1215 lines
52 KiB
Python
1215 lines
52 KiB
Python
"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
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"""
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import gc
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import logging
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import math
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import os
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import re
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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
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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 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 T5EncoderModel, T5Tokenizer
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from transformers.utils import ContextManagers
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# import easyanimate pakage
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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 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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AspectRatioBatchSampler,
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get_closest_ratio)
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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.transformer3d import Transformer3DModel
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from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
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from easyanimate.pipeline.pipeline_easyanimate_inpaint import EasyAnimateInpaintPipeline
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from easyanimate.utils.IDDIM import IDDPM
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from easyanimate.utils.lora_utils import create_network, merge_lora, unmerge_lora
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from easyanimate.utils.utils import save_videos_grid
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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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def auto_scale_lr(effective_bs, lr, rule='linear', base_batch_size=256):
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assert rule in ['linear', 'sqrt']
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# scale by world size
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if rule == 'sqrt':
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scale_ratio = math.sqrt(effective_bs / base_batch_size)
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elif rule == 'linear':
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scale_ratio = effective_bs / base_batch_size
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lr *= scale_ratio
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logger.info(f'Automatically adapt lr to {lr:.7f} (using {rule} scaling rule).')
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return lr
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def log_validation(vae, text_encoder, tokenizer, 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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transformer3d_val = 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 args.train_mode != "normal":
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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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)
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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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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:
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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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images = []
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for i in range(len(args.validation_prompts)):
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if args.train_mode != "normal":
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image = Image.open(args.validation_images[i])
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input_video = torch.tile(
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torch.from_numpy(np.array(image)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
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[1, 1, 16, 1, 1]
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) / 255
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input_video_mask = torch.zeros_like(input_video)[:, 0:1, :, :, :]
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input_video_mask[:, 1:] = 255
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with torch.no_grad():
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sample = pipeline(
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args.validation_prompts[i],
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negative_prompt = "bad detailed",
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generator = generator,
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video_length = args.sample_n_frames,
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video = input_video,
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mask_video = input_video_mask,
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height = args.sample_size,
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width = args.sample_size,
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strength = 1,
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).videos
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else:
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with torch.no_grad():
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sample = pipeline(
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args.validation_prompts[i],
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video_length = args.sample_n_frames,
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negative_prompt = "bad detailed",
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height = args.sample_size,
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width = args.sample_size,
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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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del pipeline
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del transformer3d_val
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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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return images
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# except Exception as e:
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# print(f"Eval error with info {e}")
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# return None
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
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)
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--train_data_format",
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type=str,
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default="webvid",
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help=(
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'The format of training data. Support `"webvid"`'
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' (default), `"normal"`.'
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),
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. "
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),
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)
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parser.add_argument(
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"--train_data_meta",
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type=str,
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default=None,
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help=(
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"A csv containing the training data. "
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--validation_prompts",
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type=str,
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default=None,
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nargs="+",
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_images",
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type=str,
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default=None,
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nargs="+",
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help=("A set of images evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="sd-model-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
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)
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parser.add_argument("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-4,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument(
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"--non_ema_revision",
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type=str,
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default=None,
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required=False,
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help=(
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"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
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" remote repository specified with --pretrained_model_name_or_path."
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),
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--prediction_type",
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type=str,
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default=None,
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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.",
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)
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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|
parser.add_argument(
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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
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|
" training using `--resume_from_checkpoint`."
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),
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)
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parser.add_argument(
|
|
"--checkpoints_total_limit",
|
|
type=int,
|
|
default=None,
|
|
help=("Max number of checkpoints to store."),
|
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)
|
|
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.'
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|
),
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)
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|
parser.add_argument(
|
|
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
|
|
)
|
|
parser.add_argument(
|
|
"--validation_epochs",
|
|
type=int,
|
|
default=5,
|
|
help="Run validation every X epochs.",
|
|
)
|
|
parser.add_argument(
|
|
"--validation_steps",
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|
type=int,
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|
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(
|
|
"--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(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Run train_sampling_steps.",
|
|
)
|
|
parser.add_argument(
|
|
"--sample_size",
|
|
type=int,
|
|
default=256,
|
|
help="Sample size of the video.",
|
|
)
|
|
parser.add_argument(
|
|
"--sample_stride",
|
|
type=int,
|
|
default=4,
|
|
help="Sample stride of the video.",
|
|
)
|
|
parser.add_argument(
|
|
"--sample_n_frames",
|
|
type=int,
|
|
default=4,
|
|
help="Num frame of video.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_mode",
|
|
type=str,
|
|
default="normal",
|
|
help=(
|
|
'The format of training data. Support `"normal"`'
|
|
' (default), `"inpaint"`.'
|
|
),
|
|
)
|
|
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(
|
|
'--tokenizer_max_length',
|
|
type=int,
|
|
default=120,
|
|
help='Max length of tokenizer'
|
|
)
|
|
|
|
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)
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer3d) to half-precision
|
|
# as these weights are only used for inference, keeping weights in full precision is not required.
|
|
weight_dtype = torch.float32
|
|
if accelerator.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
|
|
# Load scheduler, tokenizer and models.
|
|
# noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
train_diffusion = IDDPM(str(args.train_sampling_steps), learn_sigma=True, pred_sigma=True, snr=args.snr_loss)
|
|
tokenizer = T5Tokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
|
|
)
|
|
|
|
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()):
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
vae = Choosen_AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
|
|
)
|
|
|
|
transformer3d = Transformer3DModel.from_pretrained_2d(
|
|
args.pretrained_model_name_or_path, subfolder="transformer",
|
|
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
|
|
)
|
|
|
|
# Freeze vae and text_encoder and set transformer3d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer3d.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:
|
|
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:
|
|
models[0].save_pretrained(os.path.join(output_dir, "transformer"))
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
for i in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
load_model = Transformer3DModel.from_pretrained_2d(
|
|
input_dir, subfolder="transformer",
|
|
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
|
|
)
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
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 = auto_scale_lr(
|
|
args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes,
|
|
args.learning_rate,
|
|
'sqrt'
|
|
)
|
|
|
|
# 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
|
|
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)
|
|
|
|
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,
|
|
)
|
|
|
|
if args.train_data_format == "normal":
|
|
# Get the training dataset
|
|
train_dataset = VideoDataset(
|
|
args.train_data_meta, args.train_data_dir,
|
|
sample_size=args.sample_size, sample_stride=args.sample_stride, sample_n_frames=args.sample_n_frames,
|
|
enable_bucket=args.enable_bucket, enable_inpaint=True if args.train_mode != "normal" else False,
|
|
)
|
|
else:
|
|
# Get the training dataset
|
|
train_dataset = WebVid10M(
|
|
args.train_data_meta, args.train_data_dir,
|
|
sample_size=args.sample_size, sample_stride=args.sample_stride, sample_n_frames=args.sample_n_frames,
|
|
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.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.sample_size for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
batch_sampler = AspectRatioBatchSampler(
|
|
sampler=RandomSampler(train_dataset), dataset=train_dataset.dataset, train_data_format=args.train_data_format,
|
|
batch_size=args.train_batch_size, video_folder = args.train_data_dir, drop_last=True,
|
|
aspect_ratios=aspect_ratio_sample_size
|
|
)
|
|
def collate_fn(examples):
|
|
# Create new output
|
|
new_examples = {}
|
|
new_examples["pixel_values"] = []
|
|
new_examples["text"] = []
|
|
|
|
# Get ratio
|
|
pixel_value = examples[0]["pixel_values"]
|
|
f, h, w, c = np.shape(pixel_value)
|
|
closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size)
|
|
if args.random_ratio_crop:
|
|
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)
|
|
]
|
|
random_sample_size = [int(x / 16) * 16 for x in random_sample_size]
|
|
|
|
# For!
|
|
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.RandomHorizontalFlip(),
|
|
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.RandomHorizontalFlip(),
|
|
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"])
|
|
|
|
new_examples["pixel_values"] = torch.stack([example for example in new_examples["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:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
shuffle=True,
|
|
persistent_workers=True if args.dataloader_num_workers != 0 else False,
|
|
batch_size=args.train_batch_size,
|
|
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`.
|
|
transformer3d, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, text_encoder, network, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
text_encoder.to(accelerator.device)
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if accelerator.is_main_process:
|
|
tracker_config = dict(vars(args))
|
|
tracker_config.pop("validation_prompts")
|
|
tracker_config.pop("validation_images")
|
|
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:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
|
|
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,
|
|
)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
for step, batch in enumerate(train_dataloader):
|
|
# Data batch sanity check
|
|
if epoch == first_epoch and step == 0:
|
|
pixel_values, texts = batch['pixel_values'].cpu(), batch['text']
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True)
|
|
for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{'-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'}.gif", rescale=True)
|
|
if args.train_mode != "normal":
|
|
mask_pixel_values, texts = 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, (pixel_value, text) in enumerate(zip(mask_pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/mask_{'-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'}.gif", rescale=True)
|
|
|
|
with accelerator.accumulate(network):
|
|
# Convert images to latent space
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
if args.train_mode != "normal":
|
|
mask_pixel_values = batch["mask_pixel_values"].to(weight_dtype)
|
|
|
|
if args.random_frame_crop:
|
|
select_frames = [_tmp for _tmp in list(range(4, args.sample_n_frames + 4, 4))]
|
|
select_frames_prob = [_tmp ** 2 for _tmp in select_frames]
|
|
select_frames_prob = np.array(select_frames_prob) / sum(select_frames_prob)
|
|
select_frames_prob = np.array(select_frames) / sum(select_frames)
|
|
|
|
temp_n_frames = np.random.choice(select_frames, p = select_frames_prob)
|
|
pixel_values = pixel_values[:, :temp_n_frames, :, :]
|
|
|
|
video_length = pixel_values.shape[1]
|
|
with torch.no_grad():
|
|
# This way will be slow when batch grows up
|
|
# pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
|
|
# latents = vae.encode(pixel_values.to(dtype=weight_dtype)).latent_dist
|
|
# latents = latents.sample()
|
|
# latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
|
|
|
|
# Convert images to latent space
|
|
if vae.quant_conv.weight.ndim==5:
|
|
# This way is quicker when batch grows up
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
mini_batch = 21
|
|
new_pixel_values = []
|
|
for i in range(0, pixel_values.shape[2], mini_batch):
|
|
pixel_values_bs = pixel_values[:, :, i: i + mini_batch, :, :]
|
|
pixel_values_bs = vae.encode(pixel_values_bs)[0]
|
|
pixel_values_bs = pixel_values_bs.sample()
|
|
new_pixel_values.append(pixel_values_bs)
|
|
# if i == pixel_values.shape[2] - 1:
|
|
# break
|
|
# with torch.no_grad():
|
|
# pixel_values_bs = pixel_values[:, :, i: i + mini_batch + 1, :, :].to(dtype=weight_dtype)
|
|
# pixel_values_bs = vae.encode(pixel_values_bs)[0]
|
|
# pixel_values_bs = pixel_values_bs.sample()
|
|
# new_pixel_values.append(pixel_values_bs if i == 0 else pixel_values_bs[:, :, 1:, :, :])
|
|
latents = torch.cat(new_pixel_values, dim = 2)
|
|
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 * 0.18215
|
|
|
|
if args.train_mode != "normal":
|
|
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)
|
|
|
|
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]
|
|
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, args.train_sampling_steps, (bsz,), device=latents.device)
|
|
timesteps = timesteps.long()
|
|
|
|
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
|
|
loss_term = train_diffusion.training_losses(
|
|
transformer3d,
|
|
latents,
|
|
timesteps,
|
|
model_kwargs=dict(
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=prompt_ids.attention_mask.to(latents.device),
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
|
|
return_dict=False
|
|
)
|
|
)
|
|
loss = loss_term['loss'].mean()
|
|
|
|
# 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 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)
|
|
|
|
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}")
|
|
|
|
if accelerator.is_main_process:
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
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,
|
|
tokenizer,
|
|
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() |