#!/usr/bin/env python # coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the Lciense is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import argparse import gc import itertools import json import logging import math import os import random import shutil import warnings from contextlib import nullcontext from pathlib import Path import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ( DistributedDataParallelKwargs, ProjectConfiguration, set_seed, ) from huggingface_hub import create_repo, hf_hub_download, upload_folder from huggingface_hub.utils import insecure_hashlib from packaging import version from peft import LoraConfig, set_peft_model_state_dict from peft.utils import get_peft_model_state_dict from PIL import Image from PIL.ImageOps import exif_transpose from safetensors.torch import load_file, save_file from torch.utils.data import Dataset from torchvision import transforms from torchvision.transforms.functional import crop from tqdm.auto import tqdm from transformers import AutoTokenizer, PretrainedConfig import diffusers from diffusers import ( AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, EDMEulerScheduler, EulerDiscreteScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, ) from diffusers.loaders import LoraLoaderMixin from diffusers.optimization import get_scheduler from diffusers.training_utils import ( _set_state_dict_into_text_encoder, cast_training_params, compute_snr, ) from diffusers.utils import ( check_min_version, convert_all_state_dict_to_peft, convert_state_dict_to_diffusers, convert_state_dict_to_kohya, convert_unet_state_dict_to_peft, is_wandb_available, ) from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module if is_wandb_available(): import wandb # Will error if the minimal version of diffusers is not installed. Remove at your own risks. # check_min_version("0.28.0.dev0") logger = get_logger(__name__) from utils import extend_mask_with_bezier, mask_paint2bbox def determine_scheduler_type(pretrained_model_name_or_path, revision): model_index_filename = "model_index.json" if os.path.isdir(pretrained_model_name_or_path): model_index = os.path.join(pretrained_model_name_or_path, model_index_filename) else: model_index = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=model_index_filename, revision=revision, ) with open(model_index, "r") as f: scheduler_type = json.load(f)["scheduler"][1] return scheduler_type def parse_args(input_args=None): parser = argparse.ArgumentParser(description="Simple example of a training script.") # parser.add_argument( # "--ref_image_path", # type=str, # default=None, # required=True, # help="Path to ref_image_path", # ) parser.add_argument( "--category", type=str, default="", help="the category of the image, for decompose", ) parser.add_argument( "--regular_prob", type=float, default=0.5, help="probability of using regular image", ) parser.add_argument( "--regular_dir", type=str, default=None, required=True, help="Path to regular img and mask", ) parser.add_argument( "--loss_reweight_object", type=float, default=1, ) parser.add_argument( "--loss_reweight_background", type=float, default=1, ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--pretrained_vae_model_name_or_path", type=str, default=None, help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--instance_data_dir", type=str, default=None, help=("A folder containing the training data. "), ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing the target image. By " "default, the standard Image Dataset maps out 'file_name' " "to 'image'.", ) parser.add_argument( "--caption_column", type=str, default=None, help="The column of the dataset containing the instance prompt for each image", ) parser.add_argument( "--repeats", type=int, default=1, help="How many times to repeat the training data.", ) parser.add_argument( "--class_data_dir", type=str, default=None, required=False, help="A folder containing the training data of class images.", ) parser.add_argument( "--instance_prompt", type=str, default=None, required=True, help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'", ) parser.add_argument( "--class_prompt", type=str, default=None, help="The prompt to specify images in the same class as provided instance images.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is used during validation to verify that the model is learning.", ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images that should be generated during validation with `validation_prompt`.", ) parser.add_argument( "--validation_epochs", type=int, default=50, help=( "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`." ), ) parser.add_argument( "--do_edm_style_training", default=False, action="store_true", help="Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.", ) parser.add_argument( "--with_prior_preservation", default=False, action="store_true", help="Flag to add prior preservation loss.", ) parser.add_argument( "--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.", ) parser.add_argument( "--num_class_images", type=int, default=100, help=( "Minimal class images for prior preservation loss. If there are not enough images already present in" " class_data_dir, additional images will be sampled with class_prompt." ), ) parser.add_argument( "--output_dir", type=str, default="lora-dreambooth-model", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--output_kohya_format", action="store_true", help="Flag to additionally generate final state dict in the Kohya format so that it becomes compatible with A111, Comfy, Kohya, etc.", ) parser.add_argument( "--seed", type=int, default=None, help="A seed for reproducible training." ) parser.add_argument( "--resolution", type=int, default=1024, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--train_text_encoder", action="store_true", help="Whether to train the text encoder. If set, the text encoder should be float32 precision.", ) parser.add_argument( "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.", ) parser.add_argument("--num_train_epochs", type=int, default=1) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--text_encoder_lr", type=float, default=5e-6, help="Text encoder learning rate to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " "More details here: https://arxiv.org/abs/2303.09556.", ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.", ) parser.add_argument( "--lr_num_cycles", type=int, default=1, help="Number of hard resets of the lr in cosine_with_restarts scheduler.", ) parser.add_argument( "--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument( "--optimizer", type=str, default="AdamW", help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'), ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW", ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers.", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers.", ) parser.add_argument( "--prodigy_beta3", type=float, default=None, help="coefficients for computing the Prodidy stepsize using running averages. If set to None, " "uses the value of square root of beta2. Ignored if optimizer is adamW", ) parser.add_argument( "--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay", ) parser.add_argument( "--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params", ) parser.add_argument( "--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer and Prodigy optimizers.", ) parser.add_argument( "--prodigy_use_bias_correction", type=bool, default=True, help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW", ) parser.add_argument( "--prodigy_safeguard_warmup", type=bool, default=True, help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. " "Ignored if optimizer is adamW", ) parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm." ) parser.add_argument( "--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--prior_generation_precision", type=str, default=None, choices=["no", "fp32", "fp16", "bf16"], help=( "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32." ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers.", ) parser.add_argument( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--use_dora", action="store_true", default=False, help=( "Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. " "Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`" ), ) if input_args is not None: args = parser.parse_args(input_args) else: args = parser.parse_args() if args.dataset_name is None and args.instance_data_dir is None: raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`") if args.dataset_name is not None and args.instance_data_dir is not None: raise ValueError( "Specify only one of `--dataset_name` or `--instance_data_dir`" ) env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.with_prior_preservation: if args.class_data_dir is None: raise ValueError("You must specify a data directory for class images.") if args.class_prompt is None: raise ValueError("You must specify prompt for class images.") else: # logger is not available yet if args.class_data_dir is not None: warnings.warn( "You need not use --class_data_dir without --with_prior_preservation." ) if args.class_prompt is not None: warnings.warn( "You need not use --class_prompt without --with_prior_preservation." ) return args class DreamBoothDataset(Dataset): """ A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images. """ def __init__( self, instance_data_root, instance_prompt, class_prompt, class_data_root=None, class_num=None, size=1024, repeats=1, center_crop=False, ): self.size = size self.center_crop = center_crop self.instance_prompt = instance_prompt self.custom_instance_prompts = None self.class_prompt = class_prompt import modules.inpaint_worker as inpaint_worker from utils.add_fooocus_inpaint_head_patch import load_inpaint_head global pipe _device, _dtype = pipe.vae.device, pipe.vae.dtype inpaint_head_model = load_inpaint_head( "./models/fooocus_inpaint/fooocus_inpaint_head.pth", _device ).to(device=_device, dtype=_dtype) print("Processing image and mask to latent") train_resize = transforms.Resize( size, interpolation=transforms.InterpolationMode.BILINEAR ) train_crop = ( transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size) ) train_flip = transforms.RandomHorizontalFlip(p=1.0) train_transforms = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) INSTANCE_REPEAT_NUM = 10 REGULAR_REPEAT_NUM = 3 USE_BBOX_MASK = False # image processing to prepare for using SD-XL micro-conditioning self.original_sizes = [] self.crop_top_lefts = [] self.pixel_values = [] if True: self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): raise ValueError("Instance images root doesn't exists.") self.instance_images = [] self.instance_masks = [] # self.ref_images = [] for path in list(Path(instance_data_root).iterdir()): image = Image.open(path) mask_path = str(path).replace("/image", "/mask").replace(".jpg", ".png") if not Path(mask_path).exists(): print(f"Warning! mask of image {path} not exist! Ignore..") continue mask_img = Image.open(mask_path).resize(image.size) self.instance_images.extend(itertools.repeat(image, repeats)) self.instance_masks.extend(itertools.repeat(mask_img, repeats)) # break with torch.no_grad(): # for _ in tqdm( range(INSTANCE_REPEAT_NUM) ): for image, mask in zip(self.instance_images, self.instance_masks): image = exif_transpose(image) mask = exif_transpose(mask) if not image.mode == "RGB": image = image.convert("RGB") self.original_sizes.append((image.height, image.width)) image = train_resize(image) mask = train_resize(mask) if args.random_flip and random.random() < 0.5: # flip image = train_flip(image) mask = train_flip(mask) 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) mask = train_crop(mask) else: y1, x1, h, w = train_crop.get_params( image, (args.resolution, args.resolution) ) image = crop(image, y1, x1, h, w) mask = crop(mask, y1, x1, h, w) crop_top_left = (y1, x1) self.crop_top_lefts.append(crop_top_left) mask = extend_mask_with_bezier( ( np.asarray(mask)[:, :, 0] if mask.mode == "RGB" else np.asarray(mask) ), extend_ratio=random.randint(1, 10) / 10.0, random_width=random.randint(1, 20), ) if USE_BBOX_MASK: mask = mask_paint2bbox(mask) mask = Image.fromarray(mask) for interesting_are in [1]: inpaint_work = inpaint_worker.InpaintWorker( image=np.asarray(image), mask=( np.asarray(mask)[:, :, 0] if mask.mode == "RGB" else np.asarray(mask) ), use_fill=True, k=interesting_are, path_upscale_models="./models/upscale_models/fooocus_upscaler_s409985e5.bin", ) pixel_fill = pipe.image_processor.preprocess( Image.fromarray(inpaint_work.interested_fill) ) pixel_image = pipe.image_processor.preprocess( Image.fromarray(inpaint_work.interested_image) ) pixel_mask = pipe.mask_processor.preprocess( Image.fromarray(inpaint_work.interested_mask) ) pixel_fill = pixel_fill.to(device=_device, dtype=_dtype) pixel_image = pixel_image.to(device=_device, dtype=_dtype) pixel_mask = pixel_mask.to( device=_device, dtype=torch.float32 ) masked_image = pixel_image * (pixel_mask < 0.5) latent_fill = pipe._encode_vae_image( image=pixel_fill, generator=None ) latent_masked_image = pipe._encode_vae_image( image=masked_image, generator=None ) latent_image = pipe._encode_vae_image( image=pixel_image, generator=None ) B, C, H, W = latent_fill.shape latent_mask = torch.nn.functional.interpolate( pixel_mask, size=(H * 8, W * 8), mode="bilinear" ).round() latent_mask = ( torch.nn.functional.max_pool2d(latent_mask, (8, 8)) .round() .to(latent_fill.device) ) feed = torch.cat( [latent_mask, latent_masked_image], dim=1, ) inpaint_head_feature = inpaint_head_model(feed).to("cpu") # self.pixel_values.append((inpaint_head_feature, pixel_image)) self.pixel_values.append( ( inpaint_head_feature, latent_image, latent_mask, "instance", ) ) self.num_instance_images = len(self.pixel_values) # ! get regular image global regular_prompts_list print("Processing regular image and mask to latent") regular_data_root = args.regular_dir regular_prompts_list = [name for name in os.listdir(regular_data_root)] if args.regular_prob > 0: with torch.no_grad(): # ! load regular image and mask for prompt in tqdm(regular_prompts_list): all_images_and_masks = os.listdir( os.path.join(regular_data_root, prompt) ) files_name = set( [name.split("-")[0] for name in all_images_and_masks] ) for file_name in tqdm(files_name): _image = Image.open( f"{regular_data_root}/{prompt}/{file_name}-image.png" ) _mask = Image.open( f"{regular_data_root}/{prompt}/{file_name}-mask.png" ) for _ in range(REGULAR_REPEAT_NUM): image = exif_transpose(_image) mask = exif_transpose(_mask) if not image.mode == "RGB": image = image.convert("RGB") self.original_sizes.append((image.height, image.width)) image = train_resize(image) mask = train_resize(mask) if args.random_flip and random.random() < 0.5: # flip image = train_flip(image) mask = train_flip(mask) 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) mask = train_crop(mask) else: y1, x1, h, w = train_crop.get_params( image, (args.resolution, args.resolution) ) image = crop(image, y1, x1, h, w) mask = crop(mask, y1, x1, h, w) crop_top_left = (y1, x1) self.crop_top_lefts.append(crop_top_left) mask = extend_mask_with_bezier( ( np.asarray(mask)[:, :, 0] if mask.mode == "RGB" else np.asarray(mask) ), extend_ratio=random.randint(1, 10) / 10.0, random_width=random.randint(1, 20), ) if USE_BBOX_MASK: mask = mask_paint2bbox(mask) mask = Image.fromarray(mask) interesting_are = 1 inpaint_work = inpaint_worker.InpaintWorker( image=np.asarray(image), mask=( np.asarray(mask)[:, :, 0] if mask.mode == "RGB" else np.asarray(mask) ), use_fill=True, k=interesting_are, path_upscale_models="./models/upscale_models/fooocus_upscaler_s409985e5.bin", ) pixel_fill = pipe.image_processor.preprocess( Image.fromarray(inpaint_work.interested_fill) ) pixel_image = pipe.image_processor.preprocess( Image.fromarray(inpaint_work.interested_image) ) pixel_mask = pipe.mask_processor.preprocess( Image.fromarray(inpaint_work.interested_mask) ) pixel_fill = pixel_fill.to(device=_device, dtype=_dtype) pixel_image = pixel_image.to( device=_device, dtype=_dtype ) pixel_mask = pixel_mask.to( device=_device, dtype=torch.float32 ) masked_image = pixel_image * (pixel_mask < 0.5) latent_fill = pipe._encode_vae_image( image=pixel_fill, generator=None ) latent_masked_image = pipe._encode_vae_image( image=masked_image, generator=None ) latent_image = pipe._encode_vae_image( image=pixel_image, generator=None ) B, C, H, W = latent_fill.shape latent_mask = torch.nn.functional.interpolate( pixel_mask, size=(H * 8, W * 8), mode="bilinear" ).round() latent_mask = ( torch.nn.functional.max_pool2d(latent_mask, (8, 8)) .round() .to(latent_fill.device) ) feed = torch.cat( [latent_mask, latent_masked_image], dim=1, ) inpaint_head_feature = inpaint_head_model(feed).to( "cpu" ) self.pixel_values.append( ( inpaint_head_feature, latent_image, latent_mask, prompt, ) ) inpaint_head_model.to("cpu") del inpaint_head_model if torch.cuda.is_available(): torch.cuda.empty_cache() self.num_all_images = len(self.pixel_values) self.num_regular_images = self.num_all_images - self.num_instance_images self._length = self.num_all_images self.class_data_root = None def __len__(self): return self._length def __getitem__(self, index): example = {} self.num_regular_images self.num_all_images self.num_instance_images regular_prob = args.regular_prob if random.random() < regular_prob and self.num_regular_images > 0: img_id = self.num_instance_images + index % self.num_regular_images else: img_id = index % self.num_instance_images instance_image = self.pixel_values[img_id] original_size = self.original_sizes[img_id] crop_top_left = self.crop_top_lefts[img_id] example["instance_images"] = instance_image example["original_size"] = original_size example["crop_top_left"] = crop_top_left example["instance_prompt"] = self.instance_prompt return example def collate_fn(examples, with_prior_preservation=False): pixel_values = [example["instance_images"] for example in examples] prompts = [example["instance_prompt"] for example in examples] original_sizes = [example["original_size"] for example in examples] crop_top_lefts = [example["crop_top_left"] 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: pixel_values += [example["class_images"] for example in examples] prompts += [example["class_prompt"] for example in examples] original_sizes += [example["original_size"] for example in examples] crop_top_lefts += [example["crop_top_left"] for example in examples] # pixel_values = torch.stack(pixel_values) # pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() batch = { "pixel_values": pixel_values, "prompts": prompts, "original_sizes": original_sizes, "crop_top_lefts": crop_top_lefts, } 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): text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids return text_input_ids # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None): prompt_embeds_list = [] for i, text_encoder in enumerate(text_encoders): if tokenizers is not None: tokenizer = tokenizers[i] text_input_ids = tokenize_prompt(tokenizer, prompt) else: assert text_input_ids_list is not None text_input_ids = text_input_ids_list[i] 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, pooled_prompt_embeds 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 `huggingface-cli login` to authenticate with the Hub." ) if args.do_edm_style_training and args.snr_gamma is not None: raise ValueError( "Min-SNR formulation is not supported when conducting EDM-style training." ) 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." ) logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration( project_dir=args.output_dir, logging_dir=logging_dir ) kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, kwargs_handlers=[kwargs], ) # 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." ) # 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) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) print("Loading sdxl pipeline") from FooocusSDXLInpaintAllInOnePipeline import FooocusSDXLInpaintPipeline global pipe pipe = FooocusSDXLInpaintPipeline.from_pretrained( "frankjoshua/juggernautXL_v8Rundiffusion", # torch_dtype=torch.float16, # variant="fp16", use_safetensors=True, ).to("cuda") # pipe.text_encoder.to("cpu") # pipe.text_encoder_2.to("cpu") print("Loading fooocus unet") pipe.only_load_fooocus_unet_and_cover_pipe_unet_for_train( fooocus_model_path="./models/fooocus_inpaint/inpaint_v26.fooocus.patch", ) # Load the tokenizers tokenizer_one = pipe.tokenizer tokenizer_two = pipe.tokenizer_2 # Load scheduler and models scheduler_type = determine_scheduler_type( args.pretrained_model_name_or_path, args.revision ) if "EDM" in scheduler_type: args.do_edm_style_training = True noise_scheduler = EDMEulerScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler" ) logger.info("Performing EDM-style training!") elif args.do_edm_style_training: noise_scheduler = EulerDiscreteScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler" ) logger.info("Performing EDM-style training!") else: noise_scheduler = DDPMScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler" ) text_encoder_one = pipe.text_encoder text_encoder_two = pipe.text_encoder_2 vae = pipe.vae latents_mean = latents_std = None if hasattr(vae.config, "latents_mean") and vae.config.latents_mean is not None: latents_mean = torch.tensor(vae.config.latents_mean).view(1, 4, 1, 1) if hasattr(vae.config, "latents_std") and vae.config.latents_std is not None: latents_std = torch.tensor(vae.config.latents_std).view(1, 4, 1, 1) unet = pipe.unet # We only train the additional adapter LoRA layers vae.requires_grad_(False) text_encoder_one.requires_grad_(False) text_encoder_two.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 if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16: # 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." ) # Move unet, vae and text_encoder to device and cast to weight_dtype unet.to(accelerator.device, dtype=weight_dtype) # The VAE is always 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) 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" ) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() if args.train_text_encoder: text_encoder_one.gradient_checkpointing_enable() text_encoder_two.gradient_checkpointing_enable() # now we will add new LoRA weights to the attention layers unet_lora_config = LoraConfig( r=args.rank, use_dora=args.use_dora, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=[ "to_k", # "to_q", "to_v", "to_out.0", # "conv1", # "conv2", # "attn1.to_k", "attn1.to_q", "attn1.to_v", # "attn1.to_out.0" ], ) unet.add_adapter(unet_lora_config) # The text encoder comes from 🤗 transformers, so we cannot directly modify it. # So, instead, we monkey-patch the forward calls of its attention-blocks. def unwrap_model(model): model = accelerator.unwrap_model(model) model = model._orig_mod if is_compiled_module(model) else model return model # 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_one_lora_layers_to_save = None text_encoder_two_lora_layers_to_save = None for model in models: if isinstance(model, type(unwrap_model(unet))): unet_lora_layers_to_save = convert_state_dict_to_diffusers( get_peft_model_state_dict(model) ) elif isinstance(model, type(unwrap_model(text_encoder_one))): text_encoder_one_lora_layers_to_save = ( convert_state_dict_to_diffusers( get_peft_model_state_dict(model) ) ) elif isinstance(model, type(unwrap_model(text_encoder_two))): text_encoder_two_lora_layers_to_save = ( convert_state_dict_to_diffusers( get_peft_model_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() StableDiffusionXLPipeline.save_lora_weights( output_dir, unet_lora_layers=unet_lora_layers_to_save, text_encoder_lora_layers=text_encoder_one_lora_layers_to_save, text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save, ) def load_model_hook(models, input_dir): unet_ = None text_encoder_one_ = None text_encoder_two_ = None while len(models) > 0: model = models.pop() if isinstance(model, type(unwrap_model(unet))): unet_ = model elif isinstance(model, type(unwrap_model(text_encoder_one))): text_encoder_one_ = model elif isinstance(model, type(unwrap_model(text_encoder_two))): text_encoder_two_ = model else: raise ValueError(f"unexpected save model: {model.__class__}") lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir) unet_state_dict = { f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.") } unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict) incompatible_keys = set_peft_model_state_dict( unet_, unet_state_dict, adapter_name="default" ) if incompatible_keys is not None: # check only for unexpected keys unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None) if unexpected_keys: logger.warning( f"Loading adapter weights from state_dict led to unexpected keys not found in the model: " f" {unexpected_keys}. " ) if args.train_text_encoder: # Do we need to call `scale_lora_layers()` here? _set_state_dict_into_text_encoder( lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_ ) _set_state_dict_into_text_encoder( lora_state_dict, prefix="text_encoder_2.", text_encoder=text_encoder_two_, ) # Make sure the trainable params are in float32. This is again needed since the base models # are in `weight_dtype`. More details: # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804 if args.mixed_precision == "fp16": models = [unet_] if args.train_text_encoder: models.extend([text_encoder_one_, text_encoder_two_]) # only upcast trainable parameters (LoRA) into fp32 cast_training_params(models) 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 and torch.cuda.is_available(): 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 ) # Make sure the trainable params are in float32. if args.mixed_precision == "fp16": models = [unet] if args.train_text_encoder: models.extend([text_encoder_one, text_encoder_two]) # only upcast trainable parameters (LoRA) into fp32 cast_training_params(models, dtype=torch.float32) unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters())) if args.train_text_encoder: text_lora_parameters_one = list( filter(lambda p: p.requires_grad, text_encoder_one.parameters()) ) text_lora_parameters_two = list( filter(lambda p: p.requires_grad, text_encoder_two.parameters()) ) # Optimization parameters unet_lora_parameters_with_lr = { "params": unet_lora_parameters, "lr": args.learning_rate, } if args.train_text_encoder: # different learning rate for text encoder and unet text_lora_parameters_one_with_lr = { "params": text_lora_parameters_one, "weight_decay": args.adam_weight_decay_text_encoder, "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate, } text_lora_parameters_two_with_lr = { "params": text_lora_parameters_two, "weight_decay": args.adam_weight_decay_text_encoder, "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate, } params_to_optimize = [ unet_lora_parameters_with_lr, text_lora_parameters_one_with_lr, text_lora_parameters_two_with_lr, ] else: params_to_optimize = [unet_lora_parameters_with_lr] # Optimizer creation if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"): logger.warning( f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]." "Defaulting to adamW" ) args.optimizer = "adamw" if args.use_8bit_adam and not args.optimizer.lower() == "adamw": logger.warning( f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was " f"set to {args.optimizer.lower()}" ) if args.optimizer.lower() == "adamw": 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 = optimizer_class( params_to_optimize, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) if args.optimizer.lower() == "prodigy": try: import prodigyopt except ImportError: raise ImportError( "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`" ) optimizer_class = prodigyopt.Prodigy if args.learning_rate <= 0.1: logger.warning( "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0" ) if args.train_text_encoder and args.text_encoder_lr: logger.warning( f"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:" f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. " f"When using prodigy only learning_rate is used as the initial learning rate." ) # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be # --learning_rate params_to_optimize[1]["lr"] = args.learning_rate params_to_optimize[2]["lr"] = args.learning_rate optimizer = optimizer_class( params_to_optimize, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), beta3=args.prodigy_beta3, weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, decouple=args.prodigy_decouple, use_bias_correction=args.prodigy_use_bias_correction, safeguard_warmup=args.prodigy_safeguard_warmup, ) # Dataset and DataLoaders creation: train_dataset = DreamBoothDataset( instance_data_root=args.instance_data_dir, instance_prompt=args.instance_prompt, class_prompt=args.class_prompt, class_data_root=args.class_data_dir if args.with_prior_preservation else None, class_num=args.num_class_images, size=args.resolution, repeats=args.repeats, center_crop=args.center_crop, ) 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, ) del vae del pipe.vae gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() # Computes additional embeddings/ids required by the SDXL UNet. # regular text embeddings (when `train_text_encoder` is not True) # pooled text embeddings # 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]) add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype) return add_time_ids if not args.train_text_encoder: tokenizers = [tokenizer_one, tokenizer_two] text_encoders = [text_encoder_one, text_encoder_two] def compute_text_embeddings(prompt, text_encoders, tokenizers): with torch.no_grad(): prompt_embeds, pooled_prompt_embeds = encode_prompt( text_encoders, tokenizers, prompt ) prompt_embeds = prompt_embeds.to(accelerator.device) pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device) return prompt_embeds, pooled_prompt_embeds # If no type of tuning is done on the text_encoder and custom instance prompts are NOT # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid # the redundant encoding. if not args.train_text_encoder and not train_dataset.custom_instance_prompts: instance_prompt_hidden_states, instance_pooled_prompt_embeds = ( compute_text_embeddings(args.instance_prompt, text_encoders, tokenizers) ) global prompt_embeds_dict, unet_add_text_embeds_dict prompt_embeds_dict = {"instance": instance_prompt_hidden_states.to("cpu")} unet_add_text_embeds_dict = { "instance": instance_pooled_prompt_embeds.to("cpu") } print("Encoding regular prompts") global regular_prompts_list for _prompt in tqdm(regular_prompts_list): prompt = _prompt.replace("_", " ") prompt_embeds, pooled_prompt_embeds = compute_text_embeddings( prompt, text_encoders, tokenizers ) prompt_embeds_dict[_prompt] = prompt_embeds.to("cpu") unet_add_text_embeds_dict[_prompt] = pooled_prompt_embeds.to("cpu") # Handle class prompt for prior-preservation. if args.with_prior_preservation: if not args.train_text_encoder: class_prompt_hidden_states, class_pooled_prompt_embeds = ( compute_text_embeddings(args.class_prompt, text_encoders, tokenizers) ) # Clear the memory here if not args.train_text_encoder and not train_dataset.custom_instance_prompts: del tokenizers, text_encoders gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images), # pack the statically computed variables appropriately here. This is so that we don't # have to pass them to the dataloader. # if not train_dataset.custom_instance_prompts: # if not args.train_text_encoder: # prompt_embeds = instance_prompt_hidden_states # unet_add_text_embeds = instance_pooled_prompt_embeds # if args.with_prior_preservation: # prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0) # unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0) # # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the # # batch prompts on all training steps # else: # tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt) # tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt) # if args.with_prior_preservation: # class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt) # class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt) # tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0) # tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0) # 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_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler, ) = accelerator.prepare( unet, text_encoder_one, text_encoder_two, 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_name = ( "dreambooth-lora-sd-xl" if "playground" not in args.pretrained_model_name_or_path else "dreambooth-lora-playground" ) accelerator.init_trackers(tracker_name, config=vars(args)) # Train! total_batch_size = ( args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps ) logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num 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, ) def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype) schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device) timesteps = timesteps.to(accelerator.device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < n_dim: sigma = sigma.unsqueeze(-1) return sigma del text_encoder_one, text_encoder_two del pipe.text_encoder del pipe.text_encoder_2 gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() from utils import inject_fooocus_inpaint_head for epoch in range(first_epoch, args.num_train_epochs): unet.train() if args.train_text_encoder: text_encoder_one.train() text_encoder_two.train() # set top parameter requires_grad = True for gradient checkpointing works accelerator.unwrap_model( text_encoder_one ).text_model.embeddings.requires_grad_(True) accelerator.unwrap_model( text_encoder_two ).text_model.embeddings.requires_grad_(True) for step, batch in enumerate(train_dataloader): with accelerator.accumulate(unet): # pixel_values = batch["pixel_values"].to(dtype=vae.dtype) assert len(batch["pixel_values"]) == 1, "only support batch size 1" # (inpaint_head_feature, pixel_values) = batch["pixel_values"][0] (inpaint_head_feature, model_input, latent_mask, prompt) = batch[ "pixel_values" ][0] inject_fooocus_inpaint_head( unet, inpaint_head_feature, parallel_train=True ) prompt_embeds = prompt_embeds_dict[prompt].to(accelerator.device) # print(f"training on {prompt}") unet_add_text_embeds = unet_add_text_embeds_dict[prompt].to( accelerator.device ) # encode batch prompts when custom prompts are provided for each image - # if train_dataset.custom_instance_prompts: # if not args.train_text_encoder: # prompt_embeds, unet_add_text_embeds = compute_text_embeddings( # prompts, text_encoders, tokenizers # ) # else: # tokens_one = tokenize_prompt(tokenizer_one, prompts) # tokens_two = tokenize_prompt(tokenizer_two, prompts) # Convert images to latent space # model_input = vae.encode(pixel_values).latent_dist.sample() # if latents_mean is None and latents_std is None: # model_input = model_input * vae.config.scaling_factor # if args.pretrained_vae_model_name_or_path is None: # model_input = model_input.to(weight_dtype) # else: # latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype) # latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype) # model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std # model_input = model_input.to(dtype=weight_dtype) # Sample noise that we'll add to the latents noise = torch.randn_like(model_input) bsz = model_input.shape[0] # Sample a random timestep for each image if not args.do_edm_style_training: timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device, ) timesteps = timesteps.long() else: # in EDM formulation, the model is conditioned on the pre-conditioned noise levels # instead of discrete timesteps, so here we sample indices to get the noise levels # from `scheduler.timesteps` indices = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,) ) timesteps = noise_scheduler.timesteps[indices].to( device=model_input.device ) # 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 ) # For EDM-style training, we first obtain the sigmas based on the continuous timesteps. # We then precondition the final model inputs based on these sigmas instead of the timesteps. # Follow: Section 5 of https://arxiv.org/abs/2206.00364. if args.do_edm_style_training: sigmas = get_sigmas( timesteps, len(noisy_model_input.shape), noisy_model_input.dtype ) if "EDM" in scheduler_type: inp_noisy_latents = noise_scheduler.precondition_inputs( noisy_model_input, sigmas ) else: inp_noisy_latents = noisy_model_input / ((sigmas**2 + 1) ** 0.5) # time ids add_time_ids = torch.cat( [ compute_time_ids(original_size=s, crops_coords_top_left=c) for s, c in zip( batch["original_sizes"], batch["crop_top_lefts"] ) ] ) # Calculate the elements to repeat depending on the use of prior-preservation and custom captions. if not train_dataset.custom_instance_prompts: # None elems_to_repeat_text_embeds = ( bsz // 2 if args.with_prior_preservation else bsz ) else: elems_to_repeat_text_embeds = 1 # Predict the noise residual if not args.train_text_encoder: unet_added_conditions = { "time_ids": add_time_ids, "text_embeds": unet_add_text_embeds.repeat( elems_to_repeat_text_embeds, 1 ), } prompt_embeds_input = prompt_embeds.repeat( elems_to_repeat_text_embeds, 1, 1 ) model_pred = unet( ( inp_noisy_latents if args.do_edm_style_training else noisy_model_input ), timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions, return_dict=False, )[0] else: unet_added_conditions = {"time_ids": add_time_ids} prompt_embeds, pooled_prompt_embeds = encode_prompt( text_encoders=[text_encoder_one, text_encoder_two], tokenizers=None, prompt=None, text_input_ids_list=[tokens_one, tokens_two], ) unet_added_conditions.update( { "text_embeds": pooled_prompt_embeds.repeat( elems_to_repeat_text_embeds, 1 ) } ) prompt_embeds_input = prompt_embeds.repeat( elems_to_repeat_text_embeds, 1, 1 ) model_pred = unet( ( inp_noisy_latents if args.do_edm_style_training else noisy_model_input ), timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions, return_dict=False, )[0] weighting = None if args.do_edm_style_training: # Similar to the input preconditioning, the model predictions are also preconditioned # on noised model inputs (before preconditioning) and the sigmas. # Follow: Section 5 of https://arxiv.org/abs/2206.00364. if "EDM" in scheduler_type: model_pred = noise_scheduler.precondition_outputs( noisy_model_input, model_pred, sigmas ) else: if noise_scheduler.config.prediction_type == "epsilon": model_pred = model_pred * (-sigmas) + noisy_model_input elif noise_scheduler.config.prediction_type == "v_prediction": model_pred = model_pred * ( -sigmas / (sigmas**2 + 1) ** 0.5 ) + (noisy_model_input / (sigmas**2 + 1)) # We are not doing weighting here because it tends result in numerical problems. # See: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051 # There might be other alternatives for weighting as well: # https://github.com/huggingface/diffusers/pull/7126#discussion_r1505404686 if "EDM" not in scheduler_type: weighting = (sigmas**-2.0).float() # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = model_input if args.do_edm_style_training else noise elif noise_scheduler.config.prediction_type == "v_prediction": target = ( model_input if args.do_edm_style_training else 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 prior loss if weighting is not None: prior_loss = torch.mean( ( weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2 ).reshape(target_prior.shape[0], -1), 1, ) prior_loss = prior_loss.mean() else: prior_loss = F.mse_loss( model_pred_prior.float(), target_prior.float(), reduction="mean", ) if args.snr_gamma is None: if weighting is not None: loss = torch.mean( ( weighting.float() * (model_pred.float() - target.float()) ** 2 ).reshape(target.shape[0], -1), 1, ) loss = loss.mean() else: obj_loss_weight = args.loss_reweight_object bg_loss_weight = args.loss_reweight_background mask_reweight = ( latent_mask * (obj_loss_weight - bg_loss_weight) + bg_loss_weight ) model_pred = model_pred.float() * mask_reweight target = target.float() * mask_reweight loss = F.mse_loss(model_pred, target, reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/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) base_weight = ( torch.stack( [snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1 ).min(dim=1)[0] / snr ) if noise_scheduler.config.prediction_type == "v_prediction": # Velocity objective needs to be floored to an SNR weight of one. mse_loss_weights = base_weight + 1 else: # Epsilon and sample both use the same loss weights. mse_loss_weights = base_weight 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() if args.with_prior_preservation: # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = ( itertools.chain( unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two, ) 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: 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 # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = unwrap_model(unet) unet = unet.to(torch.float32) unet_lora_layers = convert_state_dict_to_diffusers( get_peft_model_state_dict(unet) ) if args.train_text_encoder: text_encoder_one = unwrap_model(text_encoder_one) text_encoder_lora_layers = convert_state_dict_to_diffusers( get_peft_model_state_dict(text_encoder_one.to(torch.float32)) ) text_encoder_two = unwrap_model(text_encoder_two) text_encoder_2_lora_layers = convert_state_dict_to_diffusers( get_peft_model_state_dict(text_encoder_two.to(torch.float32)) ) else: text_encoder_lora_layers = None text_encoder_2_lora_layers = None StableDiffusionXLPipeline.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_layers, text_encoder_lora_layers=text_encoder_lora_layers, text_encoder_2_lora_layers=text_encoder_2_lora_layers, ) if args.output_kohya_format: lora_state_dict = load_file( f"{args.output_dir}/pytorch_lora_weights.safetensors" ) peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict) kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict) save_file( kohya_state_dict, f"{args.output_dir}/pytorch_lora_weights_kohya.safetensors", ) accelerator.end_training() if __name__ == "__main__": args = parse_args() main(args)