File size: 47,479 Bytes
78360e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 |
import os
import time
import random
import functools
from typing import List, Optional, Tuple, Union
from pathlib import Path
from einops import rearrange
import torch
import torch.distributed as dist
from hyvideo.constants import PROMPT_TEMPLATE, NEGATIVE_PROMPT, PRECISION_TO_TYPE, NEGATIVE_PROMPT_I2V
from hyvideo.vae import load_vae
from hyvideo.modules import load_model
from hyvideo.text_encoder import TextEncoder
from hyvideo.utils.data_utils import align_to, get_closest_ratio, generate_crop_size_list
from hyvideo.modules.posemb_layers import get_nd_rotary_pos_embed, get_nd_rotary_pos_embed_new
from hyvideo.diffusion.schedulers import FlowMatchDiscreteScheduler
from hyvideo.diffusion.pipelines import HunyuanVideoPipeline
from hyvideo.diffusion.pipelines import HunyuanVideoAudioPipeline
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
import cv2
from wan.utils.utils import resize_lanczos, calculate_new_dimensions
from hyvideo.data_kits.audio_preprocessor import encode_audio, get_facemask
from transformers import WhisperModel
from transformers import AutoFeatureExtractor
from hyvideo.data_kits.face_align import AlignImage
import librosa
def get_audio_feature(feature_extractor, audio_path, duration):
audio_input, sampling_rate = librosa.load(audio_path, duration=duration, sr=16000)
assert sampling_rate == 16000
audio_features = []
window = 750*640
for i in range(0, len(audio_input), window):
audio_feature = feature_extractor(audio_input[i:i+window],
sampling_rate=sampling_rate,
return_tensors="pt",
device="cuda"
).input_features
audio_features.append(audio_feature)
audio_features = torch.cat(audio_features, dim=-1)
return audio_features, len(audio_input) // 640
def pad_image(crop_img, size, color=(255, 255, 255), resize_ratio=1):
crop_h, crop_w = crop_img.shape[:2]
target_w, target_h = size
scale_h, scale_w = target_h / crop_h, target_w / crop_w
if scale_w > scale_h:
resize_h = int(target_h*resize_ratio)
resize_w = int(crop_w / crop_h * resize_h)
else:
resize_w = int(target_w*resize_ratio)
resize_h = int(crop_h / crop_w * resize_w)
crop_img = cv2.resize(crop_img, (resize_w, resize_h))
pad_left = (target_w - resize_w) // 2
pad_top = (target_h - resize_h) // 2
pad_right = target_w - resize_w - pad_left
pad_bottom = target_h - resize_h - pad_top
crop_img = cv2.copyMakeBorder(crop_img, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=color)
return crop_img
def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
num_images, num_image_patches, embed_dim = image_features.shape
batch_size, sequence_length = input_ids.shape
left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
# 1. Create a mask to know where special image tokens are
special_image_token_mask = input_ids == self.config.image_token_index
num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
# Compute the maximum embed dimension
max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged image-text sequence.
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
# `torch.cumsum` computes how each image token shifts subsequent text token positions.
# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1
nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
if left_padding:
new_token_positions += nb_image_pad[:, None] # offset for left padding
text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
# 3. Create the full embedding, already padded to the maximum position
final_embedding = torch.zeros(
batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
final_attention_mask = torch.zeros(
batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
)
if labels is not None:
final_labels = torch.full(
(batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
)
# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
# set the corresponding tensors into their correct target device.
target_device = inputs_embeds.device
batch_indices, non_image_indices, text_to_overwrite = (
batch_indices.to(target_device),
non_image_indices.to(target_device),
text_to_overwrite.to(target_device),
)
attention_mask = attention_mask.to(target_device)
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
if labels is not None:
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
# 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
image_to_overwrite = torch.full(
(batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
)
image_to_overwrite[batch_indices, text_to_overwrite] = False
image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
if image_to_overwrite.sum() != image_features.shape[:-1].numel():
raise ValueError(
f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
)
final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
final_attention_mask |= image_to_overwrite
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
indices_to_mask = new_token_positions[batch_indices, pad_indices]
final_embedding[batch_indices, indices_to_mask] = 0
if labels is None:
final_labels = None
return final_embedding, final_attention_mask, final_labels, position_ids
def patched_llava_forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layer: Optional[int] = None,
vision_feature_select_strategy: Optional[str] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
):
from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
)
vision_feature_select_strategy = (
vision_feature_select_strategy
if vision_feature_select_strategy is not None
else self.config.vision_feature_select_strategy
)
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if pixel_values is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
)
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
image_features = None
if pixel_values is not None:
image_features = self.get_image_features(
pixel_values=pixel_values,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
)
inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
image_features, inputs_embeds, input_ids, attention_mask, labels
)
cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
num_logits_to_keep=num_logits_to_keep,
)
logits = outputs[0]
loss = None
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return LlavaCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_features if pixel_values is not None else None,
)
def adapt_model(model, audio_block_name):
modules_dict= { k: m for k, m in model.named_modules()}
for model_layer, avatar_layer in model.double_stream_map.items():
module = modules_dict[f"{audio_block_name}.{avatar_layer}"]
target = modules_dict[f"double_blocks.{model_layer}"]
setattr(target, "audio_adapter", module )
delattr(model, audio_block_name)
class DataPreprocess(object):
def __init__(self):
self.llava_size = (336, 336)
self.llava_transform = transforms.Compose(
[
transforms.Resize(self.llava_size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.4082107), (0.26862954, 0.26130258, 0.27577711)),
]
)
def get_batch(self, image , size, pad = False):
image = np.asarray(image)
if pad:
llava_item_image = pad_image(image.copy(), self.llava_size)
else:
llava_item_image = image.copy()
uncond_llava_item_image = np.ones_like(llava_item_image) * 255
if pad:
cat_item_image = pad_image(image.copy(), size)
else:
cat_item_image = image.copy()
llava_item_tensor = self.llava_transform(Image.fromarray(llava_item_image.astype(np.uint8)))
uncond_llava_item_tensor = self.llava_transform(Image.fromarray(uncond_llava_item_image))
cat_item_tensor = torch.from_numpy(cat_item_image.copy()).permute((2, 0, 1)) / 255.0
# batch = {
# "pixel_value_llava": llava_item_tensor.unsqueeze(0),
# "uncond_pixel_value_llava": uncond_llava_item_tensor.unsqueeze(0),
# 'pixel_value_ref': cat_item_tensor.unsqueeze(0),
# }
return llava_item_tensor.unsqueeze(0), uncond_llava_item_tensor.unsqueeze(0), cat_item_tensor.unsqueeze(0)
class Inference(object):
def __init__(
self,
i2v,
custom,
avatar,
enable_cfg,
vae,
vae_kwargs,
text_encoder,
model,
text_encoder_2=None,
pipeline=None,
feature_extractor=None,
wav2vec=None,
align_instance=None,
device=None,
):
self.i2v = i2v
self.custom = custom
self.avatar = avatar
self.enable_cfg = enable_cfg
self.vae = vae
self.vae_kwargs = vae_kwargs
self.text_encoder = text_encoder
self.text_encoder_2 = text_encoder_2
self.model = model
self.pipeline = pipeline
self.feature_extractor=feature_extractor
self.wav2vec=wav2vec
self.align_instance=align_instance
self.device = "cuda"
@classmethod
def from_pretrained(cls, model_filepath, model_type, base_model_type, text_encoder_filepath, dtype = torch.bfloat16, VAE_dtype = torch.float16, mixed_precision_transformer =torch.bfloat16 , quantizeTransformer = False, save_quantized = False, **kwargs):
device = "cuda"
import transformers
transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.forward = patched_llava_forward # force legacy behaviour to be able to use tansformers v>(4.47)
transformers.models.llava.modeling_llava.LlavaForConditionalGeneration._merge_input_ids_with_image_features = _merge_input_ids_with_image_features
torch.set_grad_enabled(False)
text_len = 512
latent_channels = 16
precision = "bf16"
vae_precision = "fp32" if VAE_dtype == torch.float32 else "bf16"
embedded_cfg_scale = 6
filepath = model_filepath[0]
i2v_condition_type = None
i2v_mode = False
custom = False
custom_audio = False
avatar = False
if base_model_type == "hunyuan_i2v":
model_id = "HYVideo-T/2"
i2v_condition_type = "token_replace"
i2v_mode = True
elif base_model_type == "hunyuan_custom":
model_id = "HYVideo-T/2-custom"
custom = True
elif base_model_type == "hunyuan_custom_audio":
model_id = "HYVideo-T/2-custom-audio"
custom_audio = True
custom = True
elif base_model_type == "hunyuan_custom_edit":
model_id = "HYVideo-T/2-custom-edit"
custom = True
elif base_model_type == "hunyuan_avatar":
model_id = "HYVideo-T/2-avatar"
text_len = 256
avatar = True
else:
model_id = "HYVideo-T/2-cfgdistill"
if i2v_mode and i2v_condition_type == "latent_concat":
in_channels = latent_channels * 2 + 1
image_embed_interleave = 2
elif i2v_mode and i2v_condition_type == "token_replace":
in_channels = latent_channels
image_embed_interleave = 4
else:
in_channels = latent_channels
image_embed_interleave = 1
out_channels = latent_channels
pinToMemory = kwargs.pop("pinToMemory", False)
partialPinning = kwargs.pop("partialPinning", False)
factor_kwargs = kwargs | {"device": "meta", "dtype": PRECISION_TO_TYPE[precision]}
if embedded_cfg_scale and i2v_mode:
factor_kwargs["guidance_embed"] = True
model = load_model(
model = model_id,
i2v_condition_type = i2v_condition_type,
in_channels=in_channels,
out_channels=out_channels,
factor_kwargs=factor_kwargs,
)
from mmgp import offload
# model = Inference.load_state_dict(args, model, model_filepath)
# model_filepath ="c:/temp/hc/mp_rank_00_model_states_video.pt"
offload.load_model_data(model, model_filepath, do_quantize= quantizeTransformer and not save_quantized, pinToMemory = pinToMemory, partialPinning = partialPinning)
pass
# offload.save_model(model, "hunyuan_video_avatar_edit_720_bf16.safetensors")
# offload.save_model(model, "hunyuan_video_avatar_edit_720_quanto_bf16_int8.safetensors", do_quantize= True)
if save_quantized:
from wgp import save_quantized_model
save_quantized_model(model, model_type, filepath, dtype, None)
model.mixed_precision = mixed_precision_transformer
if model.mixed_precision :
model._lock_dtype = torch.float32
model.lock_layers_dtypes(torch.float32)
model.eval()
# ============================= Build extra models ========================
# VAE
if custom or avatar:
vae_configpath = "ckpts/hunyuan_video_custom_VAE_config.json"
vae_filepath = "ckpts/hunyuan_video_custom_VAE_fp32.safetensors"
# elif avatar:
# vae_configpath = "ckpts/config_vae_avatar.json"
# vae_filepath = "ckpts/vae_avatar.pt"
else:
vae_configpath = "ckpts/hunyuan_video_VAE_config.json"
vae_filepath = "ckpts/hunyuan_video_VAE_fp32.safetensors"
# config = AutoencoderKLCausal3D.load_config("ckpts/hunyuan_video_VAE_config.json")
# config = AutoencoderKLCausal3D.load_config("c:/temp/hvae/config_vae.json")
vae, _, s_ratio, t_ratio = load_vae( "884-16c-hy", vae_path= vae_filepath, vae_config_path= vae_configpath, vae_precision= vae_precision, device= "cpu", )
vae._model_dtype = torch.float32 if VAE_dtype == torch.float32 else (torch.float16 if avatar else torch.bfloat16)
vae._model_dtype = torch.float32 if VAE_dtype == torch.float32 else torch.bfloat16
vae_kwargs = {"s_ratio": s_ratio, "t_ratio": t_ratio}
enable_cfg = False
# Text encoder
if i2v_mode:
text_encoder = "llm-i2v"
tokenizer = "llm-i2v"
prompt_template = "dit-llm-encode-i2v"
prompt_template_video = "dit-llm-encode-video-i2v"
elif custom or avatar :
text_encoder = "llm-i2v"
tokenizer = "llm-i2v"
prompt_template = "dit-llm-encode"
prompt_template_video = "dit-llm-encode-video"
enable_cfg = True
else:
text_encoder = "llm"
tokenizer = "llm"
prompt_template = "dit-llm-encode"
prompt_template_video = "dit-llm-encode-video"
if prompt_template_video is not None:
crop_start = PROMPT_TEMPLATE[prompt_template_video].get( "crop_start", 0 )
elif prompt_template is not None:
crop_start = PROMPT_TEMPLATE[prompt_template].get("crop_start", 0)
else:
crop_start = 0
max_length = text_len + crop_start
# prompt_template
prompt_template = PROMPT_TEMPLATE[prompt_template] if prompt_template is not None else None
# prompt_template_video
prompt_template_video = PROMPT_TEMPLATE[prompt_template_video] if prompt_template_video is not None else None
text_encoder = TextEncoder(
text_encoder_type=text_encoder,
max_length=max_length,
text_encoder_precision="fp16",
tokenizer_type=tokenizer,
i2v_mode=i2v_mode,
prompt_template=prompt_template,
prompt_template_video=prompt_template_video,
hidden_state_skip_layer=2,
apply_final_norm=False,
reproduce=True,
device="cpu",
image_embed_interleave=image_embed_interleave,
text_encoder_path = text_encoder_filepath
)
text_encoder_2 = TextEncoder(
text_encoder_type="clipL",
max_length=77,
text_encoder_precision="fp16",
tokenizer_type="clipL",
reproduce=True,
device="cpu",
)
feature_extractor = None
wav2vec = None
align_instance = None
if avatar or custom_audio:
feature_extractor = AutoFeatureExtractor.from_pretrained("ckpts/whisper-tiny/")
wav2vec = WhisperModel.from_pretrained("ckpts/whisper-tiny/").to(device="cpu", dtype=torch.float32)
wav2vec._model_dtype = torch.float32
wav2vec.requires_grad_(False)
if avatar:
align_instance = AlignImage("cuda", det_path="ckpts/det_align/detface.pt")
align_instance.facedet.model.to("cpu")
adapt_model(model, "audio_adapter_blocks")
elif custom_audio:
adapt_model(model, "audio_models")
return cls(
i2v=i2v_mode,
custom=custom,
avatar=avatar,
enable_cfg = enable_cfg,
vae=vae,
vae_kwargs=vae_kwargs,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
model=model,
feature_extractor=feature_extractor,
wav2vec=wav2vec,
align_instance=align_instance,
device=device,
)
class HunyuanVideoSampler(Inference):
def __init__(
self,
i2v,
custom,
avatar,
enable_cfg,
vae,
vae_kwargs,
text_encoder,
model,
text_encoder_2=None,
pipeline=None,
feature_extractor=None,
wav2vec=None,
align_instance=None,
device=0,
):
super().__init__(
i2v,
custom,
avatar,
enable_cfg,
vae,
vae_kwargs,
text_encoder,
model,
text_encoder_2=text_encoder_2,
pipeline=pipeline,
feature_extractor=feature_extractor,
wav2vec=wav2vec,
align_instance=align_instance,
device=device,
)
self.i2v_mode = i2v
self.enable_cfg = enable_cfg
self.pipeline = self.load_diffusion_pipeline(
avatar = self.avatar,
vae=self.vae,
text_encoder=self.text_encoder,
text_encoder_2=self.text_encoder_2,
model=self.model,
device=self.device,
)
if self.i2v_mode:
self.default_negative_prompt = NEGATIVE_PROMPT_I2V
else:
self.default_negative_prompt = NEGATIVE_PROMPT
@property
def _interrupt(self):
return self.pipeline._interrupt
@_interrupt.setter
def _interrupt(self, value):
self.pipeline._interrupt =value
def load_diffusion_pipeline(
self,
avatar,
vae,
text_encoder,
text_encoder_2,
model,
scheduler=None,
device=None,
progress_bar_config=None,
#data_type="video",
):
"""Load the denoising scheduler for inference."""
if scheduler is None:
scheduler = FlowMatchDiscreteScheduler(
shift=6.0,
reverse=True,
solver="euler",
)
if avatar:
pipeline = HunyuanVideoAudioPipeline(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
transformer=model,
scheduler=scheduler,
progress_bar_config=progress_bar_config,
)
else:
pipeline = HunyuanVideoPipeline(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
transformer=model,
scheduler=scheduler,
progress_bar_config=progress_bar_config,
)
return pipeline
def get_rotary_pos_embed_new(self, video_length, height, width, concat_dict={}, enable_riflex = False):
target_ndim = 3
ndim = 5 - 2
latents_size = [(video_length-1)//4+1 , height//8, width//8]
if isinstance(self.model.patch_size, int):
assert all(s % self.model.patch_size == 0 for s in latents_size), \
f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), " \
f"but got {latents_size}."
rope_sizes = [s // self.model.patch_size for s in latents_size]
elif isinstance(self.model.patch_size, list):
assert all(s % self.model.patch_size[idx] == 0 for idx, s in enumerate(latents_size)), \
f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), " \
f"but got {latents_size}."
rope_sizes = [s // self.model.patch_size[idx] for idx, s in enumerate(latents_size)]
if len(rope_sizes) != target_ndim:
rope_sizes = [1] * (target_ndim - len(rope_sizes)) + rope_sizes # time axis
head_dim = self.model.hidden_size // self.model.heads_num
rope_dim_list = self.model.rope_dim_list
if rope_dim_list is None:
rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)]
assert sum(rope_dim_list) == head_dim, "sum(rope_dim_list) should equal to head_dim of attention layer"
freqs_cos, freqs_sin = get_nd_rotary_pos_embed_new(rope_dim_list,
rope_sizes,
theta=256,
use_real=True,
theta_rescale_factor=1,
concat_dict=concat_dict,
L_test = (video_length - 1) // 4 + 1,
enable_riflex = enable_riflex
)
return freqs_cos, freqs_sin
def get_rotary_pos_embed(self, video_length, height, width, enable_riflex = False):
target_ndim = 3
ndim = 5 - 2
# 884
vae = "884-16c-hy"
if "884" in vae:
latents_size = [(video_length - 1) // 4 + 1, height // 8, width // 8]
elif "888" in vae:
latents_size = [(video_length - 1) // 8 + 1, height // 8, width // 8]
else:
latents_size = [video_length, height // 8, width // 8]
if isinstance(self.model.patch_size, int):
assert all(s % self.model.patch_size == 0 for s in latents_size), (
f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), "
f"but got {latents_size}."
)
rope_sizes = [s // self.model.patch_size for s in latents_size]
elif isinstance(self.model.patch_size, list):
assert all(
s % self.model.patch_size[idx] == 0
for idx, s in enumerate(latents_size)
), (
f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.model.patch_size}), "
f"but got {latents_size}."
)
rope_sizes = [
s // self.model.patch_size[idx] for idx, s in enumerate(latents_size)
]
if len(rope_sizes) != target_ndim:
rope_sizes = [1] * (target_ndim - len(rope_sizes)) + rope_sizes # time axis
head_dim = self.model.hidden_size // self.model.heads_num
rope_dim_list = self.model.rope_dim_list
if rope_dim_list is None:
rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)]
assert (
sum(rope_dim_list) == head_dim
), "sum(rope_dim_list) should equal to head_dim of attention layer"
freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
rope_dim_list,
rope_sizes,
theta=256,
use_real=True,
theta_rescale_factor=1,
L_test = (video_length - 1) // 4 + 1,
enable_riflex = enable_riflex
)
return freqs_cos, freqs_sin
def generate(
self,
input_prompt,
input_ref_images = None,
audio_guide = None,
input_frames = None,
input_masks = None,
input_video = None,
fps = 24,
height=192,
width=336,
frame_num=129,
seed=None,
n_prompt=None,
sampling_steps=50,
guide_scale=1.0,
shift=5.0,
embedded_guidance_scale=6.0,
batch_size=1,
num_videos_per_prompt=1,
i2v_resolution="720p",
image_start=None,
enable_RIFLEx = False,
i2v_condition_type: str = "token_replace",
i2v_stability=True,
VAE_tile_size = None,
joint_pass = False,
cfg_star_switch = False,
fit_into_canvas = True,
conditioning_latents_size = 0,
**kwargs,
):
if VAE_tile_size != None:
self.vae.tile_sample_min_tsize = VAE_tile_size["tile_sample_min_tsize"]
self.vae.tile_latent_min_tsize = VAE_tile_size["tile_latent_min_tsize"]
self.vae.tile_sample_min_size = VAE_tile_size["tile_sample_min_size"]
self.vae.tile_latent_min_size = VAE_tile_size["tile_latent_min_size"]
self.vae.tile_overlap_factor = VAE_tile_size["tile_overlap_factor"]
self.vae.enable_tiling()
i2v_mode= self.i2v_mode
if not self.enable_cfg:
guide_scale=1.0
# ========================================================================
# Arguments: seed
# ========================================================================
if isinstance(seed, torch.Tensor):
seed = seed.tolist()
if seed is None:
seeds = [
random.randint(0, 1_000_000)
for _ in range(batch_size * num_videos_per_prompt)
]
elif isinstance(seed, int):
seeds = [
seed + i
for _ in range(batch_size)
for i in range(num_videos_per_prompt)
]
elif isinstance(seed, (list, tuple)):
if len(seed) == batch_size:
seeds = [
int(seed[i]) + j
for i in range(batch_size)
for j in range(num_videos_per_prompt)
]
elif len(seed) == batch_size * num_videos_per_prompt:
seeds = [int(s) for s in seed]
else:
raise ValueError(
f"Length of seed must be equal to number of prompt(batch_size) or "
f"batch_size * num_videos_per_prompt ({batch_size} * {num_videos_per_prompt}), got {seed}."
)
else:
raise ValueError(
f"Seed must be an integer, a list of integers, or None, got {seed}."
)
from wan.utils.utils import seed_everything
seed_everything(seed)
generator = [torch.Generator("cuda").manual_seed(seed) for seed in seeds]
# generator = [torch.Generator(self.device).manual_seed(seed) for seed in seeds]
# ========================================================================
# Arguments: target_width, target_height, target_frame_num
# ========================================================================
if width <= 0 or height <= 0 or frame_num <= 0:
raise ValueError(
f"`height` and `width` and `frame_num` must be positive integers, got height={height}, width={width}, frame_num={frame_num}"
)
if (frame_num - 1) % 4 != 0:
raise ValueError(
f"`frame_num-1` must be a multiple of 4, got {frame_num}"
)
target_height = align_to(height, 16)
target_width = align_to(width, 16)
target_frame_num = frame_num
audio_strength = 1
if input_ref_images != None:
# ip_cfg_scale = 3.0
ip_cfg_scale = 0
denoise_strength = 1
# guide_scale=7.5
# shift=13
name = "person"
input_ref_images = input_ref_images[0]
# ========================================================================
# Arguments: prompt, new_prompt, negative_prompt
# ========================================================================
if not isinstance(input_prompt, str):
raise TypeError(f"`prompt` must be a string, but got {type(input_prompt)}")
input_prompt = [input_prompt.strip()]
# negative prompt
if n_prompt is None or n_prompt == "":
n_prompt = self.default_negative_prompt
if guide_scale == 1.0:
n_prompt = ""
if not isinstance(n_prompt, str):
raise TypeError(
f"`negative_prompt` must be a string, but got {type(n_prompt)}"
)
n_prompt = [n_prompt.strip()]
# ========================================================================
# Scheduler
# ========================================================================
scheduler = FlowMatchDiscreteScheduler(
shift=shift,
reverse=True,
solver="euler"
)
self.pipeline.scheduler = scheduler
# ---------------------------------
# Reference condition
# ---------------------------------
img_latents = None
semantic_images = None
denoise_strength = 0
ip_cfg_scale = 0
if i2v_mode:
if i2v_resolution == "720p":
bucket_hw_base_size = 960
elif i2v_resolution == "540p":
bucket_hw_base_size = 720
elif i2v_resolution == "360p":
bucket_hw_base_size = 480
else:
raise ValueError(f"i2v_resolution: {i2v_resolution} must be in [360p, 540p, 720p]")
# semantic_images = [Image.open(i2v_image_path).convert('RGB')]
semantic_images = [image_start.convert('RGB')] #
origin_size = semantic_images[0].size
h, w = origin_size
h, w = calculate_new_dimensions(height, width, h, w, fit_into_canvas)
closest_size = (w, h)
# crop_size_list = generate_crop_size_list(bucket_hw_base_size, 32)
# aspect_ratios = np.array([round(float(h)/float(w), 5) for h, w in crop_size_list])
# closest_size, closest_ratio = get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list)
ref_image_transform = transforms.Compose([
transforms.Resize(closest_size),
transforms.CenterCrop(closest_size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
semantic_image_pixel_values = [ref_image_transform(semantic_image) for semantic_image in semantic_images]
semantic_image_pixel_values = torch.cat(semantic_image_pixel_values).unsqueeze(0).unsqueeze(2).to(self.device)
with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=True):
img_latents = self.pipeline.vae.encode(semantic_image_pixel_values).latent_dist.mode() # B, C, F, H, W
img_latents.mul_(self.pipeline.vae.config.scaling_factor)
target_height, target_width = closest_size
# ========================================================================
# Build Rope freqs
# ========================================================================
if input_ref_images == None:
freqs_cos, freqs_sin = self.get_rotary_pos_embed(target_frame_num, target_height, target_width, enable_RIFLEx)
else:
if self.avatar:
w, h = input_ref_images.size
target_height, target_width = calculate_new_dimensions(target_height, target_width, h, w, fit_into_canvas)
if target_width != w or target_height != h:
input_ref_images = input_ref_images.resize((target_width,target_height), resample=Image.Resampling.LANCZOS)
concat_dict = {'mode': 'timecat', 'bias': -1}
freqs_cos, freqs_sin = self.get_rotary_pos_embed_new(129, target_height, target_width, concat_dict)
else:
if input_frames != None:
target_height, target_width = input_frames.shape[-3:-1]
elif input_video != None:
target_height, target_width = input_video.shape[-2:]
concat_dict = {'mode': 'timecat-w', 'bias': -1}
freqs_cos, freqs_sin = self.get_rotary_pos_embed_new(target_frame_num, target_height, target_width, concat_dict, enable_RIFLEx)
n_tokens = freqs_cos.shape[0]
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
# ========================================================================
# Pipeline inference
# ========================================================================
pixel_value_llava, uncond_pixel_value_llava, pixel_value_ref = None, None, None
if input_ref_images == None:
name = None
else:
pixel_value_llava, uncond_pixel_value_llava, pixel_value_ref = DataPreprocess().get_batch(input_ref_images, (target_width, target_height), pad = self.custom)
ref_latents, uncond_audio_prompts, audio_prompts, face_masks, motion_exp, motion_pose = None, None, None, None, None, None
bg_latents = None
if input_video != None:
pixel_value_bg = input_video.unsqueeze(0)
pixel_value_mask = torch.zeros_like(input_video).unsqueeze(0)
if input_frames != None:
pixel_value_video_bg = input_frames.permute(-1,0,1,2).unsqueeze(0).float()
pixel_value_video_mask = input_masks.unsqueeze(-1).repeat(1,1,1,3).permute(-1,0,1,2).unsqueeze(0).float()
pixel_value_video_bg = pixel_value_video_bg.div_(127.5).add_(-1.)
if input_video != None:
pixel_value_bg = torch.cat([pixel_value_bg, pixel_value_video_bg], dim=2)
pixel_value_mask = torch.cat([ pixel_value_mask, pixel_value_video_mask], dim=2)
else:
pixel_value_bg = pixel_value_video_bg
pixel_value_mask = pixel_value_video_mask
pixel_value_video_mask, pixel_value_video_bg = None, None
if input_video != None or input_frames != None:
if pixel_value_bg.shape[2] < frame_num:
padding_shape = list(pixel_value_bg.shape[0:2]) + [frame_num-pixel_value_bg.shape[2]] + list(pixel_value_bg.shape[3:])
pixel_value_bg = torch.cat([pixel_value_bg, torch.full(padding_shape, -1, dtype=pixel_value_bg.dtype, device= pixel_value_bg.device ) ], dim=2)
pixel_value_mask = torch.cat([ pixel_value_mask, torch.full(padding_shape, 255, dtype=pixel_value_mask.dtype, device= pixel_value_mask.device ) ], dim=2)
bg_latents = self.vae.encode(pixel_value_bg).latent_dist.sample()
pixel_value_mask = pixel_value_mask.div_(127.5).add_(-1.)
mask_latents = self.vae.encode(pixel_value_mask).latent_dist.sample()
bg_latents = torch.cat([bg_latents, mask_latents], dim=1)
bg_latents.mul_(self.vae.config.scaling_factor)
if self.avatar:
if n_prompt == None or len(n_prompt) == 0:
n_prompt = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, Lens changes"
uncond_pixel_value_llava = pixel_value_llava.clone()
pixel_value_ref = pixel_value_ref.unsqueeze(0)
self.align_instance.facedet.model.to("cuda")
face_masks = get_facemask(pixel_value_ref.to("cuda")*255, self.align_instance, area=3.0)
# iii = (face_masks.squeeze(0).squeeze(0).permute(1,2,0).repeat(1,1,3)*255).cpu().numpy().astype(np.uint8)
# image = Image.fromarray(iii)
# image.save("mask.png")
# jjj = (pixel_value_ref.squeeze(0).squeeze(0).permute(1,2,0)*255).cpu().numpy().astype(np.uint8)
self.align_instance.facedet.model.to("cpu")
# pixel_value_ref = pixel_value_ref.clone().repeat(1,129,1,1,1)
pixel_value_ref = pixel_value_ref.repeat(1,1+4*2,1,1,1)
pixel_value_ref = pixel_value_ref * 2 - 1
pixel_value_ref_for_vae = rearrange(pixel_value_ref, "b f c h w -> b c f h w")
vae_dtype = self.vae.dtype
with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_dtype != torch.float32):
ref_latents = self.vae.encode(pixel_value_ref_for_vae).latent_dist.sample()
ref_latents = torch.cat( [ref_latents[:,:, :1], ref_latents[:,:, 1:2].repeat(1,1,31,1,1), ref_latents[:,:, -1:]], dim=2)
pixel_value_ref, pixel_value_ref_for_vae = None, None
if hasattr(self.vae.config, 'shift_factor') and self.vae.config.shift_factor:
ref_latents.sub_(self.vae.config.shift_factor).mul_(self.vae.config.scaling_factor)
else:
ref_latents.mul_(self.vae.config.scaling_factor)
# out_latents= ref_latents / self.vae.config.scaling_factor
# image = self.vae.decode(out_latents, return_dict=False, generator=generator)[0]
# image = image.clamp(-1, 1)
# from wan.utils.utils import cache_video
# cache_video( tensor=image, save_file="decode.mp4", fps=25, nrow=1, normalize=True, value_range=(-1, 1))
motion_pose = np.array([25] * 4)
motion_exp = np.array([30] * 4)
motion_pose = torch.from_numpy(motion_pose).unsqueeze(0)
motion_exp = torch.from_numpy(motion_exp).unsqueeze(0)
face_masks = torch.nn.functional.interpolate(face_masks.float().squeeze(2),
(ref_latents.shape[-2],
ref_latents.shape[-1]),
mode="bilinear").unsqueeze(2).to(dtype=ref_latents.dtype)
if audio_guide != None:
audio_input, audio_len = get_audio_feature(self.feature_extractor, audio_guide, duration = frame_num/fps )
audio_prompts = audio_input[0]
weight_dtype = audio_prompts.dtype
if self.custom:
audio_len = min(audio_len, frame_num)
audio_input = audio_input[:, :audio_len]
audio_prompts = encode_audio(self.wav2vec, audio_prompts.to(dtype=self.wav2vec.dtype), fps, num_frames=audio_len)
audio_prompts = audio_prompts.to(self.model.dtype)
segment_size = 129 if self.avatar else frame_num
if audio_prompts.shape[1] <= segment_size:
audio_prompts = torch.cat([audio_prompts, torch.zeros_like(audio_prompts[:, :1]).repeat(1,segment_size-audio_prompts.shape[1], 1, 1, 1)], dim=1)
else:
audio_prompts = torch.cat([audio_prompts, torch.zeros_like(audio_prompts[:, :1]).repeat(1, 5, 1, 1, 1)], dim=1)
uncond_audio_prompts = torch.zeros_like(audio_prompts[:,:129])
samples = self.pipeline(
prompt=input_prompt,
height=target_height,
width=target_width,
video_length=target_frame_num,
num_inference_steps=sampling_steps,
guidance_scale=guide_scale,
negative_prompt=n_prompt,
num_videos_per_prompt=num_videos_per_prompt,
generator=generator,
output_type="pil",
name = name,
pixel_value_ref = pixel_value_ref,
ref_latents=ref_latents, # [1, 16, 1, h//8, w//8]
pixel_value_llava=pixel_value_llava, # [1, 3, 336, 336]
uncond_pixel_value_llava=uncond_pixel_value_llava,
face_masks=face_masks, # [b f h w]
audio_prompts=audio_prompts,
uncond_audio_prompts=uncond_audio_prompts,
motion_exp=motion_exp,
motion_pose=motion_pose,
fps= torch.from_numpy(np.array(fps)),
bg_latents = bg_latents,
audio_strength = audio_strength,
denoise_strength=denoise_strength,
ip_cfg_scale=ip_cfg_scale,
freqs_cis=(freqs_cos, freqs_sin),
n_tokens=n_tokens,
embedded_guidance_scale=embedded_guidance_scale,
data_type="video" if target_frame_num > 1 else "image",
is_progress_bar=True,
vae_ver="884-16c-hy",
enable_tiling=True,
i2v_mode=i2v_mode,
i2v_condition_type=i2v_condition_type,
i2v_stability=i2v_stability,
img_latents=img_latents,
semantic_images=semantic_images,
joint_pass = joint_pass,
cfg_star_rescale = cfg_star_switch,
callback = callback,
callback_steps = callback_steps,
)[0]
if samples == None:
return None
samples = samples.squeeze(0)
return samples
|