdm(frames, desc="Saving video"):
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frame = np.array(frame)
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writer.append_data(frame)
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writer.close()
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save_path_tmp = save_path + "-temp.mp4"
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if high_quality_save:
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cache_video(
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tensor=gen_video_samples.unsqueeze(0),
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save_file=save_path_tmp,
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fps=fps,
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nrow=1,
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normalize=True,
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value_range=(-1, 1)
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)
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else:
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video_audio = (gen_video_samples+1)/2 # C T H W
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video_audio = video_audio.permute(1, 2, 3, 0).cpu().numpy()
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video_audio = np.clip(video_audio * 255, 0, 255).astype(np.uint8) # to [0, 255]
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save_video(video_audio, save_path_tmp, fps=fps, quality=quality)
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# crop audio according to video length
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_, T, _, _ = gen_video_samples.shape
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duration = T / fps
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save_path_crop_audio = save_path + "-cropaudio.wav"
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final_command = [
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"ffmpeg",
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"-i",
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vocal_audio_list[0],
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"-t",
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f'{duration}',
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save_path_crop_audio,
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]
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subprocess.run(final_command, check=True)
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save_path = save_path + ".mp4"
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if high_quality_save:
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final_command = [
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"ffmpeg",
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"-y",
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"-i", save_path_tmp,
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"-i", save_path_crop_audio,
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"-c:v", "libx264",
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"-crf", "0",
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"-preset", "veryslow",
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"-c:a", "aac",
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"-shortest",
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save_path,
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]
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subprocess.run(final_command, check=True)
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os.remove(save_path_tmp)
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os.remove(save_path_crop_audio)
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else:
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final_command = [
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"ffmpeg",
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"-y",
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"-i",
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save_path_tmp,
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"-i",
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save_path_crop_audio,
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"-c:v",
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"libx264",
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"-c:a",
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"aac",
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"-shortest",
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save_path,
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]
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subprocess.run(final_command, check=True)
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os.remove(save_path_tmp)
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os.remove(save_path_crop_audio)
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class MomentumBuffer:
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def __init__(self, momentum: float):
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self.momentum = momentum
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self.running_average = 0
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def update(self, update_value: torch.Tensor):
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new_average = self.momentum * self.running_average
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self.running_average = update_value + new_average
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def project(
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v0: torch.Tensor, # [B, C, T, H, W]
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v1: torch.Tensor, # [B, C, T, H, W]
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):
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dtype = v0.dtype
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v0, v1 = v0.double(), v1.double()
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v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3, -4])
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v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3, -4], keepdim=True) * v1
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v0_orthogonal = v0 - v0_parallel
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return v0_parallel.to(dtype), v0_orthogonal.to(dtype)
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def adaptive_projected_guidance(
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diff: torch.Tensor, # [B, C, T, H, W]
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pred_cond: torch.Tensor, # [B, C, T, H, W]
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momentum_buffer: MomentumBuffer = None,
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eta: float = 0.0,
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norm_threshold: float = 55,
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):
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if momentum_buffer is not None:
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momentum_buffer.update(diff)
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diff = momentum_buffer.running_average
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if norm_threshold > 0:
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ones = torch.ones_like(diff)
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diff_norm = diff.norm(p=2, dim=[-1, -2, -3, -4], keepdim=True)
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print(f"diff_norm: {diff_norm}")
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scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
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diff = diff * scale_factor
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diff_parallel, diff_orthogonal = project(diff, pred_cond)
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normalized_update = diff_orthogonal + eta * diff_parallel
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return normalized_update
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