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#!/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 itertools | |
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, | |
DPMSolverMultistepScheduler, | |
StableDiffusionXLPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict | |
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("0.21.0.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, vae_path=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: openrail++ | |
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}. | |
Special VAE used for training: {vae_path}. | |
""" | |
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( | |
"--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( | |
"--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=1024, | |
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." | |
) | |
parser.add_argument( | |
"--rank", | |
type=int, | |
default=4, | |
help=("The dimension of the LoRA update matrices."), | |
) | |
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.") | |
return args | |
class DreamBoothDataset(Dataset): | |
""" | |
A dataset to prepare the instance and class images with the prompts for fine-tuning the model. | |
It pre-processes the images. | |
""" | |
def __init__( | |
self, | |
instance_data_root, | |
class_data_root=None, | |
class_num=None, | |
size=1024, | |
center_crop=False, | |
): | |
self.size = size | |
self.center_crop = center_crop | |
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._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) | |
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) | |
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) | |
return example | |
def collate_fn(examples, with_prior_preservation=False): | |
pixel_values = [example["instance_images"] 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] | |
pixel_values = torch.stack(pixel_values) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
batch = {"pixel_values": pixel_values} | |
return batch | |
class PromptDataset(Dataset): | |
"A simple dataset to prepare the prompts to generate class images on multiple GPUs." | |
def __init__(self, prompt, num_samples): | |
self.prompt = prompt | |
self.num_samples = num_samples | |
def __len__(self): | |
return self.num_samples | |
def __getitem__(self, index): | |
example = {} | |
example["prompt"] = self.prompt | |
example["index"] = index | |
return example | |
def tokenize_prompt(tokenizer, prompt): | |
text_inputs = tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
return text_input_ids | |
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt | |
def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None): | |
prompt_embeds_list = [] | |
for i, text_encoder in enumerate(text_encoders): | |
if tokenizers is not None: | |
tokenizer = tokenizers[i] | |
text_input_ids = tokenize_prompt(tokenizer, prompt) | |
else: | |
assert text_input_ids_list is not None | |
text_input_ids = text_input_ids_list[i] | |
prompt_embeds = text_encoder( | |
text_input_ids.to(text_encoder.device), | |
output_hidden_states=True, | |
) | |
# 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 | |
# 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 = StableDiffusionXLPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
torch_dtype=torch_dtype, | |
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, private=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_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 | |
) | |
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 | |
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.warn( | |
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." | |
) | |
unet.enable_xformers_memory_efficient_attention() | |
else: | |
raise ValueError("xformers is not available. Make sure it is installed correctly") | |
if args.gradient_checkpointing: | |
unet.enable_gradient_checkpointing() | |
if args.train_text_encoder: | |
text_encoder_one.gradient_checkpointing_enable() | |
text_encoder_two.gradient_checkpointing_enable() | |
# 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, rank=args.rank | |
) | |
unet_lora_attn_procs[name] = module | |
unet_lora_parameters.extend(module.parameters()) | |
unet.set_attn_processor(unet_lora_attn_procs) | |
# The text encoder comes from 🤗 transformers, so we cannot directly modify it. | |
# So, instead, we monkey-patch the forward calls of its attention-blocks. | |
if args.train_text_encoder: | |
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16 | |
text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder( | |
text_encoder_one, dtype=torch.float32, rank=args.rank | |
) | |
text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder( | |
text_encoder_two, dtype=torch.float32, rank=args.rank | |
) | |
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format | |
def save_model_hook(models, weights, output_dir): | |
if accelerator.is_main_process: | |
# there are only two options here. Either are just the unet attn processor layers | |
# or there are the unet and text encoder atten layers | |
unet_lora_layers_to_save = None | |
text_encoder_one_lora_layers_to_save = None | |
text_encoder_two_lora_layers_to_save = None | |
for model in models: | |
if isinstance(model, type(accelerator.unwrap_model(unet))): | |
unet_lora_layers_to_save = unet_attn_processors_state_dict(model) | |
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))): | |
text_encoder_one_lora_layers_to_save = text_encoder_lora_state_dict(model) | |
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))): | |
text_encoder_two_lora_layers_to_save = text_encoder_lora_state_dict(model) | |
else: | |
raise ValueError(f"unexpected save model: {model.__class__}") | |
# make sure to pop weight so that corresponding model is not saved again | |
weights.pop() | |
StableDiffusionXLPipeline.save_lora_weights( | |
output_dir, | |
unet_lora_layers=unet_lora_layers_to_save, | |
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save, | |
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save, | |
) | |
def load_model_hook(models, input_dir): | |
unet_ = None | |
text_encoder_one_ = None | |
text_encoder_two_ = None | |
while len(models) > 0: | |
model = models.pop() | |
if isinstance(model, type(accelerator.unwrap_model(unet))): | |
unet_ = model | |
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))): | |
text_encoder_one_ = model | |
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))): | |
text_encoder_two_ = model | |
else: | |
raise ValueError(f"unexpected save model: {model.__class__}") | |
lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir) | |
LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_) | |
text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k} | |
LoraLoaderMixin.load_lora_into_text_encoder( | |
text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_ | |
) | |
text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k} | |
LoraLoaderMixin.load_lora_into_text_encoder( | |
text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_ | |
) | |
accelerator.register_save_state_pre_hook(save_model_hook) | |
accelerator.register_load_state_pre_hook(load_model_hook) | |
# Enable TF32 for faster training on Ampere GPUs, | |
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices | |
if args.allow_tf32: | |
torch.backends.cuda.matmul.allow_tf32 = True | |
if args.scale_lr: | |
args.learning_rate = ( | |
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes | |
) | |
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs | |
if args.use_8bit_adam: | |
try: | |
import bitsandbytes as bnb | |
except ImportError: | |
raise ImportError( | |
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." | |
) | |
optimizer_class = bnb.optim.AdamW8bit | |
else: | |
optimizer_class = torch.optim.AdamW | |
# Optimizer creation | |
params_to_optimize = ( | |
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two) | |
if args.train_text_encoder | |
else unet_lora_parameters | |
) | |
optimizer = optimizer_class( | |
params_to_optimize, | |
lr=args.learning_rate, | |
betas=(args.adam_beta1, args.adam_beta2), | |
weight_decay=args.adam_weight_decay, | |
eps=args.adam_epsilon, | |
) | |
# Computes additional embeddings/ids required by the SDXL UNet. | |
# regular text emebddings (when `train_text_encoder` is not True) | |
# pooled text embeddings | |
# time ids | |
def compute_time_ids(): | |
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids | |
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) | |
add_time_ids = list(original_size + crops_coords_top_left + target_size) | |
add_time_ids = torch.tensor([add_time_ids]) | |
add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype) | |
return add_time_ids | |
if not args.train_text_encoder: | |
tokenizers = [tokenizer_one, tokenizer_two] | |
text_encoders = [text_encoder_one, text_encoder_two] | |
def compute_text_embeddings(prompt, text_encoders, tokenizers): | |
with torch.no_grad(): | |
prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt) | |
prompt_embeds = prompt_embeds.to(accelerator.device) | |
pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device) | |
return prompt_embeds, pooled_prompt_embeds | |
# Handle instance prompt. | |
instance_time_ids = compute_time_ids() | |
if not args.train_text_encoder: | |
instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings( | |
args.instance_prompt, text_encoders, tokenizers | |
) | |
# Handle class prompt for prior-preservation. | |
if args.with_prior_preservation: | |
class_time_ids = compute_time_ids() | |
if not args.train_text_encoder: | |
class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings( | |
args.class_prompt, text_encoders, tokenizers | |
) | |
# Clear the memory here. | |
if not args.train_text_encoder: | |
del tokenizers, text_encoders | |
gc.collect() | |
torch.cuda.empty_cache() | |
# Pack the statically computed variables appropriately. This is so that we don't | |
# have to pass them to the dataloader. | |
add_time_ids = instance_time_ids | |
if args.with_prior_preservation: | |
add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0) | |
if not args.train_text_encoder: | |
prompt_embeds = instance_prompt_hidden_states | |
unet_add_text_embeds = instance_pooled_prompt_embeds | |
if args.with_prior_preservation: | |
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0) | |
unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0) | |
else: | |
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt) | |
tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt) | |
if args.with_prior_preservation: | |
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt) | |
class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt) | |
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0) | |
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0) | |
# Dataset and DataLoaders creation: | |
train_dataset = DreamBoothDataset( | |
instance_data_root=args.instance_data_dir, | |
class_data_root=args.class_data_dir if args.with_prior_preservation else None, | |
class_num=args.num_class_images, | |
size=args.resolution, | |
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, | |
) | |
# Scheduler and math around the number of training steps. | |
overrode_max_train_steps = False | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if args.max_train_steps is None: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
overrode_max_train_steps = True | |
lr_scheduler = get_scheduler( | |
args.lr_scheduler, | |
optimizer=optimizer, | |
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, | |
num_training_steps=args.max_train_steps * accelerator.num_processes, | |
num_cycles=args.lr_num_cycles, | |
power=args.lr_power, | |
) | |
# Prepare everything with our `accelerator`. | |
if args.train_text_encoder: | |
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler | |
) | |
else: | |
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, optimizer, train_dataloader, lr_scheduler | |
) | |
# We need to recalculate our total training steps as the size of the training dataloader may have changed. | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if overrode_max_train_steps: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
# Afterwards we recalculate our number of training epochs | |
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
# We need to initialize the trackers we use, and also store our configuration. | |
# The trackers initializes automatically on the main process. | |
if accelerator.is_main_process: | |
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() | |
if args.train_text_encoder: | |
text_encoder_one.train() | |
text_encoder_two.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=vae.dtype) | |
# Convert images to latent space | |
model_input = vae.encode(pixel_values).latent_dist.sample() | |
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) | |
# 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) | |
# Calculate the elements to repeat depending on the use of prior-preservation. | |
elems_to_repeat = bsz // 2 if args.with_prior_preservation else bsz | |
# Predict the noise residual | |
if not args.train_text_encoder: | |
unet_added_conditions = { | |
"time_ids": add_time_ids.repeat(elems_to_repeat, 1), | |
"text_embeds": unet_add_text_embeds.repeat(elems_to_repeat, 1), | |
} | |
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1) | |
model_pred = unet( | |
noisy_model_input, | |
timesteps, | |
prompt_embeds_input, | |
added_cond_kwargs=unet_added_conditions, | |
).sample | |
else: | |
unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat, 1)} | |
prompt_embeds, pooled_prompt_embeds = encode_prompt( | |
text_encoders=[text_encoder_one, text_encoder_two], | |
tokenizers=None, | |
prompt=None, | |
text_input_ids_list=[tokens_one, tokens_two], | |
) | |
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat, 1)}) | |
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1) | |
model_pred = unet( | |
noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=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 = ( | |
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two) | |
if args.train_text_encoder | |
else unet_lora_parameters | |
) | |
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad() | |
# Checks if the accelerator has performed an optimization step behind the scenes | |
if accelerator.sync_gradients: | |
progress_bar.update(1) | |
global_step += 1 | |
if accelerator.is_main_process: | |
if global_step % args.checkpointing_steps == 0: | |
# _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 | |
if not args.train_text_encoder: | |
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 | |
) | |
pipeline = StableDiffusionXLPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
vae=vae, | |
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, | |
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} | |
with torch.cuda.amp.autocast(): | |
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) | |
if args.train_text_encoder: | |
text_encoder_one = accelerator.unwrap_model(text_encoder_one) | |
text_encoder_lora_layers = text_encoder_lora_state_dict(text_encoder_one.to(torch.float32)) | |
text_encoder_two = accelerator.unwrap_model(text_encoder_two) | |
text_encoder_2_lora_layers = text_encoder_lora_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, | |
) | |
# 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, | |
torch_dtype=weight_dtype, | |
) | |
pipeline = StableDiffusionXLPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, vae=vae, 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) | |
# load attention processors | |
pipeline.load_lora_weights(args.output_dir) | |
# run inference | |
images = [] | |
if args.validation_prompt and args.num_validation_images > 0: | |
pipeline = pipeline.to(accelerator.device) | |
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, | |
vae_path=args.pretrained_vae_model_name_or_path, | |
) | |
upload_folder( | |
repo_id=repo_id, | |
folder_path=args.output_dir, | |
commit_message="End of training", | |
ignore_patterns=["step_*", "epoch_*"], | |
token=args.hub_token | |
) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
args = parse_args() | |
main(args) |