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diff_train.py
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diff_train.py
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import argparse
import hashlib
import itertools
import math
import os
import pickle
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from datasets import ObjectAttributeDataset, get_classnames
from utils.draw_utils import concat_h
logger = get_logger(__name__)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
# 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_loc",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default="nolevel",
choices = ["nolevel","classlevel","instancelevel_blip","instancelevel_random"],
help="The prompt to specify images in the same class as provided instance images.",
)
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("--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="text-inversion-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("--generation_seed", type=int, default=1024, help="A seed for generation images.")
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(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
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("--save_steps", type=int, default=500, help="Save images every X updates steps.")
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(
"--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(
"--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("-j","--num_workers", type=int, default=4)
parser.add_argument("--modelsavesteps", type=int, default=1000, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--duplication",
type=str,
default= "nodup",
choices = ['nodup','dup_both','dup_image'],
help="Duplicate the training data or not",
)
parser.add_argument(
"--unet_from_scratch",
type=str,
default= "no",
choices = ['no','yes'],
help="Duplicate the training data or not",
)
parser.add_argument(
"--weight_pc", type=float, default=0.05, help="Percentage of points to sample more."
)
parser.add_argument(
"--dup_weight", type=int, default=5, help="How likely dup points are, over the others."
)
parser.add_argument(
"--rand_noise_lam", type=float, default=0, help="How much gaussian noise to add to text encoder embedding during training"
)
parser.add_argument(
"--mixup_noise_lam", type=float, default=0, help="How much mixup noise to add to text encoder embedding during training"
)
parser.add_argument(
"--trainspecial", type=str, default=None, choices = ['allcaps','randrepl','randwordadd','wordrepeat'],help="which caps to use"
)
parser.add_argument(
"--trainspecial_prob", type=float, default=0.1, help="for special training, intervention probability"
)
parser.add_argument(
"--trainsubset", type=float, default=None, help="percentage of training data to use"
)
################ End of args #####################
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
return args
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"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 get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
OBJECTS = get_classnames(args.instance_data_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="wandb",
logging_dir=logging_dir,
)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(f"{args.output_dir}/generations", exist_ok=True)
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name,
revision=args.revision,
use_fast=False,
)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import ipdb; ipdb.set_trace()
# 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 models and create wrapper for stable diffusion
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
if args.unet_from_scratch == 'no':
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
else:
import json
unet_config = json.loads(open('./unet_config.json').read())
unet = UNet2DConditionModel(**unet_config)
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder.gradient_checkpointing_enable()
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
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.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,
)
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = ObjectAttributeDataset(
instance_data_root=args.instance_data_dir,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
random_flip = args.random_flip,
prompt_json = args.instance_prompt_loc,
duplication = args.duplication,
args = args
)
temp = list(train_dataset.prompts.values())
choicelist = [x[0] for x in temp]
# choicelist = list(itertools.chain(*))
if args.trainsubset is not None:
train_dataset = torch.utils.data.Subset(train_dataset, list(range(0, int(len(train_dataset)*args.trainsubset))))
choicelist = choicelist[:int(len(train_dataset)*args.trainsubset)]
if args.duplication in ['dup_both','dup_image']:
sampler = torch.utils.data.WeightedRandomSampler(train_dataset.samplingweights, len(train_dataset), replacement=True)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=False,
collate_fn=lambda examples: collate_fn(examples),
num_workers=args.num_workers,
sampler = sampler
)
else:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples),
num_workers=args.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
# LINEAR = "linear"
# COSINE = "cosine"
# COSINE_WITH_RESTARTS = "cosine_with_restarts"
# POLYNOMIAL = "polynomial"
# CONSTANT = "constant"
# CONSTANT_WITH_WARMUP = "constant_with_warmup"
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# 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.
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
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.
import wandb
if accelerator.is_main_process:
# accelerator.init_trackers("diffrep_ft",
# init_kwargs={"wandb":{"name":f"{args.output_dir}_{args.class_prompt}"}, "settings":wandb.Settings(start_method="fork")},
# config=vars(args))
init_kwargs = {"wandb":{"name":f"{args.output_dir}_{args.class_prompt}","settings":{"console": "off"}}}
accelerator.init_trackers("diffrep_ft",
init_kwargs=init_kwargs,
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}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
if accelerator.is_main_process:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision, safety_checker=None,
).to(accelerator.device)
pipeline.set_use_memory_efficient_attention_xformers(True)
genseed = args.generation_seed
if args.class_prompt in ['instancelevel_blip','instancelevel_ogcap']: # and args.duplication == 'dup_image':
# choicelist = list(itertools.chain(*list(train_dataset.prompts.values())))
rand_prompts = np.random.choice(choicelist,len(OBJECTS))
if args.class_prompt in ['instancelevel_random']:
temp = np.random.choice(choicelist,len(OBJECTS))
# temp = np.random.choice(list(train_dataset.prompts.values()),len(OBJECTS))
rand_prompts = []
for p in temp:
rand_prompts.append(tokenizer.decode(eval(p)))
print(rand_prompts)
for count,object in enumerate(OBJECTS):
if count > 2:
break
if args.class_prompt == 'nolevel':
genseed+=1
prompt = f"An image"
elif args.class_prompt == 'classlevel':
prompt = f"An image of {object}"
elif args.class_prompt in ['instancelevel_blip','instancelevel_ogcap','instancelevel_random']:
# import ipdb; ipdb.set_trace()
prompt = rand_prompts[count]
print('first one------------')
print(prompt)
save_path = os.path.join(args.output_dir,"generations", f"{global_step:04d}_{object}.png")
generator = torch.Generator("cuda").manual_seed(genseed)
images = pipeline(prompt=prompt, height=args.resolution, width=args.resolution,
num_inference_steps=50, num_images_per_prompt=4, generator=generator).images
concat_h(*images, pad=4).save(save_path)
for epoch in range(args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
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 * 0.18215
# 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)
# Get the text embedding for conditioning
# import ipdb; ipdb.set_trace()
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
if args.rand_noise_lam > 0:
encoder_hidden_states = encoder_hidden_states + args.rand_noise_lam*torch.randn_like(encoder_hidden_states)
if args.mixup_noise_lam > 0:
lam = np.random.beta(args.mixup_noise_lam, 1)
index = torch.randperm(encoder_hidden_states.shape[0]).cuda()
encoder_hidden_states = lam*encoder_hidden_states + (1-lam)*encoder_hidden_states[index,:]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).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(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:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.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 global_step % args.save_steps == 0:
if accelerator.is_main_process:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision, safety_checker=None,
).to(accelerator.device)
genseed = args.generation_seed
for count,object in enumerate(OBJECTS):
if count > 2:
break
if args.class_prompt == 'nolevel':
genseed+=1
prompt = f"An image"
elif args.class_prompt == 'classlevel':
prompt = f"An image of {object}"
elif args.class_prompt in ['instancelevel_blip','instancelevel_ogcap','instancelevel_random']:
prompt = rand_prompts[count]
print('_______in the loop______-')
print(prompt)
save_path = os.path.join(args.output_dir,"generations", f"{global_step:04d}_{object}.png")
generator = torch.Generator("cuda").manual_seed(genseed)
images = pipeline(prompt=prompt, height=args.resolution, width=args.resolution,
num_inference_steps=50, num_images_per_prompt=4,
generator=generator).images
concat_h(*images, pad=4).save(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 and global_step % args.modelsavesteps == 0:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(os.path.join(args.output_dir, f'checkpoint_{global_step}'))
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(os.path.join(args.output_dir, 'checkpoint'))
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
assert not (args.duplication == 'dup_image' and args.class_prompt == 'instancelevel_ogcap'), "Duplicating just the image in original captions scenario is not acceptable"
if args.trainspecial:
if args.class_prompt != 'instancelevel_blip':
raise Exception("Cant train special without blip captions")
if args.trainsubset is not None:
args.output_dir = f"{args.output_dir}_{args.trainsubset}subset"
if args.unet_from_scratch == 'no':
args.output_dir = f"{args.output_dir}_{args.class_prompt}_{args.duplication}"
else:
args.output_dir = f"{args.output_dir}_{args.class_prompt}_{args.duplication}_unetfromscr"
if args.duplication in ['dup_both','dup_image']:
args.output_dir = f"{args.output_dir}_{args.weight_pc}_{args.dup_weight}"
if args.rand_noise_lam > 0:
args.output_dir = f"{args.output_dir}_glam{args.rand_noise_lam}"
if args.mixup_noise_lam > 0:
args.output_dir = f"{args.output_dir}_mixlam{args.mixup_noise_lam}"
if args.trainspecial is not None:
args.output_dir = f"{args.output_dir}_special_{args.trainspecial}_{args.trainspecial_prob}"
# TODO: adding noise in text case
main(args)