#!/usr/bin/env python # coding=utf-8 # Copyright 2023 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 hashlib import logging import math import os import shutil import warnings from pathlib import Path from typing import Dict 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 ProjectConfiguration, set_seed from huggingface_hub import create_repo, upload_folder from packaging import version from PIL import Image from PIL.ImageOps import exif_transpose from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm from transformers import AutoTokenizer, PretrainedConfig import diffusers from diffusers import ( AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel, ) from diffusers.loaders import LoraLoaderMixin from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0 from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.import_utils import is_xformers_available # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.18.0.dev0") logger = get_logger(__name__) def save_model_card( repo_id: str, images=None, base_model=str, train_text_encoder=False, prompt=str, repo_folder=None, ): img_str = "" for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f"![img_{i}](./image_{i}.png)\n" yaml = f""" --- license: creativeml-openrail-m base_model: {base_model} instance_prompt: {prompt} tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- """ model_card = f""" # LoRA DreamBooth - {repo_id} These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n {img_str} LoRA for the text encoder was enabled: {train_text_encoder}. """ with open(os.path.join(repo_folder, "README.md"), "w") as f: f.write(yaml + model_card) 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( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--instance_data_dir", type=str, default=None, required=True, help="A folder containing the training data of instance images.", ) 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", ) 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( "--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=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--crops_coords_top_left_h", type=int, default=0, help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), ) parser.add_argument( "--crops_coords_top_left_w", type=int, default=0, help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), ) 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( "--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=5e-4, help="Initial learning rate (after the potential warmup period) 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( "--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( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") 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." ) if input_args is not None: args = parser.parse_args(input_args) else: args = parser.parse_args() 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.") if args.train_text_encoder and args.pre_compute_text_embeddings: raise ValueError("`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`") 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 and the tokenizes prompts. """ def __init__( self, instance_data_root, instance_prompt, class_data_root=None, class_prompt=None, class_num=None, size=1024, center_crop=False, instance_prompt_hidden_states=None, class_prompt_hidden_states=None, instance_unet_added_conditions=None, class_unet_added_conditions=None, ): self.size = size self.center_crop = center_crop self.instance_prompt_hidden_states = instance_prompt_hidden_states self.class_prompt_hidden_states = class_prompt_hidden_states self.instance_unet_added_conditions = instance_unet_added_conditions self.class_unet_added_conditions = class_unet_added_conditions 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_path = list(Path(instance_data_root).iterdir()) self.num_instance_images = len(self.instance_images_path) self.instance_prompt = instance_prompt self._length = self.num_instance_images if class_data_root is not None: self.class_data_root = Path(class_data_root) self.class_data_root.mkdir(parents=True, exist_ok=True) self.class_images_path = list(self.class_data_root.iterdir()) if class_num is not None: self.num_class_images = min(len(self.class_images_path), class_num) else: self.num_class_images = len(self.class_images_path) self._length = max(self.num_class_images, self.num_instance_images) self.class_prompt = class_prompt else: self.class_data_root = None self.image_transforms = transforms.Compose( [ transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def __len__(self): return self._length def __getitem__(self, index): example = {} instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) instance_image = exif_transpose(instance_image) if not instance_image.mode == "RGB": instance_image = instance_image.convert("RGB") example["instance_images"] = self.image_transforms(instance_image) example["instance_prompt_ids"] = self.instance_prompt_hidden_states example["instance_added_cond_kwargs"] = self.instance_unet_added_conditions if self.class_data_root: class_image = Image.open(self.class_images_path[index % self.num_class_images]) class_image = exif_transpose(class_image) if not class_image.mode == "RGB": class_image = class_image.convert("RGB") example["class_images"] = self.image_transforms(class_image) example["class_prompt_ids"] = self.class_prompt_hidden_states example["class_added_cond_kwargs"] = self.class_unet_added_conditions return example def collate_fn(examples, with_prior_preservation=False): has_attention_mask = "instance_attention_mask" in examples[0] input_ids = [example["instance_prompt_ids"] for example in examples] pixel_values = [example["instance_images"] for example in examples] add_text_embeds = [example["instance_added_cond_kwargs"]["text_embeds"] for example in examples] add_time_ids = [example["instance_added_cond_kwargs"]["time_ids"] for example in examples] if has_attention_mask: attention_mask = [example["instance_attention_mask"] for example in examples] # Concat class and instance examples for prior preservation. # We do this to avoid doing two forward passes. if with_prior_preservation: input_ids += [example["class_prompt_ids"] for example in examples] pixel_values += [example["class_images"] for example in examples] add_text_embeds += [example["class_added_cond_kwargs"]["text_embeds"] for example in examples] add_time_ids += [example["class_added_cond_kwargs"]["time_ids"] for example in examples] if has_attention_mask: attention_mask += [example["class_attention_mask"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = torch.cat(input_ids, dim=0) add_text_embeds = torch.cat(add_text_embeds, dim=0) add_time_ids = torch.cat(add_time_ids, dim=0) batch = { "input_ids": input_ids, "pixel_values": pixel_values, "unet_added_conditions": {"text_embeds": add_text_embeds, "time_ids": add_time_ids}, } if has_attention_mask: batch["attention_mask"] = attention_mask return batch class PromptDataset(Dataset): "A simple dataset to prepare the prompts to generate class images on multiple GPUs." def __init__(self, prompt, num_samples): self.prompt = prompt self.num_samples = num_samples def __len__(self): return self.num_samples def __getitem__(self, index): example = {} example["prompt"] = self.prompt example["index"] = index return example # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt def encode_prompt(text_encoders, tokenizers, prompt): prompt_embeds_list = [] for tokenizer, text_encoder in zip(tokenizers, text_encoders): 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 untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = text_encoder( text_input_ids.to(text_encoder.device), output_hidden_states=True, ) # We are only ALWAYS interested in the pooled output of the final text encoder pooled_prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.hidden_states[-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 unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]: """ Returns: a state dict containing just the attention processor parameters. """ attn_processors = unet.attn_processors attn_processors_state_dict = {} for attn_processor_key, attn_processor in attn_processors.items(): for parameter_key, parameter in attn_processor.state_dict().items(): attn_processors_state_dict[f"{attn_processor_key}.{parameter_key}"] = parameter return attn_processors_state_dict def main(args): logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) if args.report_to == "wandb": if not is_wandb_available(): raise ImportError("Make sure to install wandb if you want to use it for logging during training.") import wandb if args.train_text_encoder: raise NotImplementedError("Text encoder training not yet supported.") # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Generate class images if prior preservation is enabled. if args.with_prior_preservation: class_images_dir = Path(args.class_data_dir) if not class_images_dir.exists(): class_images_dir.mkdir(parents=True) cur_class_images = len(list(class_images_dir.iterdir())) if cur_class_images < args.num_class_images: torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 if args.prior_generation_precision == "fp32": torch_dtype = torch.float32 elif args.prior_generation_precision == "fp16": torch_dtype = torch.float16 elif args.prior_generation_precision == "bf16": torch_dtype = torch.bfloat16 pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None, revision=args.revision, ) pipeline.set_progress_bar_config(disable=True) num_new_images = args.num_class_images - cur_class_images logger.info(f"Number of class images to sample: {num_new_images}.") sample_dataset = PromptDataset(args.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) sample_dataloader = accelerator.prepare(sample_dataloader) pipeline.to(accelerator.device) for example in tqdm( sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process ): images = pipeline(example["prompt"]).images for i, image in enumerate(images): hash_image = hashlib.sha1(image.tobytes()).hexdigest() image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" image.save(image_filename) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load the tokenizers tokenizer_one = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False ) tokenizer_two = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False ) # import correct text encoder classes text_encoder_cls_one = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision ) text_encoder_cls_two = import_model_class_from_model_name_or_path( args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2" ) # Load scheduler and models noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") text_encoder_one = text_encoder_cls_one.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) text_encoder_two = text_encoder_cls_two.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision ) vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision ) # 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 weigths (vae, non-lora text_encoder and non-lora unet) to half-precision # as these weights are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move unet, vae and text_encoder to device and cast to weight_dtype # The VAE is in float32 to avoid NaN losses. unet.to(accelerator.device, dtype=weight_dtype) 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.warn( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") # now we will add new LoRA weights to the attention layers # Set correct lora layers unet_lora_attn_procs = {} unet_lora_parameters = [] for name, attn_processor in unet.attn_processors.items(): cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] lora_attn_processor_class = ( LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor ) module = lora_attn_processor_class(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim) unet_lora_attn_procs[name] = module unet_lora_parameters.extend(module.parameters()) unet.set_attn_processor(unet_lora_attn_procs) # unet_lora_layers = AttnProcsLayers(unet.attn_processors) # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format def save_model_hook(models, weights, output_dir): # 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 for model in models: unet_lora_layers_to_save = unet_attn_processors_state_dict(model) # make sure to pop weight so that corresponding model is not saved again weights.pop() LoraLoaderMixin.save_lora_weights( output_dir, unet_lora_layers=unet_lora_layers_to_save, text_encoder_lora_layers=None, ) def load_model_hook(models, input_dir): unet_ = None text_encoder_ = None while len(models) > 0: model = models.pop() if isinstance(model, type(accelerator.unwrap_model(unet))): unet_ = model else: raise ValueError(f"unexpected save model: {model.__class__}") lora_state_dict, network_alpha = LoraLoaderMixin.lora_state_dict(input_dir) LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alpha=network_alpha, unet=unet_) LoraLoaderMixin.load_lora_into_text_encoder( lora_state_dict, network_alpha=network_alpha, text_encoder=text_encoder_ ) accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." ) optimizer_class = bnb.optim.AdamW8bit else: optimizer_class = torch.optim.AdamW # Optimizer creation params_to_optimize = unet_lora_parameters optimizer = optimizer_class( params_to_optimize, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # We ALWAYS pre-compute the additional condition embeddings needed for SDXL # UNet as the model is already big and it uses two text encoders. # TODO: when we add support for text encoder training, will reivist. tokenizers = [tokenizer_one, tokenizer_two] text_encoders = [text_encoder_one, text_encoder_two] # Here, we compute not just the text embeddings but also the additional embeddings # needed for the SD XL UNet to operate. def compute_embeddings(prompt, text_encoders, tokenizers): original_size = (args.resolution, args.resolution) target_size = (args.resolution, args.resolution) crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w) with torch.no_grad(): prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt) add_text_embeds = pooled_prompt_embeds # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids add_time_ids = list(original_size + crops_coords_top_left + target_size) add_time_ids = torch.tensor([add_time_ids]) prompt_embeds = prompt_embeds.to(accelerator.device) add_text_embeds = add_text_embeds.to(accelerator.device) add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype) unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} return prompt_embeds, unet_added_cond_kwargs instance_prompt_hidden_states, instance_unet_added_conditions = compute_embeddings( args.instance_prompt, text_encoders, tokenizers ) class_prompt_hidden_states, class_unet_added_conditions = None, None if args.with_prior_preservation: class_prompt_hidden_states, class_unet_added_conditions = compute_embeddings( args.class_prompt, text_encoders, tokenizers ) del tokenizers, text_encoders gc.collect() torch.cuda.empty_cache() # Dataset and DataLoaders creation: train_dataset = DreamBoothDataset( instance_data_root=args.instance_data_dir, instance_prompt=args.instance_prompt, class_data_root=args.class_data_dir if args.with_prior_preservation else None, class_prompt=args.class_prompt, class_num=args.num_class_images, size=args.resolution, center_crop=args.center_crop, instance_prompt_hidden_states=instance_prompt_hidden_states, class_prompt_hidden_states=class_prompt_hidden_states, instance_unet_added_conditions=instance_unet_added_conditions, class_unet_added_conditions=class_unet_added_conditions, ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation), num_workers=args.dataloader_num_workers, ) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, num_cycles=args.lr_num_cycles, power=args.lr_power, ) # Prepare everything with our `accelerator`. 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: accelerator.init_trackers("dreambooth-lora-sd-xl", 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 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) resume_global_step = global_step * args.gradient_accumulation_steps first_epoch = global_step // num_update_steps_per_epoch resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps) # Only show the progress bar once on each machine. progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") for epoch in range(first_epoch, args.num_train_epochs): unet.train() for step, batch in enumerate(train_dataloader): # Skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) continue with accelerator.accumulate(unet): # pixel_values = batch["pixel_values"].to(dtype=weight_dtype) # Convert images to latent space model_input = vae.encode(batch["pixel_values"]).latent_dist.sample() model_input = model_input * vae.config.scaling_factor model_input = model_input.to(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 timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device ) timesteps = timesteps.long() # Add noise to the model input according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps) # Predict the noise residual model_pred = unet( noisy_model_input, timesteps, batch["input_ids"], added_cond_kwargs=batch["unet_added_conditions"] ).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(model_input, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") if args.with_prior_preservation: # Chunk the noise and model_pred into two parts and compute the loss on each part separately. model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) target, target_prior = torch.chunk(target, 2, dim=0) # Compute instance loss loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = unet_lora_parameters accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 if accelerator.is_main_process: if global_step % args.checkpointing_steps == 0: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) if global_step >= args.max_train_steps: break if accelerator.is_main_process: if args.validation_prompt is not None and epoch % args.validation_epochs == 0: logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) # create pipeline pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, unet=accelerator.unwrap_model(unet), revision=args.revision, torch_dtype=weight_dtype, ) # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it scheduler_args = {} if "variance_type" in pipeline.scheduler.config: variance_type = pipeline.scheduler.config.variance_type if variance_type in ["learned", "learned_range"]: variance_type = "fixed_small" scheduler_args["variance_type"] = variance_type pipeline.scheduler = DPMSolverMultistepScheduler.from_config( pipeline.scheduler.config, **scheduler_args ) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) # run inference generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None pipeline_args = {"prompt": args.validation_prompt} images = [ pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images) ] for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { "validation": [ wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images) ] } ) del pipeline torch.cuda.empty_cache() # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = accelerator.unwrap_model(unet) unet = unet.to(torch.float32) unet_lora_layers = unet_attn_processors_state_dict(unet) LoraLoaderMixin.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_layers, text_encoder_lora_layers=None, ) # Final inference # Load previous pipeline pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype ) # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it scheduler_args = {} if "variance_type" in pipeline.scheduler.config: variance_type = pipeline.scheduler.config.variance_type if variance_type in ["learned", "learned_range"]: variance_type = "fixed_small" scheduler_args["variance_type"] = variance_type pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args) pipeline = pipeline.to(accelerator.device) # load attention processors pipeline.load_lora_weights(args.output_dir) # run inference images = [] if args.validation_prompt and args.num_validation_images > 0: generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None images = [ pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0] for _ in range(args.num_validation_images) ] for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { "test": [ wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images) ] } ) if args.push_to_hub: save_model_card( repo_id, images=images, base_model=args.pretrained_model_name_or_path, train_text_encoder=args.train_text_encoder, prompt=args.instance_prompt, repo_folder=args.output_dir, ) 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)