1154 lines
48 KiB
Python
1154 lines
48 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 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.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 datasets.utils.info_utils import VerificationMode
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from diffusers import AutoencoderKL, PixArtAlphaPipeline
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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 omegaconf import OmegaConf
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from packaging import version
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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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from datasets import load_dataset
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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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AspectRatioBatchImageSampler,
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get_closest_ratio)
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from easyanimate.data.dataset_image import CC15M
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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.utils.IDDIM import IDDPM
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from easyanimate.utils.lora_utils import create_network, merge_lora, 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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DATASET_NAME_MAPPING = {
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"lambdalabs/pokemon-blip-captions": ("image", "text"),
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}
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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, transformer2d, network, args, accelerator, weight_dtype, global_step):
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try:
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logger.info("Running validation... ")
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transformer2d_val = Transformer2DModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="transformer"
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).to(weight_dtype)
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transformer2d_val.load_state_dict(accelerator.unwrap_model(transformer2d).state_dict())
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pipeline = PixArtAlphaPipeline.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=transformer2d_val,
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revision=args.revision,
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variant=args.variant,
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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.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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with torch.no_grad():
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with torch.autocast("cuda"):
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image = 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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height = args.resolution,
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width = args.resolution,
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).images[0]
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os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
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image.save(os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.jpg"))
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images.append(image)
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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np_images = np.stack([np.asarray(img) for img in images])
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tracker.writer.add_images("validation", np_images, global_step, dataformats="NHWC")
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elif tracker.name == "wandb":
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tracker.log(
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{
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"validation": [
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wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
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for i, image in enumerate(images)
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]
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}
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)
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else:
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logger.warn(f"image logging not implemented for {tracker.name}")
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del pipeline
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del transformer2d_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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"--dataset_name",
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type=str,
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default=None,
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that 🤗 Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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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="diffusers",
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help=(
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'The format of training data. Support `"diffusers"`'
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' (default), `"cc15m"`.'
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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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nargs="+",
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help=(
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"Image folders containing the training data. Folders contents must follow the structure described in"
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" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
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" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
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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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"--image_column", type=str, default="image", help="The column of the dataset containing an image."
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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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_steps` 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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"--resolution",
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type=int,
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default=512,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--center_crop",
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default=False,
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action="store_true",
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help=(
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"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
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" cropped. The images will be resized to the resolution first before cropping."
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),
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)
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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("--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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"--snr_gamma",
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type=float,
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default=None,
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help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
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"More details here: https://arxiv.org/abs/2303.09556.",
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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",
|
|
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"
|
|
" training using `--resume_from_checkpoint`."
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),
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)
|
|
parser.add_argument(
|
|
"--checkpoints_total_limit",
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|
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(
|
|
"--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"
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),
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)
|
|
|
|
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(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Run train_sampling_steps.",
|
|
)
|
|
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'
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
# 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 transformer2d) 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)
|
|
# pipeline = PixArtAlphaPipeline.from_pretrained(args.pretrained_model_name_or_path, low_cpu_mem_usage=False)
|
|
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)]
|
|
|
|
# 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.
|
|
transformer2d = Transformer2DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="transformer"
|
|
)
|
|
if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
|
|
Choosen_AutoencoderKL = AutoencoderKLMagvit
|
|
else:
|
|
Choosen_AutoencoderKL = AutoencoderKL
|
|
|
|
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
|
|
vae = Choosen_AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
|
|
# Freeze vae and text_encoder and set transformer2d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer2d.requires_grad_(False)
|
|
|
|
# Lora will work with this...
|
|
network = create_network(
|
|
1.0,
|
|
args.rank,
|
|
args.network_alpha,
|
|
text_encoder,
|
|
transformer2d,
|
|
neuron_dropout=None,
|
|
add_lora_in_attn_temporal=False,
|
|
)
|
|
network.apply_to(text_encoder, transformer2d, 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 = transformer2d.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."
|
|
)
|
|
transformer2d.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 = Transformer2DModel.from_pretrained(input_dir, subfolder="transformer")
|
|
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:
|
|
transformer2d.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,
|
|
)
|
|
|
|
# Get the datasets: you can either provide your own training and evaluation files (see below)
|
|
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
|
|
|
|
if args.train_data_format == "diffusers":
|
|
if args.enable_bucket:
|
|
raise ValueError("enable_bucket can't be used when train_data_format==diffusers")
|
|
|
|
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
|
# download the dataset.
|
|
data_files = {}
|
|
if args.train_data_dir is not None:
|
|
data_files["train"] = [os.path.join(_train_data_dir, "**") for _train_data_dir in args.train_data_dir]
|
|
dataset = load_dataset(
|
|
"imagefolder",
|
|
data_files=data_files,
|
|
cache_dir=args.cache_dir,
|
|
verification_mode=VerificationMode.NO_CHECKS
|
|
)
|
|
# See more about loading custom images at
|
|
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize inputs and targets.
|
|
column_names = dataset["train"].column_names
|
|
|
|
# 6. Get the column names for input/target.
|
|
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
|
|
if args.image_column is None:
|
|
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
|
|
else:
|
|
image_column = args.image_column
|
|
if image_column not in column_names:
|
|
raise ValueError(
|
|
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
if args.caption_column is None:
|
|
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
|
|
else:
|
|
caption_column = args.caption_column
|
|
if caption_column not in column_names:
|
|
raise ValueError(
|
|
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
|
|
# Preprocessing the datasets.
|
|
train_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
|
|
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
def preprocess_train(examples):
|
|
images = [image.convert("RGB") for image in examples[image_column]]
|
|
examples["pixel_values"] = [train_transforms(image) for image in images]
|
|
examples["text"] = [caption for caption in examples[caption_column]]
|
|
return examples
|
|
|
|
with accelerator.main_process_first():
|
|
if args.max_train_samples is not None:
|
|
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
|
|
# Set the training transforms
|
|
train_dataset = dataset["train"].with_transform(preprocess_train)
|
|
|
|
def collate_fn(examples):
|
|
pixel_values = torch.stack([example["pixel_values"] for example in examples])
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
input_ids = [example["text"] for example in examples]
|
|
return {"pixel_values": pixel_values, "text": input_ids}
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
else:
|
|
# Get the training dataset
|
|
train_dataset = CC15M(
|
|
args.train_data_meta,
|
|
args.train_data_dir[0],
|
|
resolution=args.resolution,
|
|
enable_bucket=args.enable_bucket
|
|
)
|
|
|
|
if args.enable_bucket:
|
|
aspect_ratio_sample_size = {key : [x / 512 * args.resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
batch_sampler = AspectRatioBatchImageSampler(
|
|
sampler=RandomSampler(train_dataset), dataset=train_dataset.dataset,
|
|
batch_size=args.train_batch_size, train_folder = args.train_data_dir[0], 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"]
|
|
h, w, c = np.shape(pixel_value)
|
|
closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size)
|
|
|
|
# For!
|
|
for example in examples:
|
|
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(2, 0, 1).unsqueeze(0).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)[0])
|
|
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`.
|
|
transformer2d, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer2d, text_encoder, network, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
transformer2d.to(accelerator.device, dtype=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("train_data_dir")
|
|
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):
|
|
with accelerator.accumulate(network):
|
|
# Convert images to latent space
|
|
if vae.quant_conv.weight.ndim==5:
|
|
pixel_values = batch["pixel_values"]
|
|
if pixel_values.ndim==4:
|
|
pixel_values = pixel_values.unsqueeze(2)
|
|
latents = vae.encode(pixel_values.to(weight_dtype)).latent_dist.sample()
|
|
latents = latents.permute(0, 2, 1, 3, 4).flatten(0, 1)
|
|
else:
|
|
latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample()
|
|
latents = latents * vae.config.scaling_factor
|
|
|
|
# Get the text embedding for conditioning
|
|
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}
|
|
if unwrap_model(transformer2d).config.sample_size == 128:
|
|
bs, height, width = batch["pixel_values"].size()[0], batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1]
|
|
resolution = torch.tensor([height, width]).repeat(bs, 1)
|
|
aspect_ratio = torch.tensor([float(height / width)]).repeat(bs, 1)
|
|
resolution = resolution.to(dtype=encoder_hidden_states.dtype, device=latents.device)
|
|
aspect_ratio = aspect_ratio.to(dtype=encoder_hidden_states.dtype, device=latents.device)
|
|
added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
|
|
|
|
loss_term = train_diffusion.training_losses(
|
|
transformer2d,
|
|
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,
|
|
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,
|
|
transformer2d,
|
|
network,
|
|
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
|
|
|
|
# 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() |