From ce7ff41bfb8db69c93649d52517fe74ae2f95120 Mon Sep 17 00:00:00 2001 From: Bubbliiiing <47347516+bubbliiiing@users.noreply.github.com> Date: Tue, 28 Oct 2025 11:48:20 +0800 Subject: [PATCH] Update Wan2.2 S2V Training code. (#364) --- requirements.txt | 1 + scripts/fantasytalking/README_TRAIN.md | 4 +- scripts/fantasytalking/train.py | 6 +- scripts/qwenimage/train_edit.py | 4 + scripts/qwenimage/train_edit_lora.py | 4 + scripts/wan2.1_vace/train.py | 2 + scripts/wan2.2/README_TRAIN_S2V.md | 217 ++ scripts/wan2.2/train_s2v.py | 2103 +++++++++++++++++ scripts/wan2.2/train_s2v.sh | 39 + scripts/wan2.2_vace_fun/train.py | 2 + videox_fun/data/dataset_video.py | 202 ++ videox_fun/models/wan_audio_encoder.py | 23 +- videox_fun/models/wan_transformer3d.py | 4 + videox_fun/models/wan_transformer3d_s2v.py | 61 +- videox_fun/pipeline/pipeline_wan2_2_s2v.py | 5 +- .../pipeline/pipeline_wan2_2_vace_fun.py | 2 +- 16 files changed, 2661 insertions(+), 18 deletions(-) create mode 100755 scripts/wan2.2/README_TRAIN_S2V.md create mode 100644 scripts/wan2.2/train_s2v.py create mode 100644 scripts/wan2.2/train_s2v.sh diff --git a/requirements.txt b/requirements.txt index d795621..ff393ff 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,7 @@ einops safetensors timm tomesd +librosa torch>=2.1.2 torchdiffeq torchsde diff --git a/scripts/fantasytalking/README_TRAIN.md b/scripts/fantasytalking/README_TRAIN.md index c476abc..fbe85e3 100755 --- a/scripts/fantasytalking/README_TRAIN.md +++ b/scripts/fantasytalking/README_TRAIN.md @@ -23,9 +23,9 @@ Some parameters in the sh file can be confusing, and they are explained in this - `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the videos at the center, but instead, it trains the videos after grouping them into buckets based on resolution. - `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts. - `random_hw_adapt` is used to enable automatic height and width scaling for videos. When `random_hw_adapt` is enabled, for training videos, the height and width will be set to `video_sample_size` as the maximum and `512` as the minimum. - - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=512`, the resolution of video inputs for training is `512x512x49`. + - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=768`, the resolution of video inputs for training is `512x512x49`, `768x768x49`. - `training_with_video_token_length` specifies training the model according to token length. For training videos, the height and width will be set to `video_sample_size` as the maximum and `256` as the minimum. - - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `512x512x21`. + - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=768`, the resolution of video inputs for training is `256x256x49`, `512x512x49`, `768x768x21`. - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`. - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512). - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768). diff --git a/scripts/fantasytalking/train.py b/scripts/fantasytalking/train.py index 668a180..728a655 100644 --- a/scripts/fantasytalking/train.py +++ b/scripts/fantasytalking/train.py @@ -1485,6 +1485,8 @@ def main(): batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (4, 1)) else: batch['text'] = batch['text'] * 4 + audio = audio * 4 + sample_rate = sample_rate * 4 elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: pixel_values = torch.tile(pixel_values, (2, 1, 1, 1, 1)) if args.enable_text_encoder_in_dataloader: @@ -1492,7 +1494,9 @@ def main(): batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (2, 1)) else: batch['text'] = batch['text'] * 2 - + audio = audio * 2 + sample_rate = sample_rate * 2 + clip_pixel_values = batch["clip_pixel_values"].to(weight_dtype) mask_pixel_values = batch["mask_pixel_values"].to(weight_dtype) mask = batch["mask"].to(weight_dtype) diff --git a/scripts/qwenimage/train_edit.py b/scripts/qwenimage/train_edit.py index 6364cd5..b1d30ec 100644 --- a/scripts/qwenimage/train_edit.py +++ b/scripts/qwenimage/train_edit.py @@ -613,6 +613,8 @@ def parse_args(): def main(): args = parse_args() + if args.train_batch_size >= 2: + raise ValueError("This code does not support args.train_batch_size >= 2 now.") if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( @@ -1493,6 +1495,7 @@ def main(): with torch.no_grad(): if args.train_mode == "qwen_image_edit": if source_exist: + # Take the first batch condition_images prompt_in_pixel_values = condition_images[0][:1] else: prompt_in_pixel_values = None @@ -1500,6 +1503,7 @@ def main(): txt = [template.format(e) for e in batch['text']] else: if source_exist: + # Take the first batch condition_images prompt_in_pixel_values = condition_images[0] else: prompt_in_pixel_values = None diff --git a/scripts/qwenimage/train_edit_lora.py b/scripts/qwenimage/train_edit_lora.py index cc77480..100e0ca 100644 --- a/scripts/qwenimage/train_edit_lora.py +++ b/scripts/qwenimage/train_edit_lora.py @@ -616,6 +616,8 @@ def parse_args(): def main(): args = parse_args() + if args.train_batch_size >= 2: + raise ValueError("This code does not support args.train_batch_size >= 2 now.") if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( @@ -1507,6 +1509,7 @@ def main(): with torch.no_grad(): if args.train_mode == "qwen_image_edit": if source_exist: + # Take the first batch condition_images prompt_in_pixel_values = condition_images[0][:1] else: prompt_in_pixel_values = None @@ -1514,6 +1517,7 @@ def main(): txt = [template.format(e) for e in batch['text']] else: if source_exist: + # Take the first batch condition_images prompt_in_pixel_values = condition_images[0] else: prompt_in_pixel_values = None diff --git a/scripts/wan2.1_vace/train.py b/scripts/wan2.1_vace/train.py index 2c6a0d4..8a2c2c2 100644 --- a/scripts/wan2.1_vace/train.py +++ b/scripts/wan2.1_vace/train.py @@ -639,6 +639,8 @@ def parse_args(): def main(): args = parse_args() + if args.train_batch_size >= 2: + raise ValueError("This code does not support args.train_batch_size >= 2 now.") if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( diff --git a/scripts/wan2.2/README_TRAIN_S2V.md b/scripts/wan2.2/README_TRAIN_S2V.md new file mode 100755 index 0000000..58960e1 --- /dev/null +++ b/scripts/wan2.2/README_TRAIN_S2V.md @@ -0,0 +1,217 @@ +## Training Code + +The default training commands for the different versions are as follows: + +We can choose whether to use fsdp in Wan-S2V, which can save a lot of video memory. + +The metadata_control.json is a little different from normal json in Wan-S2V, you need to add a audio_path. + +```json +[ + { + "file_path": "train/00000001.mp4", + "audio_path": "wav/00000001.wav", + "control_file_path": "control/00000001.mp4", + "text": "A group of young men in suits and sunglasses are walking down a city street.", + "type": "video" + }, + ..... +] +``` + +Some parameters in the sh file can be confusing, and they are explained in this document: + +- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the videos at the center, but instead, it trains the videos after grouping them into buckets based on resolution. +- `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts. +- `random_hw_adapt` is used to enable automatic height and width scaling for videos. When `random_hw_adapt` is enabled, for training videos, the height and width will be set to `video_sample_size` as the maximum and `512` as the minimum. + - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=768`, the resolution of video inputs for training is `512x512x49`, `768x768x49`. +- `training_with_video_token_length` specifies training the model according to token length. For training videos, the height and width will be set to `video_sample_size` as the maximum and `256` as the minimum. + - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=768`, the resolution of video inputs for training is `256x256x49`, `512x512x49`, `768x768x21`. + - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`. + - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512). + - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768). + - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024). + - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes. +- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint. + +Wan-S2V without deepspeed: + +```sh +export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-S2V-14B" +export DATASET_NAME="datasets/internal_datasets/" +export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json" +# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. +# export NCCL_IB_DISABLE=1 +# export NCCL_P2P_DISABLE=1 +NCCL_DEBUG=INFO + +accelerate launch --mixed_precision="bf16" scripts/wan2.2/train_s2v.py \ + --config_path="config/wan2.2/wan_civitai_s2v.yaml" \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_data_dir=$DATASET_NAME \ + --train_data_meta=$DATASET_META_NAME \ + --video_sample_size=640 \ + --token_sample_size=640 \ + --video_sample_stride=1 \ + --video_sample_n_frames=80 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --dataloader_num_workers=8 \ + --num_train_epochs=100 \ + --checkpointing_steps=50 \ + --learning_rate=2e-05 \ + --lr_scheduler="constant_with_warmup" \ + --lr_warmup_steps=100 \ + --seed=42 \ + --output_dir="output_dir" \ + --gradient_checkpointing \ + --mixed_precision="bf16" \ + --adam_weight_decay=3e-2 \ + --adam_epsilon=1e-10 \ + --vae_mini_batch=1 \ + --max_grad_norm=0.05 \ + --random_hw_adapt \ + --training_with_video_token_length \ + --enable_bucket \ + --uniform_sampling \ + --low_vram \ + --trainable_modules "." +``` + +Wan-S2V with deepspeed zero-2: + +```sh +export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-S2V-14B" +export DATASET_NAME="datasets/internal_datasets/" +export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json" +# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. +# export NCCL_IB_DISABLE=1 +# export NCCL_P2P_DISABLE=1 +NCCL_DEBUG=INFO + +accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/wan2.2/train_s2v.py \ + --config_path="config/wan2.2/wan_civitai_s2v.yaml" \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_data_dir=$DATASET_NAME \ + --train_data_meta=$DATASET_META_NAME \ + --video_sample_size=640 \ + --token_sample_size=640 \ + --video_sample_stride=1 \ + --video_sample_n_frames=80 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --dataloader_num_workers=8 \ + --num_train_epochs=100 \ + --checkpointing_steps=50 \ + --learning_rate=2e-05 \ + --lr_scheduler="constant_with_warmup" \ + --lr_warmup_steps=100 \ + --seed=42 \ + --output_dir="output_dir" \ + --gradient_checkpointing \ + --mixed_precision="bf16" \ + --adam_weight_decay=3e-2 \ + --adam_epsilon=1e-10 \ + --vae_mini_batch=1 \ + --max_grad_norm=0.05 \ + --random_hw_adapt \ + --training_with_video_token_length \ + --enable_bucket \ + --uniform_sampling \ + --low_vram \ + --trainable_modules "." +``` + +Wan-S2V with deepspeed zero-3: + +```sh +python scripts/zero_to_bf16.py output_dir/checkpoint-{our-num-steps} output_dir/checkpoint-{your-num-steps}-outputs --max_shard_size 80GB --safe_serialization +``` + +Training shell command is as follows: +```sh +export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-S2V-14B" +export DATASET_NAME="datasets/internal_datasets/" +export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json" +# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. +# export NCCL_IB_DISABLE=1 +# export NCCL_P2P_DISABLE=1 +NCCL_DEBUG=INFO + +accelerate launch --zero_stage 3 --zero3_save_16bit_model true --zero3_init_flag true --use_deepspeed --deepspeed_config_file config/zero_stage3_config.json --deepspeed_multinode_launcher standard scripts/wan2.2/train_s2v.py \ + --config_path="config/wan2.2/wan_civitai_s2v.yaml" \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_data_dir=$DATASET_NAME \ + --train_data_meta=$DATASET_META_NAME \ + --video_sample_size=640 \ + --token_sample_size=640 \ + --video_sample_stride=1 \ + --video_sample_n_frames=80 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --dataloader_num_workers=8 \ + --num_train_epochs=100 \ + --checkpointing_steps=50 \ + --learning_rate=2e-05 \ + --lr_scheduler="constant_with_warmup" \ + --lr_warmup_steps=100 \ + --seed=42 \ + --output_dir="output_dir" \ + --gradient_checkpointing \ + --mixed_precision="bf16" \ + --adam_weight_decay=3e-2 \ + --adam_epsilon=1e-10 \ + --vae_mini_batch=1 \ + --max_grad_norm=0.05 \ + --random_hw_adapt \ + --training_with_video_token_length \ + --enable_bucket \ + --uniform_sampling \ + --low_vram \ + --trainable_modules "." +``` + +Wan-S2V with FSDP: + +Wan with FSDP is suitable for 14B Wan at high resolutions. Training shell command is as follows: +```sh +export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-S2V-14B" +export DATASET_NAME="datasets/internal_datasets/" +export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json" +# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. +# export NCCL_IB_DISABLE=1 +# export NCCL_P2P_DISABLE=1 +NCCL_DEBUG=INFO + +accelerate launch --mixed_precision="bf16" --use_fsdp --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap=AudioAttentionBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False scripts/wan2.2/train_s2v.py \ + --config_path="config/wan2.2/wan_civitai_s2v.yaml" \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_data_dir=$DATASET_NAME \ + --train_data_meta=$DATASET_META_NAME \ + --video_sample_size=640 \ + --token_sample_size=640 \ + --video_sample_stride=1 \ + --video_sample_n_frames=80 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --dataloader_num_workers=8 \ + --num_train_epochs=100 \ + --checkpointing_steps=50 \ + --learning_rate=2e-05 \ + --lr_scheduler="constant_with_warmup" \ + --lr_warmup_steps=100 \ + --seed=42 \ + --output_dir="output_dir" \ + --gradient_checkpointing \ + --mixed_precision="bf16" \ + --adam_weight_decay=3e-2 \ + --adam_epsilon=1e-10 \ + --vae_mini_batch=1 \ + --max_grad_norm=0.05 \ + --random_hw_adapt \ + --training_with_video_token_length \ + --enable_bucket \ + --uniform_sampling \ + --low_vram \ + --trainable_modules "." +``` \ No newline at end of file diff --git a/scripts/wan2.2/train_s2v.py b/scripts/wan2.2/train_s2v.py new file mode 100644 index 0000000..6f65a29 --- /dev/null +++ b/scripts/wan2.2/train_s2v.py @@ -0,0 +1,2103 @@ +"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py +""" +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and + +import argparse +import gc +import logging +import math +import os +import pickle +import shutil +import sys + +import accelerate +import diffusers +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import torchvision.transforms.functional as TF +import transformers +import random +from accelerate import Accelerator, FullyShardedDataParallelPlugin +from accelerate.logging import get_logger +from accelerate.state import AcceleratorState +from accelerate.utils import ProjectConfiguration, set_seed +from diffusers import DDIMScheduler, FlowMatchEulerDiscreteScheduler +from diffusers.optimization import get_scheduler +from diffusers.training_utils import (EMAModel, + compute_density_for_timestep_sampling, + compute_loss_weighting_for_sd3) +from diffusers.utils import check_min_version, deprecate, is_wandb_available +from diffusers.utils.torch_utils import is_compiled_module +from einops import rearrange +from omegaconf import OmegaConf +from packaging import version +from PIL import Image +from torch.distributed.fsdp.fully_sharded_data_parallel import ( + FullOptimStateDictConfig, FullStateDictConfig, ShardedStateDictConfig, ShardedOptimStateDictConfig) +from torch.utils.data import RandomSampler +from torch.utils.tensorboard import SummaryWriter +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import AutoTokenizer +from transformers.utils import ContextManagers + +import datasets + +current_file_path = os.path.abspath(__file__) +project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))] +for project_root in project_roots: + sys.path.insert(0, project_root) if project_root not in sys.path else None + +from videox_fun.data.bucket_sampler import (ASPECT_RATIO_512, + ASPECT_RATIO_RANDOM_CROP_512, + ASPECT_RATIO_RANDOM_CROP_PROB, + AspectRatioBatchImageVideoSampler, + RandomSampler, get_closest_ratio) +from videox_fun.data.dataset_image_video import (ImageVideoDataset, + ImageVideoSampler, + get_random_mask) +from videox_fun.data.dataset_video import VideoSpeechControlDataset +from videox_fun.models import (AutoencoderKLWan, AutoencoderKLWan3_8, WanT5EncoderModel, WanAudioEncoder, + Wan2_2Transformer3DModel_S2V) +from videox_fun.pipeline import Wan2_2S2VPipeline, Wan2_2I2VPipeline +from videox_fun.utils.discrete_sampler import DiscreteSampling +from videox_fun.utils.utils import get_image_to_video_latent, save_videos_grid + +if is_wandb_available(): + import wandb + + +def filter_kwargs(cls, kwargs): + import inspect + sig = inspect.signature(cls.__init__) + valid_params = set(sig.parameters.keys()) - {'self', 'cls'} + filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params} + return filtered_kwargs + +def get_random_downsample_ratio(sample_size, image_ratio=[], + all_choices=False, rng=None): + def _create_special_list(length): + if length == 1: + return [1.0] + if length >= 2: + first_element = 0.75 + remaining_sum = 1.0 - first_element + other_elements_value = remaining_sum / (length - 1) + special_list = [first_element] + [other_elements_value] * (length - 1) + return special_list + + if sample_size >= 1536: + number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio + elif sample_size >= 1024: + number_list = [1, 1.25, 1.5, 2] + image_ratio + elif sample_size >= 768: + number_list = [1, 1.25, 1.5] + image_ratio + elif sample_size >= 512: + number_list = [1] + image_ratio + else: + number_list = [1] + + if all_choices: + return number_list + + number_list_prob = np.array(_create_special_list(len(number_list))) + if rng is None: + return np.random.choice(number_list, p = number_list_prob) + else: + return rng.choice(number_list, p = number_list_prob) + +def resize_mask(mask, latent, process_first_frame_only=True): + latent_size = latent.size() + batch_size, channels, num_frames, height, width = mask.shape + + if process_first_frame_only: + target_size = list(latent_size[2:]) + target_size[0] = 1 + first_frame_resized = F.interpolate( + mask[:, :, 0:1, :, :], + size=target_size, + mode='trilinear', + align_corners=False + ) + + target_size = list(latent_size[2:]) + target_size[0] = target_size[0] - 1 + if target_size[0] != 0: + remaining_frames_resized = F.interpolate( + mask[:, :, 1:, :, :], + size=target_size, + mode='trilinear', + align_corners=False + ) + resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2) + else: + resized_mask = first_frame_resized + else: + target_size = list(latent_size[2:]) + resized_mask = F.interpolate( + mask, + size=target_size, + mode='trilinear', + align_corners=False + ) + return resized_mask + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +check_min_version("0.18.0.dev0") + +logger = get_logger(__name__, log_level="INFO") + +def log_validation(vae, text_encoder, tokenizer, transformer3d, args, config, accelerator, weight_dtype, global_step): + try: + logger.info("Running validation... ") + + if args.boundary_type == "full": + sub_path = config['transformer_additional_kwargs'].get('transformer_low_noise_model_subpath', 'transformer') + + transformer3d_val = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict()) + + transformer3d_2_val = None + else: + if args.boundary_type == "low": + sub_path = config['transformer_additional_kwargs'].get('transformer_low_noise_model_subpath', 'transformer') + + transformer3d_val = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict()) + + sub_path = config['transformer_additional_kwargs'].get('transformer_high_noise_model_subpath', 'transformer') + transformer3d_2_val = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + else: + sub_path = config['transformer_additional_kwargs'].get('transformer_low_noise_model_subpath', 'transformer') + + transformer3d_val = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + + sub_path = config['transformer_additional_kwargs'].get('transformer_high_noise_model_subpath', 'transformer') + transformer3d_2_val = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + transformer3d_2_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict()) + + scheduler = FlowMatchEulerDiscreteScheduler( + **filter_kwargs(FlowMatchEulerDiscreteScheduler, OmegaConf.to_container(config['scheduler_kwargs'])) + ) + + pipeline = Wan2_2S2VPipeline( + vae=accelerator.unwrap_model(vae).to(weight_dtype), + text_encoder=accelerator.unwrap_model(text_encoder), + tokenizer=tokenizer, + transformer=transformer3d_val, + transformer_2=transformer3d_2_val, + scheduler=scheduler, + ) + pipeline = pipeline.to(accelerator.device) + + if args.seed is None: + generator = None + else: + generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) + + images = [] + for i in range(len(args.validation_prompts)): + with torch.no_grad(): + if args.train_mode != "normal": + with torch.autocast("cuda", dtype=weight_dtype): + video_length = int((args.video_sample_n_frames - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if args.video_sample_n_frames != 1 else 1 + input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size]) + sample = pipeline( + args.validation_prompts[i], + num_frames = video_length, + negative_prompt = "bad detailed", + height = args.video_sample_size, + width = args.video_sample_size, + guidance_scale = 6.0, + generator = generator, + + video = input_video, + mask_video = input_video_mask, + ).videos + os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True) + save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif")) + + video_length = 1 + input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size]) + sample = pipeline( + args.validation_prompts[i], + num_frames = video_length, + negative_prompt = "bad detailed", + height = args.video_sample_size, + width = args.video_sample_size, + guidance_scale = 6.0, + generator = generator, + + video = input_video, + mask_video = input_video_mask, + ).videos + os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True) + save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif")) + else: + with torch.autocast("cuda", dtype=weight_dtype): + sample = pipeline( + args.validation_prompts[i], + num_frames = args.video_sample_n_frames, + negative_prompt = "bad detailed", + height = args.video_sample_size, + width = args.video_sample_size, + generator = generator + ).videos + os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True) + save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif")) + + sample = pipeline( + args.validation_prompts[i], + num_frames = 1, + negative_prompt = "bad detailed", + height = args.video_sample_size, + width = args.video_sample_size, + generator = generator + ).videos + os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True) + save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif")) + + del pipeline + del transformer3d_val + gc.collect() + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + return images + except Exception as e: + gc.collect() + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + print(f"Eval error with info {e}") + return None + +def linear_decay(initial_value, final_value, total_steps, current_step): + if current_step >= total_steps: + return final_value + current_step = max(0, current_step) + step_size = (final_value - initial_value) / total_steps + current_value = initial_value + step_size * current_step + return current_value + +def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None): + u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator) + t = 1 / (1 + torch.exp(-u)) * (high - low) + low + return torch.clip(t.to(torch.int32), low, high - 1) + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1." + ) + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + required=True, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + required=False, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--variant", + type=str, + default=None, + help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", + ) + parser.add_argument( + "--train_data_dir", + type=str, + default=None, + help=( + "A folder containing the training data. " + ), + ) + parser.add_argument( + "--train_data_meta", + type=str, + default=None, + help=( + "A csv containing the training data. " + ), + ) + parser.add_argument( + "--max_train_samples", + type=int, + default=None, + help=( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ), + ) + parser.add_argument( + "--validation_prompts", + type=str, + default=None, + nargs="+", + help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."), + ) + parser.add_argument( + "--output_dir", + type=str, + default="sd-model-finetuned", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--cache_dir", + type=str, + default=None, + help="The directory where the downloaded models and datasets will be stored.", + ) + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") + parser.add_argument( + "--random_flip", + action="store_true", + help="whether to randomly flip images horizontally", + ) + parser.add_argument( + "--use_came", + action="store_true", + help="whether to use came", + ) + parser.add_argument( + "--multi_stream", + action="store_true", + help="whether to use cuda multi-stream", + ) + parser.add_argument( + "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." + ) + parser.add_argument( + "--vae_mini_batch", type=int, default=32, help="mini batch size for vae." + ) + parser.add_argument("--num_train_epochs", type=int, default=100) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." + ) + parser.add_argument( + "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." + ) + parser.add_argument( + "--allow_tf32", + action="store_true", + help=( + "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" + ), + ) + parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.") + parser.add_argument( + "--non_ema_revision", + type=str, + default=None, + required=False, + help=( + "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" + " remote repository specified with --pretrained_model_name_or_path." + ), + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help=( + "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." + ), + ) + parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") + parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") + parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") + parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") + parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") + parser.add_argument( + "--prediction_type", + type=str, + default=None, + help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.", + ) + parser.add_argument( + "--hub_model_id", + type=str, + default=None, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=( + "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." + ), + ) + parser.add_argument( + "--report_model_info", action="store_true", help="Whether or not to report more info about model (such as norm, grad)." + ) + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." + ), + ) + parser.add_argument( + "--report_to", + type=str, + default="tensorboard", + help=( + 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' + ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' + ), + ) + parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") + parser.add_argument( + "--checkpointing_steps", + type=int, + default=500, + help=( + "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" + " training using `--resume_from_checkpoint`." + ), + ) + parser.add_argument( + "--checkpoints_total_limit", + type=int, + default=None, + help=("Max number of checkpoints to store."), + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + help=( + "Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' + ), + ) + parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.") + parser.add_argument( + "--validation_epochs", + type=int, + default=5, + help="Run validation every X epochs.", + ) + parser.add_argument( + "--validation_steps", + type=int, + default=2000, + help="Run validation every X steps.", + ) + parser.add_argument( + "--tracker_project_name", + type=str, + default="text2image-fine-tune", + help=( + "The `project_name` argument passed to Accelerator.init_trackers for" + " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator" + ), + ) + + parser.add_argument( + "--snr_loss", action="store_true", help="Whether or not to use snr_loss." + ) + parser.add_argument( + "--uniform_sampling", action="store_true", help="Whether or not to use uniform_sampling." + ) + parser.add_argument( + "--enable_text_encoder_in_dataloader", action="store_true", help="Whether or not to use text encoder in dataloader." + ) + parser.add_argument( + "--enable_bucket", action="store_true", help="Whether enable bucket sample in datasets." + ) + parser.add_argument( + "--random_ratio_crop", action="store_true", help="Whether enable random ratio crop sample in datasets." + ) + parser.add_argument( + "--random_frame_crop", action="store_true", help="Whether enable random frame crop sample in datasets." + ) + parser.add_argument( + "--random_hw_adapt", action="store_true", help="Whether enable random adapt height and width in datasets." + ) + parser.add_argument( + "--training_with_video_token_length", action="store_true", help="The training stage of the model in training.", + ) + parser.add_argument( + "--auto_tile_batch_size", action="store_true", help="Whether to auto tile batch size.", + ) + parser.add_argument( + "--motion_sub_loss", action="store_true", help="Whether enable motion sub loss." + ) + parser.add_argument( + "--motion_sub_loss_ratio", type=float, default=0.25, help="The ratio of motion sub loss." + ) + parser.add_argument( + "--train_sampling_steps", + type=int, + default=1000, + help="Run train_sampling_steps.", + ) + parser.add_argument( + "--keep_all_node_same_token_length", + action="store_true", + help="Reference of the length token.", + ) + parser.add_argument( + "--token_sample_size", + type=int, + default=512, + help="Sample size of the token.", + ) + parser.add_argument( + "--video_sample_size", + type=int, + default=512, + help="Sample size of the video.", + ) + parser.add_argument( + "--motion_frames", + type=int, + default=73, + help="Motion frames of s2v.", + ) + parser.add_argument( + "--fix_sample_size", + nargs=2, type=int, default=None, + help="Fix Sample size [height, width] when using bucket and collate_fn." + ) + parser.add_argument( + "--video_sample_stride", + type=int, + default=4, + help="Sample stride of the video.", + ) + parser.add_argument( + "--video_sample_n_frames", + type=int, + default=17, + help="Num frame of video.", + ) + parser.add_argument( + "--video_repeat", + type=int, + default=0, + help="Num of repeat video.", + ) + parser.add_argument( + "--config_path", + type=str, + default=None, + help=( + "The config of the model in training." + ), + ) + parser.add_argument( + "--transformer_path", + type=str, + default=None, + help=("If you want to load the weight from other transformers, input its path."), + ) + parser.add_argument( + "--vae_path", + type=str, + default=None, + help=("If you want to load the weight from other vaes, input its path."), + ) + + parser.add_argument( + '--trainable_modules', + nargs='+', + help='Enter a list of trainable modules' + ) + parser.add_argument( + '--trainable_modules_low_learning_rate', + nargs='+', + default=[], + help='Enter a list of trainable modules with lower learning rate' + ) + parser.add_argument( + '--tokenizer_max_length', + type=int, + default=512, + help='Max length of tokenizer' + ) + parser.add_argument( + "--use_deepspeed", action="store_true", help="Whether or not to use deepspeed." + ) + parser.add_argument( + "--use_fsdp", action="store_true", help="Whether or not to use fsdp." + ) + parser.add_argument( + "--low_vram", action="store_true", help="Whether enable low_vram mode." + ) + parser.add_argument( + "--boundary_type", + type=str, + default="low", + help=( + 'The format of training data. Support `"low"` and `"high"`' + ), + ) + parser.add_argument( + "--control_ref_image", + type=str, + default="first_frame", + help=( + 'The format of training data. Support `"first_frame"`' + ' (default), `"random"`.' + ), + ) + parser.add_argument( + "--abnormal_norm_clip_start", + type=int, + default=1000, + help=( + 'When do we start doing additional processing on abnormal gradients. ' + ), + ) + parser.add_argument( + "--initial_grad_norm_ratio", + type=int, + default=5, + help=( + 'The initial gradient is relative to the multiple of the max_grad_norm. ' + ), + ) + parser.add_argument( + "--weighting_scheme", + type=str, + default="none", + choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"], + help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'), + ) + parser.add_argument( + "--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme." + ) + parser.add_argument( + "--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme." + ) + parser.add_argument( + "--mode_scale", + type=float, + default=1.29, + help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.", + ) + + args = parser.parse_args() + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + # default to using the same revision for the non-ema model if not specified + if args.non_ema_revision is None: + args.non_ema_revision = args.revision + + return args + + +def main(): + args = parse_args() + + if args.report_to == "wandb" and args.hub_token is not None: + raise ValueError( + "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token." + " Please use `huggingface-cli login` to authenticate with the Hub." + ) + + if args.non_ema_revision is not None: + deprecate( + "non_ema_revision!=None", + "0.15.0", + message=( + "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to" + " use `--variant=non_ema` instead." + ), + ) + logging_dir = os.path.join(args.output_dir, args.logging_dir) + + config = OmegaConf.load(args.config_path) + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=args.report_to, + project_config=accelerator_project_config, + ) + + deepspeed_plugin = accelerator.state.deepspeed_plugin if hasattr(accelerator.state, "deepspeed_plugin") else None + fsdp_plugin = accelerator.state.fsdp_plugin if hasattr(accelerator.state, "fsdp_plugin") else None + if deepspeed_plugin is not None: + zero_stage = int(deepspeed_plugin.zero_stage) + fsdp_stage = 0 + print(f"Using DeepSpeed Zero stage: {zero_stage}") + + args.use_deepspeed = True + if zero_stage == 3: + print(f"Auto set save_state to True because zero_stage == 3") + args.save_state = True + elif fsdp_plugin is not None: + from torch.distributed.fsdp import ShardingStrategy + zero_stage = 0 + if fsdp_plugin.sharding_strategy is ShardingStrategy.FULL_SHARD: + fsdp_stage = 3 + elif fsdp_plugin.sharding_strategy is None: # The fsdp_plugin.sharding_strategy is None in FSDP 2. + fsdp_stage = 3 + elif fsdp_plugin.sharding_strategy is ShardingStrategy.SHARD_GRAD_OP: + fsdp_stage = 2 + else: + fsdp_stage = 0 + print(f"Using FSDP stage: {fsdp_stage}") + + args.use_fsdp = True + if fsdp_stage == 3: + print(f"Auto set save_state to True because fsdp_stage == 3") + args.save_state = True + else: + zero_stage = 0 + fsdp_stage = 0 + print("DeepSpeed is not enabled.") + + if accelerator.is_main_process: + writer = SummaryWriter(log_dir=logging_dir) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if args.seed is not None: + set_seed(args.seed) + rng = np.random.default_rng(np.random.PCG64(args.seed + accelerator.process_index)) + torch_rng = torch.Generator(accelerator.device).manual_seed(args.seed + accelerator.process_index) + else: + rng = None + torch_rng = None + index_rng = np.random.default_rng(np.random.PCG64(43)) + print(f"Init rng with seed {args.seed + accelerator.process_index}. Process_index is {accelerator.process_index}") + + # Handle the repository creation + if accelerator.is_main_process: + if args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer3d) to half-precision + # as these weights are only used for inference, keeping weights in full precision is not required. + weight_dtype = torch.float32 + if accelerator.mixed_precision == "fp16": + weight_dtype = torch.float16 + args.mixed_precision = accelerator.mixed_precision + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + args.mixed_precision = accelerator.mixed_precision + + # Load scheduler, tokenizer and models. + noise_scheduler = FlowMatchEulerDiscreteScheduler( + **filter_kwargs(FlowMatchEulerDiscreteScheduler, OmegaConf.to_container(config['scheduler_kwargs'])) + ) + + # Get Tokenizer + tokenizer = AutoTokenizer.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')), + ) + + def deepspeed_zero_init_disabled_context_manager(): + """ + returns either a context list that includes one that will disable zero.Init or an empty context list + """ + deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None + if deepspeed_plugin is None: + return [] + + return [deepspeed_plugin.zero3_init_context_manager(enable=False)] + + # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3. + # For this to work properly all models must be run through `accelerate.prepare`. But accelerate + # will try to assign the same optimizer with the same weights to all models during + # `deepspeed.initialize`, which of course doesn't work. + # + # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2 + # frozen models from being partitioned during `zero.Init` which gets called during + # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding + # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded. + with ContextManagers(deepspeed_zero_init_disabled_context_manager()): + # Get Text encoder + text_encoder = WanT5EncoderModel.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')), + additional_kwargs=OmegaConf.to_container(config['text_encoder_kwargs']), + low_cpu_mem_usage=True, + torch_dtype=weight_dtype, + ) + text_encoder = text_encoder.eval() + # Get Vae + Chosen_AutoencoderKL = { + "AutoencoderKLWan": AutoencoderKLWan, + "AutoencoderKLWan3_8": AutoencoderKLWan3_8 + }[config['vae_kwargs'].get('vae_type', 'AutoencoderKLWan')] + vae = Chosen_AutoencoderKL.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, config['vae_kwargs'].get('vae_subpath', 'vae')), + additional_kwargs=OmegaConf.to_container(config['vae_kwargs']), + ) + vae.eval() + # Get Audio Encoder + audio_encoder = WanAudioEncoder( + os.path.join(args.pretrained_model_name_or_path, config['audio_encoder_kwargs'].get('audio_encoder_subpath', 'audio_encoder')), + "cpu" + ) + audio_encoder.eval() + + # Get Transformer + if args.boundary_type == "low" or args.boundary_type == "full": + sub_path = config['transformer_additional_kwargs'].get('transformer_low_noise_model_subpath', 'transformer') + else: + sub_path = config['transformer_additional_kwargs'].get('transformer_high_noise_model_subpath', 'transformer') + transformer3d = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, sub_path), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + low_cpu_mem_usage=True, + ).to(weight_dtype) + + # Freeze vae and text_encoder and set transformer3d to trainable + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + transformer3d.requires_grad_(False) + + if args.transformer_path is not None: + print(f"From checkpoint: {args.transformer_path}") + if args.transformer_path.endswith("safetensors"): + from safetensors.torch import load_file, safe_open + state_dict = load_file(args.transformer_path) + else: + state_dict = torch.load(args.transformer_path, map_location="cpu") + state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict + + m, u = transformer3d.load_state_dict(state_dict, strict=False) + print(f"missing keys: {len(m)}, unexpected keys: {len(u)}") + assert len(u) == 0 + + if args.vae_path is not None: + print(f"From checkpoint: {args.vae_path}") + if args.vae_path.endswith("safetensors"): + from safetensors.torch import load_file, safe_open + state_dict = load_file(args.vae_path) + else: + state_dict = torch.load(args.vae_path, map_location="cpu") + state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict + + m, u = vae.load_state_dict(state_dict, strict=False) + print(f"missing keys: {len(m)}, unexpected keys: {len(u)}") + assert len(u) == 0 + + # A good trainable modules is showed below now. + # For 3D Patch: trainable_modules = ['ff.net', 'pos_embed', 'attn2', 'proj_out', 'timepositionalencoding', 'h_position', 'w_position'] + # For 2D Patch: trainable_modules = ['ff.net', 'attn2', 'timepositionalencoding', 'h_position', 'w_position'] + transformer3d.train() + if accelerator.is_main_process: + accelerator.print( + f"Trainable modules '{args.trainable_modules}'." + ) + for name, param in transformer3d.named_parameters(): + for trainable_module_name in args.trainable_modules + args.trainable_modules_low_learning_rate: + if trainable_module_name in name: + param.requires_grad = True + break + + # Create EMA for the transformer3d. + if args.use_ema: + if zero_stage == 3: + raise NotImplementedError("FSDP does not support EMA.") + + ema_transformer3d = Wan2_2Transformer3DModel_S2V.from_pretrained( + os.path.join(args.pretrained_model_name_or_path, config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')), + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']), + ).to(weight_dtype) + + ema_transformer3d = EMAModel(ema_transformer3d.parameters(), model_cls=Wan2_2Transformer3DModel_S2V, model_config=ema_transformer3d.config) + + # `accelerate` 0.16.0 will have better support for customized saving + if version.parse(accelerate.__version__) >= version.parse("0.16.0"): + # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format + if fsdp_stage != 0: + def save_model_hook(models, weights, output_dir): + accelerate_state_dict = accelerator.get_state_dict(models[-1], unwrap=True) + if accelerator.is_main_process: + from safetensors.torch import save_file + + safetensor_save_path = os.path.join(output_dir, f"diffusion_pytorch_model.safetensors") + accelerate_state_dict = {k: v.to(dtype=weight_dtype) for k, v in accelerate_state_dict.items()} + save_file(accelerate_state_dict, safetensor_save_path, metadata={"format": "pt"}) + + with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file: + pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file) + + def load_model_hook(models, input_dir): + pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl") + if os.path.exists(pkl_path): + with open(pkl_path, 'rb') as file: + loaded_number, _ = pickle.load(file) + batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0) + print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.") + + elif zero_stage == 3: + # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format + def save_model_hook(models, weights, output_dir): + if accelerator.is_main_process: + with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file: + pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file) + + def load_model_hook(models, input_dir): + pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl") + if os.path.exists(pkl_path): + with open(pkl_path, 'rb') as file: + loaded_number, _ = pickle.load(file) + batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0) + print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.") + else: + # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format + def save_model_hook(models, weights, output_dir): + if accelerator.is_main_process: + if args.use_ema: + ema_transformer3d.save_pretrained(os.path.join(output_dir, "transformer_ema")) + + models[0].save_pretrained(os.path.join(output_dir, "transformer")) + if not args.use_deepspeed: + weights.pop() + + with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file: + pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file) + + def load_model_hook(models, input_dir): + if args.use_ema: + ema_path = os.path.join(input_dir, "transformer_ema") + _, ema_kwargs = Wan2_2Transformer3DModel_S2V.load_config(ema_path, return_unused_kwargs=True) + load_model = Wan2_2Transformer3DModel_S2V.from_pretrained( + input_dir, subfolder="transformer_ema", + transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) + ) + load_model = EMAModel(load_model.parameters(), model_cls=Wan2_2Transformer3DModel_S2V, model_config=load_model.config) + load_model.load_state_dict(ema_kwargs) + + ema_transformer3d.load_state_dict(load_model.state_dict()) + ema_transformer3d.to(accelerator.device) + del load_model + + for i in range(len(models)): + # pop models so that they are not loaded again + model = models.pop() + + # load diffusers style into model + load_model = Wan2_2Transformer3DModel_S2V.from_pretrained( + input_dir, subfolder="transformer" + ) + model.register_to_config(**load_model.config) + + model.load_state_dict(load_model.state_dict()) + del load_model + + pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl") + if os.path.exists(pkl_path): + with open(pkl_path, 'rb') as file: + loaded_number, _ = pickle.load(file) + batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0) + print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.") + + accelerator.register_save_state_pre_hook(save_model_hook) + accelerator.register_load_state_pre_hook(load_model_hook) + + if args.gradient_checkpointing: + transformer3d.enable_gradient_checkpointing() + + # Enable TF32 for faster training on Ampere GPUs, + # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices + if args.allow_tf32: + torch.backends.cuda.matmul.allow_tf32 = True + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes + ) + + # Initialize the optimizer + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" + ) + + optimizer_cls = bnb.optim.AdamW8bit + elif args.use_came: + try: + from came_pytorch import CAME + except: + raise ImportError( + "Please install came_pytorch to use CAME. You can do so by running `pip install came_pytorch`" + ) + + optimizer_cls = CAME + else: + optimizer_cls = torch.optim.AdamW + + trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters())) + trainable_params_optim = [ + {'params': [], 'lr': args.learning_rate}, + {'params': [], 'lr': args.learning_rate / 2}, + ] + in_already = [] + for name, param in transformer3d.named_parameters(): + high_lr_flag = False + if name in in_already: + continue + for trainable_module_name in args.trainable_modules: + if trainable_module_name in name: + in_already.append(name) + high_lr_flag = True + trainable_params_optim[0]['params'].append(param) + if accelerator.is_main_process: + print(f"Set {name} to lr : {args.learning_rate}") + break + if high_lr_flag: + continue + for trainable_module_name in args.trainable_modules_low_learning_rate: + if trainable_module_name in name: + in_already.append(name) + trainable_params_optim[1]['params'].append(param) + if accelerator.is_main_process: + print(f"Set {name} to lr : {args.learning_rate / 2}") + break + + if args.use_came: + optimizer = optimizer_cls( + trainable_params_optim, + lr=args.learning_rate, + # weight_decay=args.adam_weight_decay, + betas=(0.9, 0.999, 0.9999), + eps=(1e-30, 1e-16) + ) + else: + optimizer = optimizer_cls( + trainable_params_optim, + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + # Get the training dataset + sample_n_frames_bucket_interval = vae.config.temporal_compression_ratio + spatial_compression_ratio = vae.config.spatial_compression_ratio + + if args.fix_sample_size is not None and args.enable_bucket: + args.video_sample_size = max(max(args.fix_sample_size), args.video_sample_size) + args.training_with_video_token_length = False + args.random_hw_adapt = False + + # Get the dataset + train_dataset = VideoSpeechControlDataset( + args.train_data_meta, args.train_data_dir, + video_sample_size=args.video_sample_size, video_sample_stride=args.video_sample_stride, video_sample_n_frames=args.video_sample_n_frames, + enable_bucket=args.enable_bucket, enable_inpaint=True, enable_motion_info=True, motion_frames=args.motion_frames + ) + def worker_init_fn(_seed): + _seed = _seed * 256 + def _worker_init_fn(worker_id): + print(f"worker_init_fn with {_seed + worker_id}") + np.random.seed(_seed + worker_id) + random.seed(_seed + worker_id) + return _worker_init_fn + + if args.enable_bucket: + aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()} + batch_sampler_generator = torch.Generator().manual_seed(args.seed) + batch_sampler = AspectRatioBatchImageVideoSampler( + sampler=RandomSampler(train_dataset, generator=batch_sampler_generator), dataset=train_dataset.dataset, + batch_size=args.train_batch_size, train_folder = args.train_data_dir, drop_last=True, + aspect_ratios=aspect_ratio_sample_size, + ) + + def collate_fn(examples): + def get_length_to_frame_num(token_length): + if args.video_sample_size > 256: + sample_sizes = list(range(256, args.video_sample_size + 1, 128)) + + if sample_sizes[-1] != args.video_sample_size: + sample_sizes.append(args.video_sample_size) + else: + sample_sizes = [args.video_sample_size] + + length_to_frame_num = { + sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval for sample_size in sample_sizes + } + + return length_to_frame_num + + def get_random_downsample_ratio(sample_size, image_ratio=[], + all_choices=False, rng=None): + def _create_special_list(length): + if length == 1: + return [1.0] + if length >= 2: + first_element = 0.90 + remaining_sum = 1.0 - first_element + other_elements_value = remaining_sum / (length - 1) + special_list = [first_element] + [other_elements_value] * (length - 1) + return special_list + + if sample_size >= 1536: + number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio + elif sample_size >= 1024: + number_list = [1, 1.25, 1.5, 2] + image_ratio + elif sample_size >= 768: + number_list = [1, 1.25, 1.5] + image_ratio + elif sample_size >= 512: + number_list = [1] + image_ratio + else: + number_list = [1] + + if all_choices: + return number_list + + number_list_prob = np.array(_create_special_list(len(number_list))) + if rng is None: + return np.random.choice(number_list, p = number_list_prob) + else: + return rng.choice(number_list, p = number_list_prob) + + # Get token length + target_token_length = args.video_sample_n_frames * args.token_sample_size * args.token_sample_size + length_to_frame_num = get_length_to_frame_num(target_token_length) + + # Create new output + new_examples = {} + new_examples["target_token_length"] = target_token_length + new_examples["pixel_values"] = [] + new_examples["motion_pixel_values"] = [] + new_examples["text"] = [] + # Used in Control Mode + new_examples["control_pixel_values"] = [] + new_examples["ref_pixel_values"] = [] + new_examples["clip_idx"] = [] + + new_examples["audio"] = [] + new_examples["sample_rate"] = [] + new_examples["fps"] = [] + + # Used in Inpaint mode + new_examples["clip_pixel_values"] = [] + + # Get downsample ratio in image and videos + pixel_value = examples[0]["pixel_values"] + f, h, w, c = np.shape(pixel_value) + + if args.random_hw_adapt: + if args.training_with_video_token_length: + local_min_size = np.min(np.array([np.mean(np.array([np.shape(example["pixel_values"])[1], np.shape(example["pixel_values"])[2]])) for example in examples])) + # The video will be resized to a lower resolution than its own. + choice_list = [length for length in list(length_to_frame_num.keys()) if length < local_min_size * 1.25] + if len(choice_list) == 0: + choice_list = list(length_to_frame_num.keys()) + local_video_sample_size = np.random.choice(choice_list) + batch_video_length = length_to_frame_num[local_video_sample_size] + random_downsample_ratio = args.video_sample_size / local_video_sample_size + else: + random_downsample_ratio = get_random_downsample_ratio(args.video_sample_size) + batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval + else: + random_downsample_ratio = 1 + batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval + + aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()} + aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()} + + if args.fix_sample_size is not None: + fix_sample_size = [int(x / 16) * 16 for x in args.fix_sample_size] + elif args.random_ratio_crop: + if rng is None: + random_sample_size = aspect_ratio_random_crop_sample_size[ + np.random.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB) + ] + else: + random_sample_size = aspect_ratio_random_crop_sample_size[ + rng.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB) + ] + random_sample_size = [int(x / 16) * 16 for x in random_sample_size] + else: + closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size) + closest_size = [int(x / 16) * 16 for x in closest_size] + + min_example_length = min( + [example["pixel_values"].shape[0] for example in examples] + ) + batch_video_length = int(min(batch_video_length, min_example_length)) + + # Magvae needs the number of frames to be 4n. + batch_video_length = (batch_video_length) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + + if batch_video_length <= 0: + batch_video_length = 1 + + for example in examples: + # To 0~1 + pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous() + pixel_values = pixel_values / 255. + + motion_pixel_values = torch.from_numpy(example["motion_pixel_values"]).permute(0, 3, 1, 2).contiguous() + motion_pixel_values = motion_pixel_values / 255. + + control_pixel_values = torch.from_numpy(example["control_pixel_values"]).permute(0, 3, 1, 2).contiguous() + control_pixel_values = control_pixel_values / 255. + + if args.fix_sample_size is not None: + # Get adapt hw for resize + fix_sample_size = list(map(lambda x: int(x), fix_sample_size)) + transform = transforms.Compose([ + transforms.Resize(fix_sample_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC + transforms.CenterCrop(fix_sample_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + elif args.random_ratio_crop: + # Get adapt hw for resize + b, c, h, w = pixel_values.size() + th, tw = random_sample_size + if th / tw > h / w: + nh = int(th) + nw = int(w / h * nh) + else: + nw = int(tw) + nh = int(h / w * nw) + + transform = transforms.Compose([ + transforms.Resize([nh, nw]), + transforms.CenterCrop([int(x) for x in random_sample_size]), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + else: + # Get adapt hw for resize + closest_size = list(map(lambda x: int(x), closest_size)) + if closest_size[0] / h > closest_size[1] / w: + resize_size = closest_size[0], int(w * closest_size[0] / h) + else: + resize_size = int(h * closest_size[1] / w), closest_size[1] + + transform = transforms.Compose([ + transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC + transforms.CenterCrop(closest_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ]) + + new_examples["pixel_values"].append(transform(pixel_values)[:batch_video_length]) + new_examples["motion_pixel_values"].append(transform(motion_pixel_values)) + new_examples["control_pixel_values"].append(transform(control_pixel_values)) + new_examples["text"].append(example["text"]) + + if args.control_ref_image == "first_frame": + clip_index = 0 + else: + def _create_special_list(length): + if length == 1: + return [1.0] + if length >= 2: + first_element = 0.40 + remaining_sum = 1.0 - first_element + other_elements_value = remaining_sum / (length - 1) + special_list = [first_element] + [other_elements_value] * (length - 1) + return special_list + number_list_prob = np.array(_create_special_list(len(new_examples["pixel_values"][-1]))) + clip_index = np.random.choice(list(range(len(new_examples["pixel_values"][-1]))), p = number_list_prob) + + ref_pixel_values = new_examples["pixel_values"][-1][clip_index].unsqueeze(0) + new_examples["ref_pixel_values"].append(ref_pixel_values) + + clip_pixel_values = new_examples["pixel_values"][-1][clip_index].permute(1, 2, 0).contiguous() + clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255 + new_examples["clip_pixel_values"].append(clip_pixel_values) + new_examples["clip_idx"].append(clip_index) + + audio_length = np.shape(example["audio"])[0] + batch_audio_length = int(audio_length / pixel_values.size()[0] * batch_video_length) + new_examples["audio"].append(example["audio"][:batch_audio_length]) + new_examples["sample_rate"].append(example["sample_rate"]) + new_examples["fps"].append(example["fps"]) + + # Limit the number of frames to the same + new_examples["pixel_values"] = torch.stack([example for example in new_examples["pixel_values"]]) + new_examples["motion_pixel_values"] = torch.stack([example for example in new_examples["motion_pixel_values"]]) + new_examples["control_pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["control_pixel_values"]]) + new_examples["ref_pixel_values"] = torch.stack([example for example in new_examples["ref_pixel_values"]]) + new_examples["clip_pixel_values"] = torch.stack([example for example in new_examples["clip_pixel_values"]]) + new_examples["clip_idx"] = torch.tensor(new_examples["clip_idx"]) + new_examples["audio"] = torch.stack([example for example in new_examples["audio"]]) + new_examples["sample_rate"] = new_examples["sample_rate"] + new_examples["fps"] = new_examples["fps"] + + # Encode prompts when enable_text_encoder_in_dataloader=True + if args.enable_text_encoder_in_dataloader: + prompt_ids = tokenizer( + new_examples['text'], + max_length=args.tokenizer_max_length, + padding="max_length", + add_special_tokens=True, + truncation=True, + return_tensors="pt" + ) + encoder_hidden_states = text_encoder( + prompt_ids.input_ids + )[0] + new_examples['encoder_attention_mask'] = prompt_ids.attention_mask + new_examples['encoder_hidden_states'] = encoder_hidden_states + + return new_examples + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_sampler=batch_sampler, + collate_fn=collate_fn, + persistent_workers=True if args.dataloader_num_workers != 0 else False, + num_workers=args.dataloader_num_workers, + worker_init_fn=worker_init_fn(args.seed + accelerator.process_index) + ) + else: + # DataLoaders creation: + batch_sampler_generator = torch.Generator().manual_seed(args.seed) + batch_sampler = ImageVideoSampler(RandomSampler(train_dataset, generator=batch_sampler_generator), train_dataset, args.train_batch_size) + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_sampler=batch_sampler, + persistent_workers=True if args.dataloader_num_workers != 0 else False, + num_workers=args.dataloader_num_workers, + worker_init_fn=worker_init_fn(args.seed + accelerator.process_index) + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps * accelerator.num_processes, + ) + + # Prepare everything with our `accelerator`. + transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + transformer3d, optimizer, train_dataloader, lr_scheduler + ) + + if fsdp_stage != 0: + from functools import partial + from videox_fun.dist import set_multi_gpus_devices, shard_model + shard_fn = partial(shard_model, device_id=accelerator.device, param_dtype=weight_dtype) + text_encoder = shard_fn(text_encoder) + + if args.use_ema: + ema_transformer3d.to(accelerator.device) + + # Move text_encode and vae to gpu and cast to weight_dtype + vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype) + if not args.enable_text_encoder_in_dataloader: + text_encoder.to(accelerator.device if not args.low_vram else "cpu") + audio_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=torch.float32) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + tracker_config = dict(vars(args)) + keys_to_pop = [k for k, v in tracker_config.items() if isinstance(v, list)] + for k in keys_to_pop: + tracker_config.pop(k) + print(f"Removed tracker_config['{k}']") + accelerator.init_trackers(args.tracker_project_name, tracker_config) + + # Function for unwrapping if model was compiled with `torch.compile`. + def unwrap_model(model): + model = accelerator.unwrap_model(model) + model = model._orig_mod if is_compiled_module(model) else model + return model + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + global_step = 0 + first_epoch = 0 + + # Potentially load in the weights and states from a previous save + if args.resume_from_checkpoint: + if args.resume_from_checkpoint != "latest": + path = os.path.basename(args.resume_from_checkpoint) + else: + # Get the most recent checkpoint + dirs = os.listdir(args.output_dir) + dirs = [d for d in dirs if d.startswith("checkpoint")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] if len(dirs) > 0 else None + + if path is None: + accelerator.print( + f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." + ) + args.resume_from_checkpoint = None + initial_global_step = 0 + else: + global_step = int(path.split("-")[1]) + + initial_global_step = global_step + + pkl_path = os.path.join(os.path.join(args.output_dir, path), "sampler_pos_start.pkl") + if os.path.exists(pkl_path): + with open(pkl_path, 'rb') as file: + _, first_epoch = pickle.load(file) + else: + first_epoch = global_step // num_update_steps_per_epoch + print(f"Load pkl from {pkl_path}. Get first_epoch = {first_epoch}.") + + accelerator.print(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(args.output_dir, path)) + else: + initial_global_step = 0 + + progress_bar = tqdm( + range(0, args.max_train_steps), + initial=initial_global_step, + desc="Steps", + # Only show the progress bar once on each machine. + disable=not accelerator.is_local_main_process, + ) + + if args.multi_stream: + # create extra cuda streams to speedup inpaint vae computation + vae_stream_1 = torch.cuda.Stream() + vae_stream_2 = torch.cuda.Stream() + else: + vae_stream_1 = None + vae_stream_2 = None + + # Calculate the index we need + boundary = config['transformer_additional_kwargs'].get('boundary', 0.900) + split_timesteps = args.train_sampling_steps * boundary + differences = torch.abs(noise_scheduler.timesteps - split_timesteps) + closest_index = torch.argmin(differences).item() + if args.boundary_type == "high" or args.boundary_type == "low": + print(f"The boundary is {boundary} and the boundary_type is {args.boundary_type}. The closest_index we calculate is {closest_index}") + if args.boundary_type == "high": + start_num_idx = 0 + train_sampling_steps = closest_index + elif args.boundary_type == "low": + start_num_idx = closest_index + train_sampling_steps = args.train_sampling_steps - closest_index + else: + start_num_idx = 0 + train_sampling_steps = args.train_sampling_steps + + idx_sampling = DiscreteSampling(train_sampling_steps, start_num_idx=start_num_idx, uniform_sampling=args.uniform_sampling) + + for epoch in range(first_epoch, args.num_train_epochs): + train_loss = 0.0 + batch_sampler.sampler.generator = torch.Generator().manual_seed(args.seed + epoch) + for step, batch in enumerate(train_dataloader): + # Data batch sanity check + if epoch == first_epoch and step == 0: + pixel_values, texts = batch['pixel_values'].cpu(), batch['text'] + pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w") + os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True) + for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)): + pixel_value = pixel_value[None, ...] + gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}' + save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}.gif", rescale=True) + ref_pixel_values = batch["ref_pixel_values"].cpu() + ref_pixel_values = rearrange(ref_pixel_values, "b f c h w -> b c f h w") + for idx, (ref_pixel_value, text) in enumerate(zip(ref_pixel_values, texts)): + ref_pixel_value = ref_pixel_value[None, ...] + gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}' + save_videos_grid(ref_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_ref.gif", rescale=True) + + motion_pixel_values = batch["motion_pixel_values"].cpu() + motion_pixel_values = rearrange(motion_pixel_values, "b f c h w -> b c f h w") + for idx, (motion_pixel_value, text) in enumerate(zip(motion_pixel_values, texts)): + motion_pixel_value = motion_pixel_value[None, ...] + gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}' + save_videos_grid(motion_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_motion.gif", rescale=True) + + clip_pixel_values, texts = batch['clip_pixel_values'].cpu(), batch['text'] + for idx, (clip_pixel_value, text) in enumerate(zip(clip_pixel_values, texts)): + pixel_value = pixel_value[None, ...] + Image.fromarray(np.uint8(clip_pixel_value)).save(f"{args.output_dir}/sanity_check/clip_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.png") + + with accelerator.accumulate(transformer3d): + # Convert images to latent space + pixel_values = batch["pixel_values"].to(weight_dtype) + motion_pixel_values = batch["motion_pixel_values"].to(weight_dtype) + control_pixel_values = batch["control_pixel_values"].to(weight_dtype) + ref_pixel_values = batch["ref_pixel_values"].to(weight_dtype) + clip_idx = batch["clip_idx"] + audio = batch["audio"] + sample_rate = batch["sample_rate"] + fps = batch["fps"] + + # Increase the batch size when the length of the latent sequence of the current sample is small + if args.auto_tile_batch_size and args.training_with_video_token_length and zero_stage != 3: + if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: + pixel_values = torch.tile(pixel_values, (4, 1, 1, 1, 1)) + motion_pixel_values = torch.tile(motion_pixel_values, (4, 1, 1, 1, 1)) + control_pixel_values = torch.tile(control_pixel_values, (4, 1, 1, 1, 1)) + ref_pixel_values = torch.tile(ref_pixel_values, (4, 1, 1, 1, 1)) + clip_idx = torch.tile(clip_idx, (4,)) + audio = torch.tile(audio, (4, 1)) + sample_rate = sample_rate * 4 + fps = fps * 4 + if args.enable_text_encoder_in_dataloader: + batch['encoder_hidden_states'] = torch.tile(batch['encoder_hidden_states'], (4, 1, 1)) + batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (4, 1)) + else: + batch['text'] = batch['text'] * 4 + elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: + pixel_values = torch.tile(pixel_values, (2, 1, 1, 1, 1)) + motion_pixel_values = torch.tile(motion_pixel_values, (2, 1, 1, 1, 1)) + control_pixel_values = torch.tile(control_pixel_values, (2, 1, 1, 1, 1)) + ref_pixel_values = torch.tile(ref_pixel_values, (2, 1, 1, 1, 1)) + clip_idx = torch.tile(clip_idx, (2,)) + audio = torch.tile(audio, (2, 1)) + sample_rate = sample_rate * 2 + fps = fps * 2 + if args.enable_text_encoder_in_dataloader: + batch['encoder_hidden_states'] = torch.tile(batch['encoder_hidden_states'], (2, 1, 1)) + batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (2, 1)) + else: + batch['text'] = batch['text'] * 2 + + clip_pixel_values = batch["clip_pixel_values"].to(weight_dtype) + # Increase the batch size when the length of the latent sequence of the current sample is small + if args.auto_tile_batch_size and args.training_with_video_token_length and zero_stage != 3: + if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: + clip_pixel_values = torch.tile(clip_pixel_values, (4, 1, 1, 1)) + elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: + clip_pixel_values = torch.tile(clip_pixel_values, (2, 1, 1, 1)) + + if args.random_frame_crop: + def _create_special_list(length): + if length == 1: + return [1.0] + if length >= 2: + last_element = 0.90 + remaining_sum = 1.0 - last_element + other_elements_value = remaining_sum / (length - 1) + special_list = [other_elements_value] * (length - 1) + [last_element] + return special_list + select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval + 1, args.video_sample_n_frames + sample_n_frames_bucket_interval, sample_n_frames_bucket_interval))] + select_frames_prob = np.array(_create_special_list(len(select_frames))) + + if len(select_frames) != 0: + if rng is None: + temp_n_frames = np.random.choice(select_frames, p = select_frames_prob) + else: + temp_n_frames = rng.choice(select_frames, p = select_frames_prob) + else: + temp_n_frames = 1 + + # Magvae needs the number of frames to be 4n. + temp_n_frames = (temp_n_frames) // sample_n_frames_bucket_interval + + pixel_values = pixel_values[:, :temp_n_frames, :, :] + control_pixel_values = control_pixel_values[:, :temp_n_frames, :, :] + + # Keep all node same token length to accelerate the traning when resolution grows. + if args.keep_all_node_same_token_length: + if args.token_sample_size > 256: + numbers_list = list(range(256, args.token_sample_size + 1, 128)) + + if numbers_list[-1] != args.token_sample_size: + numbers_list.append(args.token_sample_size) + else: + numbers_list = [256] + numbers_list = [_number * _number * args.video_sample_n_frames for _number in numbers_list] + + actual_token_length = index_rng.choice(numbers_list) + actual_video_length = (min( + actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames + )) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + actual_video_length = int(max(actual_video_length, 1)) + + # Magvae needs the number of frames to be 4n. + actual_video_length = (actual_video_length) // sample_n_frames_bucket_interval + + pixel_values = pixel_values[:, :actual_video_length, :, :] + control_pixel_values = control_pixel_values[:, :actual_video_length, :, :] + + if args.low_vram: + torch.cuda.empty_cache() + vae.to(accelerator.device) + if not args.enable_text_encoder_in_dataloader: + text_encoder.to("cpu") + audio_encoder.to("cpu") + + with torch.no_grad(): + # This way is quicker when batch grows up + def _batch_encode_vae(pixel_values): + pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w") + bs = args.vae_mini_batch + new_pixel_values = [] + for i in range(0, pixel_values.shape[0], bs): + pixel_values_bs = pixel_values[i : i + bs] + pixel_values_bs = vae.encode(pixel_values_bs)[0] + pixel_values_bs = pixel_values_bs.sample() + new_pixel_values.append(pixel_values_bs) + return torch.cat(new_pixel_values, dim = 0) + + if rng is None: + zero_tail_frames = np.random.choice([0, 1], p = [0.90, 0.10]) + else: + zero_tail_frames = rng.choice([0, 1], p = [0.90, 0.10]) + if zero_tail_frames: + if rng is None: + zero_frames_num = np.random.randint(1, control_pixel_values.size()[1]) + else: + zero_frames_num = rng.integers(1, control_pixel_values.size()[1]) + control_pixel_values[:, zero_frames_num:] = torch.ones_like(control_pixel_values[:, zero_frames_num:]) * -1 + + # Make control pixel values to zero + for bs_index in range(control_pixel_values.size()[0]): + if rng is None: + zero_init_control_latents_conv_in = np.random.choice([0, 1], p = [0.90, 0.10]) + else: + zero_init_control_latents_conv_in = rng.choice([0, 1], p = [0.90, 0.10]) + + if zero_init_control_latents_conv_in: + control_pixel_values[bs_index] = torch.ones_like(control_pixel_values[bs_index]) * -1 + # Encode control latents + pad_control_pixel_values = torch.cat([control_pixel_values[:, 0:1, :].repeat(1, 1, 1, 1, 1), control_pixel_values], dim=1) + control_latents = _batch_encode_vae(pad_control_pixel_values)[:, :, 1:] + + # Encode Reference latents + ref_latents = _batch_encode_vae(ref_pixel_values) + + # Encode Motion latents + if rng is None: + zero_motion_pixel_values = np.random.choice([0, 1], p = [0.90, 0.10]) + else: + zero_motion_pixel_values = rng.choice([0, 1], p = [0.90, 0.10]) + if zero_motion_pixel_values: + height, width = control_pixel_values.size()[-2], control_pixel_values.size()[-1] + motion_pixel_values = torch.zeros([1, args.motion_frames, 3, height, width], dtype=control_latents.dtype, device=control_latents.device) + + has_motion_pixel_values = torch.sum(motion_pixel_values) == 0 + if torch.sum(clip_idx) != 0: + init_first_frame = False + else: + if rng is None: + init_first_frame = np.random.choice([0, 1], p = [0.50, 0.50]) + else: + init_first_frame = rng.choice([0, 1], p = [0.50, 0.50]) + if init_first_frame or has_motion_pixel_values: + if not has_motion_pixel_values: + motion_pixel_values[:, -6:, :] = ref_pixel_values[:, 0, :] + + motion_frames_latents_length = int((args.motion_frames - 1) / sample_n_frames_bucket_interval + 1) + local_pixel_values = torch.cat([motion_pixel_values, pixel_values], dim = 1) + local_latents = _batch_encode_vae(local_pixel_values) + latents = local_latents[:, :, motion_frames_latents_length:] + motion_latents = local_latents[:, :, :motion_frames_latents_length] + drop_motion_frames = False + else: + local_pixel_values = torch.cat([ref_pixel_values, pixel_values], dim = 1) + latents = _batch_encode_vae(local_pixel_values) + latents = latents[:, :, 1:] + motion_latents = _batch_encode_vae(motion_pixel_values) + drop_motion_frames = True + + if args.low_vram: + vae.to('cpu') + torch.cuda.empty_cache() + if not args.enable_text_encoder_in_dataloader: + text_encoder.to(accelerator.device) + audio_encoder.to(accelerator.device) + + if args.enable_text_encoder_in_dataloader: + prompt_embeds = batch['encoder_hidden_states'].to(device=latents.device) + else: + with torch.no_grad(): + prompt_ids = tokenizer( + batch['text'], + padding="max_length", + max_length=args.tokenizer_max_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt" + ) + text_input_ids = prompt_ids.input_ids + prompt_attention_mask = prompt_ids.attention_mask + + seq_lens = prompt_attention_mask.gt(0).sum(dim=1).long() + prompt_embeds = text_encoder(text_input_ids.to(latents.device), attention_mask=prompt_attention_mask.to(latents.device))[0] + prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)] + + with torch.no_grad(): + # Extract audio emb + new_audio_wav2vec_fea = [] + for index in range(len(audio)): + _audio_wav2vec_fea = audio_encoder.extract_audio_feat_without_file_load( + audio[index], sample_rate[index], return_all_layers=True + ) + new_audio_wav2vec_fea.append(_audio_wav2vec_fea) + audio_wav2vec_fea = torch.stack(new_audio_wav2vec_fea).to(device=accelerator.device, dtype=weight_dtype) + + new_audio_wav2vec_fea = [] + for index in range(len(audio)): + _audio_wav2vec_fea, num_repeat = audio_encoder.get_audio_embed_bucket_fps( + audio_wav2vec_fea[index], fps=fps[index], batch_frames=control_pixel_values.size()[1], m=0) + new_audio_wav2vec_fea.append(_audio_wav2vec_fea) + audio_wav2vec_fea = torch.stack(new_audio_wav2vec_fea).to(device=accelerator.device, dtype=weight_dtype) + + if len(audio_wav2vec_fea.shape) == 3: + audio_wav2vec_fea = audio_wav2vec_fea.permute(0, 2, 1) + elif len(audio_wav2vec_fea.shape) == 4: + audio_wav2vec_fea = audio_wav2vec_fea.permute(0, 2, 3, 1) + + for bs_index in range(audio_wav2vec_fea.size()[0]): + if rng is None: + zero_init_control_latents_conv_in = np.random.choice([0, 1], p = [0.90, 0.10]) + else: + zero_init_control_latents_conv_in = rng.choice([0, 1], p = [0.90, 0.10]) + + if zero_init_control_latents_conv_in: + audio_wav2vec_fea[bs_index] = torch.ones_like(audio_wav2vec_fea[bs_index]) * 0 + + if zero_tail_frames: + audio_wav2vec_fea[..., zero_frames_num:] = torch.zeros_like(audio_wav2vec_fea[..., zero_frames_num:]) + # audio_wav2vec_fea = audio_wav2vec_fea[..., :control_pixel_values.size()[1]] + + if args.low_vram and not args.enable_text_encoder_in_dataloader: + text_encoder.to('cpu') + audio_encoder.to("cpu") + torch.cuda.empty_cache() + + bsz, channel, num_frames, height, width = latents.size() + noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype) + + if not args.uniform_sampling: + u = compute_density_for_timestep_sampling( + weighting_scheme=args.weighting_scheme, + batch_size=bsz, + logit_mean=args.logit_mean, + logit_std=args.logit_std, + mode_scale=args.mode_scale, + ) + indices = (u * noise_scheduler.config.num_train_timesteps).long() + else: + # Sample a random timestep for each image + # timesteps = generate_timestep_with_lognorm(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng) + # timesteps = torch.randint(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng) + indices = idx_sampling(bsz, generator=torch_rng, device=latents.device) + indices = indices.long().cpu() + timesteps = noise_scheduler.timesteps[indices].to(device=latents.device) + + def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): + sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype) + schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device) + timesteps = timesteps.to(accelerator.device) + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < n_dim: + sigma = sigma.unsqueeze(-1) + return sigma + + # Add noise according to flow matching. + # zt = (1 - texp) * x + texp * z1 + sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=latents.dtype) + noisy_latents = (1.0 - sigmas) * latents + sigmas * noise + + # Add noise + target = noise - latents + + target_shape = (vae.latent_channels, num_frames, width, height) + seq_len = math.ceil( + (target_shape[2] * target_shape[3]) / + (accelerator.unwrap_model(transformer3d).config.patch_size[1] * accelerator.unwrap_model(transformer3d).config.patch_size[2]) * + target_shape[1] + ) + + with torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device): + noise_pred = transformer3d( + x=noisy_latents, + context=prompt_embeds, + t=timesteps, + seq_len=seq_len, + cond_states=control_latents, + motion_latents=motion_latents, + ref_latents=ref_latents, + audio_input=audio_wav2vec_fea, + motion_frames=[[args.motion_frames, (args.motion_frames + 3) // 4]] * bsz, + drop_motion_frames=drop_motion_frames, + ) + + def custom_mse_loss(noise_pred, target, weighting=None, threshold=50): + noise_pred = noise_pred.float() + target = target.float() + diff = noise_pred - target + mse_loss = F.mse_loss(noise_pred, target, reduction='none') + mask = (diff.abs() <= threshold).float() + masked_loss = mse_loss * mask + if weighting is not None: + masked_loss = masked_loss * weighting + final_loss = masked_loss.mean() + return final_loss + + weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas) + loss = custom_mse_loss(noise_pred.float(), target.float(), weighting.float()) + loss = loss.mean() + + if args.motion_sub_loss and noise_pred.size()[2] > 2: + gt_sub_noise = noise_pred[:, :, 1:].float() - noise_pred[:, :, :-1].float() + pre_sub_noise = target[:, :, 1:].float() - target[:, :, :-1].float() + sub_loss = F.mse_loss(gt_sub_noise, pre_sub_noise, reduction="mean") + loss = loss * (1 - args.motion_sub_loss_ratio) + sub_loss * args.motion_sub_loss_ratio + + # Gather the losses across all processes for logging (if we use distributed training). + avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + # Backpropagate + accelerator.backward(loss) + if accelerator.sync_gradients: + if not args.use_deepspeed and not args.use_fsdp: + trainable_params_grads = [p.grad for p in trainable_params if p.grad is not None] + trainable_params_total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2) for g in trainable_params_grads]), 2) + max_grad_norm = linear_decay(args.max_grad_norm * args.initial_grad_norm_ratio, args.max_grad_norm, args.abnormal_norm_clip_start, global_step) + if trainable_params_total_norm / max_grad_norm > 5 and global_step > args.abnormal_norm_clip_start: + actual_max_grad_norm = max_grad_norm / min((trainable_params_total_norm / max_grad_norm), 10) + else: + actual_max_grad_norm = max_grad_norm + else: + actual_max_grad_norm = args.max_grad_norm + + if not args.use_deepspeed and not args.use_fsdp and args.report_model_info and accelerator.is_main_process: + if trainable_params_total_norm > 1 and global_step > args.abnormal_norm_clip_start: + for name, param in transformer3d.named_parameters(): + if param.requires_grad: + writer.add_scalar(f'gradients/before_clip_norm/{name}', param.grad.norm(), global_step=global_step) + + norm_sum = accelerator.clip_grad_norm_(trainable_params, actual_max_grad_norm) + if not args.use_deepspeed and not args.use_fsdp and args.report_model_info and accelerator.is_main_process: + writer.add_scalar(f'gradients/norm_sum', norm_sum, global_step=global_step) + writer.add_scalar(f'gradients/actual_max_grad_norm', actual_max_grad_norm, global_step=global_step) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + + if args.use_ema: + ema_transformer3d.step(transformer3d.parameters()) + progress_bar.update(1) + global_step += 1 + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if global_step % args.checkpointing_steps == 0: + if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process: + # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` + if args.checkpoints_total_limit is not None: + checkpoints = os.listdir(args.output_dir) + checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] + checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) + + # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints + if len(checkpoints) >= args.checkpoints_total_limit: + num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 + removing_checkpoints = checkpoints[0:num_to_remove] + + logger.info( + f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" + ) + logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") + + for removing_checkpoint in removing_checkpoints: + removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) + shutil.rmtree(removing_checkpoint) + + gc.collect() + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + accelerator.save_state(save_path) + logger.info(f"Saved state to {save_path}") + + if accelerator.is_main_process: + if args.validation_prompts is not None and global_step % args.validation_steps == 0: + if args.use_ema: + # Store the UNet parameters temporarily and load the EMA parameters to perform inference. + ema_transformer3d.store(transformer3d.parameters()) + ema_transformer3d.copy_to(transformer3d.parameters()) + log_validation( + vae, + text_encoder, + tokenizer, + transformer3d, + args, + config, + accelerator, + weight_dtype, + global_step, + ) + if args.use_ema: + # Switch back to the original transformer3d parameters. + ema_transformer3d.restore(transformer3d.parameters()) + + logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + if accelerator.is_main_process: + if args.validation_prompts is not None and epoch % args.validation_epochs == 0: + if args.use_ema: + # Store the UNet parameters temporarily and load the EMA parameters to perform inference. + ema_transformer3d.store(transformer3d.parameters()) + ema_transformer3d.copy_to(transformer3d.parameters()) + log_validation( + vae, + text_encoder, + tokenizer, + transformer3d, + args, + config, + accelerator, + weight_dtype, + global_step, + ) + if args.use_ema: + # Switch back to the original transformer3d parameters. + ema_transformer3d.restore(transformer3d.parameters()) + + # Create the pipeline using the trained modules and save it. + accelerator.wait_for_everyone() + if accelerator.is_main_process: + transformer3d = unwrap_model(transformer3d) + if args.use_ema: + ema_transformer3d.copy_to(transformer3d.parameters()) + + if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process: + gc.collect() + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + accelerator.save_state(save_path) + logger.info(f"Saved state to {save_path}") + + accelerator.end_training() + + +if __name__ == "__main__": + main() diff --git a/scripts/wan2.2/train_s2v.sh b/scripts/wan2.2/train_s2v.sh new file mode 100644 index 0000000..a8d2d95 --- /dev/null +++ b/scripts/wan2.2/train_s2v.sh @@ -0,0 +1,39 @@ +export MODEL_NAME="models/Diffusion_Transformer/Wan2.2-S2V-14B" +export DATASET_NAME="datasets/internal_datasets/" +export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json" +# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. +# export NCCL_IB_DISABLE=1 +# export NCCL_P2P_DISABLE=1 +NCCL_DEBUG=INFO + +accelerate launch --mixed_precision="bf16" scripts/wan2.2/train_s2v.py \ + --config_path="config/wan2.2/wan_civitai_s2v.yaml" \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_data_dir=$DATASET_NAME \ + --train_data_meta=$DATASET_META_NAME \ + --video_sample_size=640 \ + --token_sample_size=640 \ + --video_sample_stride=1 \ + --video_sample_n_frames=80 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --dataloader_num_workers=8 \ + --num_train_epochs=100 \ + --checkpointing_steps=50 \ + --learning_rate=2e-05 \ + --lr_scheduler="constant_with_warmup" \ + --lr_warmup_steps=100 \ + --seed=42 \ + --output_dir="output_dir" \ + --gradient_checkpointing \ + --mixed_precision="bf16" \ + --adam_weight_decay=3e-2 \ + --adam_epsilon=1e-10 \ + --vae_mini_batch=1 \ + --max_grad_norm=0.05 \ + --random_hw_adapt \ + --training_with_video_token_length \ + --enable_bucket \ + --uniform_sampling \ + --low_vram \ + --trainable_modules "." diff --git a/scripts/wan2.2_vace_fun/train.py b/scripts/wan2.2_vace_fun/train.py index abf7c5b..f59c067 100644 --- a/scripts/wan2.2_vace_fun/train.py +++ b/scripts/wan2.2_vace_fun/train.py @@ -683,6 +683,8 @@ def parse_args(): def main(): args = parse_args() + if args.train_batch_size >= 2: + raise ValueError("This code does not support args.train_batch_size >= 2 now.") if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( diff --git a/videox_fun/data/dataset_video.py b/videox_fun/data/dataset_video.py index 84ad7a4..6b52be7 100644 --- a/videox_fun/data/dataset_video.py +++ b/videox_fun/data/dataset_video.py @@ -352,6 +352,208 @@ class VideoSpeechDataset(Dataset): return sample +class VideoSpeechControlDataset(Dataset): + def __init__( + self, + ann_path, data_root=None, + video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16, + enable_bucket=False, enable_inpaint=False, + audio_sr=16000, + text_drop_ratio=0.1, + enable_motion_info=False, + motion_frames=73, + ): + print(f"loading annotations from {ann_path} ...") + self.dataset = json.load(open(ann_path, 'r')) + self.length = len(self.dataset) + print(f"data scale: {self.length}") + + self.data_root = data_root + self.video_sample_stride = video_sample_stride + self.video_sample_n_frames = video_sample_n_frames + self.enable_bucket = enable_bucket + self.enable_inpaint = enable_inpaint + self.audio_sr = audio_sr + self.text_drop_ratio = text_drop_ratio + self.enable_motion_info = enable_motion_info + self.motion_frames = motion_frames + + video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size) + self.pixel_transforms = transforms.Compose( + [ + transforms.Resize(video_sample_size[0]), + transforms.CenterCrop(video_sample_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), + ] + ) + + self.video_sample_size = video_sample_size + + def get_batch(self, idx): + video_dict = self.dataset[idx] + video_id, text = video_dict['file_path'], video_dict['text'] + audio_id = video_dict['audio_path'] + control_video_id = video_dict['control_file_path'] + + if self.data_root is None: + video_path = video_id + else: + video_path = os.path.join(self.data_root, video_id) + + if self.data_root is None: + audio_path = audio_id + else: + audio_path = os.path.join(self.data_root, audio_id) + + if self.data_root is None: + control_video_id = control_video_id + else: + control_video_id = os.path.join(self.data_root, control_video_id) + + if not os.path.exists(audio_path): + raise FileNotFoundError(f"Audio file not found for {video_path}") + + # Video information + with VideoReader_contextmanager(video_path, num_threads=2) as video_reader: + total_frames = len(video_reader) + fps = video_reader.get_avg_fps() + if fps <= 0: + raise ValueError(f"Video has negative fps: {video_path}") + local_video_sample_stride = self.video_sample_stride + new_fps = int(fps // local_video_sample_stride) + while new_fps > 30: + local_video_sample_stride = local_video_sample_stride + 1 + new_fps = int(fps // local_video_sample_stride) + + max_possible_frames = (total_frames - 1) // local_video_sample_stride + 1 + actual_n_frames = min(self.video_sample_n_frames, max_possible_frames) + if actual_n_frames <= 0: + raise ValueError(f"Video too short: {video_path}") + + max_start = total_frames - (actual_n_frames - 1) * local_video_sample_stride - 1 + start_frame = random.randint(0, max_start) if max_start > 0 else 0 + frame_indices = [start_frame + i * local_video_sample_stride for i in range(actual_n_frames)] + + try: + sample_args = (video_reader, frame_indices) + pixel_values = func_timeout( + VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args + ) + except FunctionTimedOut: + raise ValueError(f"Read {idx} timeout.") + except Exception as e: + raise ValueError(f"Failed to extract frames from video. Error is {e}.") + + _, height, width, channel = np.shape(pixel_values) + if self.enable_motion_info: + motion_pixel_values = np.ones([self.motion_frames, height, width, channel]) * 127.5 + if start_frame > 0: + motion_max_possible_frames = (start_frame - 1) // local_video_sample_stride + 1 + motion_frame_indices = [0 + i * local_video_sample_stride for i in range(motion_max_possible_frames)] + motion_frame_indices = motion_frame_indices[-self.motion_frames:] + + _motion_sample_args = (video_reader, motion_frame_indices) + _motion_pixel_values = func_timeout( + VIDEO_READER_TIMEOUT, get_video_reader_batch, args=_motion_sample_args + ) + motion_pixel_values[-len(motion_frame_indices):] = _motion_pixel_values + + if not self.enable_bucket: + motion_pixel_values = torch.from_numpy(motion_pixel_values).permute(0, 3, 1, 2).contiguous() + motion_pixel_values = motion_pixel_values / 255. + motion_pixel_values = self.pixel_transforms(motion_pixel_values) + else: + motion_pixel_values = None + + if not self.enable_bucket: + pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous() + pixel_values = pixel_values / 255. + pixel_values = self.pixel_transforms(pixel_values) + + # Audio information + start_time = start_frame / fps + end_time = (start_frame + (actual_n_frames - 1) * local_video_sample_stride) / fps + duration = end_time - start_time + + audio_input, sample_rate = librosa.load(audio_path, sr=self.audio_sr) + start_sample = int(start_time * self.audio_sr) + end_sample = int(end_time * self.audio_sr) + + if start_sample >= len(audio_input): + raise ValueError(f"Audio file too short: {audio_path}") + else: + audio_segment = audio_input[start_sample:end_sample] + target_len = int(duration * self.audio_sr) + if len(audio_segment) < target_len: + raise ValueError(f"Audio file too short: {audio_path}") + + # Control information + with VideoReader_contextmanager(control_video_id, num_threads=2) as control_video_reader: + try: + sample_args = (control_video_reader, frame_indices) + control_pixel_values = func_timeout( + VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args + ) + resized_frames = [] + for i in range(len(control_pixel_values)): + frame = control_pixel_values[i] + resized_frame = resize_frame(frame, max(self.video_sample_size)) + resized_frames.append(resized_frame) + control_pixel_values = np.array(control_pixel_values) + except FunctionTimedOut: + raise ValueError(f"Read {idx} timeout.") + except Exception as e: + raise ValueError(f"Failed to extract frames from video. Error is {e}.") + + if not self.enable_bucket: + control_pixel_values = torch.from_numpy(control_pixel_values).permute(0, 3, 1, 2).contiguous() + control_pixel_values = control_pixel_values / 255. + del control_video_reader + else: + control_pixel_values = control_pixel_values + + if not self.enable_bucket: + control_pixel_values = self.video_transforms(control_pixel_values) + + if random.random() < self.text_drop_ratio: + text = '' + + return pixel_values, motion_pixel_values, control_pixel_values, text, audio_segment, sample_rate, new_fps + + def __len__(self): + return self.length + + def __getitem__(self, idx): + while True: + sample = {} + try: + pixel_values, motion_pixel_values, control_pixel_values, text, audio, sample_rate, new_fps = self.get_batch(idx) + sample["pixel_values"] = pixel_values + sample["motion_pixel_values"] = motion_pixel_values + sample["control_pixel_values"] = control_pixel_values + sample["text"] = text + sample["audio"] = torch.from_numpy(audio).float() # 转为 tensor + sample["sample_rate"] = sample_rate + sample["fps"] = new_fps + sample["idx"] = idx + break + except Exception as e: + print(f"Error processing {idx}: {e}, retrying with random idx...") + idx = random.randint(0, self.length - 1) + + if self.enable_inpaint and not self.enable_bucket: + mask = get_random_mask(pixel_values.size(), image_start_only=True) + mask_pixel_values = pixel_values * (1 - mask) + torch.zeros_like(pixel_values) * mask + sample["mask_pixel_values"] = mask_pixel_values + sample["mask"] = mask + + clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous() + clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255 + sample["clip_pixel_values"] = clip_pixel_values + + return sample + + if __name__ == "__main__": if 1: dataset = VideoDataset( diff --git a/videox_fun/models/wan_audio_encoder.py b/videox_fun/models/wan_audio_encoder.py index 4e2a83b..5dc652c 100644 --- a/videox_fun/models/wan_audio_encoder.py +++ b/videox_fun/models/wan_audio_encoder.py @@ -75,8 +75,8 @@ class WanAudioEncoder(ModelMixin, ConfigMixin, FromOriginalModelMixin): audio_input, sample_rate = librosa.load(audio_path, sr=16000) input_values = self.processor( - audio_input, sampling_rate=sample_rate, - return_tensors="pt").input_values + audio_input, sampling_rate=sample_rate, return_tensors="pt" + ).input_values # INFERENCE @@ -92,6 +92,25 @@ class WanAudioEncoder(ModelMixin, ConfigMixin, FromOriginalModelMixin): z = feat.to(dtype) # Encoding for the motion return z + + def extract_audio_feat_without_file_load(self, audio_input, sample_rate, return_all_layers=False, dtype=torch.float32): + input_values = self.processor( + audio_input, sampling_rate=sample_rate, return_tensors="pt" + ).input_values + + # INFERENCE + # retrieve logits & take argmax + res = self.model( + input_values.to(self.model.device), output_hidden_states=True) + if return_all_layers: + feat = torch.cat(res.hidden_states) + else: + feat = res.hidden_states[-1] + feat = linear_interpolation( + feat, input_fps=50, output_fps=self.video_rate) + + z = feat.to(dtype) # Encoding for the motion + return z def get_audio_embed_bucket(self, audio_embed, diff --git a/videox_fun/models/wan_transformer3d.py b/videox_fun/models/wan_transformer3d.py index cd23b84..4812625 100755 --- a/videox_fun/models/wan_transformer3d.py +++ b/videox_fun/models/wan_transformer3d.py @@ -676,8 +676,12 @@ class WanTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin): def _set_gradient_checkpointing(self, *args, **kwargs): if "value" in kwargs: self.gradient_checkpointing = kwargs["value"] + if hasattr(self, "motioner") and hasattr(self.motioner, "gradient_checkpointing"): + self.motioner.gradient_checkpointing = kwargs["value"] elif "enable" in kwargs: self.gradient_checkpointing = kwargs["enable"] + if hasattr(self, "motioner") and hasattr(self.motioner, "gradient_checkpointing"): + self.motioner.gradient_checkpointing = kwargs["enable"] else: raise ValueError("Invalid set gradient checkpointing") diff --git a/videox_fun/models/wan_transformer3d_s2v.py b/videox_fun/models/wan_transformer3d_s2v.py index 5af96b5..4b763c4 100644 --- a/videox_fun/models/wan_transformer3d_s2v.py +++ b/videox_fun/models/wan_transformer3d_s2v.py @@ -530,10 +530,26 @@ class Wan2_2Transformer3DModel_S2V(Wan2_2Transformer3DModel): context_lens = torch.ones( attn_hidden_states.shape[0], dtype=torch.long, device=attn_hidden_states.device ) * attn_audio_emb.shape[1] - residual_out = self.audio_injector.injector[audio_attn_id]( - x=attn_hidden_states, - context=attn_audio_emb, - context_lens=context_lens) + + if torch.is_grad_enabled() and self.gradient_checkpointing: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + residual_out = torch.utils.checkpoint.checkpoint( + create_custom_forward(self.audio_injector.injector[audio_attn_id]), + attn_hidden_states, + attn_audio_emb, + context_lens, + **ckpt_kwargs + ) + else: + residual_out = self.audio_injector.injector[audio_attn_id]( + x=attn_hidden_states, + context=attn_audio_emb, + context_lens=context_lens) residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames) hidden_states[:, :self.original_seq_len] = hidden_states[:, :self.original_seq_len] + residual_out @@ -585,15 +601,30 @@ class Wan2_2Transformer3DModel_S2V(Wan2_2Transformer3DModel): # Embeddings x = [self.patch_embedding(u.unsqueeze(0)) for u in x] + if isinstance(motion_frames[0], list): + motion_frames_0 = motion_frames[0][0] + motion_frames_1 = motion_frames[0][1] + else: + motion_frames_0 = motion_frames[0] + motion_frames_1 = motion_frames[1] # Audio process - audio_input = torch.cat([audio_input[..., 0:1].repeat(1, 1, 1, motion_frames[0]), audio_input], dim=-1) - audio_emb_res = self.casual_audio_encoder(audio_input) + audio_input = torch.cat([audio_input[..., 0:1].repeat(1, 1, 1, motion_frames_0), audio_input], dim=-1) + if torch.is_grad_enabled() and self.gradient_checkpointing: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + audio_emb_res = torch.utils.checkpoint.checkpoint(create_custom_forward(self.casual_audio_encoder), audio_input, **ckpt_kwargs) + else: + audio_emb_res = self.casual_audio_encoder(audio_input) if self.enbale_adain: audio_emb_global, audio_emb = audio_emb_res - self.audio_emb_global = audio_emb_global[:, motion_frames[1]:].clone() + self.audio_emb_global = audio_emb_global[:, motion_frames_1:].clone() else: audio_emb = audio_emb_res - self.merged_audio_emb = audio_emb[:, motion_frames[1]:, :] + self.merged_audio_emb = audio_emb[:, motion_frames_1:, :] # Cond states cond = [self.cond_encoder(c.unsqueeze(0)) for c in cond_states] @@ -677,6 +708,7 @@ class Wan2_2Transformer3DModel_S2V(Wan2_2Transformer3DModel): sinusoidal_embedding_1d(self.freq_dim, t).float()) e0 = self.time_projection(e).unflatten(1, (6, self.dim)) assert e.dtype == torch.float32 and e0.dtype == torch.float32 + if self.zero_timestep: e = e[:-1] zero_e0 = e0[-1:] @@ -840,8 +872,17 @@ class Wan2_2Transformer3DModel_S2V(Wan2_2Transformer3DModel): # Unpatchify x = x[:, :self.original_seq_len] - # Head - x = self.head(x, e) + # head + if torch.is_grad_enabled() and self.gradient_checkpointing: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.head), x, e, **ckpt_kwargs) + else: + x = self.head(x, e) x = self.unpatchify(x, original_grid_sizes) x = torch.stack(x) if self.teacache is not None and cond_flag: diff --git a/videox_fun/pipeline/pipeline_wan2_2_s2v.py b/videox_fun/pipeline/pipeline_wan2_2_s2v.py index 64ad75a..b927eaa 100644 --- a/videox_fun/pipeline/pipeline_wan2_2_s2v.py +++ b/videox_fun/pipeline/pipeline_wan2_2_s2v.py @@ -731,8 +731,9 @@ class Wan2_2S2VPipeline(DiffusionPipeline): pose_latents_input = torch.cat([cond_latents] * 2) if do_classifier_free_guidance else cond_latents motion_latents_input = torch.cat([motion_latents] * 2) if do_classifier_free_guidance else motion_latents - audio_emb_input = torch.cat([audio_input * 0] + [audio_input]) if do_classifier_free_guidance else audio_emb + audio_emb_input = torch.cat([audio_input * 0] + [audio_input]) if do_classifier_free_guidance else audio_input ref_image_latentes_input = torch.cat([ref_image_latentes] * 2) if do_classifier_free_guidance else ref_image_latentes + motion_frames=[[self.motion_frames, (self.motion_frames + 3) // 4]] * 2 if do_classifier_free_guidance else [[self.motion_frames, (self.motion_frames + 3) // 4]] timestep = t.expand(latent_model_input.shape[0]) if self.transformer_2 is not None: @@ -754,7 +755,7 @@ class Wan2_2S2VPipeline(DiffusionPipeline): motion_latents=motion_latents_input, ref_latents=ref_image_latentes_input, audio_input=audio_emb_input, - motion_frames=[self.motion_frames, (self.motion_frames + 3) // 4], + motion_frames=motion_frames, drop_motion_frames=drop_first_motion and r == 0, ) # perform guidance diff --git a/videox_fun/pipeline/pipeline_wan2_2_vace_fun.py b/videox_fun/pipeline/pipeline_wan2_2_vace_fun.py index 162f8fb..c8b0d99 100644 --- a/videox_fun/pipeline/pipeline_wan2_2_vace_fun.py +++ b/videox_fun/pipeline/pipeline_wan2_2_vace_fun.py @@ -694,7 +694,7 @@ class Wan2_2VaceFunPipeline(DiffusionPipeline): subject_ref_images = new_subject_ref_images vace_latents = self.vace_encode_frames(input_video, subject_ref_images, masks=mask_condition, vae=self.vae) - mask_latents = self.vace_encode_masks(mask_condition, subject_ref_images) + mask_latents = self.vace_encode_masks(mask_condition, subject_ref_images, vae_stride=[4, self.vae.spatial_compression_ratio, self.vae.spatial_compression_ratio]) vace_context = self.vace_latent(vace_latents, mask_latents) # 5. Prepare latents.