2028 lines
98 KiB
Python
2028 lines
98 KiB
Python
"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
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"""
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import gc
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import logging
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import math
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import os
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import pickle
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import random
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import shutil
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import sys
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import accelerate
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import diffusers
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import torchvision.transforms.functional as TF
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import transformers
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from accelerate import Accelerator, FullyShardedDataParallelPlugin
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from accelerate.logging import get_logger
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from accelerate.state import AcceleratorState
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from accelerate.utils import ProjectConfiguration, set_seed
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from diffusers import DDIMScheduler, FlowMatchEulerDiscreteScheduler
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import (EMAModel,
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compute_density_for_timestep_sampling,
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compute_loss_weighting_for_sd3)
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from diffusers.utils import check_min_version, deprecate, is_wandb_available
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from diffusers.utils.torch_utils import is_compiled_module
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from einops import rearrange
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from omegaconf import OmegaConf
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from packaging import version
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from PIL import Image
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from torch.utils.data import RandomSampler
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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from transformers import AutoTokenizer
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from transformers.utils import ContextManagers
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import datasets
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current_file_path = os.path.abspath(__file__)
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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)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.data import (ASPECT_RATIO_512, ASPECT_RATIO_RANDOM_CROP_512,
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ASPECT_RATIO_RANDOM_CROP_PROB,
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AspectRatioBatchImageVideoSampler,
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ImageVideoDataset, ImageVideoSampler,
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RandomSampler, VideoAnimateDataset,
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get_closest_ratio, get_random_mask,
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process_pose_file, process_pose_params)
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from videox_fun.models import (AutoencoderKLWan, AutoencoderKLWan3_8,
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CLIPModel, Wan2_2Transformer3DModel,
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Wan2_2Transformer3DModel_Animate,
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WanT5EncoderModel)
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from videox_fun.pipeline import Wan2_2AnimatePipeline
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from videox_fun.utils.discrete_sampler import DiscreteSampling
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from videox_fun.utils.lora_utils import (convert_peft_lora_to_kohya_lora,
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create_network, merge_lora,
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unmerge_lora)
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from videox_fun.utils.tqdm_bar import PauseAwareTqdm
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from videox_fun.utils.utils import (calculate_dimensions, get_image,
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get_image_latent,
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get_image_to_video_latent,
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get_video_to_video_latent,
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save_videos_grid)
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if is_wandb_available():
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import wandb
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def filter_kwargs(cls, kwargs):
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import inspect
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sig = inspect.signature(cls.__init__)
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valid_params = set(sig.parameters.keys()) - {'self', 'cls'}
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filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
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return filtered_kwargs
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def linear_decay(initial_value, final_value, total_steps, current_step):
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if current_step >= total_steps:
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return final_value
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current_step = max(0, current_step)
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step_size = (final_value - initial_value) / total_steps
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current_value = initial_value + step_size * current_step
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return current_value
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def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None):
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u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator)
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t = 1 / (1 + torch.exp(-u)) * (high - low) + low
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return torch.clip(t.to(torch.int32), low, high - 1)
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def resize_mask(mask, latent, process_first_frame_only=True):
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latent_size = latent.size()
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batch_size, channels, num_frames, height, width = mask.shape
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if process_first_frame_only:
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target_size = list(latent_size[2:])
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target_size[0] = 1
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first_frame_resized = F.interpolate(
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mask[:, :, 0:1, :, :],
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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target_size = list(latent_size[2:])
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target_size[0] = target_size[0] - 1
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if target_size[0] != 0:
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remaining_frames_resized = F.interpolate(
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mask[:, :, 1:, :, :],
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
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else:
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resized_mask = first_frame_resized
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else:
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target_size = list(latent_size[2:])
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resized_mask = F.interpolate(
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mask,
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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return resized_mask
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def get_i2v_mask(lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"):
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if mask_pixel_values is None:
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msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device)
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else:
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msk = mask_pixel_values.clone()
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msk[:, :mask_len] = 1
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msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
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msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
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msk = msk.transpose(1, 2)
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return msk
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.18.0.dev0")
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logger = get_logger(__name__, log_level="INFO")
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def log_validation(vae, text_encoder, tokenizer, clip_image_encoder, transformer3d, network, args, config, accelerator, weight_dtype, global_step):
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try:
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is_deepspeed = type(transformer3d).__name__ == 'DeepSpeedEngine'
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if is_deepspeed:
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origin_config = transformer3d.config
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transformer3d.config = accelerator.unwrap_model(transformer3d).config
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with torch.no_grad(), torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
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logger.info("Running validation... ")
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scheduler = FlowMatchEulerDiscreteScheduler(
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**filter_kwargs(FlowMatchEulerDiscreteScheduler, OmegaConf.to_container(config['scheduler_kwargs']))
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)
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if args.boundary_type == "full":
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transformer3d_1 = accelerator.unwrap_model(transformer3d) if type(transformer3d).__name__ == 'DistributedDataParallel' else transformer3d
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transformer3d_2 = None
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else:
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if args.boundary_type == "low":
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transformer3d_1 = accelerator.unwrap_model(transformer3d) if type(transformer3d).__name__ == 'DistributedDataParallel' else transformer3d
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sub_path = config['transformer_additional_kwargs'].get('transformer_high_noise_model_subpath', 'transformer')
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transformer3d_2 = Wan2_2Transformer3DModel.from_pretrained(
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os.path.join(args.pretrained_model_name_or_path, sub_path),
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transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
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low_cpu_mem_usage=True,
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).to(weight_dtype)
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else:
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sub_path = config['transformer_additional_kwargs'].get('transformer_low_noise_model_subpath', 'transformer')
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transformer3d_1 = Wan2_2Transformer3DModel.from_pretrained(
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os.path.join(args.pretrained_model_name_or_path, sub_path),
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transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
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low_cpu_mem_usage=True,
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).to(weight_dtype)
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transformer3d_2 = accelerator.unwrap_model(transformer3d) if type(transformer3d).__name__ == 'DistributedDataParallel' else transformer3d
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pipeline = Wan2_2AnimatePipeline(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer3d_1,
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transformer_2=transformer3d_2,
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scheduler=scheduler,
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clip_image_encoder=clip_image_encoder,
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)
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pipeline = pipeline.to(accelerator.device)
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if args.seed is None:
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generator = None
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else:
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rank_seed = args.seed + accelerator.process_index
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generator = torch.Generator(device=accelerator.device).manual_seed(rank_seed)
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logger.info(f"Rank {accelerator.process_index} using seed: {rank_seed}")
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images = []
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for i in range(len(args.validation_prompts)):
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src_root_path = args.validation_paths[i]
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src_pose_path = os.path.join(src_root_path, "src_pose.mp4")
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src_face_path = os.path.join(src_root_path, "src_face.mp4")
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src_ref_path = os.path.join(src_root_path, "src_ref.png")
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src_bg_path = os.path.join(src_root_path, "src_bg.mp4")
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src_mask_path = os.path.join(src_root_path, "src_mask.mp4")
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import cv2
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cap = cv2.VideoCapture(src_pose_path)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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cap.release()
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width, height = calculate_dimensions(args.video_sample_size * args.video_sample_size, width / height)
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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
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fps = 16
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pose_video, _, _, _ = get_video_to_video_latent(src_pose_path, video_length=video_length, sample_size=[height, width], fps=fps, ref_image=None)
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face_video, _, _, _ = get_video_to_video_latent(src_face_path, video_length=video_length, sample_size=[512, 512], fps=fps, ref_image=None)
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ref_image = get_image(src_ref_path)
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if os.path.exists(src_bg_path):
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bg_video, _, _, _ = get_video_to_video_latent(src_bg_path, video_length=video_length, sample_size=[height, width], fps=fps, ref_image=None)
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mask_video, _, _, _ = get_video_to_video_latent(src_mask_path, video_length=video_length, sample_size=[height, width], fps=fps, ref_image=None)
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mask_video = mask_video[:, :1]
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replace_flag = True
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else:
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bg_video = None
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mask_video = None
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replace_flag = False
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sample = pipeline(
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args.validation_prompts[i],
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segment_frame_length = 77,
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negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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height = height,
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width = width,
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generator = generator,
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guidance_scale = 4.5,
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num_inference_steps = 25,
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pose_video = pose_video,
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face_video = face_video,
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ref_image = ref_image,
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bg_video = bg_video,
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mask_video = mask_video,
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replace_flag = replace_flag,
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).videos
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os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
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save_videos_grid(
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sample,
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os.path.join(
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args.output_dir,
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f"sample/sample-{global_step}-rank{accelerator.process_index}-image-{i}.mp4"
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)
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)
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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transformer3d.to(accelerator.device, dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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if is_deepspeed:
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transformer3d.config = origin_config
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except Exception as e:
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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print(f"Eval error on rank {accelerator.process_index} with info {e}")
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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transformer3d.to(accelerator.device, dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
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)
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. "
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),
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)
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parser.add_argument(
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"--train_data_meta",
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type=str,
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default=None,
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help=(
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"A csv containing the training data. "
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--validation_prompts",
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type=str,
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default=None,
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nargs="+",
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_paths",
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type=str,
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default=None,
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nargs="+",
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help=("A set of control videos evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="sd-model-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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)
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parser.add_argument(
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"--use_came",
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action="store_true",
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help="whether to use came",
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)
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parser.add_argument(
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"--multi_stream",
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action="store_true",
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help="whether to use cuda multi-stream",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
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)
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|
parser.add_argument("--num_train_epochs", type=int, default=100)
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|
parser.add_argument(
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|
"--max_train_steps",
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type=int,
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|
default=None,
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|
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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|
parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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|
help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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|
parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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|
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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|
)
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|
parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-4,
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|
help="Initial learning rate (after the potential warmup period) to use.",
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)
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|
parser.add_argument(
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"--scale_lr",
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|
action="store_true",
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default=False,
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|
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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|
parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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|
help=(
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|
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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|
' "constant", "constant_with_warmup"]'
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),
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)
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|
parser.add_argument(
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|
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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|
)
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|
parser.add_argument(
|
|
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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|
)
|
|
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(
|
|
"--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(
|
|
"--rank",
|
|
type=int,
|
|
default=128,
|
|
help=("The dimension of the LoRA update matrices."),
|
|
)
|
|
parser.add_argument(
|
|
"--network_alpha",
|
|
type=int,
|
|
default=64,
|
|
help=("The dimension of the LoRA update matrices."),
|
|
)
|
|
parser.add_argument(
|
|
"--use_peft_lora", action="store_true", help="Whether or not to use peft lora."
|
|
)
|
|
parser.add_argument(
|
|
"--train_text_encoder",
|
|
action="store_true",
|
|
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
|
|
)
|
|
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(
|
|
"--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("--save_state", action="store_true", help="Whether or not to save state.")
|
|
|
|
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(
|
|
"--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`.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_skip_name",
|
|
type=str,
|
|
default=None,
|
|
help=("The module is not trained in loras. "),
|
|
)
|
|
parser.add_argument(
|
|
"--target_name",
|
|
type=str,
|
|
default=None,
|
|
help=("The module is trained in loras. "),
|
|
)
|
|
|
|
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 Clip Image Encoder
|
|
clip_image_encoder = CLIPModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, config['image_encoder_kwargs'].get('image_encoder_subpath', 'image_encoder')),
|
|
)
|
|
clip_image_encoder = clip_image_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_Animate.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)
|
|
clip_image_encoder.requires_grad_(False)
|
|
|
|
# Lora will work with this...
|
|
if args.use_peft_lora:
|
|
from peft import (LoraConfig, get_peft_model_state_dict,
|
|
inject_adapter_in_model)
|
|
lora_config = LoraConfig(r=args.rank, lora_alpha=args.network_alpha, target_modules=args.target_name.split(","))
|
|
transformer3d = inject_adapter_in_model(lora_config, transformer3d)
|
|
|
|
network = None
|
|
else:
|
|
network = create_network(
|
|
1.0,
|
|
args.rank,
|
|
args.network_alpha,
|
|
text_encoder,
|
|
transformer3d,
|
|
neuron_dropout=None,
|
|
target_name=args.target_name,
|
|
skip_name=args.lora_skip_name,
|
|
)
|
|
network = network.to(weight_dtype)
|
|
network.apply_to(text_encoder, transformer3d, args.train_text_encoder and not args.training_with_video_token_length, True)
|
|
|
|
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)}")
|
|
|
|
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)}")
|
|
|
|
# `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 or zero_stage == 3:
|
|
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"lora_diffusion_pytorch_model.safetensors")
|
|
if args.use_peft_lora:
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(models[-1]), accelerate_state_dict)
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
safetensor_kohya_format_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model_compatible_with_comfyui.safetensors")
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
else:
|
|
network_state_dict = {}
|
|
for key in accelerate_state_dict:
|
|
if "network" in key:
|
|
network_state_dict[key.replace("network.", "")] = accelerate_state_dict[key].to(weight_dtype)
|
|
save_file(network_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}.")
|
|
|
|
else:
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
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"lora_diffusion_pytorch_model.safetensors")
|
|
if args.use_peft_lora:
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(models[-1]), accelerate_state_dict)
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
safetensor_kohya_format_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model_compatible_with_comfyui.safetensors")
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
else:
|
|
network_state_dict = {}
|
|
for key in accelerate_state_dict:
|
|
if "network" in key:
|
|
network_state_dict[key.replace("network.", "")] = accelerate_state_dict[key].to(weight_dtype)
|
|
save_file(network_state_dict, safetensor_save_path, metadata={"format": "pt"})
|
|
|
|
if not args.use_deepspeed:
|
|
for _ in range(len(weights)):
|
|
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):
|
|
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 Exception:
|
|
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
|
|
|
|
if args.use_peft_lora:
|
|
logging.info("Add peft parameters")
|
|
trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
trainable_params_optim = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
else:
|
|
logging.info("Add network parameters")
|
|
trainable_params = list(filter(lambda p: p.requires_grad, network.parameters()))
|
|
trainable_params_optim = network.prepare_optimizer_params(args.learning_rate / 2, args.learning_rate, args.learning_rate)
|
|
|
|
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 = VideoAnimateDataset(
|
|
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,
|
|
video_repeat=args.video_repeat,
|
|
enable_bucket=args.enable_bucket,
|
|
)
|
|
|
|
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["text"] = []
|
|
# Used in Control pixel values
|
|
new_examples["control_pixel_values"] = []
|
|
# Used in Face pixel values
|
|
new_examples["face_pixel_values"] = []
|
|
# Used in Ref pixel values
|
|
new_examples["ref_pixel_values"] = []
|
|
# Used in Background pixel values
|
|
new_examples["background_pixel_values"] = []
|
|
# Used in Mask pixel values
|
|
new_examples["mask"] = []
|
|
new_examples["clip_pixel_values"] = []
|
|
|
|
# Get downsample ratio in image and videos
|
|
pixel_value = examples[0]["pixel_values"]
|
|
data_type = examples[0]["data_type"]
|
|
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 + 1.
|
|
batch_video_length = (batch_video_length - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
|
|
|
|
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.
|
|
|
|
control_pixel_values = torch.from_numpy(example["control_pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
control_pixel_values = control_pixel_values / 255.
|
|
|
|
face_pixel_values = torch.from_numpy(example["face_pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
face_pixel_values = face_pixel_values / 255.
|
|
|
|
ref_pixel_values = torch.from_numpy(example["ref_pixel_values"]).unsqueeze(0).permute(0, 3, 1, 2).contiguous()
|
|
ref_pixel_values = ref_pixel_values / 255.
|
|
|
|
background_pixel_values = torch.from_numpy(example["background_pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
background_pixel_values = background_pixel_values / 255.
|
|
|
|
mask = torch.from_numpy(example["mask"]).permute(0, 3, 1, 2).contiguous()
|
|
mask = mask / 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),
|
|
])
|
|
|
|
transform_no_normalize = transforms.Compose([
|
|
transforms.Resize(fix_sample_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(fix_sample_size),
|
|
])
|
|
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),
|
|
])
|
|
|
|
transform_no_normalize = transforms.Compose([
|
|
transforms.Resize([nh, nw]),
|
|
transforms.CenterCrop([int(x) for x in random_sample_size]),
|
|
])
|
|
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),
|
|
])
|
|
|
|
transform_no_normalize = transforms.Compose([
|
|
transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(closest_size),
|
|
])
|
|
|
|
transform_512 = transforms.Compose([
|
|
transforms.Resize((512, 512), interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop((512, 512)),
|
|
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["control_pixel_values"].append(transform(control_pixel_values))
|
|
new_examples["face_pixel_values"].append(transform_512(face_pixel_values)[:batch_video_length])
|
|
new_examples["ref_pixel_values"].append(transform(ref_pixel_values))
|
|
new_examples["background_pixel_values"].append(transform(background_pixel_values)[:batch_video_length])
|
|
new_examples["mask"].append(transform_no_normalize(mask)[:batch_video_length])
|
|
new_examples["clip_pixel_values"].append(torch.from_numpy(example["clip_pixel_values"]))
|
|
new_examples["text"].append(example["text"])
|
|
|
|
# Limit the number of frames to the same
|
|
new_examples["pixel_values"] = torch.stack([example for example in new_examples["pixel_values"]])
|
|
new_examples["control_pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["control_pixel_values"]])
|
|
new_examples["face_pixel_values"] = torch.stack([example for example in new_examples["face_pixel_values"]])
|
|
new_examples["ref_pixel_values"] = torch.stack([example for example in new_examples["ref_pixel_values"]])
|
|
new_examples["background_pixel_values"] = torch.stack([example for example in new_examples["background_pixel_values"]])
|
|
new_examples["clip_pixel_values"] = torch.stack([example for example in new_examples["clip_pixel_values"]])
|
|
new_examples["mask"] = torch.stack([example for example in new_examples["mask"]])
|
|
|
|
# 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`.
|
|
if args.use_peft_lora:
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
else:
|
|
transformer3d.network = network
|
|
transformer3d = transformer3d.to(dtype=weight_dtype)
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
if fsdp_stage != 0 or zero_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)
|
|
|
|
# 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)
|
|
transformer3d.to(accelerator.device, dtype=weight_dtype)
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
clip_image_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
|
|
# 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
|
|
|
|
# function for saving/removing
|
|
def save_model(ckpt_file, unwrapped_nw):
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
|
|
if isinstance(unwrapped_nw, dict):
|
|
from safetensors.torch import save_file
|
|
save_file(unwrapped_nw, ckpt_file, metadata={"format": "pt"})
|
|
return ckpt_file
|
|
unwrapped_nw.save_weights(ckpt_file, weight_dtype, None)
|
|
|
|
progress_bar = PauseAwareTqdm(
|
|
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()
|
|
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']
|
|
control_pixel_values = batch["control_pixel_values"].cpu()
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
control_pixel_values = rearrange(control_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, control_pixel_value, text) in enumerate(zip(pixel_values, control_pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
control_pixel_value = control_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]}.mp4", rescale=True)
|
|
save_videos_grid(control_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_control.mp4", rescale=True)
|
|
|
|
face_pixel_values = batch["face_pixel_values"].cpu()
|
|
face_pixel_values = rearrange(face_pixel_values, "b f c h w -> b c f h w")
|
|
for idx, (face_pixel_value, text) in enumerate(zip(face_pixel_values, texts)):
|
|
face_pixel_value = face_pixel_value[None, ...]
|
|
gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'
|
|
save_videos_grid(face_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_face.mp4", 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)
|
|
|
|
background_pixel_values = batch["background_pixel_values"].cpu()
|
|
background_pixel_values = rearrange(background_pixel_values, "b f c h w -> b c f h w")
|
|
for idx, (bg_pixel_value, text) in enumerate(zip(background_pixel_values, texts)):
|
|
bg_pixel_value = bg_pixel_value[None, ...]
|
|
gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'
|
|
save_videos_grid(bg_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_bg.mp4", rescale=True)
|
|
|
|
clip_pixel_values, mask, texts = batch['clip_pixel_values'].cpu(), batch['mask'].cpu(), batch['text']
|
|
mask = rearrange(mask, "b f c h w -> b c f h w")
|
|
for idx, (clip_pixel_value, pixel_value, text) in enumerate(zip(clip_pixel_values, mask, 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")
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/mask_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.mp4", rescale=True)
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
# Convert images to latent space
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
control_pixel_values = batch["control_pixel_values"].to(weight_dtype)
|
|
face_pixel_values = batch["face_pixel_values"].to(weight_dtype)
|
|
ref_pixel_values = batch["ref_pixel_values"].to(weight_dtype)
|
|
background_pixel_values = batch["background_pixel_values"].to(weight_dtype)
|
|
mask = batch["mask"].to(weight_dtype)
|
|
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]:
|
|
pixel_values = torch.tile(pixel_values, (4, 1, 1, 1, 1))
|
|
control_pixel_values = torch.tile(control_pixel_values, (4, 1, 1, 1, 1))
|
|
face_pixel_values = torch.tile(face_pixel_values, (4, 1, 1, 1, 1))
|
|
ref_pixel_values = torch.tile(ref_pixel_values, (4, 1, 1, 1, 1))
|
|
background_pixel_values = torch.tile(background_pixel_values, (4, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (4, 1, 1, 1, 1))
|
|
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (4, 1, 1, 1))
|
|
|
|
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))
|
|
control_pixel_values = torch.tile(control_pixel_values, (2, 1, 1, 1, 1))
|
|
face_pixel_values = torch.tile(face_pixel_values, (2, 1, 1, 1, 1))
|
|
ref_pixel_values = torch.tile(ref_pixel_values, (2, 1, 1, 1, 1))
|
|
background_pixel_values = torch.tile(background_pixel_values, (2, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (2, 1, 1, 1, 1))
|
|
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (2, 1, 1, 1))
|
|
|
|
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
|
|
|
|
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 + 1.
|
|
temp_n_frames = (temp_n_frames - 1) // sample_n_frames_bucket_interval + 1
|
|
|
|
pixel_values = pixel_values[:, :temp_n_frames, :, :]
|
|
control_pixel_values = control_pixel_values[:, :temp_n_frames, :, :]
|
|
face_pixel_values = face_pixel_values[:, :temp_n_frames, :, :]
|
|
background_pixel_values = background_pixel_values[:, :temp_n_frames, :, :]
|
|
mask = mask[:, :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
|
|
) - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
|
|
actual_video_length = int(max(actual_video_length, 1))
|
|
|
|
# Magvae needs the number of frames to be 4n + 1.
|
|
actual_video_length = (actual_video_length - 1) // sample_n_frames_bucket_interval + 1
|
|
|
|
pixel_values = pixel_values[:, :actual_video_length, :, :]
|
|
control_pixel_values = control_pixel_values[:, :actual_video_length, :, :]
|
|
face_pixel_values = face_pixel_values[:, :actual_video_length, :, :]
|
|
background_pixel_values = background_pixel_values[:, :actual_video_length, :, :]
|
|
mask = mask[:, :actual_video_length, :, :]
|
|
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
vae.to(accelerator.device)
|
|
clip_image_encoder.to(accelerator.device)
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_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 vae_stream_1 is not None:
|
|
vae_stream_1.wait_stream(torch.cuda.current_stream())
|
|
with torch.cuda.stream(vae_stream_1):
|
|
latents = _batch_encode_vae(pixel_values)
|
|
else:
|
|
latents = _batch_encode_vae(pixel_values)
|
|
|
|
control_latents = _batch_encode_vae(control_pixel_values)
|
|
ref_latents = _batch_encode_vae(ref_pixel_values)
|
|
|
|
if rng is None:
|
|
refert_num = np.random.choice([0, 1], p = [0.75, 0.25])
|
|
else:
|
|
refert_num = rng.choice([0, 1], p = [0.75, 0.25])
|
|
|
|
background_pixel_values[:, :refert_num, :] = pixel_values[:, :refert_num, :]
|
|
mask[:, :refert_num, :] = torch.zeros_like(mask[:, :refert_num, :])
|
|
for bs_index in range(background_pixel_values.size()[0]):
|
|
if rng is None:
|
|
zero_init_background_pixel_values_conv_in = np.random.choice([0, 1], p = [0.90, 0.10])
|
|
else:
|
|
zero_init_background_pixel_values_conv_in = rng.choice([0, 1], p = [0.90, 0.10])
|
|
|
|
if zero_init_background_pixel_values_conv_in:
|
|
background_pixel_values[bs_index] = background_pixel_values[bs_index] * 0
|
|
mask[bs_index] = torch.ones_like(mask[bs_index])
|
|
background_latents = _batch_encode_vae(background_pixel_values)
|
|
|
|
mask = rearrange(mask, "b f c h w -> b c f h w")
|
|
mask = torch.concat(
|
|
[
|
|
torch.repeat_interleave(mask[:, :, 0:1], repeats=4, dim=2),
|
|
mask[:, :, 1:]
|
|
], dim=2
|
|
)
|
|
mask = mask.view(mask.shape[0], mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4])
|
|
mask = mask.transpose(1, 2)
|
|
mask = resize_mask(1 - mask, latents)
|
|
y_reft = torch.concat([mask, background_latents], dim=1).to(device=accelerator.device, dtype=weight_dtype)
|
|
|
|
mask_ref = get_i2v_mask(1, latents.size(-2), latents.size(-1), 1, device=accelerator.device)
|
|
y_ref = torch.concat([mask_ref, ref_latents], dim=1).to(device=accelerator.device, dtype=weight_dtype)
|
|
y = torch.concat([y_ref, y_reft], dim=2)
|
|
|
|
face_pixel_values = rearrange(face_pixel_values, "b c f h w -> b f c h w")
|
|
|
|
clip_context = []
|
|
for clip_pixel_value in clip_pixel_values:
|
|
clip_image = Image.fromarray(np.uint8(clip_pixel_value.float().cpu().numpy()))
|
|
clip_image = TF.to_tensor(clip_image).sub_(0.5).div_(0.5).to(clip_image_encoder.device, weight_dtype)
|
|
_clip_context = clip_image_encoder([clip_image[:, None, :, :]])
|
|
clip_context.append(_clip_context)
|
|
clip_context = torch.cat(clip_context)
|
|
|
|
latents = torch.cat([ref_latents, latents], dim=2)
|
|
|
|
# wait for latents = vae.encode(pixel_values) to complete
|
|
if vae_stream_1 is not None:
|
|
torch.cuda.current_stream().wait_stream(vae_stream_1)
|
|
|
|
if args.low_vram:
|
|
vae.to('cpu')
|
|
clip_image_encoder.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_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)]
|
|
|
|
if args.low_vram and not args.enable_text_encoder_in_dataloader:
|
|
text_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]
|
|
)
|
|
|
|
# Predict the noise residual
|
|
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,
|
|
y=y,
|
|
clip_fea=clip_context,
|
|
pose_latents=control_latents,
|
|
face_pixel_values=face_pixel_values,
|
|
)
|
|
|
|
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:
|
|
accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
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()
|
|
# Keep the checkpoint out of the progress bar rate: a minute-long save would
|
|
# otherwise land in the next step's interval and be shown as a slow step. The
|
|
# save also stages the whole state in host RAM (safetensors materializes every
|
|
# tensor as bytes) and leaves the freed blocks in the allocator caches, so the
|
|
# cache flushes run inside the same window.
|
|
with progress_bar.paused():
|
|
if not args.save_state:
|
|
if args.use_peft_lora:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(transformer3d))
|
|
save_model(safetensor_save_path, network_state_dict)
|
|
|
|
safetensor_kohya_format_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}-compatible_with_comfyui.safetensors")
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(network))
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(accelerator_save_path)
|
|
logger.info(f"Saved state to {accelerator_save_path}")
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
with progress_bar.paused():
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
clip_image_encoder,
|
|
transformer3d,
|
|
network,
|
|
args,
|
|
config,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
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 args.validation_prompts is not None and epoch % args.validation_epochs == 0:
|
|
with progress_bar.paused():
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
clip_image_encoder,
|
|
transformer3d,
|
|
network,
|
|
args,
|
|
config,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
# Close the bar before the end-of-run checkpoint: tqdm keeps redrawing a live bar whenever
|
|
# something else writes to the console. PauseAwareTqdm.close() rebases the closing line onto
|
|
# the smoothed rate, so the worker warm-up and the first dataloader fetch do not dilute it.
|
|
progress_bar.close()
|
|
|
|
# Create the pipeline using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process:
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
if not args.save_state:
|
|
if args.use_peft_lora:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(transformer3d))
|
|
save_model(safetensor_save_path, network_state_dict)
|
|
|
|
safetensor_kohya_format_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}-compatible_with_comfyui.safetensors")
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(network))
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(accelerator_save_path)
|
|
logger.info(f"Saved state to {accelerator_save_path}")
|
|
|
|
accelerator.end_training()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|