2272 lines
108 KiB
Python
2272 lines
108 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 copy
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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 shutil
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import pickle
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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 transformers
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from accelerate import Accelerator
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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 AutoencoderKL, DDPMScheduler, LCMScheduler
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import EMAModel
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from diffusers.utils import check_min_version, deprecate, is_wandb_available
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from diffusers.utils.import_utils import is_xformers_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 huggingface_hub import create_repo, upload_folder
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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 tqdm.auto import tqdm
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from transformers import (BertModel, BertTokenizer, CLIPImageProcessor,
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CLIPVisionModelWithProjection,
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T5EncoderModel, T5Tokenizer)
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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))]
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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 transformers import (CLIPImageProcessor, CLIPVisionModelWithProjection,
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T5EncoderModel, T5Tokenizer)
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from transformers.utils import ContextManagers
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from easyanimate.data.bucket_sampler import (ASPECT_RATIO_512,
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ASPECT_RATIO_RANDOM_CROP_512,
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ASPECT_RATIO_RANDOM_CROP_PROB,
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AspectRatioBatchImageSampler,
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AspectRatioBatchImageVideoSampler,
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AspectRatioBatchSampler,
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RandomSampler, get_closest_ratio)
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from easyanimate.data.dataset_image import CC15M
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from easyanimate.data.dataset_image_video import (ImageVideoDataset,
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ImageVideoSampler,
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get_random_mask)
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from easyanimate.data.dataset_video import VideoDataset, WebVid10M
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from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
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from easyanimate.models.transformer2d import Transformer2DModel
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from easyanimate.models.transformer3d import (HunyuanTransformer3DModel,
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Transformer3DModel)
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from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import EasyAnimatePipeline_Multi_Text_Encoder
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from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import EasyAnimatePipeline_Multi_Text_Encoder_Inpaint
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from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
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from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
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EasyAnimateInpaintPipeline
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from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import (
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get_2d_rotary_pos_embed, get_resize_crop_region_for_grid)
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from easyanimate.pipeline.pipeline_pixart_magvit import \
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PixArtAlphaMagvitPipeline
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from easyanimate.utils import gaussian_diffusion as gd
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from easyanimate.utils.respace import SpacedDiffusion, space_timesteps
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from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
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from easyanimate.utils.lora_utils import (create_network, merge_lora,
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unmerge_lora)
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if is_wandb_available():
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import wandb
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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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rotary_pos_embed_cache = {}
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def _get_2d_rotary_pos_embed_cached(embed_dim, crops_coords, grid_size):
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tmp_key = (embed_dim, crops_coords, grid_size)
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print('embed_dim=%d crops_coords=%s grid_size=%s in_cache=%d cache_size=%d' % (
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embed_dim, crops_coords, grid_size, tmp_key in rotary_pos_embed_cache, len(rotary_pos_embed_cache)))
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if tmp_key not in rotary_pos_embed_cache:
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tmp_embed = get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size)
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tmp_embed_gpu = (tmp_embed[0].cuda(), tmp_embed[1].cuda())
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print('\tembed_dim=%d data_size=%s' % (embed_dim, tmp_embed[0].shape))
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rotary_pos_embed_cache[tmp_key] = tmp_embed_gpu
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return tmp_embed_gpu
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else:
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return rotary_pos_embed_cache[tmp_key]
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def encode_prompt(
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tokenizer,
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text_encoder,
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prompt: str,
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device: torch.device,
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dtype: torch.dtype,
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max_sequence_length = None,
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text_encoder_index = 0,
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):
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if max_sequence_length is None:
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if text_encoder_index == 0:
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max_length = 77
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if text_encoder_index == 1:
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max_length = 256
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else:
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max_length = max_sequence_length
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text_inputs = tokenizer(
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prompt,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_attention_mask=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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prompt_attention_mask = text_inputs.attention_mask.to(device)
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prompt_embeds = text_encoder(
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text_input_ids,
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attention_mask=prompt_attention_mask,
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)[0]
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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return prompt_embeds, prompt_attention_mask
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def get_random_downsample_ratio(sample_size, image_ratio=[],
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all_choices=False, rng=None):
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def _create_special_list(length):
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if length == 1:
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return [1.0]
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if length >= 2:
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first_element = 0.75
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remaining_sum = 1.0 - first_element
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other_elements_value = remaining_sum / (length - 1)
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special_list = [first_element] + [other_elements_value] * (length - 1)
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return special_list
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if sample_size >= 1536:
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number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio
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elif sample_size >= 1024:
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number_list = [1, 1.25, 1.5, 2] + image_ratio
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elif sample_size >= 768:
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number_list = [1, 1.25, 1.5] + image_ratio
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elif sample_size >= 512:
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number_list = [1] + image_ratio
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else:
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number_list = [1]
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if all_choices:
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return number_list
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number_list_prob = np.array(_create_special_list(len(number_list)))
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if rng is None:
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return np.random.choice(number_list, p = number_list_prob)
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else:
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return rng.choice(number_list, p = number_list_prob)
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def log_validation(vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, network, config, args, accelerator, weight_dtype, global_step):
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try:
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logger.info("Running validation... ")
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# Get Transformer
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if config.get('enable_multi_text_encoder', False):
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Choosen_Transformer3DModel = HunyuanTransformer3DModel
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else:
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Choosen_Transformer3DModel = Transformer3DModel
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transformer3d_val = Choosen_Transformer3DModel.from_pretrained_2d(
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args.pretrained_model_name_or_path, subfolder="transformer",
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transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
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).to(weight_dtype)
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transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict())
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if config.get('enable_multi_text_encoder', False):
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if args.train_mode != "normal":
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clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="image_encoder"
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)
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clip_image_processor = CLIPImageProcessor.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="image_encoder"
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)
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pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=accelerator.unwrap_model(vae).to(weight_dtype),
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text_encoder=accelerator.unwrap_model(text_encoder),
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text_encoder_2=accelerator.unwrap_model(text_encoder_2),
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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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transformer=transformer3d_val,
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torch_dtype=weight_dtype,
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clip_image_encoder=clip_image_encoder,
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clip_image_processor=clip_image_processor,
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scheduler=LCMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
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)
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else:
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pipeline = EasyAnimatePipeline_Multi_Text_Encoder.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=accelerator.unwrap_model(vae).to(weight_dtype),
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text_encoder=accelerator.unwrap_model(text_encoder),
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text_encoder_2=accelerator.unwrap_model(text_encoder_2),
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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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transformer=transformer3d_val,
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scheduler=LCMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
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torch_dtype=weight_dtype
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)
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else:
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if args.train_mode != "normal":
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clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="image_encoder"
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)
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clip_image_processor = CLIPImageProcessor.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="image_encoder"
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)
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pipeline = EasyAnimateInpaintPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=accelerator.unwrap_model(vae).to(weight_dtype),
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text_encoder=accelerator.unwrap_model(text_encoder),
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tokenizer=tokenizer,
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transformer=transformer3d_val,
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torch_dtype=weight_dtype,
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clip_image_encoder=clip_image_encoder,
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clip_image_processor=clip_image_processor,
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scheduler=LCMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
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)
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else:
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pipeline = EasyAnimatePipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=accelerator.unwrap_model(vae).to(weight_dtype),
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text_encoder=accelerator.unwrap_model(text_encoder),
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tokenizer=tokenizer,
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transformer=transformer3d_val,
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scheduler=LCMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
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torch_dtype=weight_dtype
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)
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pipeline = pipeline.to(accelerator.device)
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pipeline = merge_lora(
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pipeline, None, 1, accelerator.device, state_dict=accelerator.unwrap_model(network).state_dict(), transformer_only=True
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)
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if args.enable_xformers_memory_efficient_attention and not config.get('enable_multi_text_encoder', False):
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pipeline.enable_xformers_memory_efficient_attention()
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if args.seed is None:
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generator = None
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else:
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
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for i in range(len(args.validation_prompts)):
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with torch.no_grad():
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if args.train_mode != "normal":
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with torch.autocast("cuda", dtype=weight_dtype):
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video_length = int(args.video_sample_n_frames // vae.mini_batch_encoder * vae.mini_batch_encoder) if args.video_sample_n_frames != 1 else 1
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input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size])
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sample = pipeline(
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args.validation_prompts[i],
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video_length = args.video_sample_n_frames,
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# negative_prompt = "bad detailed",
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height = args.video_sample_size,
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width = args.video_sample_size,
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guidance_scale = 0,
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generator = generator,
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num_inference_steps=4,
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video = input_video,
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mask_video = input_video_mask,
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clip_image = clip_image,
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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(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif"))
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video_length = 1
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input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size])
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sample = pipeline(
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args.validation_prompts[i],
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video_length = 1,
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# negative_prompt = "bad detailed",
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height = args.video_sample_size,
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width = args.video_sample_size,
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generator = generator,
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num_inference_steps=4,
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guidance_scale = 0,
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video = input_video,
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mask_video = input_video_mask,
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clip_image = clip_image,
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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(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif"))
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else:
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with torch.autocast("cuda", dtype=weight_dtype):
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sample = pipeline(
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args.validation_prompts[i],
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video_length = args.video_sample_n_frames,
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negative_prompt = "bad detailed",
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height = args.video_sample_size,
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width = args.video_sample_size,
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num_inference_steps=4,
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guidance_scale = 0,
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generator = generator
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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(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif"))
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sample = pipeline(
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args.validation_prompts[i],
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video_length = 1,
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negative_prompt = "bad detailed",
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height = args.video_sample_size,
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width = args.video_sample_size,
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num_inference_steps=4,
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guidance_scale = 0,
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generator = generator
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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(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif"))
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del pipeline
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del transformer3d_val
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if args.train_mode != "normal":
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del clip_image_encoder
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del clip_image_processor
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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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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 with info {e}")
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return None
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# From LatentConsistencyModel.get_guidance_scale_embedding
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def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
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"""
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See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
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Args:
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timesteps (`torch.Tensor`):
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generate embedding vectors at these timesteps
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embedding_dim (`int`, *optional*, defaults to 512):
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dimension of the embeddings to generate
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dtype:
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data type of the generated embeddings
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Returns:
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`torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
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"""
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assert len(w.shape) == 1
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w = w * 1000.0
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half_dim = embedding_dim // 2
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emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
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emb = w.to(dtype)[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0, 1))
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assert emb.shape == (w.shape[0], embedding_dim)
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return emb
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
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return x[(...,) + (None,) * dims_to_append]
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# From LCMScheduler.get_scalings_for_boundary_condition_discrete
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def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
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scaled_timestep = timestep_scaling * timestep
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c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
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c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
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return c_skip, c_out
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# Compare LCMScheduler.step, Step 4
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def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):
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alphas = extract_into_tensor(alphas, timesteps, sample.shape)
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sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
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if prediction_type == "epsilon":
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pred_x_0 = (sample - sigmas * model_output) / alphas
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elif prediction_type == "sample":
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pred_x_0 = model_output
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elif prediction_type == "v_prediction":
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pred_x_0 = alphas * sample - sigmas * model_output
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else:
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raise ValueError(
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f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
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f" are supported."
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)
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|
return pred_x_0
|
|
|
|
|
|
# Based on step 4 in DDIMScheduler.step
|
|
def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):
|
|
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
|
|
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
|
|
if prediction_type == "epsilon":
|
|
pred_epsilon = model_output
|
|
elif prediction_type == "sample":
|
|
pred_epsilon = (sample - alphas * model_output) / sigmas
|
|
elif prediction_type == "v_prediction":
|
|
pred_epsilon = alphas * model_output + sigmas * sample
|
|
else:
|
|
raise ValueError(
|
|
f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
|
|
f" are supported."
|
|
)
|
|
|
|
return pred_epsilon
|
|
|
|
|
|
def extract_into_tensor(a, t, x_shape):
|
|
b, *_ = t.shape
|
|
out = a.gather(-1, t)
|
|
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
|
|
|
class DDIMSolver:
|
|
def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
|
|
# DDIM sampling parameters
|
|
step_ratio = timesteps // ddim_timesteps
|
|
self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
|
|
self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
|
|
self.ddim_alpha_cumprods_prev = np.asarray(
|
|
[alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
|
|
)
|
|
# convert to torch tensors
|
|
self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
|
|
self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
|
|
self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
|
|
|
|
def to(self, device):
|
|
self.ddim_timesteps = self.ddim_timesteps.to(device)
|
|
self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
|
|
self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
|
|
return self
|
|
|
|
def ddim_step(self, pred_x0, pred_noise, timestep_index):
|
|
alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
|
|
dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
|
|
x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
|
|
return x_prev
|
|
|
|
|
|
@torch.no_grad()
|
|
def update_ema(target_params, source_params, rate=0.99):
|
|
"""
|
|
Update target parameters to be closer to those of source parameters using
|
|
an exponential moving average.
|
|
|
|
:param target_params: the target parameter sequence.
|
|
:param source_params: the source parameter sequence.
|
|
:param rate: the EMA rate (closer to 1 means slower).
|
|
"""
|
|
for targ, src in zip(target_params, source_params):
|
|
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
|
|
|
|
def linear_decay(initial_value, final_value, total_steps, current_step):
|
|
if current_step >= total_steps:
|
|
return final_value
|
|
current_step = max(0, current_step)
|
|
step_size = (final_value - initial_value) / total_steps
|
|
current_value = initial_value + step_size * current_step
|
|
return current_value
|
|
|
|
def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None):
|
|
u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator)
|
|
t = 1 / (1 + torch.exp(-u)) * (high - low) + low
|
|
return torch.clip(t.to(torch.int32), low, high - 1)
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
|
parser.add_argument(
|
|
"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
|
|
)
|
|
parser.add_argument(
|
|
"--pretrained_model_name_or_path",
|
|
type=str,
|
|
default=None,
|
|
required=True,
|
|
help="Path to pretrained model or model identifier from huggingface.co/models.",
|
|
)
|
|
parser.add_argument(
|
|
"--revision",
|
|
type=str,
|
|
default=None,
|
|
required=False,
|
|
help="Revision of pretrained model identifier from huggingface.co/models.",
|
|
)
|
|
parser.add_argument(
|
|
"--variant",
|
|
type=str,
|
|
default=None,
|
|
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
|
|
)
|
|
parser.add_argument(
|
|
"--train_data_dir",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"A folder containing the training data. "
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--train_data_meta",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"A csv containing the training data. "
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--max_train_samples",
|
|
type=int,
|
|
default=None,
|
|
help=(
|
|
"For debugging purposes or quicker training, truncate the number of training examples to this "
|
|
"value if set."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--validation_prompts",
|
|
type=str,
|
|
default=None,
|
|
nargs="+",
|
|
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
|
|
)
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
type=str,
|
|
default="sd-model-finetuned",
|
|
help="The output directory where the model predictions and checkpoints will be written.",
|
|
)
|
|
parser.add_argument(
|
|
"--cache_dir",
|
|
type=str,
|
|
default=None,
|
|
help="The directory where the downloaded models and datasets will be stored.",
|
|
)
|
|
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
|
parser.add_argument(
|
|
"--random_flip",
|
|
action="store_true",
|
|
help="whether to randomly flip images horizontally",
|
|
)
|
|
parser.add_argument(
|
|
"--use_came",
|
|
action="store_true",
|
|
help="whether to use came",
|
|
)
|
|
parser.add_argument(
|
|
"--multi_stream",
|
|
action="store_true",
|
|
help="whether to use cuda multi-stream",
|
|
)
|
|
parser.add_argument(
|
|
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
|
|
)
|
|
parser.add_argument(
|
|
"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
|
|
)
|
|
parser.add_argument("--num_train_epochs", type=int, default=100)
|
|
parser.add_argument(
|
|
"--max_train_steps",
|
|
type=int,
|
|
default=None,
|
|
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
|
|
)
|
|
parser.add_argument(
|
|
"--gradient_accumulation_steps",
|
|
type=int,
|
|
default=1,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
|
)
|
|
parser.add_argument(
|
|
"--gradient_checkpointing",
|
|
action="store_true",
|
|
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
|
|
)
|
|
parser.add_argument(
|
|
"--learning_rate",
|
|
type=float,
|
|
default=1e-4,
|
|
help="Initial learning rate (after the potential warmup period) to use.",
|
|
)
|
|
parser.add_argument(
|
|
"--scale_lr",
|
|
action="store_true",
|
|
default=False,
|
|
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
|
|
)
|
|
parser.add_argument(
|
|
"--lr_scheduler",
|
|
type=str,
|
|
default="constant",
|
|
help=(
|
|
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
|
' "constant", "constant_with_warmup"]'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
|
|
)
|
|
parser.add_argument(
|
|
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
|
|
)
|
|
parser.add_argument(
|
|
"--allow_tf32",
|
|
action="store_true",
|
|
help=(
|
|
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
|
|
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
|
|
),
|
|
)
|
|
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
|
|
parser.add_argument(
|
|
"--non_ema_revision",
|
|
type=str,
|
|
default=None,
|
|
required=False,
|
|
help=(
|
|
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
|
|
" remote repository specified with --pretrained_model_name_or_path."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--dataloader_num_workers",
|
|
type=int,
|
|
default=0,
|
|
help=(
|
|
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
|
|
),
|
|
)
|
|
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
|
|
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
|
|
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
|
|
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
|
|
parser.add_argument(
|
|
"--prediction_type",
|
|
type=str,
|
|
default=None,
|
|
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
|
|
)
|
|
parser.add_argument(
|
|
"--hub_model_id",
|
|
type=str,
|
|
default=None,
|
|
help="The name of the repository to keep in sync with the local `output_dir`.",
|
|
)
|
|
parser.add_argument(
|
|
"--logging_dir",
|
|
type=str,
|
|
default="logs",
|
|
help=(
|
|
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
|
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--report_model_info", action="store_true", help="Whether or not to report more info about model (such as norm, grad)."
|
|
)
|
|
parser.add_argument(
|
|
"--mixed_precision",
|
|
type=str,
|
|
default=None,
|
|
choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
|
|
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
|
|
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--report_to",
|
|
type=str,
|
|
default="tensorboard",
|
|
help=(
|
|
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
|
|
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
|
|
),
|
|
)
|
|
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
parser.add_argument(
|
|
"--checkpointing_steps",
|
|
type=int,
|
|
default=500,
|
|
help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--checkpoints_total_limit",
|
|
type=int,
|
|
default=None,
|
|
help=("Max number of checkpoints to store."),
|
|
)
|
|
parser.add_argument(
|
|
"--resume_from_checkpoint",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
|
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
|
|
)
|
|
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(
|
|
"--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(
|
|
"--not_sigma_loss", action="store_true", help="Whether or not to not use sigma_loss."
|
|
)
|
|
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(
|
|
"--noise_share_in_frames", action="store_true", help="Whether enable noise share in frames."
|
|
)
|
|
parser.add_argument(
|
|
"--noise_share_in_frames_ratio", type=float, default=0.5, help="Noise share ratio.",
|
|
)
|
|
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(
|
|
"--keep_all_node_same_token_length",
|
|
action="store_true",
|
|
help="Reference of the length token.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Run train_sampling_steps.",
|
|
)
|
|
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(
|
|
"--image_sample_size",
|
|
type=int,
|
|
default=512,
|
|
help="Sample size of the video.",
|
|
)
|
|
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=120,
|
|
help='Max length of tokenizer'
|
|
)
|
|
parser.add_argument(
|
|
"--use_deepspeed", action="store_true", help="Whether or not to use deepspeed."
|
|
)
|
|
parser.add_argument(
|
|
"--low_vram", action="store_true", help="Whether enable low_vram mode."
|
|
)
|
|
parser.add_argument(
|
|
"--train_mode",
|
|
type=str,
|
|
default="normal",
|
|
help=(
|
|
'The format of training data. Support `"normal"`'
|
|
' (default), `"inpaint"`.'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--abnormal_norm_clip_start",
|
|
type=int,
|
|
default=1000,
|
|
help=(
|
|
'When do we start doing additional processing on abnormal gradients. '
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--initial_grad_norm_ratio",
|
|
type=int,
|
|
default=5,
|
|
help=(
|
|
'The initial gradient is relative to the multiple of the max_grad_norm. '
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--w_min",
|
|
type=float,
|
|
default=5.0,
|
|
required=False,
|
|
help=(
|
|
"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
|
|
" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
|
|
" compared to the original paper."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--w_max",
|
|
type=float,
|
|
default=15.0,
|
|
required=False,
|
|
help=(
|
|
"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
|
|
" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
|
|
" compared to the original paper."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--num_ddim_timesteps",
|
|
type=int,
|
|
default=50,
|
|
help="The number of timesteps to use for DDIM sampling.",
|
|
)
|
|
parser.add_argument(
|
|
"--loss_type",
|
|
type=str,
|
|
default="l2",
|
|
choices=["l2", "huber"],
|
|
help="The type of loss to use for the LCD loss.",
|
|
)
|
|
parser.add_argument(
|
|
"--huber_c",
|
|
type=float,
|
|
default=0.001,
|
|
help="The huber loss parameter. Only used if `--loss_type=huber`.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_dropout",
|
|
type=float,
|
|
default=0.0,
|
|
help="The dropout probability for the dropout layer added before applying the LoRA to each layer input.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_target_modules",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will"
|
|
" be used. By default, LoRA will be applied to all conv and linear layers."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--timestep_scaling_factor",
|
|
type=float,
|
|
default=10.0,
|
|
help=(
|
|
"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The"
|
|
" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically"
|
|
" suffice."
|
|
),
|
|
)
|
|
|
|
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,
|
|
)
|
|
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.
|
|
if args.not_sigma_loss:
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
else:
|
|
train_diffusion = SpacedDiffusion(
|
|
use_timesteps=space_timesteps(1000, str(args.train_sampling_steps)), betas=gd.get_named_beta_schedule("linear", 1000),
|
|
model_mean_type=(gd.ModelMeanType.EPSILON), model_var_type=((gd.ModelVarType.LEARNED_RANGE)),
|
|
loss_type=gd.LossType.MSE, snr=args.snr_loss, return_startx=False,
|
|
)
|
|
|
|
# DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us
|
|
alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
|
|
sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
|
|
# Initialize the DDIM ODE solver for distillation.
|
|
solver = DDIMSolver(
|
|
noise_scheduler.alphas_cumprod.numpy(),
|
|
timesteps=noise_scheduler.config.num_train_timesteps,
|
|
ddim_timesteps=args.num_ddim_timesteps,
|
|
)
|
|
|
|
if config.get('enable_multi_text_encoder', False):
|
|
tokenizer = BertTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
|
|
)
|
|
tokenizer_2 = T5Tokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision
|
|
)
|
|
else:
|
|
tokenizer = T5Tokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
|
|
)
|
|
tokenizer_2 = None
|
|
|
|
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)]
|
|
|
|
|
|
if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
|
|
Choosen_AutoencoderKL = AutoencoderKLMagvit
|
|
else:
|
|
Choosen_AutoencoderKL = AutoencoderKL
|
|
|
|
# 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()):
|
|
if config.get('enable_multi_text_encoder', False):
|
|
text_encoder = BertModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
text_encoder_2 = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
else:
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
text_encoder_2 = None
|
|
|
|
vae = Choosen_AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant,
|
|
vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs'])
|
|
)
|
|
|
|
if config.get('enable_multi_text_encoder', False):
|
|
Choosen_Transformer3DModel = HunyuanTransformer3DModel
|
|
else:
|
|
Choosen_Transformer3DModel = Transformer3DModel
|
|
|
|
transformer3d = Choosen_Transformer3DModel.from_pretrained_2d(
|
|
args.pretrained_model_name_or_path, subfolder="transformer",
|
|
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
|
|
)
|
|
teacher_transformer3d = Choosen_Transformer3DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="transformer",
|
|
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
|
|
)
|
|
|
|
if args.train_mode != "normal":
|
|
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="image_encoder"
|
|
)
|
|
image_processor = CLIPImageProcessor.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="image_encoder"
|
|
)
|
|
|
|
# Freeze vae and text_encoder and set transformer3d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
teacher_transformer3d.requires_grad_(False)
|
|
if config.get('enable_multi_text_encoder', False):
|
|
text_encoder_2.requires_grad_(False)
|
|
transformer3d.requires_grad_(False)
|
|
if args.train_mode != "normal":
|
|
image_encoder.requires_grad_(False)
|
|
|
|
# Lora will work with this...
|
|
network = create_network(
|
|
1.0,
|
|
args.rank,
|
|
args.network_alpha,
|
|
text_encoder,
|
|
transformer3d,
|
|
neuron_dropout=None,
|
|
add_lora_in_attn_temporal=True,
|
|
)
|
|
network.apply_to(text_encoder, transformer3d, args.train_text_encoder, 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)}")
|
|
assert len(u) == 0
|
|
|
|
if args.vae_path is not None:
|
|
print(f"From checkpoint: {args.vae_path}")
|
|
if args.vae_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(args.vae_path)
|
|
else:
|
|
state_dict = torch.load(args.vae_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = vae.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
assert len(u) == 0
|
|
|
|
if args.enable_xformers_memory_efficient_attention and not config.get('enable_multi_text_encoder', False):
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warn(
|
|
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
|
|
)
|
|
transformer3d.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
# `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
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(models[-1]))
|
|
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:
|
|
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
|
|
|
|
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.mini_batch_encoder if vae.quant_conv.weight.ndim==5 else 4
|
|
|
|
train_dataset = ImageVideoDataset(
|
|
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,
|
|
image_sample_size=args.image_sample_size,
|
|
enable_bucket=args.enable_bucket, enable_inpaint=True if args.train_mode != "normal" else False,
|
|
)
|
|
|
|
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()}
|
|
aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.video_sample_size for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_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,
|
|
)
|
|
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]
|
|
else:
|
|
numbers_list = None
|
|
|
|
def get_length_to_frame_num(token_length):
|
|
if args.image_sample_size > args.video_sample_size:
|
|
sample_sizes = list(range(args.video_sample_size, args.image_sample_size + 1, 128))
|
|
|
|
if sample_sizes[-1] != args.image_sample_size:
|
|
sample_sizes.append(args.image_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 collate_fn(examples):
|
|
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"] = []
|
|
if args.train_mode != "normal":
|
|
new_examples["mask_pixel_values"] = []
|
|
new_examples["mask"] = []
|
|
new_examples["clip_pixel_values"] = []
|
|
|
|
# Get ratio
|
|
pixel_value = examples[0]["pixel_values"]
|
|
data_type = examples[0]["data_type"]
|
|
f, h, w, c = np.shape(pixel_value)
|
|
if data_type == 'image':
|
|
random_downsample_ratio = 1 if not args.random_hw_adapt else get_random_downsample_ratio(args.image_sample_size, image_ratio=[args.image_sample_size / args.video_sample_size], rng=rng)
|
|
|
|
aspect_ratio_sample_size = {key : [x / 512 * args.image_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.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
else:
|
|
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]))
|
|
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())
|
|
if rng is None:
|
|
local_video_sample_size = np.random.choice(choice_list)
|
|
else:
|
|
local_video_sample_size = rng.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, rng=rng)
|
|
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()}
|
|
|
|
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]
|
|
if 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]
|
|
|
|
for example in examples:
|
|
if args.random_ratio_crop:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
b, c, h, w = pixel_values.size()
|
|
th, tw = random_sample_size
|
|
if th / tw > h / w:
|
|
nh = int(th)
|
|
nw = int(w / h * nh)
|
|
else:
|
|
nw = int(tw)
|
|
nh = int(h / w * nw)
|
|
|
|
transform = transforms.Compose([
|
|
transforms.Resize([nh, nw]),
|
|
transforms.CenterCrop([int(x) for x in random_sample_size]),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
else:
|
|
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]
|
|
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
transform = transforms.Compose([
|
|
transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(closest_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
new_examples["pixel_values"].append(transform(pixel_values))
|
|
new_examples["text"].append(example["text"])
|
|
batch_video_length = int(
|
|
min(
|
|
batch_video_length,
|
|
len(pixel_values) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval,
|
|
)
|
|
)
|
|
if batch_video_length == 0:
|
|
batch_video_length = 1
|
|
|
|
if args.train_mode != "normal":
|
|
mask = get_random_mask(new_examples["pixel_values"][-1].size())
|
|
mask_pixel_values = new_examples["pixel_values"][-1] * (1 - mask) + torch.ones_like(new_examples["pixel_values"][-1]) * -1 * mask
|
|
new_examples["mask_pixel_values"].append(mask_pixel_values)
|
|
new_examples["mask"].append(mask)
|
|
|
|
def get_random_clip_index(low, high):
|
|
if high - low <= 1.1:
|
|
return low
|
|
values = np.arange(low, high)
|
|
probabilities = np.ones(len(values)) * 0.5 / (len(values) - 1)
|
|
probabilities[0] = 0.5
|
|
return np.random.choice(values, p=probabilities)
|
|
|
|
clip_index = get_random_clip_index(0, len(new_examples["pixel_values"][-1]))
|
|
clip_pixel_values = new_examples["pixel_values"][-1][clip_index].permute(1, 2, 0).contiguous()
|
|
clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
|
|
new_examples["clip_pixel_values"].append(clip_pixel_values)
|
|
|
|
new_examples["pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["pixel_values"]])
|
|
if args.train_mode != "normal":
|
|
new_examples["mask_pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["mask_pixel_values"]])
|
|
new_examples["mask"] = torch.stack([example[:batch_video_length] for example in new_examples["mask"]])
|
|
new_examples["clip_pixel_values"] = torch.stack([example for example in new_examples["clip_pixel_values"]])
|
|
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,
|
|
)
|
|
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,
|
|
)
|
|
|
|
# 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`.
|
|
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
network, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
if args.train_mode != "normal":
|
|
image_encoder.to(accelerator.device, dtype=weight_dtype)
|
|
transformer3d.to(accelerator.device, dtype=weight_dtype)
|
|
teacher_transformer3d.to(accelerator.device)
|
|
text_encoder.to(accelerator.device)
|
|
if config.get('enable_multi_text_encoder', False):
|
|
text_encoder_2.to(accelerator.device)
|
|
alpha_schedule = alpha_schedule.to(accelerator.device)
|
|
sigma_schedule = sigma_schedule.to(accelerator.device)
|
|
# Move the ODE solver to accelerator.device.
|
|
solver = solver.to(accelerator.device)
|
|
|
|
# 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))
|
|
tracker_config.pop("validation_prompts")
|
|
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}.")
|
|
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(os.path.join(os.path.join(args.output_dir, path), "lora_diffusion_pytorch_model.safetensors"))
|
|
m, u = accelerator.unwrap_model(network).load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
|
|
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}")
|
|
unwrapped_nw.save_weights(ckpt_file, weight_dtype, None)
|
|
|
|
progress_bar = tqdm(
|
|
range(0, args.max_train_steps),
|
|
initial=initial_global_step,
|
|
desc="Steps",
|
|
# Only show the progress bar once on each machine.
|
|
disable=not accelerator.is_local_main_process,
|
|
)
|
|
|
|
if args.multi_stream and args.train_mode != "normal":
|
|
# 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
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
batch_sampler.sampler.generator = torch.Generator().manual_seed(args.seed + epoch)
|
|
for step, batch in enumerate(train_dataloader):
|
|
# Data batch sanity check
|
|
if epoch == first_epoch and step == 0:
|
|
pixel_values, texts = batch['pixel_values'].cpu(), batch['text']
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
if pixel_values.ndim==4:
|
|
pixel_values = pixel_values.unsqueeze(2)
|
|
os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True)
|
|
for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}.gif", rescale=True)
|
|
if args.train_mode != "normal":
|
|
clip_pixel_values, mask_pixel_values, texts = batch['clip_pixel_values'].cpu(), batch['mask_pixel_values'].cpu(), batch['text']
|
|
mask_pixel_values = rearrange(mask_pixel_values, "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_pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
Image.fromarray(np.uint8(clip_pixel_value)).save(f"{args.output_dir}/sanity_check/clip_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.png")
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/mask_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.gif", rescale=True)
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
# Convert images to latent space
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
if args.training_with_video_token_length:
|
|
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))
|
|
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))
|
|
batch['text'] = batch['text'] * 2
|
|
|
|
if args.train_mode != "normal":
|
|
clip_pixel_values = batch["clip_pixel_values"]
|
|
mask_pixel_values = batch["mask_pixel_values"].to(weight_dtype)
|
|
mask = batch["mask"].to(weight_dtype)
|
|
if args.training_with_video_token_length:
|
|
if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]:
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (4, 1, 1, 1))
|
|
mask_pixel_values = torch.tile(mask_pixel_values, (4, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (4, 1, 1, 1, 1))
|
|
elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]:
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (2, 1, 1, 1))
|
|
mask_pixel_values = torch.tile(mask_pixel_values, (2, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (2, 1, 1, 1, 1))
|
|
|
|
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
|
|
|
|
if args.keep_all_node_same_token_length:
|
|
actual_token_length = index_rng.choice(numbers_list)
|
|
actual_video_length = min(actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval
|
|
actual_video_length = int(max(actual_video_length, 1))
|
|
else:
|
|
actual_video_length = None
|
|
|
|
if args.random_frame_crop:
|
|
select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval, 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 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)
|
|
if args.keep_all_node_same_token_length:
|
|
temp_n_frames = min(actual_video_length, temp_n_frames)
|
|
|
|
pixel_values = pixel_values[:, :temp_n_frames, :, :]
|
|
|
|
if args.train_mode != "normal":
|
|
mask_pixel_values = mask_pixel_values[:, :temp_n_frames, :, :]
|
|
mask = mask[:, :temp_n_frames, :, :]
|
|
|
|
video_length = pixel_values.shape[1]
|
|
if args.train_mode != "normal":
|
|
t2v_flag = [(_mask == 1).all() for _mask in mask]
|
|
new_t2v_flag = []
|
|
for _mask in t2v_flag:
|
|
if _mask and np.random.rand() < 0.90:
|
|
new_t2v_flag.append(0)
|
|
else:
|
|
new_t2v_flag.append(1)
|
|
t2v_flag = torch.from_numpy(np.array(new_t2v_flag)).to(accelerator.device, dtype=weight_dtype)
|
|
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
vae.to(accelerator.device)
|
|
|
|
with torch.no_grad():
|
|
if vae.quant_conv.weight.ndim==5:
|
|
# This way is quicker when batch grows up
|
|
def _slice_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 = _slice_vae(pixel_values)
|
|
else:
|
|
latents = _slice_vae(pixel_values)
|
|
else:
|
|
# This way is quicker when batch grows up
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c 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.to(dtype=weight_dtype)).latent_dist
|
|
pixel_values_bs = pixel_values_bs.sample()
|
|
new_pixel_values.append(pixel_values_bs)
|
|
latents = torch.cat(new_pixel_values, dim = 0)
|
|
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
|
|
latents = latents * vae.config.scaling_factor
|
|
|
|
if args.train_mode != "normal":
|
|
if vae.quant_conv.weight.ndim==5:
|
|
def _mask_slice_vae(mask):
|
|
mask = rearrange(mask, "b f c h w -> b c f h w")
|
|
mask = torch.tile(mask, [1, 3, 1, 1, 1])
|
|
bs = args.vae_mini_batch
|
|
new_mask = []
|
|
for i in range(0, mask.shape[0], bs):
|
|
mask_bs = mask[i : i + bs]
|
|
mask_bs = vae.encode(mask_bs)[0]
|
|
mask_bs = mask_bs.sample()
|
|
new_mask.append(mask_bs)
|
|
return torch.cat(new_mask, dim = 0)
|
|
if vae_stream_2 is not None:
|
|
vae_stream_2.wait_stream(torch.cuda.current_stream())
|
|
with torch.cuda.stream(vae_stream_2):
|
|
mask = _mask_slice_vae(mask)
|
|
else:
|
|
mask = _mask_slice_vae(mask)
|
|
|
|
mask_pixel_values = rearrange(mask_pixel_values, "b f c h w -> b c f h w")
|
|
bs = args.vae_mini_batch
|
|
new_mask_pixel_values = []
|
|
for i in range(0, mask_pixel_values.shape[0], bs):
|
|
mask_pixel_values_bs = mask_pixel_values[i : i + bs]
|
|
mask_pixel_values_bs = vae.encode(mask_pixel_values_bs)[0]
|
|
mask_pixel_values_bs = mask_pixel_values_bs.sample()
|
|
new_mask_pixel_values.append(mask_pixel_values_bs)
|
|
mask_latents = torch.cat(new_mask_pixel_values, dim = 0)
|
|
|
|
if vae_stream_2 is not None:
|
|
torch.cuda.current_stream().wait_stream(vae_stream_2)
|
|
|
|
inpaint_latents = torch.concat([mask, mask_latents], dim=1)
|
|
else:
|
|
mask_pixel_values = rearrange(mask_pixel_values, "b f c h w -> (b f) c h w")
|
|
bs = args.vae_mini_batch
|
|
new_mask_pixel_values = []
|
|
for i in range(0, mask_pixel_values.shape[0], bs):
|
|
mask_pixel_values_bs = mask_pixel_values[i : i + bs]
|
|
mask_pixel_values_bs = vae.encode(mask_pixel_values_bs.to(dtype=weight_dtype)).latent_dist
|
|
mask_pixel_values_bs = mask_pixel_values_bs.sample()
|
|
new_mask_pixel_values.append(mask_pixel_values_bs)
|
|
mask_latents = torch.cat(new_mask_pixel_values, dim = 0)
|
|
mask_latents = rearrange(mask_latents, "(b f) c h w -> b c f h w", f=video_length)
|
|
|
|
mask = rearrange(batch['mask'], "b f c h w -> (b f) c h w")
|
|
mask = torch.nn.functional.interpolate(
|
|
mask, size=(mask_latents.size()[-2], mask_latents.size()[-1])
|
|
)
|
|
mask = rearrange(mask, "(b f) c h w -> b c f h w", f=video_length)
|
|
inpaint_latents = torch.concat([mask, mask_latents], dim=1)
|
|
|
|
inpaint_latents = t2v_flag[:, None, None, None, None] * inpaint_latents
|
|
|
|
with torch.no_grad():
|
|
clip_encoder_hidden_states = []
|
|
for clip_pixel_value in clip_pixel_values:
|
|
image = Image.fromarray(np.uint8(clip_pixel_value.cpu().numpy()))
|
|
inputs = image_processor(images=image, return_tensors="pt")
|
|
inputs["pixel_values"] = inputs["pixel_values"].to(accelerator.device, dtype=weight_dtype)
|
|
if config.get('enable_multi_text_encoder', False):
|
|
outputs = image_encoder(**inputs).last_hidden_state[0, 1:]
|
|
else:
|
|
outputs = image_encoder(**inputs).image_embeds[0]
|
|
clip_encoder_hidden_states.append(outputs)
|
|
clip_encoder_hidden_states = torch.stack(clip_encoder_hidden_states)
|
|
bsc = clip_encoder_hidden_states.shape[0]
|
|
|
|
if config.get('enable_multi_text_encoder', False):
|
|
clip_attention_mask = torch.ones([bsc, unwrap_model(transformer3d).n_query]) if np.random.rand() < 0.50 else torch.zeros([bsc, unwrap_model(transformer3d).n_query])
|
|
else:
|
|
clip_attention_mask = torch.ones([bsc, 8]) if np.random.rand() < 0.50 else torch.zeros([bsc, 8])
|
|
clip_attention_mask = clip_attention_mask.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
inpaint_latents = inpaint_latents * vae.config.scaling_factor
|
|
|
|
# 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')
|
|
torch.cuda.empty_cache()
|
|
text_encoder.to(accelerator.device)
|
|
if text_encoder_2 is not None:
|
|
text_encoder_2.to(accelerator.device)
|
|
|
|
if config.get('enable_multi_text_encoder', False):
|
|
def encode_prompt(
|
|
tokenizer,
|
|
text_encoder,
|
|
prompt: str,
|
|
device: torch.device,
|
|
dtype: torch.dtype,
|
|
max_sequence_length = None,
|
|
text_encoder_index = 0,
|
|
):
|
|
if max_sequence_length is None:
|
|
if text_encoder_index == 0:
|
|
max_length = 77
|
|
if text_encoder_index == 1:
|
|
max_length = 256
|
|
else:
|
|
max_length = max_sequence_length
|
|
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_attention_mask=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids.to(device)
|
|
prompt_attention_mask = text_inputs.attention_mask.to(device)
|
|
|
|
prompt_embeds = text_encoder(
|
|
text_input_ids,
|
|
attention_mask=prompt_attention_mask,
|
|
)[0]
|
|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
|
|
return prompt_embeds, prompt_attention_mask
|
|
|
|
with torch.no_grad():
|
|
prompt_embeds, prompt_attention_mask = \
|
|
encode_prompt(tokenizer, text_encoder, batch['text'], latents.device, dtype=weight_dtype, text_encoder_index=0)
|
|
prompt_embeds_2, prompt_attention_mask_2 = \
|
|
encode_prompt(tokenizer_2, text_encoder_2, batch['text'], latents.device, dtype=weight_dtype, text_encoder_index=1)
|
|
|
|
un_prompt_embeds, un_prompt_attention_mask = \
|
|
encode_prompt(tokenizer, text_encoder, ['' for _ in batch['text']], latents.device, dtype=weight_dtype, text_encoder_index=0)
|
|
un_prompt_embeds_2, un_prompt_attention_mask_2 = \
|
|
encode_prompt(tokenizer_2, text_encoder_2, ['' for _ in batch['text']], latents.device, dtype=weight_dtype, text_encoder_index=1)
|
|
|
|
else:
|
|
with torch.no_grad():
|
|
prompt_ids = tokenizer(
|
|
batch['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.to(latents.device),
|
|
attention_mask=prompt_ids.attention_mask.to(latents.device),
|
|
return_dict=False
|
|
)[0]
|
|
|
|
if args.low_vram:
|
|
text_encoder.to('cpu')
|
|
if text_encoder_2 is not None:
|
|
text_encoder_2.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
bsz = latents.shape[0]
|
|
|
|
# 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.
|
|
# For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]
|
|
topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
|
|
index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
|
|
start_timesteps = solver.ddim_timesteps[index]
|
|
timesteps = start_timesteps - topk
|
|
timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
|
|
|
|
# 3. Get boundary scalings for start_timesteps and (end) timesteps.
|
|
c_skip_start, c_out_start = scalings_for_boundary_conditions(
|
|
start_timesteps, timestep_scaling=args.timestep_scaling_factor
|
|
)
|
|
c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
|
|
c_skip, c_out = scalings_for_boundary_conditions(
|
|
timesteps, timestep_scaling=args.timestep_scaling_factor
|
|
)
|
|
c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
|
|
|
|
if args.noise_share_in_frames:
|
|
def generate_noise(bs, channel, length, height, width, ratio=0.5, generator=None, device="cuda", dtype=None):
|
|
noise = torch.randn(bs, channel, length, height, width, generator=generator, device=device, dtype=dtype)
|
|
for i in range(1, length):
|
|
noise[:, :, i, :, :] = ratio * noise[:, :, i - 1, :, :] + (1 - ratio) * noise[:, :, i, :, :]
|
|
return noise
|
|
noise = generate_noise(*latents.size(), ratio=args.noise_share_in_frames_ratio, device=latents.device, generator=torch_rng, dtype=weight_dtype)
|
|
else:
|
|
noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype)
|
|
|
|
height, width = batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1]
|
|
|
|
grid_height = height // 8 // accelerator.unwrap_model(transformer3d).config.patch_size
|
|
grid_width = width // 8 // accelerator.unwrap_model(transformer3d).config.patch_size
|
|
base_size = 512 // 8 // accelerator.unwrap_model(transformer3d).config.patch_size
|
|
grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)
|
|
image_rotary_emb = get_2d_rotary_pos_embed(
|
|
accelerator.unwrap_model(transformer3d).inner_dim // accelerator.unwrap_model(transformer3d).num_heads, grid_crops_coords, (grid_height, grid_width)
|
|
)
|
|
|
|
style = torch.tensor([0], device=latents.device)
|
|
|
|
target_size = (height, width)
|
|
add_time_ids = list((1024, 1024) + target_size + (0, 0))
|
|
add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)
|
|
|
|
prompt_embeds = prompt_embeds.to(device=latents.device)
|
|
prompt_attention_mask = prompt_attention_mask.to(device=latents.device)
|
|
prompt_embeds_2 = prompt_embeds_2.to(device=latents.device)
|
|
prompt_attention_mask_2 = prompt_attention_mask_2.to(device=latents.device)
|
|
add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=latents.device).repeat(
|
|
bsz, 1
|
|
)
|
|
style = style.to(device=latents.device).repeat(bsz)
|
|
|
|
noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
|
|
# 5. Sample a random guidance scale w from U[w_min, w_max]
|
|
# Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding
|
|
w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
|
|
w = w.reshape(bsz, 1, 1, 1, 1)
|
|
w = w.to(device=latents.device, dtype=latents.dtype)
|
|
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
# predict the noise residual
|
|
noise_pred = transformer3d(
|
|
noisy_model_input,
|
|
start_timesteps.to(noisy_model_input.dtype),
|
|
encoder_hidden_states=prompt_embeds,
|
|
text_embedding_mask=prompt_attention_mask,
|
|
encoder_hidden_states_t5=prompt_embeds_2,
|
|
text_embedding_mask_t5=prompt_attention_mask_2,
|
|
image_meta_size=add_time_ids,
|
|
style=style,
|
|
image_rotary_emb=image_rotary_emb,
|
|
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
|
|
clip_encoder_hidden_states=clip_encoder_hidden_states if args.train_mode != "normal" else None,
|
|
clip_attention_mask=clip_attention_mask if args.train_mode != "normal" else None,
|
|
return_dict=False
|
|
)[0]
|
|
noise_pred, _ = noise_pred.chunk(2, dim=1)
|
|
|
|
pred_x_0 = get_predicted_original_sample(
|
|
noise_pred,
|
|
start_timesteps,
|
|
noisy_model_input,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
|
|
model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
|
|
|
|
# 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the
|
|
# predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these
|
|
# estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE
|
|
# solver timestep.
|
|
with torch.no_grad():
|
|
with torch.autocast(accelerator.device.type):
|
|
# 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c
|
|
|
|
cond_teacher_output = teacher_transformer3d(
|
|
noisy_model_input,
|
|
start_timesteps.to(noisy_model_input.dtype),
|
|
encoder_hidden_states=prompt_embeds,
|
|
text_embedding_mask=prompt_attention_mask,
|
|
encoder_hidden_states_t5=prompt_embeds_2,
|
|
text_embedding_mask_t5=prompt_attention_mask_2,
|
|
image_meta_size=add_time_ids,
|
|
style=style,
|
|
image_rotary_emb=image_rotary_emb,
|
|
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
|
|
clip_encoder_hidden_states=clip_encoder_hidden_states if args.train_mode != "normal" else None,
|
|
clip_attention_mask=clip_attention_mask if args.train_mode != "normal" else None,
|
|
return_dict=False
|
|
)[0]
|
|
cond_teacher_output, _ = cond_teacher_output.chunk(2, dim=1)
|
|
cond_pred_x0 = get_predicted_original_sample(
|
|
cond_teacher_output,
|
|
start_timesteps,
|
|
noisy_model_input,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
cond_pred_noise = get_predicted_noise(
|
|
cond_teacher_output,
|
|
start_timesteps,
|
|
noisy_model_input,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
|
|
# 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0
|
|
|
|
uncond_teacher_output = teacher_transformer3d(
|
|
noisy_model_input,
|
|
start_timesteps.to(noisy_model_input.dtype),
|
|
encoder_hidden_states=un_prompt_embeds,
|
|
text_embedding_mask=un_prompt_attention_mask,
|
|
encoder_hidden_states_t5=un_prompt_embeds_2,
|
|
text_embedding_mask_t5=un_prompt_attention_mask_2,
|
|
image_meta_size=add_time_ids,
|
|
style=style,
|
|
image_rotary_emb=image_rotary_emb,
|
|
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
|
|
clip_encoder_hidden_states=torch.zeros_like(clip_encoder_hidden_states) if args.train_mode != "normal" else None,
|
|
clip_attention_mask=torch.zeros_like(clip_attention_mask) if args.train_mode != "normal" else None,
|
|
return_dict=False
|
|
)[0]
|
|
uncond_teacher_output, _ = uncond_teacher_output.chunk(2, dim=1)
|
|
|
|
uncond_pred_x0 = get_predicted_original_sample(
|
|
uncond_teacher_output,
|
|
start_timesteps,
|
|
noisy_model_input,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
uncond_pred_noise = get_predicted_noise(
|
|
uncond_teacher_output,
|
|
start_timesteps,
|
|
noisy_model_input,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
|
|
# 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)
|
|
# Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation
|
|
pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
|
|
pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)
|
|
# 4. Run one step of the ODE solver to estimate the next point x_prev on the
|
|
# augmented PF-ODE trajectory (solving backward in time)
|
|
# Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.
|
|
x_prev = solver.ddim_step(pred_x0, pred_noise, index)
|
|
|
|
print(pixel_values.size())
|
|
# 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)
|
|
# Note that we do not use a separate target network for LCM-LoRA distillation.
|
|
with torch.no_grad():
|
|
with torch.autocast(accelerator.device.type):
|
|
target_noise_pred = transformer3d(
|
|
x_prev.float(),
|
|
timesteps.to(noisy_model_input.dtype),
|
|
encoder_hidden_states=prompt_embeds,
|
|
text_embedding_mask=prompt_attention_mask,
|
|
encoder_hidden_states_t5=prompt_embeds_2,
|
|
text_embedding_mask_t5=prompt_attention_mask_2,
|
|
image_meta_size=add_time_ids,
|
|
style=style,
|
|
image_rotary_emb=image_rotary_emb,
|
|
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
|
|
clip_encoder_hidden_states=clip_encoder_hidden_states if args.train_mode != "normal" else None,
|
|
clip_attention_mask=clip_attention_mask if args.train_mode != "normal" else None,
|
|
return_dict=False
|
|
)[0]
|
|
target_noise_pred, _ = target_noise_pred.chunk(2, dim=1)
|
|
pred_x_0 = get_predicted_original_sample(
|
|
target_noise_pred,
|
|
timesteps,
|
|
x_prev,
|
|
noise_scheduler.config.prediction_type,
|
|
alpha_schedule,
|
|
sigma_schedule,
|
|
)
|
|
target = c_skip * x_prev + c_out * pred_x_0
|
|
|
|
# 10. Calculate loss
|
|
if args.loss_type == "l2":
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
elif args.loss_type == "huber":
|
|
loss = torch.mean(
|
|
torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
|
|
)
|
|
|
|
# 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 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)
|
|
if not args.save_state:
|
|
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}")
|
|
|
|
if accelerator.is_main_process:
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
text_encoder_2,
|
|
tokenizer,
|
|
tokenizer_2,
|
|
transformer3d,
|
|
network,
|
|
config,
|
|
args,
|
|
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 accelerator.is_main_process:
|
|
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
text_encoder_2,
|
|
tokenizer,
|
|
tokenizer_2,
|
|
transformer3d,
|
|
network,
|
|
config,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
# Create the pipeline using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(network))
|
|
if args.save_state:
|
|
accelerator.save_state(accelerator_save_path)
|
|
logger.info(f"Saved state to {accelerator_save_path}")
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|