#!/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 functools import gc import logging import math import os import random import shutil from pathlib import Path import accelerate import numpy as np import torch import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset from huggingface_hub import create_repo, upload_folder from packaging import version from PIL import Image from torchvision import transforms from tqdm.auto import tqdm from transformers import AutoTokenizer, PretrainedConfig import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter, UNet2DConditionModel, ) from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module MAX_SEQ_LENGTH = 77 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 image_grid(imgs, rows, cols): assert len(imgs) == rows * cols w, h = imgs[0].size grid = Image.new("RGB", size=(cols * w, rows * h)) for i, img in enumerate(imgs): grid.paste(img, box=(i % cols * w, i // cols * h)) return grid def log_validation(vae, unet, adapter, args, accelerator, weight_dtype, step): logger.info("Running validation... ") adapter = accelerator.unwrap_model(adapter) pipeline = StableDiffusionXLAdapterPipeline.from_pretrained( args.pretrained_model_name_or_path, vae=vae, unet=unet, adapter=adapter, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) if args.enable_xformers_memory_efficient_attention: pipeline.enable_xformers_memory_efficient_attention() if args.seed is None: generator = None else: generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if len(args.validation_image) == len(args.validation_prompt): validation_images = args.validation_image validation_prompts = args.validation_prompt elif len(args.validation_image) == 1: validation_images = args.validation_image * len(args.validation_prompt) validation_prompts = args.validation_prompt elif len(args.validation_prompt) == 1: validation_images = args.validation_image validation_prompts = args.validation_prompt * len(args.validation_image) else: raise ValueError( "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`" ) image_logs = [] for validation_prompt, validation_image in zip(validation_prompts, validation_images): validation_image = Image.open(validation_image).convert("RGB") validation_image = validation_image.resize((args.resolution, args.resolution)) images = [] for _ in range(args.num_validation_images): with torch.autocast("cuda"): image = pipeline( prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator ).images[0] images.append(image) image_logs.append( {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt} ) for tracker in accelerator.trackers: if tracker.name == "tensorboard": for log in image_logs: images = log["images"] validation_prompt = log["validation_prompt"] validation_image = log["validation_image"] formatted_images = [] formatted_images.append(np.asarray(validation_image)) for image in images: formatted_images.append(np.asarray(image)) formatted_images = np.stack(formatted_images) tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC") elif tracker.name == "wandb": formatted_images = [] for log in image_logs: images = log["images"] validation_prompt = log["validation_prompt"] validation_image = log["validation_image"] formatted_images.append(wandb.Image(validation_image, caption="adapter conditioning")) for image in images: image = wandb.Image(image, caption=validation_prompt) formatted_images.append(image) tracker.log({"validation": formatted_images}) else: logger.warning(f"image logging not implemented for {tracker.name}") del pipeline gc.collect() torch.cuda.empty_cache() return image_logs 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 save_model_card(repo_id: str, image_logs: dict = None, base_model: str = None, repo_folder: str = None): img_str = "" if image_logs is not None: img_str = "You can find some example images below.\n" for i, log in enumerate(image_logs): images = log["images"] validation_prompt = log["validation_prompt"] validation_image = log["validation_image"] validation_image.save(os.path.join(repo_folder, "image_control.png")) img_str += f"prompt: {validation_prompt}\n" images = [validation_image] + images image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png")) img_str += f"![images_{i})](./images_{i}.png)\n" model_description = f""" # t2iadapter-{repo_id} These are t2iadapter weights trained on {base_model} with new type of conditioning. {img_str} """ model_card = load_or_create_model_card( repo_id_or_path=repo_id, from_training=True, license="creativeml-openrail-m", base_model=base_model, model_description=model_description, inference=True, ) tags = [ "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "t2iadapter", "diffusers-training", ] model_card = populate_model_card(model_card, tags=tags) model_card.save(os.path.join(repo_folder, "README.md")) def parse_args(input_args=None): parser = argparse.ArgumentParser(description="Simple example of a ControlNet 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 an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.", ) parser.add_argument( "--adapter_model_name_or_path", type=str, default=None, help="Path to pretrained adapter model or model identifier from huggingface.co/models." " If not specified adapter weights are initialized w.r.t the configurations of SDXL.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help=( "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be" " float32 precision." ), ) 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( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--output_dir", type=str, default="t2iadapter-model", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) 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( "--detection_resolution", type=int, default=None, 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( "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." ) 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. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. " "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference." "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components." "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step" "instructions." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=3, 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-6, 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( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--dataloader_num_workers", type=int, default=1, help=("Number of subprocesses to use for data loading."), ) 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( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument( "--set_grads_to_none", action="store_true", help=( "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain" " behaviors, so disable this argument if it causes any problems. More info:" " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html" ), ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." ), ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing the target image." ) parser.add_argument( "--conditioning_image_column", type=str, default="conditioning_image", help="The column of the dataset containing the adapter conditioning image.", ) parser.add_argument( "--caption_column", type=str, default="text", help="The column of the dataset containing a caption or a list of captions.", ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--proportion_empty_prompts", type=float, default=0, help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).", ) parser.add_argument( "--validation_prompt", type=str, default=None, nargs="+", help=( "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`." " Provide either a matching number of `--validation_image`s, a single `--validation_image`" " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s." ), ) parser.add_argument( "--validation_image", type=str, default=None, nargs="+", help=( "A set of paths to the t2iadapter conditioning image be evaluated every `--validation_steps`" " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a" " a single `--validation_prompt` to be used with all `--validation_image`s, or a single" " `--validation_image` that will be used with all `--validation_prompt`s." ), ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair", ) parser.add_argument( "--validation_steps", type=int, default=100, help=( "Run validation every X steps. Validation consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`" " and logging the images." ), ) parser.add_argument( "--tracker_project_name", type=str, default="sd_xl_train_t2iadapter", help=( "The `project_name` argument passed to Accelerator.init_trackers for" " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator" ), ) 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.train_data_dir is None: raise ValueError("Specify either `--dataset_name` or `--train_data_dir`") if args.dataset_name is not None and args.train_data_dir is not None: raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`") if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1: raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].") if args.validation_prompt is not None and args.validation_image is None: raise ValueError("`--validation_image` must be set if `--validation_prompt` is set") if args.validation_prompt is None and args.validation_image is not None: raise ValueError("`--validation_prompt` must be set if `--validation_image` is set") if ( args.validation_image is not None and args.validation_prompt is not None and len(args.validation_image) != 1 and len(args.validation_prompt) != 1 and len(args.validation_image) != len(args.validation_prompt) ): raise ValueError( "Must provide either 1 `--validation_image`, 1 `--validation_prompt`," " or the same number of `--validation_prompt`s and `--validation_image`s" ) if args.resolution % 8 != 0: raise ValueError( "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the t2iadapter encoder." ) return args def get_train_dataset(args, accelerator): # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, ) else: if args.train_data_dir is not None: dataset = load_dataset( args.train_data_dir, cache_dir=args.cache_dir, ) # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script # Preprocessing the datasets. # We need to tokenize inputs and targets. 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)}" ) if args.caption_column is None: caption_column = column_names[1] logger.info(f"caption column defaulting to {caption_column}") else: caption_column = args.caption_column if 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)}" ) if args.conditioning_image_column is None: conditioning_image_column = column_names[2] logger.info(f"conditioning image column defaulting to {conditioning_image_column}") else: conditioning_image_column = args.conditioning_image_column if conditioning_image_column not in column_names: raise ValueError( f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" ) with accelerator.main_process_first(): train_dataset = dataset["train"].shuffle(seed=args.seed) if args.max_train_samples is not None: train_dataset = train_dataset.select(range(args.max_train_samples)) return train_dataset # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True): prompt_embeds_list = [] captions = [] for caption in prompt_batch: if random.random() < proportion_empty_prompts: captions.append("") elif isinstance(caption, str): captions.append(caption) elif isinstance(caption, (list, np.ndarray)): # take a random caption if there are multiple captions.append(random.choice(caption) if is_train else caption[0]) with torch.no_grad(): for tokenizer, text_encoder in zip(tokenizers, text_encoders): text_inputs = tokenizer( captions, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids prompt_embeds = text_encoder( text_input_ids.to(text_encoder.device), output_hidden_states=True, ) # 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 prepare_train_dataset(dataset, accelerator): image_transforms = transforms.Compose( [ transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(args.resolution), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) conditioning_image_transforms = transforms.Compose( [ transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(args.resolution), transforms.ToTensor(), ] ) def preprocess_train(examples): images = [image.convert("RGB") for image in examples[args.image_column]] images = [image_transforms(image) for image in images] conditioning_images = [image.convert("RGB") for image in examples[args.conditioning_image_column]] conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images] examples["pixel_values"] = images examples["conditioning_pixel_values"] = conditioning_images return examples with accelerator.main_process_first(): dataset = dataset.with_transform(preprocess_train) return dataset def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples]) conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float() prompt_ids = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples]) add_text_embeds = torch.stack([torch.tensor(example["text_embeds"]) for example in examples]) add_time_ids = torch.stack([torch.tensor(example["time_ids"]) for example in examples]) return { "pixel_values": pixel_values, "conditioning_pixel_values": conditioning_pixel_values, "prompt_ids": prompt_ids, "unet_added_conditions": {"text_embeds": add_text_embeds, "time_ids": add_time_ids}, } 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." ) 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, ) # Disable AMP for MPS. if torch.backends.mps.is_available(): accelerator.native_amp = False # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) 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, private=True, ).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 = EulerDiscreteScheduler.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, ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant ) if args.adapter_model_name_or_path: logger.info("Loading existing adapter weights.") t2iadapter = T2IAdapter.from_pretrained(args.adapter_model_name_or_path) else: logger.info("Initializing t2iadapter weights.") t2iadapter = T2IAdapter( in_channels=3, channels=(320, 640, 1280, 1280), num_res_blocks=2, downscale_factor=16, adapter_type="full_adapter_xl", ) # `accelerate` 0.16.0 will have better support for customized saving if version.parse(accelerate.__version__) >= version.parse("0.16.0"): # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format def save_model_hook(models, weights, output_dir): i = len(weights) - 1 while len(weights) > 0: weights.pop() model = models[i] sub_dir = "t2iadapter" model.save_pretrained(os.path.join(output_dir, sub_dir)) i -= 1 def load_model_hook(models, input_dir): while len(models) > 0: # pop models so that they are not loaded again model = models.pop() # load diffusers style into model load_model = T2IAdapter.from_pretrained(os.path.join(input_dir, "t2iadapter")) if args.control_type != "style": model.register_to_config(**load_model.config) model.load_state_dict(load_model.state_dict()) del load_model accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) vae.requires_grad_(False) text_encoder_one.requires_grad_(False) text_encoder_two.requires_grad_(False) t2iadapter.train() unet.train() 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") def unwrap_model(model): model = accelerator.unwrap_model(model) model = model._orig_mod if is_compiled_module(model) else model return model if args.gradient_checkpointing: unet.enable_gradient_checkpointing() # Check that all trainable models are in full precision low_precision_error_string = ( " Please make sure to always have all model weights in full float32 precision when starting training - even if" " doing mixed precision training, copy of the weights should still be float32." ) if unwrap_model(t2iadapter).dtype != torch.float32: raise ValueError( f"Controlnet loaded as datatype {unwrap_model(t2iadapter).dtype}. {low_precision_error_string}" ) # 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 = t2iadapter.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, ) # For mixed precision training we cast the text_encoder and vae weights to half-precision # as these models 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 vae, unet and text_encoder to device and cast to weight_dtype # The VAE is in float32 to avoid NaN losses. if args.pretrained_vae_model_name_or_path is not None: vae.to(accelerator.device, dtype=weight_dtype) else: vae.to(accelerator.device, dtype=torch.float32) unet.to(accelerator.device, dtype=weight_dtype) text_encoder_one.to(accelerator.device, dtype=weight_dtype) text_encoder_two.to(accelerator.device, dtype=weight_dtype) # Here, we compute not just the text embeddings but also the additional embeddings # needed for the SD XL UNet to operate. def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizers, is_train=True): 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) prompt_batch = batch[args.caption_column] prompt_embeds, pooled_prompt_embeds = encode_prompt( prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train ) 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.repeat(len(prompt_batch), 1) 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": prompt_embeds, **unet_added_cond_kwargs} 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 # Let's first compute all the embeddings so that we can free up the text encoders # from memory. text_encoders = [text_encoder_one, text_encoder_two] tokenizers = [tokenizer_one, tokenizer_two] train_dataset = get_train_dataset(args, accelerator) compute_embeddings_fn = functools.partial( compute_embeddings, proportion_empty_prompts=args.proportion_empty_prompts, text_encoders=text_encoders, tokenizers=tokenizers, ) with accelerator.main_process_first(): from datasets.fingerprint import Hasher # fingerprint used by the cache for the other processes to load the result # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401 new_fingerprint = Hasher.hash(args) train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) # Then get the training dataset ready to be passed to the dataloader. train_dataset = prepare_train_dataset(train_dataset, accelerator) train_dataloader = torch.utils.data.DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size, num_workers=args.dataloader_num_workers, ) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps, num_cycles=args.lr_num_cycles, power=args.lr_power, ) # Prepare everything with our `accelerator`. t2iadapter, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( t2iadapter, optimizer, train_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: tracker_config = dict(vars(args)) # tensorboard cannot handle list types for config tracker_config.pop("validation_prompt") tracker_config.pop("validation_image") accelerator.init_trackers(args.tracker_project_name, config=tracker_config) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num batches each epoch = {len(train_dataloader)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") global_step = 0 first_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint != "latest": path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = os.listdir(args.output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) path = dirs[-1] if len(dirs) > 0 else None if path is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) args.resume_from_checkpoint = None initial_global_step = 0 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) initial_global_step = global_step first_epoch = global_step // num_update_steps_per_epoch else: initial_global_step = 0 progress_bar = tqdm( range(0, args.max_train_steps), initial=initial_global_step, desc="Steps", # Only show the progress bar once on each machine. disable=not accelerator.is_local_main_process, ) image_logs = None for epoch in range(first_epoch, args.num_train_epochs): for step, batch in enumerate(train_dataloader): with accelerator.accumulate(t2iadapter): if args.pretrained_vae_model_name_or_path is not None: pixel_values = batch["pixel_values"].to(dtype=weight_dtype) else: pixel_values = batch["pixel_values"] # encode pixel values with batch size of at most 8 to avoid OOM latents = [] for i in range(0, pixel_values.shape[0], 8): latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample()) latents = torch.cat(latents, dim=0) latents = latents * vae.config.scaling_factor if args.pretrained_vae_model_name_or_path is None: latents = latents.to(weight_dtype) # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # Cubic sampling to sample a random timestep for each image. # For more details about why cubic sampling is used, refer to section 3.4 of https://arxiv.org/abs/2302.08453 timesteps = torch.rand((bsz,), device=latents.device) timesteps = (1 - timesteps**3) * noise_scheduler.config.num_train_timesteps timesteps = timesteps.long().to(noise_scheduler.timesteps.dtype) timesteps = timesteps.clamp(0, noise_scheduler.config.num_train_timesteps - 1) # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Scale the noisy latents for the UNet sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype) inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5) # Adapter conditioning. t2iadapter_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype) down_block_additional_residuals = t2iadapter(t2iadapter_image) down_block_additional_residuals = [ sample.to(dtype=weight_dtype) for sample in down_block_additional_residuals ] # Predict the noise residual model_pred = unet( inp_noisy_latents, timesteps, encoder_hidden_states=batch["prompt_ids"], added_cond_kwargs=batch["unet_added_conditions"], down_block_additional_residuals=down_block_additional_residuals, return_dict=False, )[0] # Denoise the latents denoised_latents = model_pred * (-sigmas) + noisy_latents weighing = sigmas**-2.0 # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = latents # we are computing loss against denoise latents elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") # MSE loss loss = torch.mean( (weighing.float() * (denoised_latents.float() - target.float()) ** 2).reshape(target.shape[0], -1), dim=1, ) loss = loss.mean() accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = t2iadapter.parameters() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad(set_to_none=args.set_grads_to_none) # 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}") if args.validation_prompt is not None and global_step % args.validation_steps == 0: image_logs = log_validation( vae, unet, t2iadapter, args, accelerator, weight_dtype, global_step, ) 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 # Create the pipeline using using the trained modules and save it. accelerator.wait_for_everyone() if accelerator.is_main_process: t2iadapter = unwrap_model(t2iadapter) t2iadapter.save_pretrained(args.output_dir) if args.push_to_hub: save_model_card( repo_id, image_logs=image_logs, base_model=args.pretrained_model_name_or_path, 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)