#!/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 License 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 re import uuid import shutil import warnings from contextlib import nullcontext from pathlib import Path from typing import List, Optional 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 StableDiffusionLoraLoaderMixin 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.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("0.30.0.dev0") logger = get_logger(__name__) 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 save_model_card( repo_id: str, use_dora: bool, images=None, base_model: str = None, train_text_encoder=False, train_text_encoder_ti=False, token_abstraction_dict=None, instance_prompt: str = None, validation_prompt: str = None, repo_folder=None, vae_path=None, ): img_str = "widget:\n" lora = "lora" if not use_dora else "dora" for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f""" - text: '{validation_prompt if validation_prompt else ' ' }' output: url: "image_{i}.png" """ if not images: img_str += f""" - text: '{instance_prompt}' """ embeddings_filename = f"{repo_folder}_emb" instance_prompt_webui = re.sub(r"", "", re.sub(r"", embeddings_filename, instance_prompt, count=1)) ti_keys = ", ".join(f'"{match}"' for match in re.findall(r"", instance_prompt)) if instance_prompt_webui != embeddings_filename: instance_prompt_sentence = f"For example, `{instance_prompt_webui}`" else: instance_prompt_sentence = "" trigger_str = f"You should use {instance_prompt} to trigger the image generation." diffusers_imports_pivotal = "" diffusers_example_pivotal = "" webui_example_pivotal = "" license = "" if "playground" in base_model: license = """\n ## License Please adhere to the licensing terms as described [here](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic/blob/main/LICENSE.md). """ if train_text_encoder_ti: trigger_str = ( "To trigger image generation of trained concept(or concepts) replace each concept identifier " "in you prompt with the new inserted tokens:\n" ) diffusers_imports_pivotal = """from huggingface_hub import hf_hub_download from safetensors.torch import load_file """ diffusers_example_pivotal = f"""embedding_path = hf_hub_download(repo_id='{repo_id}', filename='{embeddings_filename}.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=[{ti_keys}], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=[{ti_keys}], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) """ webui_example_pivotal = f"""- *Embeddings*: download **[`{embeddings_filename}.safetensors` here ๐Ÿ’พ](/{repo_id}/blob/main/{embeddings_filename}.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `{embeddings_filename}` to your prompt. {instance_prompt_sentence} (you need both the LoRA and the embeddings as they were trained together for this LoRA) """ if token_abstraction_dict: for key, value in token_abstraction_dict.items(): tokens = "".join(value) trigger_str += f""" to trigger concept `{key}` โ†’ use `{tokens}` in your prompt \n """ yaml = f"""--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - diffusers-training - text-to-image - diffusers - {lora} - template:sd-lora {img_str} base_model: {base_model} instance_prompt: {instance_prompt} license: openrail++ --- """ model_card = f""" # SDXL LoRA DreamBooth - {repo_id} ## Model description ### These are {repo_id} LoRA adaption weights for {base_model}. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`{repo_folder}.safetensors` here ๐Ÿ’พ](/{repo_id}/blob/main/{repo_folder}.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). {webui_example_pivotal} ## Use it with the [๐Ÿงจ diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch {diffusers_imports_pivotal} pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors') {diffusers_example_pivotal} image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words {trigger_str} ## Details All [Files & versions](/{repo_id}/tree/main). The weights were trained using [๐Ÿงจ diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. {train_text_encoder}. Pivotal tuning was enabled: {train_text_encoder_ti}. Special VAE used for training: {vae_path}. {license} """ with open(os.path.join(repo_folder, "README.md"), "w") as f: f.write(yaml + model_card) def log_validation( pipeline, args, accelerator, pipeline_args, epoch, is_final_validation=False, ): logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it scheduler_args = {} if not args.do_edm_style_training: 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 # Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better # way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051 if torch.backends.mps.is_available() or "playground" in args.pretrained_model_name_or_path: autocast_ctx = nullcontext() else: autocast_ctx = torch.autocast(accelerator.device.type) with autocast_ctx: images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)] for tracker in accelerator.trackers: phase_name = "test" if is_final_validation else "validation" np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { phase_name: [ wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images) ] } ) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() return images def import_model_class_from_model_name_or_path( pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder" ): text_encoder_config = PretrainedConfig.from_pretrained( pretrained_model_name_or_path, subfolder=subfolder, revision=revision ) model_class = text_encoder_config.architectures[0] if model_class == "CLIPTextModel": from transformers import CLIPTextModel return CLIPTextModel elif model_class == "CLIPTextModelWithProjection": from transformers import CLIPTextModelWithProjection return CLIPTextModelWithProjection else: raise ValueError(f"{model_class} is not supported.") def parse_args(input_args=None): parser = argparse.ArgumentParser(description="Simple example of a training script.") 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 ๐Ÿค— Datasets can understand.To load the custom captions, the training set directory needs to follow the structure of a " "datasets ImageFolder, containing both the images and the corresponding caption for each image. see: " "https://huggingface.co/docs/datasets/image_dataset for more information" ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset. In some cases, a dataset may have more than one configuration (for example " "if it contains different subsets of data within, and you only wish to load a specific subset - in that case specify the desired configuration using --dataset_config_name. Leave as " "None if there's only one config.", ) parser.add_argument( "--instance_data_dir", type=str, default=None, help="A path to local folder containing the training data of instance images. Specify this arg instead of " "--dataset_name if you wish to train using a local folder without custom captions. If you wish to train with custom captions please specify " "--dataset_name instead.", ) 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( "--token_abstraction", type=str, default="TOK", help="identifier specifying the instance(or instances) as used in instance_prompt, validation prompt, " "captions - e.g. TOK. To use multiple identifiers, please specify them in a comma separated string - e.g. " "'TOK,TOK2,TOK3' etc.", ) parser.add_argument( "--num_new_tokens_per_abstraction", type=int, default=2, help="number of new tokens inserted to the tokenizers per token_abstraction identifier when " "--train_text_encoder_ti = True. By default, each --token_abstraction (e.g. TOK) is mapped to 2 new " "tokens - ", ) 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("--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( "--clip_skip", type=int, default=None, help="Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that " "the output of the pre-final layer will be used for computing the prompt embeddings.", ) 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( "--train_text_encoder_ti", action="store_true", help=("Whether to use textual inversion"), ) parser.add_argument( "--train_text_encoder_ti_frac", type=float, default=0.5, help=("The percentage of epochs to perform textual inversion"), ) parser.add_argument( "--train_text_encoder_frac", type=float, default=1.0, help=("The percentage of epochs to perform text encoder tuning"), ) 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 Prodigy 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=None, 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("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") 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("--noise_offset", type=float, default=0, help="The scale of noise offset.") 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`" ), ) parser.add_argument( "--lora_unet_blocks", type=str, default=None, help=( "the U-net blocks to tune during training. please specify them in a comma separated string, e.g. `unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1` etc." "NOTE: By default (if not specified) - regular LoRA training is performed. " "if --use_blora is enabled, this arg will be ignored, since in B-LoRA training, targeted U-net blocks are `unet.up_blocks.0.attentions.0` and `unet.up_blocks.0.attentions.1`" ), ) parser.add_argument( "--use_blora", action="store_true", help=( "Whether to train a B-LoRA as proposed in- Implicit Style-Content Separation using B-LoRA https://arxiv.org/abs/2403.14572. " ), ) parser.add_argument( "--cache_latents", action="store_true", default=False, help="Cache the VAE latents", ) 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`") if args.train_text_encoder and args.train_text_encoder_ti: raise ValueError( "Specify only one of `--train_text_encoder` or `--train_text_encoder_ti. " "For full LoRA text encoder training check --train_text_encoder, for textual " "inversion training check `--train_text_encoder_ti`" ) if args.use_blora and args.lora_unet_blocks: warnings.warn( "You specified both `--use_blora` and `--lora_unet_blocks`, for B-LoRA training, target unet blocks are: `unet.up_blocks.0.attentions.0` and `unet.up_blocks.0.attentions.1`. " "If you wish to target different U-net blocks, don't enable `--use_blora`" ) 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 # Taken (and slightly modified) from B-LoRA repo https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py def is_belong_to_blocks(key, blocks): try: for g in blocks: if g in key: return True return False except Exception as e: raise type(e)(f"failed to is_belong_to_block, due to: {e}") def get_unet_lora_target_modules(unet, use_blora, target_blocks=None): if use_blora: content_b_lora_blocks = "unet.up_blocks.0.attentions.0" style_b_lora_blocks = "unet.up_blocks.0.attentions.1" target_blocks = [content_b_lora_blocks, style_b_lora_blocks] try: blocks = [(".").join(blk.split(".")[1:]) for blk in target_blocks] attns = [ attn_processor_name.rsplit(".", 1)[0] for attn_processor_name, _ in unet.attn_processors.items() if is_belong_to_blocks(attn_processor_name, blocks) ] target_modules = [f"{attn}.{mat}" for mat in ["to_k", "to_q", "to_v", "to_out.0"] for attn in attns] return target_modules except Exception as e: raise type(e)( f"failed to get_target_modules, due to: {e}. " f"Please check the modules specified in --lora_unet_blocks are correct" ) # Taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py class TokenEmbeddingsHandler: def __init__(self, text_encoders, tokenizers): self.text_encoders = text_encoders self.tokenizers = tokenizers self.train_ids: Optional[torch.Tensor] = None self.inserting_toks: Optional[List[str]] = None self.embeddings_settings = {} def initialize_new_tokens(self, inserting_toks: List[str]): idx = 0 for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders): assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings." assert all( isinstance(tok, str) for tok in inserting_toks ), "All elements in inserting_toks should be strings." self.inserting_toks = inserting_toks special_tokens_dict = {"additional_special_tokens": self.inserting_toks} tokenizer.add_special_tokens(special_tokens_dict) text_encoder.resize_token_embeddings(len(tokenizer)) self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks) # random initialization of new tokens std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std() print(f"{idx} text encoder's std_token_embedding: {std_token_embedding}") text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = ( torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size) .to(device=self.device) .to(dtype=self.dtype) * std_token_embedding ) self.embeddings_settings[ f"original_embeddings_{idx}" ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone() self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding inu = torch.ones((len(tokenizer),), dtype=torch.bool) inu[self.train_ids] = False self.embeddings_settings[f"index_no_updates_{idx}"] = inu print(self.embeddings_settings[f"index_no_updates_{idx}"].shape) idx += 1 def save_embeddings(self, file_path: str): assert self.train_ids is not None, "Initialize new tokens before saving embeddings." tensors = {} # text_encoder_0 - CLIP ViT-L/14, text_encoder_1 - CLIP ViT-G/14 idx_to_text_encoder_name = {0: "clip_l", 1: "clip_g"} for idx, text_encoder in enumerate(self.text_encoders): assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[0] == len( self.tokenizers[0] ), "Tokenizers should be the same." new_token_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] # New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), "clip_g" (for # text_encoder 1) to keep compatible with the ecosystem. # Note: When loading with diffusers, any name can work - simply specify in inference tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings # tensors[f"text_encoders_{idx}"] = new_token_embeddings save_file(tensors, file_path) @property def dtype(self): return self.text_encoders[0].dtype @property def device(self): return self.text_encoders[0].device @torch.no_grad() def retract_embeddings(self): for idx, text_encoder in enumerate(self.text_encoders): index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"] text_encoder.text_model.embeddings.token_embedding.weight.data[index_no_updates] = ( self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates] .to(device=text_encoder.device) .to(dtype=text_encoder.dtype) ) # for the parts that were updated, we need to normalize them # to have the same std as before std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"] index_updates = ~index_no_updates new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] off_ratio = std_token_embedding / new_embeddings.std() new_embeddings = new_embeddings * (off_ratio**0.1) text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings 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, train_text_encoder_ti, class_data_root=None, class_num=None, token_abstraction_dict=None, # token mapping for textual inversion size=(1080,1920), 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 self.token_abstraction_dict = token_abstraction_dict self.train_text_encoder_ti = train_text_encoder_ti # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory, # we load the training data using load_dataset if args.dataset_name is not None: try: from datasets import load_dataset except ImportError: raise ImportError( "You are trying to load your data using the datasets library. If you wish to train using custom " "captions please install the datasets library: `pip install datasets`. If you wish to load a " "local folder containing images only, specify --instance_data_dir instead." ) # Downloading and loading a dataset from the hub. # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, ) # Preprocessing the datasets. column_names = dataset["train"].column_names # 6. Get the column names for input/target. if args.image_column is None: image_column = column_names[0] logger.info(f"image column defaulting to {image_column}") else: image_column = args.image_column if image_column not in column_names: raise ValueError( f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" ) instance_images = dataset["train"][image_column] if args.caption_column is None: logger.info( "No caption column provided, defaulting to instance_prompt for all images. If your dataset " "contains captions/prompts for the images, make sure to specify the " "column as --caption_column" ) self.custom_instance_prompts = None else: if args.caption_column not in column_names: raise ValueError( f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" ) custom_instance_prompts = dataset["train"][args.caption_column] # create final list of captions according to --repeats self.custom_instance_prompts = [] for caption in custom_instance_prompts: self.custom_instance_prompts.extend(itertools.repeat(caption, repeats)) else: self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): raise ValueError("Instance images root doesn't exists.") instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())] self.custom_instance_prompts = None self.instance_images = [] for img in instance_images: self.instance_images.extend(itertools.repeat(img, repeats)) # image processing to prepare for using SD-XL micro-conditioning self.original_sizes = [] self.crop_top_lefts = [] self.pixel_values = [] 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]), ] ) # if using B-LoRA for single image. do not use transformations single_image = len(self.instance_images) < 2 for image in self.instance_images: if not single_image: image = exif_transpose(image) if not image.mode == "RGB": image = image.convert("RGB") self.original_sizes.append((image.height, image.width)) image = train_resize(image) if not single_image and args.random_flip and random.random() < 0.5: # flip image = train_flip(image) y1 = max(0, int(round((image.height - 1080) / 2.0))) x1 = max(0, int(round((image.width - 1920) / 2.0))) image = train_crop(image) crop_top_left = (y1, x1) self.crop_top_lefts.append(crop_top_left) image = train_transforms(image) self.pixel_values.append(image) self.num_instance_images = len(self.instance_images) 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()) self.original_sizes_class_imgs = [] self.crop_top_lefts_class_imgs = [] self.pixel_values_class_imgs = [] self.class_images = [Image.open(path) for path in self.class_images_path] for image in self.class_images: image = exif_transpose(image) if not image.mode == "RGB": image = image.convert("RGB") self.original_sizes_class_imgs.append((image.height, image.width)) image = train_resize(image) if args.random_flip and random.random() < 0.5: # flip image = train_flip(image) y1 = max(0, int(round((image.height - 1080) / 2.0))) x1 = max(0, int(round((image.width - 1920) / 2.0))) image = train_crop(image) crop_top_left = (y1, x1) self.crop_top_lefts_class_imgs.append(crop_top_left) image = train_transforms(image) self.pixel_values_class_imgs.append(image) 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) 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 def __getitem__(self, index): example = {} example["instance_images"] = self.pixel_values[index % self.num_instance_images] example["original_size"] = self.original_sizes[index % self.num_instance_images] example["crop_top_left"] = self.crop_top_lefts[index % self.num_instance_images] if self.custom_instance_prompts: caption = self.custom_instance_prompts[index % self.num_instance_images] if caption: if self.train_text_encoder_ti: # replace instances of --token_abstraction in caption with the new tokens: "" etc. for token_abs, token_replacement in self.token_abstraction_dict.items(): caption = caption.replace(token_abs, "".join(token_replacement)) example["instance_prompt"] = caption else: example["instance_prompt"] = self.instance_prompt else: # custom prompts were provided, but length does not match size of image dataset example["instance_prompt"] = self.instance_prompt if self.class_data_root: example["class_prompt"] = self.class_prompt example["class_images"] = self.pixel_values_class_imgs[index % self.num_class_images] example["class_original_size"] = self.original_sizes_class_imgs[index % self.num_class_images] example["class_crop_top_left"] = self.crop_top_lefts_class_imgs[index % self.num_class_images] 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["class_original_size"] for example in examples] crop_top_lefts += [example["class_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, add_special_tokens=False): text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, add_special_tokens=add_special_tokens, 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, clip_skip=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] if clip_skip is None: prompt_embeds = prompt_embeds[-1][-2] else: # "2" because SDXL always indexes from the penultimate layer. prompt_embeds = prompt_embeds[-1][-(clip_skip + 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) # 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: has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available() torch_dtype = torch.float16 if has_supported_fp16_accelerator 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 = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, 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 = insecure_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) model_id = args.hub_model_id or Path(args.output_dir).name repo_id = None if args.push_to_hub: repo_id = create_repo(repo_id=model_id, exist_ok=True, token=args.hub_token).repo_id # Load the tokenizers tokenizer_one = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, variant=args.variant, use_fast=False, ) tokenizer_two = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, variant=args.variant, use_fast=False, ) # import correct text encoder classes text_encoder_cls_one = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision ) text_encoder_cls_two = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2" ) # Load scheduler and models 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 = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant ) vae_path = ( args.pretrained_model_name_or_path if args.pretrained_vae_model_name_or_path is None else args.pretrained_vae_model_name_or_path ) vae = AutoencoderKL.from_pretrained( vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision, variant=args.variant, ) 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 = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant ) if args.train_text_encoder_ti: # we parse the provided token identifier (or identifiers) into a list. s.t. - "TOK" -> ["TOK"], "TOK, # TOK2" -> ["TOK", "TOK2"] etc. token_abstraction_list = "".join(args.token_abstraction.split()).split(",") logger.info(f"list of token identifiers: {token_abstraction_list}") token_abstraction_dict = {} token_idx = 0 for i, token in enumerate(token_abstraction_list): token_abstraction_dict[token] = [ f"" for j in range(args.num_new_tokens_per_abstraction) ] token_idx += args.num_new_tokens_per_abstraction - 1 # replace instances of --token_abstraction in --instance_prompt with the new tokens: "" etc. for token_abs, token_replacement in token_abstraction_dict.items(): args.instance_prompt = args.instance_prompt.replace(token_abs, "".join(token_replacement)) if args.with_prior_preservation: args.class_prompt = args.class_prompt.replace(token_abs, "".join(token_replacement)) if args.validation_prompt: args.validation_prompt = args.validation_prompt.replace(token_abs, "".join(token_replacement)) print("validation prompt:", args.validation_prompt) # initialize the new tokens for textual inversion embedding_handler = TokenEmbeddingsHandler( [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two] ) inserting_toks = [] for new_tok in token_abstraction_dict.values(): inserting_toks.extend(new_tok) embedding_handler.initialize_new_tokens(inserting_toks=inserting_toks) # 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 if args.use_blora: # if using B-LoRA, the targeted blocks to train are automatically set target_modules = get_unet_lora_target_modules(unet, use_blora=True) elif args.lora_unet_blocks: # if training specific unet blocks not in the B-LoRA scheme target_blocks_list = "".join(args.lora_unet_blocks.split()).split(",") logger.info(f"list of unet blocks to train: {target_blocks_list}") target_modules = get_unet_lora_target_modules(unet, use_blora=False, target_blocks=target_blocks_list) else: target_modules = ["to_k", "to_q", "to_v", "to_out.0"] unet_lora_config = LoraConfig( r=args.rank, use_dora=args.use_dora, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=target_modules, ) unet.add_adapter(unet_lora_config) # The text encoder comes from ๐Ÿค— transformers, so we cannot directly modify it. # So, instead, we monkey-patch the forward calls of its attention-blocks. if args.train_text_encoder: text_lora_config = LoraConfig( r=args.rank, use_dora=args.use_dora, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], ) text_encoder_one.add_adapter(text_lora_config) text_encoder_two.add_adapter(text_lora_config) # if we use textual inversion, we freeze all parameters except for the token embeddings # in text encoder elif args.train_text_encoder_ti: text_lora_parameters_one = [] for name, param in text_encoder_one.named_parameters(): if "token_embedding" in name: # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 param.data = param.to(dtype=torch.float32) param.requires_grad = True text_lora_parameters_one.append(param) else: param.requires_grad = False text_lora_parameters_two = [] for name, param in text_encoder_two.named_parameters(): if "token_embedding" in name: # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 param.data = param.to(dtype=torch.float32) param.requires_grad = True text_lora_parameters_two.append(param) else: param.requires_grad = False 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))): if args.train_text_encoder: 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))): if args.train_text_encoder: 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, ) if args.train_text_encoder_ti: embedding_handler.save_embeddings(f"{args.output_dir}/{Path(args.output_dir).name}_emb.safetensors") 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 = StableDiffusionLoraLoaderMixin.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())) # If neither --train_text_encoder nor --train_text_encoder_ti, text_encoders remain frozen during training freeze_text_encoder = not (args.train_text_encoder or args.train_text_encoder_ti) # Optimization parameters unet_lora_parameters_with_lr = {"params": unet_lora_parameters, "lr": args.learning_rate} if not freeze_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 if args.adam_weight_decay_text_encoder else args.adam_weight_decay, "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 if args.adam_weight_decay_text_encoder else args.adam_weight_decay, "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, train_text_encoder_ti=args.train_text_encoder_ti, class_data_root=args.class_data_dir if args.with_prior_preservation else None, token_abstraction_dict=token_abstraction_dict if args.train_text_encoder_ti else None, class_num=args.num_class_images, size=(1080,1920), 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, ) # 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(crops_coords_top_left, original_size=None): # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids target_size = (1080, 1920) 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, clip_skip): with torch.no_grad(): prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt, clip_skip) 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 freeze_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, args.clip_skip ) # Handle class prompt for prior-preservation. if args.with_prior_preservation: if freeze_text_encoder: class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings( args.class_prompt, text_encoders, tokenizers ) # Clear the memory here if freeze_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 --train_text_encoder_ti we need add_special_tokens to be True fo textual inversion add_special_tokens = True if args.train_text_encoder_ti else False if not train_dataset.custom_instance_prompts: if freeze_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 optimizing 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, add_special_tokens) tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, add_special_tokens) if args.with_prior_preservation: class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, add_special_tokens) class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, add_special_tokens) tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0) tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0) if args.cache_latents: latents_cache = [] for batch in tqdm(train_dataloader, desc="Caching latents"): with torch.no_grad(): print(f"{batch['pixel_values'].shape=}") batch["pixel_values"] = batch["pixel_values"].to( accelerator.device, non_blocking=True, dtype=torch.float32 ) latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist) # if args.validation_prompt is None: #del vae #if torch.cuda.is_available(): #torch.cuda.empty_cache() # Scheduler and math around the number of training steps. # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation. num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes if args.max_train_steps is None: len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes) num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps) num_training_steps_for_scheduler = ( args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes ) else: num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=num_warmup_steps_for_scheduler, num_training_steps=num_training_steps_for_scheduler, num_cycles=args.lr_num_cycles, power=args.lr_power, ) # Prepare everything with our `accelerator`. if not freeze_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 args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes: logger.warning( f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match " f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. " f"This inconsistency may result in the learning rate scheduler not functioning properly." ) # 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 if args.train_text_encoder: num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs) elif args.train_text_encoder_ti: # args.train_text_encoder_ti num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs) # flag used for textual inversion pivoted = False for epoch in range(first_epoch, args.num_train_epochs): unet.train() # if performing any kind of optimization of text_encoder params if args.train_text_encoder or args.train_text_encoder_ti: if epoch == num_train_epochs_text_encoder: print("PIVOT HALFWAY", epoch) # stopping optimization of text_encoder params # this flag is used to reset the optimizer to optimize only on unet params pivoted = True else: # still optimizing the text encoder text_encoder_one.train() text_encoder_two.train() # set top parameter requires_grad = True for gradient checkpointing works if args.train_text_encoder: 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): if pivoted: # stopping optimization of text_encoder params # re setting the optimizer to optimize only on unet params optimizer.param_groups[1]["lr"] = 0.0 optimizer.param_groups[2]["lr"] = 0.0 with accelerator.accumulate(unet): prompts = batch["prompts"] # encode batch prompts when custom prompts are provided for each image - if train_dataset.custom_instance_prompts: if freeze_text_encoder: prompt_embeds, unet_add_text_embeds = compute_text_embeddings( prompts, text_encoders, tokenizers, args.clip_skip ) else: tokens_one = tokenize_prompt(tokenizer_one, prompts, add_special_tokens) tokens_two = tokenize_prompt(tokenizer_two, prompts, add_special_tokens) if args.cache_latents: model_input = latents_cache[step].sample() else: pixel_values = batch["pixel_values"].to(dtype=vae.dtype) 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) if args.noise_offset: # https://www.crosslabs.org//blog/diffusion-with-offset-noise noise += args.noise_offset * torch.randn( (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device ) bsz = model_input.shape[0] # 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: 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 freeze_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], clip_skip=args.clip_skip, ) 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: loss = F.mse_loss(model_pred.float(), target.float(), 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. if args.with_prior_preservation: # if we're using prior preservation, we calc snr for instance loss only - # and hence only need timesteps corresponding to instance images snr_timesteps, _ = torch.chunk(timesteps, 2, dim=0) else: snr_timesteps = timesteps snr = compute_snr(noise_scheduler, snr_timesteps) base_weight = ( torch.stack([snr, args.snr_gamma * torch.ones_like(snr_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 or args.train_text_encoder_ti) else unet_lora_parameters ) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # every step, we reset the embeddings to the original embeddings. if args.train_text_encoder_ti: embedding_handler.retract_embeddings() # 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: # create pipeline if freeze_text_encoder: text_encoder_one = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant, ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant, ) pipeline = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, tokenizer=tokenizer_one, tokenizer_2=tokenizer_two, text_encoder=accelerator.unwrap_model(text_encoder_one), text_encoder_2=accelerator.unwrap_model(text_encoder_two), unet=accelerator.unwrap_model(unet), revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) pipeline_args = {"prompt": args.validation_prompt, "width":1920, "height":1080} images = log_validation( pipeline, args, accelerator, pipeline_args, epoch, ) # 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.train_text_encoder_ti: embeddings_path = f"{args.output_dir}/{args.output_dir}_emb.safetensors" embedding_handler.save_embeddings(embeddings_path) # Final inference # Load previous pipeline vae = AutoencoderKL.from_pretrained( vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) pipeline = StableDiffusionXLPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) # load attention processors pipeline.load_lora_weights(args.output_dir) # run inference images = [] if args.validation_prompt and args.num_validation_images > 0: pipeline_args = {"prompt": args.validation_prompt, "num_inference_steps": 25, "width":1920, "height":1080} images = log_validation( pipeline, args, accelerator, pipeline_args, epoch, is_final_validation=True, ) # Convert to WebUI 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}/{Path(args.output_dir).name}.safetensors") save_model_card( model_id if not args.push_to_hub else repo_id, use_dora=args.use_dora, images=images, base_model=args.pretrained_model_name_or_path, train_text_encoder=args.train_text_encoder, train_text_encoder_ti=args.train_text_encoder_ti, token_abstraction_dict=train_dataset.token_abstraction_dict, instance_prompt=args.instance_prompt, validation_prompt=args.validation_prompt, repo_folder=args.output_dir, vae_path=args.pretrained_vae_model_name_or_path, ) if args.push_to_hub: 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)