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# from accelerate.utils import write_basic_config | |
# | |
# write_basic_config() | |
import argparse | |
import logging | |
import math | |
import os | |
import shutil | |
from pathlib import Path | |
import accelerate | |
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 packaging import version | |
from tqdm.auto import tqdm | |
import diffusers | |
from diffusers import ( | |
AutoencoderKL, | |
DDPMScheduler, | |
EulerDiscreteScheduler, | |
StableDiffusionGLIGENPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.optimization import get_scheduler | |
from diffusers.utils import is_wandb_available, make_image_grid | |
from diffusers.utils.import_utils import is_xformers_available | |
from diffusers.utils.torch_utils import is_compiled_module | |
if is_wandb_available(): | |
pass | |
# Will error if the minimal version of diffusers is not installed. Remove at your own risks. | |
# check_min_version("0.28.0.dev0") | |
logger = get_logger(__name__) | |
def log_validation(vae, text_encoder, tokenizer, unet, noise_scheduler, args, accelerator, step, weight_dtype): | |
if accelerator.is_main_process: | |
print("generate test images...") | |
unet = accelerator.unwrap_model(unet) | |
vae.to(accelerator.device, dtype=torch.float32) | |
pipeline = StableDiffusionGLIGENPipeline( | |
vae, | |
text_encoder, | |
tokenizer, | |
unet, | |
EulerDiscreteScheduler.from_config(noise_scheduler.config), | |
safety_checker=None, | |
feature_extractor=None, | |
) | |
pipeline = pipeline.to(accelerator.device) | |
pipeline.set_progress_bar_config(disable=not accelerator.is_main_process) | |
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) | |
prompt = "A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky" | |
boxes = [ | |
[0.041015625, 0.548828125, 0.453125, 0.859375], | |
[0.525390625, 0.552734375, 0.93359375, 0.865234375], | |
[0.12890625, 0.015625, 0.412109375, 0.279296875], | |
[0.578125, 0.08203125, 0.857421875, 0.27734375], | |
] | |
gligen_phrases = ["a green car", "a blue truck", "a red air balloon", "a bird"] | |
images = pipeline( | |
prompt=prompt, | |
gligen_phrases=gligen_phrases, | |
gligen_boxes=boxes, | |
gligen_scheduled_sampling_beta=1.0, | |
output_type="pil", | |
num_inference_steps=50, | |
negative_prompt="artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate", | |
num_images_per_prompt=4, | |
generator=generator, | |
).images | |
os.makedirs(os.path.join(args.output_dir, "images"), exist_ok=True) | |
make_image_grid(images, 1, 4).save( | |
os.path.join(args.output_dir, "images", f"generated-images-{step:06d}-{accelerator.process_index:02d}.png") | |
) | |
vae.to(accelerator.device, dtype=weight_dtype) | |
def parse_args(input_args=None): | |
parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.") | |
parser.add_argument( | |
"--data_path", | |
type=str, | |
default="coco_train2017.pth", | |
help="Path to training dataset.", | |
) | |
parser.add_argument( | |
"--image_path", | |
type=str, | |
default="coco_train2017.pth", | |
help="Path to training images.", | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="controlnet-model", | |
help="The output directory where the model predictions and checkpoints will be written.", | |
) | |
parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.") | |
parser.add_argument( | |
"--resolution", | |
type=int, | |
default=512, | |
help=( | |
"The resolution for input images, all the images in the train/validation dataset will be resized to this" | |
" resolution" | |
), | |
) | |
parser.add_argument( | |
"--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=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-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( | |
"--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("--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( | |
"--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( | |
"--tracker_project_name", | |
type=str, | |
default="train_controlnet", | |
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" | |
), | |
) | |
args = parser.parse_args() | |
return args | |
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, | |
) | |
# 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) | |
# import correct text encoder class | |
# text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) | |
# Load scheduler and models | |
from transformers import CLIPTextModel, CLIPTokenizer | |
pretrained_model_name_or_path = "masterful/gligen-1-4-generation-text-box" | |
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer") | |
noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler") | |
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder") | |
vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae") | |
unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet") | |
# Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files) | |
def unwrap_model(model): | |
model = accelerator.unwrap_model(model) | |
model = model._orig_mod if is_compiled_module(model) else model | |
return model | |
# `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): | |
if accelerator.is_main_process: | |
i = len(weights) - 1 | |
while len(weights) > 0: | |
weights.pop() | |
model = models[i] | |
sub_dir = "unet" | |
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 = unet.from_pretrained(input_dir, subfolder="unet") | |
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) | |
unet.requires_grad_(False) | |
text_encoder.requires_grad_(False) | |
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() | |
# controlnet.enable_xformers_memory_efficient_attention() | |
else: | |
raise ValueError("xformers is not available. Make sure it is installed correctly") | |
# if args.gradient_checkpointing: | |
# controlnet.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(unet).dtype != torch.float32: | |
raise ValueError(f"Controlnet loaded as datatype {unwrap_model(unet).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 | |
) | |
optimizer_class = torch.optim.AdamW | |
# Optimizer creation | |
for n, m in unet.named_modules(): | |
if ("fuser" in n) or ("position_net" in n): | |
import torch.nn as nn | |
if isinstance(m, (nn.Linear, nn.LayerNorm)): | |
m.reset_parameters() | |
params_to_optimize = [] | |
for n, p in unet.named_parameters(): | |
if ("fuser" in n) or ("position_net" in n): | |
p.requires_grad = True | |
params_to_optimize.append(p) | |
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, | |
) | |
from dataset import COCODataset | |
train_dataset = COCODataset( | |
data_path=args.data_path, | |
image_path=args.image_path, | |
tokenizer=tokenizer, | |
image_size=args.resolution, | |
max_boxes_per_data=30, | |
) | |
print("num samples: ", len(train_dataset)) | |
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 * 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`. | |
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, optimizer, train_dataloader, lr_scheduler | |
) | |
# 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 | |
vae.to(accelerator.device, dtype=weight_dtype) | |
# unet.to(accelerator.device, dtype=weight_dtype) | |
unet.to(accelerator.device, dtype=torch.float32) | |
text_encoder.to(accelerator.device, dtype=weight_dtype) | |
# 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, | |
) | |
log_validation( | |
vae, | |
text_encoder, | |
tokenizer, | |
unet, | |
noise_scheduler, | |
args, | |
accelerator, | |
global_step, | |
weight_dtype, | |
) | |
# image_logs = None | |
for epoch in range(first_epoch, args.num_train_epochs): | |
for step, batch in enumerate(train_dataloader): | |
with accelerator.accumulate(unet): | |
# Convert images to latent space | |
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() | |
latents = latents * vae.config.scaling_factor | |
# Sample noise that we'll add to the latents | |
noise = torch.randn_like(latents) | |
bsz = latents.shape[0] | |
# Sample a random timestep for each image | |
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) | |
timesteps = timesteps.long() | |
# 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) | |
with torch.no_grad(): | |
# Get the text embedding for conditioning | |
encoder_hidden_states = text_encoder( | |
batch["caption"]["input_ids"].squeeze(1), | |
# batch['caption']['attention_mask'].squeeze(1), | |
return_dict=False, | |
)[0] | |
cross_attention_kwargs = {} | |
cross_attention_kwargs["gligen"] = { | |
"boxes": batch["boxes"], | |
"positive_embeddings": batch["text_embeddings_before_projection"], | |
"masks": batch["masks"], | |
} | |
# Predict the noise residual | |
model_pred = unet( | |
noisy_latents, | |
timesteps, | |
encoder_hidden_states=encoder_hidden_states, | |
cross_attention_kwargs=cross_attention_kwargs, | |
return_dict=False, | |
)[0] | |
# 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(latents, noise, timesteps) | |
else: | |
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") | |
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") | |
accelerator.backward(loss) | |
if accelerator.sync_gradients: | |
accelerator.clip_grad_norm_(params_to_optimize, 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 global_step % args.checkpointing_steps == 0: | |
if accelerator.is_main_process: | |
# _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:06d}") | |
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: | |
log_validation( | |
vae, | |
text_encoder, | |
tokenizer, | |
unet, | |
noise_scheduler, | |
args, | |
accelerator, | |
global_step, | |
weight_dtype, | |
) | |
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: | |
unet = unwrap_model(unet) | |
unet.save_pretrained(args.output_dir) | |
# | |
# # Run a final round of validation. | |
# image_logs = None | |
# if args.validation_prompt is not None: | |
# image_logs = log_validation( | |
# vae=vae, | |
# text_encoder=text_encoder, | |
# tokenizer=tokenizer, | |
# unet=unet, | |
# controlnet=None, | |
# args=args, | |
# accelerator=accelerator, | |
# weight_dtype=weight_dtype, | |
# step=global_step, | |
# is_final_validation=True, | |
# ) | |
# | |
# 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) | |