mirror of
https://github.com/deepseek-ai/DreamCraft3D
synced 2024-12-04 18:15:11 +00:00
1480 lines
60 KiB
Python
1480 lines
60 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import copy
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import gc
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import hashlib
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import itertools
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import logging
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import math
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import os
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import shutil
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import warnings
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from pathlib import Path
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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 ProjectConfiguration, set_seed
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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 PIL import Image
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from PIL.ImageOps import exif_transpose
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from torch.utils.data import Dataset
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from torchvision import transforms
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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 (
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AutoencoderKL,
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DDPMScheduler,
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DiffusionPipeline,
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DPMSolverMultistepScheduler,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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)
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from diffusers.loaders import (
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LoraLoaderMixin,
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text_encoder_lora_state_dict,
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)
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from diffusers.models.attention_processor import (
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AttnAddedKVProcessor,
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AttnAddedKVProcessor2_0,
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SlicedAttnAddedKVProcessor,
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)
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from diffusers.models.lora import LoRALinearLayer
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import unet_lora_state_dict
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from diffusers.utils import check_min_version, is_wandb_available
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from diffusers.utils.import_utils import is_xformers_available
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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("0.23.0.dev0")
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logger = get_logger(__name__)
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def save_model_card(
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repo_id: str,
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images=None,
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base_model=str,
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train_text_encoder=False,
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prompt=str,
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repo_folder=None,
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pipeline: DiffusionPipeline = None,
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):
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img_str = ""
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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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yaml = f"""
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---
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license: creativeml-openrail-m
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base_model: {base_model}
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instance_prompt: {prompt}
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tags:
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- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
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- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
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- text-to-image
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- diffusers
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- lora
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inference: true
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---
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"""
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model_card = f"""
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# LoRA DreamBooth - {repo_id}
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These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
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{img_str}
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LoRA for the text encoder was enabled: {train_text_encoder}.
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"""
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with open(os.path.join(repo_folder, "README.md"), "w") as f:
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f.write(yaml + model_card)
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def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path,
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subfolder="text_encoder",
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revision=revision,
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local_files_only=True,
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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 == "RobertaSeriesModelWithTransformation":
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from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
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return RobertaSeriesModelWithTransformation
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elif model_class == "T5EncoderModel":
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from transformers import T5EncoderModel
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return T5EncoderModel
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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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"--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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"--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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"--instance_data_dir",
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type=str,
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default=None,
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required=True,
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help="A folder containing the training data of instance images.",
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)
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parser.add_argument(
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"--class_data_dir",
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type=str,
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default=None,
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required=False,
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help="A folder containing the training data of class images.",
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)
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parser.add_argument(
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"--instance_prompt",
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type=str,
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default=None,
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required=True,
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help="The prompt with identifier specifying the instance",
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)
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parser.add_argument(
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"--class_prompt",
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type=str,
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default=None,
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help="The prompt to specify images in the same class as provided instance images.",
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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=50,
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help=(
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"Run dreambooth validation every X epochs. Dreambooth validation 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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"--with_prior_preservation",
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default=False,
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action="store_true",
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help="Flag to add prior preservation loss.",
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)
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parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
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parser.add_argument(
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"--num_class_images",
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type=int,
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default=100,
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help=(
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"Minimal class images for prior preservation loss. If there are not enough images already present in"
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" class_data_dir, additional images will be sampled with class_prompt."
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),
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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="lora-dreambooth-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",
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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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"--train_text_encoder",
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action="store_true",
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help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
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)
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parser.add_argument("--num_train_epochs", type=int, default=1)
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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=5e-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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"--lr_num_cycles",
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type=int,
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default=1,
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help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
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)
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parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
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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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"--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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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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),
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)
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument(
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"--prior_generation_precision",
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type=str,
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default=None,
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choices=["no", "fp32", "fp16", "bf16"],
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help=(
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"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
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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_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(
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"--pre_compute_text_embeddings",
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action="store_true",
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help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
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)
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parser.add_argument(
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"--tokenizer_max_length",
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type=int,
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default=None,
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required=False,
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help="The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.",
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)
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parser.add_argument(
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"--text_encoder_use_attention_mask",
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action="store_true",
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required=False,
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help="Whether to use attention mask for the text encoder",
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)
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parser.add_argument(
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"--validation_images",
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required=False,
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default=None,
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nargs="+",
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help="Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.",
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)
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parser.add_argument(
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"--class_labels_conditioning",
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required=False,
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default=None,
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help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
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)
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|
parser.add_argument(
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"--rank",
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type=int,
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default=4,
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help=("The dimension of the LoRA update matrices."),
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)
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|
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parser.add_argument(
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"--use_view_dependent_prompt",
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action="store_true",
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help="Whether to use view-dependent prompt.",
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)
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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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|
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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.with_prior_preservation:
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if args.class_data_dir is None:
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raise ValueError("You must specify a data directory for class images.")
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if args.class_prompt is None:
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raise ValueError("You must specify prompt for class images.")
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else:
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# logger is not available yet
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if args.class_data_dir is not None:
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warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
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if args.class_prompt is not None:
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warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
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if args.train_text_encoder and args.pre_compute_text_embeddings:
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raise ValueError("`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`")
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return args
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|
|
|
|
class DreamBoothDataset(Dataset):
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"""
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A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
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It pre-processes the images and the tokenizes prompts.
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"""
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|
|
|
def __init__(
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|
self,
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instance_data_root,
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instance_prompt,
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|
tokenizer,
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|
class_data_root=None,
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class_prompt=None,
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|
class_num=None,
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size=512,
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center_crop=False,
|
|
encoder_hidden_states=None,
|
|
class_prompt_encoder_hidden_states=None,
|
|
tokenizer_max_length=None,
|
|
use_view_dependent_prompt=False,
|
|
):
|
|
self.size = size
|
|
self.center_crop = center_crop
|
|
self.tokenizer = tokenizer
|
|
self.encoder_hidden_states = encoder_hidden_states
|
|
self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states
|
|
self.tokenizer_max_length = tokenizer_max_length
|
|
self.use_view_dependent_prompt = use_view_dependent_prompt
|
|
|
|
self.instance_data_root = Path(instance_data_root)
|
|
if not self.instance_data_root.exists():
|
|
raise ValueError("Instance images root doesn't exists.")
|
|
|
|
Image.init()
|
|
|
|
self.instance_images_path = list(Path(instance_data_root).iterdir())
|
|
self.instance_images_path = [p for p in self.instance_images_path if self._file_ext(p) in Image.EXTENSION]
|
|
print("images:", self.instance_images_path)
|
|
self.num_instance_images = len(self.instance_images_path)
|
|
self.instance_prompt = instance_prompt
|
|
self._length = self.num_instance_images
|
|
|
|
if class_data_root is not None:
|
|
self.class_data_root = Path(class_data_root)
|
|
self.class_data_root.mkdir(parents=True, exist_ok=True)
|
|
self.class_images_path = list(self.class_data_root.iterdir())
|
|
if class_num is not None:
|
|
self.num_class_images = min(len(self.class_images_path), class_num)
|
|
else:
|
|
self.num_class_images = len(self.class_images_path)
|
|
self._length = max(self.num_class_images, self.num_instance_images)
|
|
self.class_prompt = class_prompt
|
|
else:
|
|
self.class_data_root = None
|
|
|
|
self.image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
def __len__(self):
|
|
return self._length
|
|
|
|
@staticmethod
|
|
def _file_ext(fname):
|
|
return os.path.splitext(fname)[1].lower()
|
|
|
|
def __getitem__(self, index):
|
|
example = {}
|
|
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
|
|
instance_image = exif_transpose(instance_image)
|
|
|
|
if self.use_view_dependent_prompt:
|
|
angle = float(os.path.basename(self.instance_images_path[index % self.num_instance_images])[4:-4])
|
|
if angle < 45 or angle >= 315:
|
|
view = "front view"
|
|
elif 45 <= angle < 135 or 225 <= angle < 315:
|
|
view = "side view"
|
|
else:
|
|
view = "back view"
|
|
|
|
if not instance_image.mode == "RGB":
|
|
instance_image = instance_image.convert("RGB")
|
|
example["instance_images"] = self.image_transforms(instance_image)
|
|
|
|
if self.encoder_hidden_states is not None:
|
|
example["instance_prompt_ids"] = self.encoder_hidden_states
|
|
else:
|
|
# view-depnedent prompt
|
|
if self.use_view_dependent_prompt:
|
|
instance_prompt = self.instance_prompt + f", {view}"
|
|
else:
|
|
instance_prompt = self.instance_prompt
|
|
text_inputs = tokenize_prompt(
|
|
self.tokenizer, instance_prompt, tokenizer_max_length=self.tokenizer_max_length
|
|
)
|
|
example["instance_prompt_ids"] = text_inputs.input_ids
|
|
example["instance_attention_mask"] = text_inputs.attention_mask
|
|
|
|
if self.class_data_root:
|
|
class_image = Image.open(self.class_images_path[index % self.num_class_images])
|
|
class_image = exif_transpose(class_image)
|
|
|
|
if not class_image.mode == "RGB":
|
|
class_image = class_image.convert("RGB")
|
|
example["class_images"] = self.image_transforms(class_image)
|
|
|
|
if self.class_prompt_encoder_hidden_states is not None:
|
|
example["class_prompt_ids"] = self.class_prompt_encoder_hidden_states
|
|
else:
|
|
class_text_inputs = tokenize_prompt(
|
|
self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length
|
|
)
|
|
example["class_prompt_ids"] = class_text_inputs.input_ids
|
|
example["class_attention_mask"] = class_text_inputs.attention_mask
|
|
|
|
return example
|
|
|
|
|
|
def collate_fn(examples, with_prior_preservation=False):
|
|
has_attention_mask = "instance_attention_mask" in examples[0]
|
|
|
|
input_ids = [example["instance_prompt_ids"] for example in examples]
|
|
pixel_values = [example["instance_images"] for example in examples]
|
|
|
|
if has_attention_mask:
|
|
attention_mask = [example["instance_attention_mask"] for example in examples]
|
|
|
|
# Concat class and instance examples for prior preservation.
|
|
# We do this to avoid doing two forward passes.
|
|
if with_prior_preservation:
|
|
input_ids += [example["class_prompt_ids"] for example in examples]
|
|
pixel_values += [example["class_images"] for example in examples]
|
|
if has_attention_mask:
|
|
attention_mask += [example["class_attention_mask"] for example in examples]
|
|
|
|
pixel_values = torch.stack(pixel_values)
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
input_ids = torch.cat(input_ids, dim=0)
|
|
|
|
batch = {
|
|
"input_ids": input_ids,
|
|
"pixel_values": pixel_values,
|
|
}
|
|
|
|
if has_attention_mask:
|
|
batch["attention_mask"] = attention_mask
|
|
|
|
return batch
|
|
|
|
|
|
class PromptDataset(Dataset):
|
|
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
|
|
|
|
def __init__(self, prompt, num_samples):
|
|
self.prompt = prompt
|
|
self.num_samples = num_samples
|
|
|
|
def __len__(self):
|
|
return self.num_samples
|
|
|
|
def __getitem__(self, index):
|
|
example = {}
|
|
example["prompt"] = self.prompt
|
|
example["index"] = index
|
|
return example
|
|
|
|
|
|
def tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):
|
|
if tokenizer_max_length is not None:
|
|
max_length = tokenizer_max_length
|
|
else:
|
|
max_length = tokenizer.model_max_length
|
|
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
truncation=True,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
return text_inputs
|
|
|
|
|
|
def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):
|
|
text_input_ids = input_ids.to(text_encoder.device)
|
|
|
|
if text_encoder_use_attention_mask:
|
|
attention_mask = attention_mask.to(text_encoder.device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
prompt_embeds = text_encoder(
|
|
text_input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
|
|
return prompt_embeds
|
|
|
|
|
|
def main(args):
|
|
logging_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
|
|
if 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
|
|
|
|
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
|
|
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
|
|
# TODO (sayakpaul): Remove this check when gradient accumulation with two models is enabled in accelerate.
|
|
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
|
|
raise ValueError(
|
|
"Gradient accumulation is not supported when training the text encoder in distributed training. "
|
|
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
|
|
)
|
|
|
|
# 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:
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
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)
|
|
|
|
# Generate class images if prior preservation is enabled.
|
|
if args.with_prior_preservation:
|
|
class_images_dir = Path(args.class_data_dir)
|
|
if not class_images_dir.exists():
|
|
class_images_dir.mkdir(parents=True)
|
|
cur_class_images = len(list(class_images_dir.iterdir()))
|
|
|
|
if cur_class_images < args.num_class_images:
|
|
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
|
|
if args.prior_generation_precision == "fp32":
|
|
torch_dtype = torch.float32
|
|
elif args.prior_generation_precision == "fp16":
|
|
torch_dtype = torch.float16
|
|
elif args.prior_generation_precision == "bf16":
|
|
torch_dtype = torch.bfloat16
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
torch_dtype=torch_dtype,
|
|
safety_checker=None,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
pipeline.set_progress_bar_config(disable=True)
|
|
|
|
num_new_images = args.num_class_images - cur_class_images
|
|
logger.info(f"Number of class images to sample: {num_new_images}.")
|
|
|
|
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
|
|
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
|
|
|
|
sample_dataloader = accelerator.prepare(sample_dataloader)
|
|
pipeline.to(accelerator.device)
|
|
|
|
for example in tqdm(
|
|
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
|
|
):
|
|
images = pipeline(example["prompt"]).images
|
|
|
|
for i, image in enumerate(images):
|
|
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
|
|
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
|
|
image.save(image_filename)
|
|
|
|
del pipeline
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
# 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 tokenizer
|
|
if args.tokenizer_name:
|
|
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
|
|
elif args.pretrained_model_name_or_path:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
revision=args.revision,
|
|
use_fast=False,
|
|
cache_dir=CACHE_DIR,
|
|
local_files_only=True,
|
|
)
|
|
|
|
# import correct text encoder class
|
|
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
|
|
|
|
# Load scheduler and models
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler", cache_dir=CACHE_DIR, local_files_only=True)
|
|
text_encoder = text_encoder_cls.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant, cache_dir=CACHE_DIR, local_files_only=True,
|
|
)
|
|
try:
|
|
vae = AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant, cache_dir=CACHE_DIR, local_files_only=True,
|
|
)
|
|
except OSError:
|
|
# IF does not have a VAE so let's just set it to None
|
|
# We don't have to error out here
|
|
vae = None
|
|
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant, cache_dir=CACHE_DIR, local_files_only=True,
|
|
)
|
|
|
|
# We only train the additional adapter LoRA layers
|
|
if vae is not None:
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
unet.requires_grad_(False)
|
|
|
|
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) 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
|
|
unet.to(accelerator.device, dtype=weight_dtype)
|
|
if vae is not None:
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warn(
|
|
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
|
|
)
|
|
unet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
if args.gradient_checkpointing:
|
|
unet.enable_gradient_checkpointing()
|
|
if args.train_text_encoder:
|
|
text_encoder.gradient_checkpointing_enable()
|
|
|
|
# now we will add new LoRA weights to the attention layers
|
|
# It's important to realize here how many attention weights will be added and of which sizes
|
|
# The sizes of the attention layers consist only of two different variables:
|
|
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
|
|
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
|
|
|
|
# Let's first see how many attention processors we will have to set.
|
|
# For Stable Diffusion, it should be equal to:
|
|
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
|
|
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
|
|
# - up blocks (2x attention layers) * (3x transformer layers) * (3x up blocks) = 18
|
|
# => 32 layers
|
|
|
|
# Set correct lora layers
|
|
unet_lora_parameters = []
|
|
for attn_processor_name, attn_processor in unet.attn_processors.items():
|
|
# Parse the attention module.
|
|
attn_module = unet
|
|
for n in attn_processor_name.split(".")[:-1]:
|
|
attn_module = getattr(attn_module, n)
|
|
|
|
# Set the `lora_layer` attribute of the attention-related matrices.
|
|
attn_module.to_q.set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank
|
|
)
|
|
)
|
|
attn_module.to_k.set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank
|
|
)
|
|
)
|
|
attn_module.to_v.set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank
|
|
)
|
|
)
|
|
attn_module.to_out[0].set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.to_out[0].in_features,
|
|
out_features=attn_module.to_out[0].out_features,
|
|
rank=args.rank,
|
|
)
|
|
)
|
|
|
|
# Accumulate the LoRA params to optimize.
|
|
unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters())
|
|
unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters())
|
|
unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters())
|
|
unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters())
|
|
|
|
if isinstance(attn_processor, (AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0)):
|
|
attn_module.add_k_proj.set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.add_k_proj.in_features,
|
|
out_features=attn_module.add_k_proj.out_features,
|
|
rank=args.rank,
|
|
)
|
|
)
|
|
attn_module.add_v_proj.set_lora_layer(
|
|
LoRALinearLayer(
|
|
in_features=attn_module.add_v_proj.in_features,
|
|
out_features=attn_module.add_v_proj.out_features,
|
|
rank=args.rank,
|
|
)
|
|
)
|
|
unet_lora_parameters.extend(attn_module.add_k_proj.lora_layer.parameters())
|
|
unet_lora_parameters.extend(attn_module.add_v_proj.lora_layer.parameters())
|
|
|
|
# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
|
|
# So, instead, we monkey-patch the forward calls of its attention-blocks.
|
|
if args.train_text_encoder:
|
|
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
|
|
text_lora_parameters = LoraLoaderMixin._modify_text_encoder(text_encoder, dtype=torch.float32, rank=args.rank)
|
|
|
|
# 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:
|
|
# there are only two options here. Either are just the unet attn processor layers
|
|
# or there are the unet and text encoder atten layers
|
|
unet_lora_layers_to_save = None
|
|
text_encoder_lora_layers_to_save = None
|
|
|
|
for model in models:
|
|
if isinstance(model, type(accelerator.unwrap_model(unet))):
|
|
unet_lora_layers_to_save = unet_lora_state_dict(model)
|
|
elif isinstance(model, type(accelerator.unwrap_model(text_encoder))):
|
|
text_encoder_lora_layers_to_save = text_encoder_lora_state_dict(model)
|
|
else:
|
|
raise ValueError(f"unexpected save model: {model.__class__}")
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
weights.pop()
|
|
|
|
LoraLoaderMixin.save_lora_weights(
|
|
output_dir,
|
|
unet_lora_layers=unet_lora_layers_to_save,
|
|
text_encoder_lora_layers=text_encoder_lora_layers_to_save,
|
|
)
|
|
|
|
def load_model_hook(models, input_dir):
|
|
unet_ = None
|
|
text_encoder_ = None
|
|
|
|
while len(models) > 0:
|
|
model = models.pop()
|
|
|
|
if isinstance(model, type(accelerator.unwrap_model(unet))):
|
|
unet_ = model
|
|
elif isinstance(model, type(accelerator.unwrap_model(text_encoder))):
|
|
text_encoder_ = model
|
|
else:
|
|
raise ValueError(f"unexpected save model: {model.__class__}")
|
|
|
|
lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
|
|
LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
|
|
LoraLoaderMixin.load_lora_into_text_encoder(
|
|
lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_
|
|
)
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
# 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 = (
|
|
itertools.chain(unet_lora_parameters, text_lora_parameters)
|
|
if args.train_text_encoder
|
|
else unet_lora_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,
|
|
)
|
|
|
|
if args.pre_compute_text_embeddings:
|
|
|
|
def compute_text_embeddings(prompt):
|
|
with torch.no_grad():
|
|
text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)
|
|
prompt_embeds = encode_prompt(
|
|
text_encoder,
|
|
text_inputs.input_ids,
|
|
text_inputs.attention_mask,
|
|
text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
|
|
)
|
|
|
|
return prompt_embeds
|
|
|
|
pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
|
|
validation_prompt_negative_prompt_embeds = compute_text_embeddings("")
|
|
|
|
if args.validation_prompt is not None:
|
|
validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)
|
|
else:
|
|
validation_prompt_encoder_hidden_states = None
|
|
|
|
if args.class_prompt is not None:
|
|
pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)
|
|
else:
|
|
pre_computed_class_prompt_encoder_hidden_states = None
|
|
|
|
text_encoder = None
|
|
tokenizer = None
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
else:
|
|
pre_computed_encoder_hidden_states = None
|
|
validation_prompt_encoder_hidden_states = None
|
|
validation_prompt_negative_prompt_embeds = None
|
|
pre_computed_class_prompt_encoder_hidden_states = None
|
|
|
|
# Dataset and DataLoaders creation:
|
|
train_dataset = DreamBoothDataset(
|
|
instance_data_root=args.instance_data_dir,
|
|
instance_prompt=args.instance_prompt,
|
|
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
|
|
class_prompt=args.class_prompt,
|
|
class_num=args.num_class_images,
|
|
tokenizer=tokenizer,
|
|
size=args.resolution,
|
|
center_crop=args.center_crop,
|
|
encoder_hidden_states=pre_computed_encoder_hidden_states,
|
|
class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,
|
|
tokenizer_max_length=args.tokenizer_max_length,
|
|
use_view_dependent_prompt=args.use_view_dependent_prompt,
|
|
)
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_size=args.train_batch_size,
|
|
shuffle=True,
|
|
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
|
|
num_training_steps=args.max_train_steps * accelerator.num_processes,
|
|
num_cycles=args.lr_num_cycles,
|
|
power=args.lr_power,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
if args.train_text_encoder:
|
|
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
else:
|
|
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if accelerator.is_main_process:
|
|
tracker_config = vars(copy.deepcopy(args))
|
|
tracker_config.pop("validation_images")
|
|
accelerator.init_trackers("dreambooth-lora", config=tracker_config)
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
|
|
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 mos 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):
|
|
unet.train()
|
|
if args.train_text_encoder:
|
|
text_encoder.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet):
|
|
pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
|
|
|
|
if vae is not None:
|
|
# Convert images to latent space
|
|
model_input = vae.encode(pixel_values).latent_dist.sample()
|
|
model_input = model_input * vae.config.scaling_factor
|
|
else:
|
|
model_input = pixel_values
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(model_input)
|
|
bsz, channels, height, width = model_input.shape
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(
|
|
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
|
|
)
|
|
timesteps = timesteps.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)
|
|
|
|
# Get the text embedding for conditioning
|
|
if args.pre_compute_text_embeddings:
|
|
encoder_hidden_states = batch["input_ids"]
|
|
else:
|
|
encoder_hidden_states = encode_prompt(
|
|
text_encoder,
|
|
batch["input_ids"],
|
|
batch["attention_mask"],
|
|
text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
|
|
)
|
|
|
|
if accelerator.unwrap_model(unet).config.in_channels == channels * 2:
|
|
noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)
|
|
|
|
if args.class_labels_conditioning == "timesteps":
|
|
class_labels = timesteps
|
|
else:
|
|
class_labels = None
|
|
|
|
# Predict the noise residual
|
|
model_pred = unet(
|
|
noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels
|
|
).sample
|
|
|
|
# if model predicts variance, throw away the prediction. we will only train on the
|
|
# simplified training objective. This means that all schedulers using the fine tuned
|
|
# model must be configured to use one of the fixed variance variance types.
|
|
if model_pred.shape[1] == 6:
|
|
model_pred, _ = torch.chunk(model_pred, 2, dim=1)
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
if args.with_prior_preservation:
|
|
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
|
|
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
|
|
target, target_prior = torch.chunk(target, 2, dim=0)
|
|
|
|
# Compute instance loss
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
|
|
# Compute prior loss
|
|
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
|
|
|
|
# Add the prior loss to the instance loss.
|
|
loss = loss + args.prior_loss_weight * prior_loss
|
|
else:
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = (
|
|
itertools.chain(unet_lora_parameters, text_lora_parameters)
|
|
if args.train_text_encoder
|
|
else unet_lora_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:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
|
|
if 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 = {"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
|
|
|
|
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}."
|
|
)
|
|
# create pipeline
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
unet=accelerator.unwrap_model(unet),
|
|
text_encoder=None if args.pre_compute_text_embeddings else accelerator.unwrap_model(text_encoder),
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
safety_checker=None,
|
|
cache_dir=CACHE_DIR,
|
|
local_files_only=True,
|
|
)
|
|
|
|
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
|
|
scheduler_args = {}
|
|
|
|
if "variance_type" in pipeline.scheduler.config:
|
|
variance_type = pipeline.scheduler.config.variance_type
|
|
|
|
if variance_type in ["learned", "learned_range"]:
|
|
variance_type = "fixed_small"
|
|
|
|
scheduler_args["variance_type"] = variance_type
|
|
|
|
pipeline.scheduler = DPMSolverMultistepScheduler.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 else None
|
|
if args.pre_compute_text_embeddings:
|
|
pipeline_args = {
|
|
"prompt_embeds": validation_prompt_encoder_hidden_states,
|
|
"negative_prompt_embeds": validation_prompt_negative_prompt_embeds,
|
|
}
|
|
else:
|
|
pipeline_args = {"prompt": args.validation_prompt}
|
|
|
|
if args.validation_images is None:
|
|
images = []
|
|
for _ in range(args.num_validation_images):
|
|
with torch.cuda.amp.autocast():
|
|
image = pipeline(**pipeline_args, generator=generator).images[0]
|
|
images.append(image)
|
|
else:
|
|
images = []
|
|
for image in args.validation_images:
|
|
image = Image.open(image)
|
|
with torch.cuda.amp.autocast():
|
|
image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
|
|
images.append(image)
|
|
|
|
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
|
|
torch.cuda.empty_cache()
|
|
|
|
# Save the lora layers
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
unet = accelerator.unwrap_model(unet)
|
|
unet = unet.to(torch.float32)
|
|
unet_lora_layers = unet_lora_state_dict(unet)
|
|
|
|
if text_encoder is not None and args.train_text_encoder:
|
|
text_encoder = accelerator.unwrap_model(text_encoder)
|
|
text_encoder = text_encoder.to(torch.float32)
|
|
text_encoder_lora_layers = text_encoder_lora_state_dict(text_encoder)
|
|
else:
|
|
text_encoder_lora_layers = None
|
|
|
|
LoraLoaderMixin.save_lora_weights(
|
|
save_directory=args.output_dir,
|
|
unet_lora_layers=unet_lora_layers,
|
|
text_encoder_lora_layers=text_encoder_lora_layers,
|
|
)
|
|
|
|
# Final inference
|
|
# Load previous pipeline
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype,
|
|
cache_dir=CACHE_DIR, local_files_only=True, safety_checker=None,
|
|
)
|
|
|
|
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
|
|
scheduler_args = {}
|
|
|
|
if "variance_type" in pipeline.scheduler.config:
|
|
variance_type = pipeline.scheduler.config.variance_type
|
|
|
|
if variance_type in ["learned", "learned_range"]:
|
|
variance_type = "fixed_small"
|
|
|
|
scheduler_args["variance_type"] = variance_type
|
|
|
|
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args, cache_dir=CACHE_DIR, local_files_only=True,)
|
|
|
|
pipeline = pipeline.to(accelerator.device)
|
|
|
|
# load attention processors
|
|
pipeline.load_lora_weights(args.output_dir, weight_name="pytorch_lora_weights.safetensors")
|
|
|
|
# run inference
|
|
images = []
|
|
if args.validation_prompt and args.num_validation_images > 0:
|
|
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
|
|
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,
|
|
images=images,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
train_text_encoder=args.train_text_encoder,
|
|
prompt=args.instance_prompt,
|
|
repo_folder=args.output_dir,
|
|
pipeline=pipeline,
|
|
)
|
|
upload_folder(
|
|
repo_id=repo_id,
|
|
folder_path=args.output_dir,
|
|
commit_message="End of training",
|
|
ignore_patterns=["step_*", "epoch_*"],
|
|
)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args) |