mirror of
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1371 lines
57 KiB
Python
1371 lines
57 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2025 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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# limitations under the License.
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"""Fine-tuning script for Stable Diffusion XL for text2image."""
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import argparse
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import functools
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import gc
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import logging
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import math
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import os
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import random
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import shutil
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from contextlib import nullcontext
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from pathlib import Path
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import accelerate
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import datasets
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import DistributedType, ProjectConfiguration, set_seed
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from datasets import concatenate_datasets, load_dataset
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from huggingface_hub import create_repo, upload_folder
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from packaging import version
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from torchvision import transforms
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from torchvision.transforms.functional import crop
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from tqdm.auto import tqdm
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from transformers import AutoTokenizer, PretrainedConfig
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import diffusers
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from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import EMAModel, compute_snr
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from diffusers.utils import check_min_version, is_wandb_available
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from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
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from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available
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from diffusers.utils.torch_utils import is_compiled_module
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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("0.36.0.dev0")
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logger = get_logger(__name__)
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if is_torch_npu_available():
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import torch_npu
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torch.npu.config.allow_internal_format = False
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DATASET_NAME_MAPPING = {
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"lambdalabs/naruto-blip-captions": ("image", "text"),
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}
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def save_model_card(
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repo_id: str,
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images: list = None,
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validation_prompt: str = None,
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base_model: str = None,
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dataset_name: str = None,
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repo_folder: str = None,
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vae_path: str = None,
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):
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img_str = ""
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if images is not None:
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for i, image in enumerate(images):
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image.save(os.path.join(repo_folder, f"image_{i}.png"))
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img_str += f"\n"
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model_description = f"""
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# Text-to-image finetuning - {repo_id}
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This pipeline was finetuned from **{base_model}** on the **{dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: {validation_prompt}: \n
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{img_str}
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Special VAE used for training: {vae_path}.
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"""
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model_card = load_or_create_model_card(
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repo_id_or_path=repo_id,
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from_training=True,
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license="creativeml-openrail-m",
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base_model=base_model,
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model_description=model_description,
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inference=True,
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)
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tags = [
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"stable-diffusion-xl",
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"stable-diffusion-xl-diffusers",
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"text-to-image",
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"diffusers-training",
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"diffusers",
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]
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model_card = populate_model_card(model_card, tags=tags)
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model_card.save(os.path.join(repo_folder, "README.md"))
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def import_model_class_from_model_name_or_path(
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pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
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):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path, subfolder=subfolder, revision=revision
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)
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model_class = text_encoder_config.architectures[0]
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if model_class == "CLIPTextModel":
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from transformers import CLIPTextModel
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return CLIPTextModel
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elif model_class == "CLIPTextModelWithProjection":
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from transformers import CLIPTextModelWithProjection
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return CLIPTextModelWithProjection
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else:
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raise ValueError(f"{model_class} is not supported.")
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def parse_args(input_args=None):
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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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"--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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"--pretrained_vae_model_name_or_path",
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type=str,
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default=None,
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help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that 🤗 Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. Folder contents must follow the structure described in"
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" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
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" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
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),
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)
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parser.add_argument(
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"--image_column", type=str, default="image", help="The column of the dataset containing an image."
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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)
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parser.add_argument(
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"--validation_prompt",
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type=str,
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default=None,
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help="A prompt that is used during validation to verify that the model is learning.",
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)
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parser.add_argument(
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"--num_validation_images",
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type=int,
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default=4,
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help="Number of images that should be generated during validation with `validation_prompt`.",
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)
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parser.add_argument(
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"--validation_epochs",
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type=int,
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default=1,
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help=(
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"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
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" `args.validation_prompt` multiple times: `args.num_validation_images`."
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--proportion_empty_prompts",
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type=float,
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default=0,
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help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="sdxl-model-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--resolution",
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type=int,
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default=1024,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--center_crop",
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default=False,
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action="store_true",
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help=(
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"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
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" cropped. The images will be resized to the resolution first before cropping."
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),
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)
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--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 can be used both as final"
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" checkpoints in case they are better than the last checkpoint, and are also 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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"--checkpoints_total_limit",
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type=int,
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default=None,
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help=("Max number of checkpoints to store."),
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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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"--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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"--timestep_bias_strategy",
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type=str,
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default="none",
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choices=["earlier", "later", "range", "none"],
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help=(
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"The timestep bias strategy, which may help direct the model toward learning low or high frequency details."
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" Choices: ['earlier', 'later', 'range', 'none']."
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" The default is 'none', which means no bias is applied, and training proceeds normally."
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" The value of 'later' will increase the frequency of the model's final training timesteps."
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),
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)
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parser.add_argument(
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"--timestep_bias_multiplier",
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type=float,
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default=1.0,
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help=(
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"The multiplier for the bias. Defaults to 1.0, which means no bias is applied."
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" A value of 2.0 will double the weight of the bias, and a value of 0.5 will halve it."
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),
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)
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parser.add_argument(
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"--timestep_bias_begin",
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type=int,
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default=0,
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help=(
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"When using `--timestep_bias_strategy=range`, the beginning (inclusive) timestep to bias."
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" Defaults to zero, which equates to having no specific bias."
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),
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)
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parser.add_argument(
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"--timestep_bias_end",
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type=int,
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default=1000,
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help=(
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"When using `--timestep_bias_strategy=range`, the final timestep (inclusive) to bias."
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" Defaults to 1000, which is the number of timesteps that Stable Diffusion is trained on."
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),
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)
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parser.add_argument(
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"--timestep_bias_portion",
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type=float,
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default=0.25,
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help=(
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"The portion of timesteps to bias. Defaults to 0.25, which 25% of timesteps will be biased."
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" A value of 0.5 will bias one half of the timesteps. The value provided for `--timestep_bias_strategy` determines"
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" whether the biased portions are in the earlier or later timesteps."
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),
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)
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parser.add_argument(
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"--snr_gamma",
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type=float,
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default=None,
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help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
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"More details here: https://huggingface.co/papers/2303.09556.",
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)
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parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
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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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"--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(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--prediction_type",
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type=str,
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default=None,
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help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.",
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)
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument(
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"--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(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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parser.add_argument(
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"--enable_npu_flash_attention", action="store_true", help="Whether or not to use npu flash attention."
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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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parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
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parser.add_argument(
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"--image_interpolation_mode",
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type=str,
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default="lanczos",
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choices=[
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f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
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],
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help="The image interpolation method to use for resizing images.",
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)
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if input_args is not None:
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args = parser.parse_args(input_args)
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else:
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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:
|
|
args.local_rank = env_local_rank
|
|
|
|
# Sanity checks
|
|
if args.dataset_name is None and args.train_data_dir is None:
|
|
raise ValueError("Need either a dataset name or a training folder.")
|
|
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
|
|
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
|
|
|
|
return args
|
|
|
|
|
|
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
|
|
def encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):
|
|
prompt_embeds_list = []
|
|
prompt_batch = batch[caption_column]
|
|
|
|
captions = []
|
|
for caption in prompt_batch:
|
|
if random.random() < proportion_empty_prompts:
|
|
captions.append("")
|
|
elif isinstance(caption, str):
|
|
captions.append(caption)
|
|
elif isinstance(caption, (list, np.ndarray)):
|
|
# take a random caption if there are multiple
|
|
captions.append(random.choice(caption) if is_train else caption[0])
|
|
|
|
with torch.no_grad():
|
|
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
|
|
text_inputs = tokenizer(
|
|
captions,
|
|
padding="max_length",
|
|
max_length=tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
prompt_embeds = text_encoder(
|
|
text_input_ids.to(text_encoder.device),
|
|
output_hidden_states=True,
|
|
return_dict=False,
|
|
)
|
|
|
|
# We are only ALWAYS interested in the pooled output of the final text encoder
|
|
pooled_prompt_embeds = prompt_embeds[0]
|
|
prompt_embeds = prompt_embeds[-1][-2]
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
|
|
prompt_embeds_list.append(prompt_embeds)
|
|
|
|
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
|
|
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
|
|
return {"prompt_embeds": prompt_embeds.cpu(), "pooled_prompt_embeds": pooled_prompt_embeds.cpu()}
|
|
|
|
|
|
def compute_vae_encodings(batch, vae):
|
|
images = batch.pop("pixel_values")
|
|
pixel_values = torch.stack(list(images))
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)
|
|
|
|
with torch.no_grad():
|
|
model_input = vae.encode(pixel_values).latent_dist.sample()
|
|
model_input = model_input * vae.config.scaling_factor
|
|
|
|
# There might have slightly performance improvement
|
|
# by changing model_input.cpu() to accelerator.gather(model_input)
|
|
return {"model_input": model_input.cpu()}
|
|
|
|
|
|
def generate_timestep_weights(args, num_timesteps):
|
|
weights = torch.ones(num_timesteps)
|
|
|
|
# Determine the indices to bias
|
|
num_to_bias = int(args.timestep_bias_portion * num_timesteps)
|
|
|
|
if args.timestep_bias_strategy == "later":
|
|
bias_indices = slice(-num_to_bias, None)
|
|
elif args.timestep_bias_strategy == "earlier":
|
|
bias_indices = slice(0, num_to_bias)
|
|
elif args.timestep_bias_strategy == "range":
|
|
# Out of the possible 1000 timesteps, we might want to focus on eg. 200-500.
|
|
range_begin = args.timestep_bias_begin
|
|
range_end = args.timestep_bias_end
|
|
if range_begin < 0:
|
|
raise ValueError(
|
|
"When using the range strategy for timestep bias, you must provide a beginning timestep greater or equal to zero."
|
|
)
|
|
if range_end > num_timesteps:
|
|
raise ValueError(
|
|
"When using the range strategy for timestep bias, you must provide an ending timestep smaller than the number of timesteps."
|
|
)
|
|
bias_indices = slice(range_begin, range_end)
|
|
else: # 'none' or any other string
|
|
return weights
|
|
if args.timestep_bias_multiplier <= 0:
|
|
return ValueError(
|
|
"The parameter --timestep_bias_multiplier is not intended to be used to disable the training of specific timesteps."
|
|
" If it was intended to disable timestep bias, use `--timestep_bias_strategy none` instead."
|
|
" A timestep bias multiplier less than or equal to 0 is not allowed."
|
|
)
|
|
|
|
# Apply the bias
|
|
weights[bias_indices] *= args.timestep_bias_multiplier
|
|
|
|
# Normalize
|
|
weights /= weights.sum()
|
|
|
|
return weights
|
|
|
|
|
|
def main(args):
|
|
if args.report_to == "wandb" and args.hub_token is not None:
|
|
raise ValueError(
|
|
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
|
|
" Please use `hf auth login` to authenticate with the Hub."
|
|
)
|
|
|
|
logging_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
|
|
|
|
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
|
|
# due to pytorch#99272, MPS does not yet support bfloat16.
|
|
raise ValueError(
|
|
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
|
|
)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
|
|
# Disable AMP for MPS.
|
|
if torch.backends.mps.is_available():
|
|
accelerator.native_amp = False
|
|
|
|
if args.report_to == "wandb":
|
|
if not is_wandb_available():
|
|
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
|
|
import wandb
|
|
|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
logger.info(accelerator.state, main_process_only=False)
|
|
if accelerator.is_local_main_process:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
diffusers.utils.logging.set_verbosity_error()
|
|
|
|
# If passed along, set the training seed now.
|
|
if args.seed is not None:
|
|
set_seed(args.seed)
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
if args.push_to_hub:
|
|
repo_id = create_repo(
|
|
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
|
|
).repo_id
|
|
|
|
# Load the tokenizers
|
|
tokenizer_one = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
revision=args.revision,
|
|
use_fast=False,
|
|
)
|
|
tokenizer_two = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer_2",
|
|
revision=args.revision,
|
|
use_fast=False,
|
|
)
|
|
|
|
# import correct text encoder classes
|
|
text_encoder_cls_one = import_model_class_from_model_name_or_path(
|
|
args.pretrained_model_name_or_path, args.revision
|
|
)
|
|
text_encoder_cls_two = import_model_class_from_model_name_or_path(
|
|
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
|
|
)
|
|
|
|
# Load scheduler and models
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
# Check for terminal SNR in combination with SNR Gamma
|
|
text_encoder_one = text_encoder_cls_one.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
|
|
)
|
|
text_encoder_two = text_encoder_cls_two.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
|
|
)
|
|
vae_path = (
|
|
args.pretrained_model_name_or_path
|
|
if args.pretrained_vae_model_name_or_path is None
|
|
else args.pretrained_vae_model_name_or_path
|
|
)
|
|
vae = AutoencoderKL.from_pretrained(
|
|
vae_path,
|
|
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
|
|
)
|
|
|
|
# Freeze vae and text encoders.
|
|
vae.requires_grad_(False)
|
|
text_encoder_one.requires_grad_(False)
|
|
text_encoder_two.requires_grad_(False)
|
|
# Set unet as trainable.
|
|
unet.train()
|
|
|
|
# For mixed precision training we cast all non-trainable weights to half-precision
|
|
# as these weights are only used for inference, keeping weights in full precision is not required.
|
|
weight_dtype = torch.float32
|
|
if accelerator.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Move unet, vae and text_encoder to device and cast to weight_dtype
|
|
# The VAE is in float32 to avoid NaN losses.
|
|
vae.to(accelerator.device, dtype=torch.float32)
|
|
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
|
|
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# Create EMA for the unet.
|
|
if args.use_ema:
|
|
ema_unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
|
|
)
|
|
ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
|
|
if args.enable_npu_flash_attention:
|
|
if is_torch_npu_available():
|
|
logger.info("npu flash attention enabled.")
|
|
unet.enable_npu_flash_attention()
|
|
else:
|
|
raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu devices.")
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warning(
|
|
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
|
|
)
|
|
unet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
if args.use_ema:
|
|
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
|
|
|
|
for i, model in enumerate(models):
|
|
model.save_pretrained(os.path.join(output_dir, "unet"))
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
if weights:
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
if args.use_ema:
|
|
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
|
|
ema_unet.load_state_dict(load_model.state_dict())
|
|
ema_unet.to(accelerator.device)
|
|
del load_model
|
|
|
|
for _ in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.gradient_checkpointing:
|
|
unet.enable_gradient_checkpointing()
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError(
|
|
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
|
|
)
|
|
|
|
optimizer_class = bnb.optim.AdamW8bit
|
|
else:
|
|
optimizer_class = torch.optim.AdamW
|
|
|
|
# Optimizer creation
|
|
params_to_optimize = unet.parameters()
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Get the datasets: you can either provide your own training and evaluation files (see below)
|
|
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
|
|
|
|
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
|
# download the dataset.
|
|
if args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
dataset = load_dataset(
|
|
args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir
|
|
)
|
|
else:
|
|
data_files = {}
|
|
if args.train_data_dir is not None:
|
|
data_files["train"] = os.path.join(args.train_data_dir, "**")
|
|
dataset = load_dataset(
|
|
"imagefolder",
|
|
data_files=data_files,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
# See more about loading custom images at
|
|
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize inputs and targets.
|
|
column_names = dataset["train"].column_names
|
|
|
|
# 6. Get the column names for input/target.
|
|
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
|
|
if args.image_column is None:
|
|
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
|
|
else:
|
|
image_column = args.image_column
|
|
if image_column not in column_names:
|
|
raise ValueError(
|
|
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
if args.caption_column is None:
|
|
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
|
|
else:
|
|
caption_column = args.caption_column
|
|
if caption_column not in column_names:
|
|
raise ValueError(
|
|
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
|
|
# Preprocessing the datasets.
|
|
interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
|
|
if interpolation is None:
|
|
raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
|
|
train_resize = transforms.Resize(args.resolution, interpolation=interpolation)
|
|
train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
|
|
train_flip = transforms.RandomHorizontalFlip(p=1.0)
|
|
train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
|
|
|
|
def preprocess_train(examples):
|
|
images = [image.convert("RGB") for image in examples[image_column]]
|
|
# image aug
|
|
original_sizes = []
|
|
all_images = []
|
|
crop_top_lefts = []
|
|
for image in images:
|
|
original_sizes.append((image.height, image.width))
|
|
image = train_resize(image)
|
|
if args.random_flip and random.random() < 0.5:
|
|
# flip
|
|
image = train_flip(image)
|
|
if args.center_crop:
|
|
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
|
|
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
|
|
image = train_crop(image)
|
|
else:
|
|
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
|
|
image = crop(image, y1, x1, h, w)
|
|
crop_top_left = (y1, x1)
|
|
crop_top_lefts.append(crop_top_left)
|
|
image = train_transforms(image)
|
|
all_images.append(image)
|
|
|
|
examples["original_sizes"] = original_sizes
|
|
examples["crop_top_lefts"] = crop_top_lefts
|
|
examples["pixel_values"] = all_images
|
|
return examples
|
|
|
|
with accelerator.main_process_first():
|
|
if args.max_train_samples is not None:
|
|
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
|
|
# Set the training transforms
|
|
train_dataset = dataset["train"].with_transform(preprocess_train)
|
|
|
|
# Let's first compute all the embeddings so that we can free up the text encoders
|
|
# from memory. We will pre-compute the VAE encodings too.
|
|
text_encoders = [text_encoder_one, text_encoder_two]
|
|
tokenizers = [tokenizer_one, tokenizer_two]
|
|
compute_embeddings_fn = functools.partial(
|
|
encode_prompt,
|
|
text_encoders=text_encoders,
|
|
tokenizers=tokenizers,
|
|
proportion_empty_prompts=args.proportion_empty_prompts,
|
|
caption_column=args.caption_column,
|
|
)
|
|
compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)
|
|
with accelerator.main_process_first():
|
|
from datasets.fingerprint import Hasher
|
|
|
|
# fingerprint used by the cache for the other processes to load the result
|
|
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
|
|
new_fingerprint = Hasher.hash(args)
|
|
new_fingerprint_for_vae = Hasher.hash((vae_path, args))
|
|
train_dataset_with_embeddings = train_dataset.map(
|
|
compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint
|
|
)
|
|
train_dataset_with_vae = train_dataset.map(
|
|
compute_vae_encodings_fn,
|
|
batched=True,
|
|
batch_size=args.train_batch_size,
|
|
new_fingerprint=new_fingerprint_for_vae,
|
|
)
|
|
precomputed_dataset = concatenate_datasets(
|
|
[train_dataset_with_embeddings, train_dataset_with_vae.remove_columns(["image", "text"])], axis=1
|
|
)
|
|
precomputed_dataset = precomputed_dataset.with_transform(preprocess_train)
|
|
|
|
del compute_vae_encodings_fn, compute_embeddings_fn, text_encoder_one, text_encoder_two
|
|
del text_encoders, tokenizers, vae
|
|
gc.collect()
|
|
if is_torch_npu_available():
|
|
torch_npu.npu.empty_cache()
|
|
elif torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
def collate_fn(examples):
|
|
model_input = torch.stack([torch.tensor(example["model_input"]) for example in examples])
|
|
original_sizes = [example["original_sizes"] for example in examples]
|
|
crop_top_lefts = [example["crop_top_lefts"] for example in examples]
|
|
prompt_embeds = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])
|
|
pooled_prompt_embeds = torch.stack([torch.tensor(example["pooled_prompt_embeds"]) for example in examples])
|
|
|
|
return {
|
|
"model_input": model_input,
|
|
"prompt_embeds": prompt_embeds,
|
|
"pooled_prompt_embeds": pooled_prompt_embeds,
|
|
"original_sizes": original_sizes,
|
|
"crop_top_lefts": crop_top_lefts,
|
|
}
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
precomputed_dataset,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
if args.use_ema:
|
|
ema_unet.to(accelerator.device)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if accelerator.is_main_process:
|
|
accelerator.init_trackers("text2image-fine-tune-sdxl", config=vars(args))
|
|
|
|
# Function for unwrapping if torch.compile() was used in accelerate.
|
|
def unwrap_model(model):
|
|
model = accelerator.unwrap_model(model)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
if torch.backends.mps.is_available() or "playground" in args.pretrained_model_name_or_path:
|
|
autocast_ctx = nullcontext()
|
|
else:
|
|
autocast_ctx = torch.autocast(accelerator.device.type)
|
|
|
|
# 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(precomputed_dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
global_step = 0
|
|
first_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint != "latest":
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = os.listdir(args.output_dir)
|
|
dirs = [d for d in dirs if d.startswith("checkpoint")]
|
|
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
|
|
path = dirs[-1] if len(dirs) > 0 else None
|
|
|
|
if path is None:
|
|
accelerator.print(
|
|
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
|
|
)
|
|
args.resume_from_checkpoint = None
|
|
initial_global_step = 0
|
|
else:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
|
|
else:
|
|
initial_global_step = 0
|
|
|
|
progress_bar = tqdm(
|
|
range(0, args.max_train_steps),
|
|
initial=initial_global_step,
|
|
desc="Steps",
|
|
# Only show the progress bar once on each machine.
|
|
disable=not accelerator.is_local_main_process,
|
|
)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet):
|
|
# Sample noise that we'll add to the latents
|
|
model_input = batch["model_input"].to(accelerator.device)
|
|
noise = torch.randn_like(model_input)
|
|
if args.noise_offset:
|
|
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
|
noise += args.noise_offset * torch.randn(
|
|
(model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device
|
|
)
|
|
|
|
bsz = model_input.shape[0]
|
|
if args.timestep_bias_strategy == "none":
|
|
# Sample a random timestep for each image without bias.
|
|
timesteps = torch.randint(
|
|
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
|
|
)
|
|
else:
|
|
# Sample a random timestep for each image, potentially biased by the timestep weights.
|
|
# Biasing the timestep weights allows us to spend less time training irrelevant timesteps.
|
|
weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(
|
|
model_input.device
|
|
)
|
|
timesteps = torch.multinomial(weights, bsz, replacement=True).long()
|
|
|
|
# Add noise to the model input according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps).to(dtype=weight_dtype)
|
|
|
|
# time ids
|
|
def compute_time_ids(original_size, crops_coords_top_left):
|
|
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
|
|
target_size = (args.resolution, args.resolution)
|
|
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
|
add_time_ids = torch.tensor([add_time_ids], device=accelerator.device, dtype=weight_dtype)
|
|
return add_time_ids
|
|
|
|
add_time_ids = torch.cat(
|
|
[compute_time_ids(s, c) for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])]
|
|
)
|
|
|
|
# Predict the noise residual
|
|
unet_added_conditions = {"time_ids": add_time_ids}
|
|
prompt_embeds = batch["prompt_embeds"].to(accelerator.device, dtype=weight_dtype)
|
|
pooled_prompt_embeds = batch["pooled_prompt_embeds"].to(accelerator.device)
|
|
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
|
|
model_pred = unet(
|
|
noisy_model_input,
|
|
timesteps,
|
|
prompt_embeds,
|
|
added_cond_kwargs=unet_added_conditions,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if args.prediction_type is not None:
|
|
# set prediction_type of scheduler if defined
|
|
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
|
|
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
|
|
elif noise_scheduler.config.prediction_type == "sample":
|
|
# We set the target to latents here, but the model_pred will return the noise sample prediction.
|
|
target = model_input
|
|
# We will have to subtract the noise residual from the prediction to get the target sample.
|
|
model_pred = model_pred - noise
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
if args.snr_gamma is None:
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
else:
|
|
# Compute loss-weights as per Section 3.4 of https://huggingface.co/papers/2303.09556.
|
|
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
|
|
# This is discussed in Section 4.2 of the same paper.
|
|
snr = compute_snr(noise_scheduler, timesteps)
|
|
mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(
|
|
dim=1
|
|
)[0]
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
mse_loss_weights = mse_loss_weights / snr
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
mse_loss_weights = mse_loss_weights / (snr + 1)
|
|
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
|
|
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
|
|
loss = loss.mean()
|
|
|
|
# Gather the losses across all processes for logging (if we use distributed training).
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
train_loss += avg_loss.item() / args.gradient_accumulation_steps
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = unet.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
if args.use_ema:
|
|
ema_unet.step(unet.parameters())
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss}, step=global_step)
|
|
train_loss = 0.0
|
|
|
|
# DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
|
|
if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
|
|
if global_step % args.checkpointing_steps == 0:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if args.checkpoints_total_limit is not None:
|
|
checkpoints = os.listdir(args.output_dir)
|
|
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
|
|
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
|
|
|
|
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
|
|
if len(checkpoints) >= args.checkpoints_total_limit:
|
|
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
|
|
removing_checkpoints = checkpoints[0:num_to_remove]
|
|
|
|
logger.info(
|
|
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
|
|
)
|
|
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
|
|
|
|
for removing_checkpoint in removing_checkpoints:
|
|
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
|
|
shutil.rmtree(removing_checkpoint)
|
|
|
|
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 = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
if accelerator.is_main_process:
|
|
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
|
|
logger.info(
|
|
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
|
|
f" {args.validation_prompt}."
|
|
)
|
|
if args.use_ema:
|
|
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
|
|
ema_unet.store(unet.parameters())
|
|
ema_unet.copy_to(unet.parameters())
|
|
|
|
# create pipeline
|
|
vae = AutoencoderKL.from_pretrained(
|
|
vae_path,
|
|
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
pipeline = StableDiffusionXLPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
vae=vae,
|
|
unet=accelerator.unwrap_model(unet),
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
if args.prediction_type is not None:
|
|
scheduler_args = {"prediction_type": args.prediction_type}
|
|
pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
|
|
|
|
pipeline = pipeline.to(accelerator.device)
|
|
pipeline.set_progress_bar_config(disable=True)
|
|
|
|
# run inference
|
|
generator = (
|
|
torch.Generator(device=accelerator.device).manual_seed(args.seed)
|
|
if args.seed is not None
|
|
else None
|
|
)
|
|
pipeline_args = {"prompt": args.validation_prompt}
|
|
|
|
with autocast_ctx:
|
|
images = [
|
|
pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0]
|
|
for _ in range(args.num_validation_images)
|
|
]
|
|
|
|
for tracker in accelerator.trackers:
|
|
if tracker.name == "tensorboard":
|
|
np_images = np.stack([np.asarray(img) for img in images])
|
|
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
|
|
if tracker.name == "wandb":
|
|
tracker.log(
|
|
{
|
|
"validation": [
|
|
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
|
|
for i, image in enumerate(images)
|
|
]
|
|
}
|
|
)
|
|
|
|
del pipeline
|
|
if is_torch_npu_available():
|
|
torch_npu.npu.empty_cache()
|
|
elif torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
if args.use_ema:
|
|
# Switch back to the original UNet parameters.
|
|
ema_unet.restore(unet.parameters())
|
|
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
unet = unwrap_model(unet)
|
|
if args.use_ema:
|
|
ema_unet.copy_to(unet.parameters())
|
|
|
|
# Serialize pipeline.
|
|
vae = AutoencoderKL.from_pretrained(
|
|
vae_path,
|
|
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
pipeline = StableDiffusionXLPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
unet=unet,
|
|
vae=vae,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
if args.prediction_type is not None:
|
|
scheduler_args = {"prediction_type": args.prediction_type}
|
|
pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
|
|
pipeline.save_pretrained(args.output_dir)
|
|
|
|
# run inference
|
|
images = []
|
|
if args.validation_prompt and args.num_validation_images > 0:
|
|
pipeline = pipeline.to(accelerator.device)
|
|
generator = (
|
|
torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
|
|
)
|
|
|
|
with autocast_ctx:
|
|
images = [
|
|
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
|
|
for _ in range(args.num_validation_images)
|
|
]
|
|
|
|
for tracker in accelerator.trackers:
|
|
if tracker.name == "tensorboard":
|
|
np_images = np.stack([np.asarray(img) for img in images])
|
|
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
|
|
if tracker.name == "wandb":
|
|
tracker.log(
|
|
{
|
|
"test": [
|
|
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
|
|
for i, image in enumerate(images)
|
|
]
|
|
}
|
|
)
|
|
|
|
if args.push_to_hub:
|
|
save_model_card(
|
|
repo_id=repo_id,
|
|
images=images,
|
|
validation_prompt=args.validation_prompt,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
dataset_name=args.dataset_name,
|
|
repo_folder=args.output_dir,
|
|
vae_path=args.pretrained_vae_model_name_or_path,
|
|
)
|
|
upload_folder(
|
|
repo_id=repo_id,
|
|
folder_path=args.output_dir,
|
|
commit_message="End of training",
|
|
ignore_patterns=["step_*", "epoch_*"],
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)
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accelerator.end_training()
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|
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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