579 lines
22 KiB
Python
579 lines
22 KiB
Python
import argparse
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import itertools
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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 PIL
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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, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
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from diffusers.optimization import get_scheduler
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
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from huggingface_hub import HfFolder, Repository, whoami
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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 CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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logger = get_logger(__name__)
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def save_progress(text_encoder, placeholder_token_id, accelerator, args):
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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, os.path.join(args.output_dir, "learned_embeds.bin"))
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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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"--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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"--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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"--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=True,
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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("--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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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.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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}[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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h, w, = (
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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 freeze_params(params):
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for param in params:
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param.requires_grad = False
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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="tensorboard",
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logging_dir=logging_dir,
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)
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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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repo = Repository(args.output_dir, clone_from=repo_name)
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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 the tokenizer and add the placeholder token as a additional special token
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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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# Add the placeholder token in tokenizer
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num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
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if num_added_tokens == 0:
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raise ValueError(
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f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
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" `placeholder_token` that is not already in the tokenizer."
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)
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# Convert the initializer_token, placeholder_token to ids
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token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
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# Check if initializer_token is a single token or a sequence of tokens
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if len(token_ids) > 1:
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raise ValueError("The initializer token must be a single token.")
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initializer_token_id = token_ids[0]
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placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
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# Load models and create wrapper for stable diffusion
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text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
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# Resize the token embeddings as we are adding new special tokens to the tokenizer
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text_encoder.resize_token_embeddings(len(tokenizer))
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# Initialise the newly added placeholder token with the embeddings of the initializer token
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token_embeds = text_encoder.get_input_embeddings().weight.data
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token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
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# Freeze vae and unet
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freeze_params(vae.parameters())
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freeze_params(unet.parameters())
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# Freeze all parameters except for the token embeddings in text encoder
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params_to_freeze = itertools.chain(
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text_encoder.text_model.encoder.parameters(),
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text_encoder.text_model.final_layer_norm.parameters(),
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text_encoder.text_model.embeddings.position_embedding.parameters(),
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)
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freeze_params(params_to_freeze)
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if args.scale_lr:
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args.learning_rate = (
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args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
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)
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# Initialize the optimizer
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optimizer = torch.optim.AdamW(
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text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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# TODO (patil-suraj): load scheduler using args
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noise_scheduler = DDPMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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num_train_timesteps=1000,
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)
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train_dataset = TextualInversionDataset(
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data_root=args.train_data_dir,
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tokenizer=tokenizer,
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size=args.resolution,
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placeholder_token=args.placeholder_token,
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repeats=args.repeats,
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learnable_property=args.learnable_property,
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center_crop=args.center_crop,
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set="train",
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)
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train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
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# Scheduler and math around the number of training steps.
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overrode_max_train_steps = False
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
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if args.max_train_steps is None:
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args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
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overrode_max_train_steps = True
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lr_scheduler = get_scheduler(
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args.lr_scheduler,
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optimizer=optimizer,
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num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
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num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
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)
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text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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text_encoder, optimizer, train_dataloader, lr_scheduler
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)
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# Move vae and unet to device
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vae.to(accelerator.device)
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unet.to(accelerator.device)
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# Keep vae and unet in eval model as we don't train these
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vae.eval()
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unet.eval()
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# We need to recalculate our total training steps as the size of the training dataloader may have changed.
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
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if overrode_max_train_steps:
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args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
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# Afterwards we recalculate our number of training epochs
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args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
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# We need to initialize the trackers we use, and also store our configuration.
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# The trackers initializes automatically on the main process.
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if accelerator.is_main_process:
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accelerator.init_trackers("textual_inversion", config=vars(args))
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# Train!
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total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
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|
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logger.info("***** Running training *****")
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logger.info(f" Num examples = {len(train_dataset)}")
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logger.info(f" Num Epochs = {args.num_train_epochs}")
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logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
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logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
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logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
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logger.info(f" Total optimization steps = {args.max_train_steps}")
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# Only show the progress bar once on each machine.
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progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
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progress_bar.set_description("Steps")
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global_step = 0
|
|
|
|
for epoch in range(args.num_train_epochs):
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text_encoder.train()
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for step, batch in enumerate(train_dataloader):
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with accelerator.accumulate(text_encoder):
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# Convert images to latent space
|
|
latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
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|
latents = latents * 0.18215
|
|
|
|
# Sample noise that we'll add to the latents
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|
noise = torch.randn(latents.shape).to(latents.device)
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bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(
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|
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
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|
).long()
|
|
|
|
# Add noise to the latents according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
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|
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
|
|
|
# Predict the noise residual
|
|
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
|
|
|
loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
|
|
accelerator.backward(loss)
|
|
|
|
# Zero out the gradients for all token embeddings except the newly added
|
|
# embeddings for the concept, as we only want to optimize the concept embeddings
|
|
if accelerator.num_processes > 1:
|
|
grads = text_encoder.module.get_input_embeddings().weight.grad
|
|
else:
|
|
grads = text_encoder.get_input_embeddings().weight.grad
|
|
# Get the index for tokens that we want to zero the grads for
|
|
index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
|
|
grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
|
|
|
|
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
|
|
if global_step % args.save_steps == 0:
|
|
save_progress(text_encoder, placeholder_token_id, accelerator, args)
|
|
|
|
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
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
# Create the pipeline using using the trained modules and save it.
|
|
if accelerator.is_main_process:
|
|
pipeline = StableDiffusionPipeline(
|
|
text_encoder=accelerator.unwrap_model(text_encoder),
|
|
vae=vae,
|
|
unet=unet,
|
|
tokenizer=tokenizer,
|
|
scheduler=PNDMScheduler(
|
|
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
|
|
),
|
|
safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
|
|
feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
|
|
)
|
|
pipeline.save_pretrained(args.output_dir)
|
|
# Also save the newly trained embeddings
|
|
save_progress(text_encoder, placeholder_token_id, accelerator, args)
|
|
|
|
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()
|