773 lines
30 KiB
Python
773 lines
30 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2022 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 logging
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import math
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import os
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import random
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from pathlib import Path
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from typing import Optional
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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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from torch.utils.data import Dataset
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import datasets
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import diffusers
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import PIL
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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.utils import set_seed
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from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
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from diffusers.optimization import get_scheduler
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from diffusers.utils import check_min_version
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from diffusers.utils.import_utils import is_xformers_available
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from huggingface_hub import HfFolder, Repository, create_repo, whoami
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# TODO: remove and import from diffusers.utils when the new version of diffusers is released
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from packaging import version
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from PIL import Image
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
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PIL_INTERPOLATION = {
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"linear": PIL.Image.Resampling.BILINEAR,
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"bilinear": PIL.Image.Resampling.BILINEAR,
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"bicubic": PIL.Image.Resampling.BICUBIC,
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"lanczos": PIL.Image.Resampling.LANCZOS,
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"nearest": PIL.Image.Resampling.NEAREST,
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}
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else:
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PIL_INTERPOLATION = {
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"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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"nearest": PIL.Image.NEAREST,
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}
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# ------------------------------------------------------------------------------
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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("0.10.0.dev0")
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logger = get_logger(__name__)
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def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
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logger.info("Saving embeddings")
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learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
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learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
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torch.save(learned_embeds_dict, save_path)
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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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"--save_steps",
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type=int,
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default=500,
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help="Save learned_embeds.bin every X updates steps.",
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)
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parser.add_argument(
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"--only_save_embeds",
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action="store_true",
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default=False,
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help="Save only the embeddings for the new concept.",
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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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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
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)
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parser.add_argument(
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"--placeholder_token",
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type=str,
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default=None,
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required=True,
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help="A token to use as a placeholder for the concept.",
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)
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parser.add_argument(
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"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
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)
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parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
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parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
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parser.add_argument(
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"--output_dir",
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type=str,
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default="text-inversion-model",
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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("--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", action="store_true", help="Whether to center crop images before resizing to resolution"
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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=5000,
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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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"--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("--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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"--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="no",
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU."
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),
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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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"--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=(
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"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(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help=(
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
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' `--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(
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"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
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)
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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if args.train_data_dir is None:
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raise ValueError("You must specify a train data directory.")
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return args
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imagenet_templates_small = [
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"a photo of a {}",
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"a rendering of a {}",
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"a cropped photo of the {}",
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"the photo of a {}",
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"a photo of a clean {}",
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"a photo of a dirty {}",
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"a dark photo of the {}",
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"a photo of my {}",
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"a photo of the cool {}",
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"a close-up photo of a {}",
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"a bright photo of the {}",
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"a cropped photo of a {}",
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"a photo of the {}",
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"a good photo of the {}",
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"a photo of one {}",
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"a close-up photo of the {}",
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"a rendition of the {}",
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"a photo of the clean {}",
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"a rendition of a {}",
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"a photo of a nice {}",
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"a good photo of a {}",
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"a photo of the nice {}",
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"a photo of the small {}",
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"a photo of the weird {}",
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"a photo of the large {}",
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"a photo of a cool {}",
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"a photo of a small {}",
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]
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imagenet_style_templates_small = [
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"a painting in the style of {}",
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"a rendering in the style of {}",
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"a cropped painting in the style of {}",
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"the painting in the style of {}",
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"a clean painting in the style of {}",
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"a dirty painting in the style of {}",
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"a dark painting in the style of {}",
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"a picture in the style of {}",
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"a cool painting in the style of {}",
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"a close-up painting in the style of {}",
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"a bright painting in the style of {}",
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"a cropped painting in the style of {}",
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"a good painting in the style of {}",
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"a close-up painting in the style of {}",
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"a rendition in the style of {}",
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"a nice painting in the style of {}",
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"a small painting in the style of {}",
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"a weird painting in the style of {}",
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"a large painting in the style of {}",
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]
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class TextualInversionDataset(Dataset):
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def __init__(
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self,
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data_root,
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tokenizer,
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learnable_property="object", # [object, style]
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size=512,
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repeats=100,
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interpolation="bicubic",
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flip_p=0.5,
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set="train",
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placeholder_token="*",
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center_crop=False,
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):
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self.data_root = data_root
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self.tokenizer = tokenizer
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self.learnable_property = learnable_property
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self.size = size
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self.placeholder_token = placeholder_token
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self.center_crop = center_crop
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self.flip_p = flip_p
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self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
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self.num_images = len(self.image_paths)
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self._length = self.num_images
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if set == "train":
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self._length = self.num_images * repeats
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self.interpolation = {
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"linear": PIL_INTERPOLATION["linear"],
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"bilinear": PIL_INTERPOLATION["bilinear"],
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"bicubic": PIL_INTERPOLATION["bicubic"],
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"lanczos": PIL_INTERPOLATION["lanczos"],
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}[interpolation]
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self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
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self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
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def __len__(self):
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return self._length
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def __getitem__(self, i):
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example = {}
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image = Image.open(self.image_paths[i % self.num_images])
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if not image.mode == "RGB":
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image = image.convert("RGB")
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placeholder_string = self.placeholder_token
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text = random.choice(self.templates).format(placeholder_string)
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example["input_ids"] = self.tokenizer(
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text,
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padding="max_length",
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truncation=True,
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max_length=self.tokenizer.model_max_length,
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return_tensors="pt",
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).input_ids[0]
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# default to score-sde preprocessing
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img = np.array(image).astype(np.uint8)
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if self.center_crop:
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crop = min(img.shape[0], img.shape[1])
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(
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h,
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w,
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) = (
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img.shape[0],
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img.shape[1],
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)
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img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
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image = Image.fromarray(img)
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image = image.resize((self.size, self.size), resample=self.interpolation)
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image = self.flip_transform(image)
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image = np.array(image).astype(np.uint8)
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image = (image / 127.5 - 1.0).astype(np.float32)
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example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
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return example
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def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
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if token is None:
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token = HfFolder.get_token()
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if organization is None:
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username = whoami(token)["name"]
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return f"{username}/{model_id}"
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else:
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return f"{organization}/{model_id}"
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def main():
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args = parse_args()
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logging_dir = os.path.join(args.output_dir, args.logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with=args.report_to,
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logging_dir=logging_dir,
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)
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_warning()
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diffusers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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diffusers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.push_to_hub:
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if args.hub_model_id is None:
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repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
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else:
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repo_name = args.hub_model_id
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create_repo(repo_name, exist_ok=True, token=args.hub_token)
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repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
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with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# Load tokenizer
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if args.tokenizer_name:
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tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
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elif args.pretrained_model_name_or_path:
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tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
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# Load scheduler and models
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
text_encoder = CLIPTextModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
|
|
)
|
|
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
|
|
)
|
|
|
|
# Add the placeholder token in tokenizer
|
|
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
|
|
if num_added_tokens == 0:
|
|
raise ValueError(
|
|
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
|
|
" `placeholder_token` that is not already in the tokenizer."
|
|
)
|
|
|
|
# Convert the initializer_token, placeholder_token to ids
|
|
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
|
|
# Check if initializer_token is a single token or a sequence of tokens
|
|
if len(token_ids) > 1:
|
|
raise ValueError("The initializer token must be a single token.")
|
|
|
|
initializer_token_id = token_ids[0]
|
|
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
|
|
|
|
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
|
text_encoder.resize_token_embeddings(len(tokenizer))
|
|
|
|
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
|
token_embeds = text_encoder.get_input_embeddings().weight.data
|
|
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
|
|
|
|
# Freeze vae and unet
|
|
vae.requires_grad_(False)
|
|
unet.requires_grad_(False)
|
|
# Freeze all parameters except for the token embeddings in text encoder
|
|
text_encoder.text_model.encoder.requires_grad_(False)
|
|
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
|
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
|
|
|
if args.gradient_checkpointing:
|
|
# Keep unet in train mode if we are using gradient checkpointing to save memory.
|
|
# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
|
|
unet.train()
|
|
text_encoder.gradient_checkpointing_enable()
|
|
unet.enable_gradient_checkpointing()
|
|
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
unet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Initialize the optimizer
|
|
optimizer = torch.optim.AdamW(
|
|
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Dataset and DataLoaders creation:
|
|
train_dataset = TextualInversionDataset(
|
|
data_root=args.train_data_dir,
|
|
tokenizer=tokenizer,
|
|
size=args.resolution,
|
|
placeholder_token=args.placeholder_token,
|
|
repeats=args.repeats,
|
|
learnable_property=args.learnable_property,
|
|
center_crop=args.center_crop,
|
|
set="train",
|
|
)
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset, batch_size=args.train_batch_size, shuffle=True, 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 * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
text_encoder, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# For mixed precision training we cast the unet and vae weights to half-precision
|
|
# as these models 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
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Move vae and unet to device and cast to weight_dtype
|
|
unet.to(accelerator.device, dtype=weight_dtype)
|
|
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:
|
|
accelerator.init_trackers("textual_inversion", config=vars(args))
|
|
|
|
# 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
|
|
else:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
resume_global_step = global_step * args.gradient_accumulation_steps
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
|
|
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
progress_bar.set_description("Steps")
|
|
|
|
# keep original embeddings as reference
|
|
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
text_encoder.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
# Skip steps until we reach the resumed step
|
|
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
|
|
if step % args.gradient_accumulation_steps == 0:
|
|
progress_bar.update(1)
|
|
continue
|
|
|
|
with accelerator.accumulate(text_encoder):
|
|
# Convert images to latent space
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
|
|
latents = latents * 0.18215
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
|
timesteps = timesteps.long()
|
|
|
|
# Add noise to the latents according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
|
|
|
|
# Predict the noise residual
|
|
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
|
|
accelerator.backward(loss)
|
|
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Let's make sure we don't update any embedding weights besides the newly added token
|
|
index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
|
|
with torch.no_grad():
|
|
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
|
|
index_no_updates
|
|
] = orig_embeds_params[index_no_updates]
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
if global_step % args.save_steps == 0:
|
|
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
|
|
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
if accelerator.is_main_process:
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
accelerator.log(logs, step=global_step)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
# Create the pipeline using using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
if args.push_to_hub and args.only_save_embeds:
|
|
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
|
|
save_full_model = True
|
|
else:
|
|
save_full_model = not args.only_save_embeds
|
|
if save_full_model:
|
|
pipeline = StableDiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
text_encoder=accelerator.unwrap_model(text_encoder),
|
|
vae=vae,
|
|
unet=unet,
|
|
tokenizer=tokenizer,
|
|
)
|
|
pipeline.save_pretrained(args.output_dir)
|
|
# Save the newly trained embeddings
|
|
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
|
|
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
|
|
|
|
if args.push_to_hub:
|
|
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|