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import argparse | |
import logging | |
import math | |
import os | |
import random | |
from pathlib import Path | |
import jax | |
import jax.numpy as jnp | |
import numpy as np | |
import optax | |
import torch | |
import torch.utils.checkpoint | |
import transformers | |
from datasets import load_dataset | |
from flax import jax_utils | |
from flax.training import train_state | |
from flax.training.common_utils import shard | |
from huggingface_hub import create_repo, upload_folder | |
from torchvision import transforms | |
from tqdm.auto import tqdm | |
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed | |
from diffusers import ( | |
FlaxAutoencoderKL, | |
FlaxDDPMScheduler, | |
FlaxPNDMScheduler, | |
FlaxStableDiffusionPipeline, | |
FlaxUNet2DConditionModel, | |
) | |
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker | |
from diffusers.utils import check_min_version | |
# Will error if the minimal version of diffusers is not installed. Remove at your own risks. | |
check_min_version("0.15.0.dev0") | |
logger = logging.getLogger(__name__) | |
def parse_args(): | |
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( | |
"--dataset_name", | |
type=str, | |
default=None, | |
help=( | |
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," | |
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," | |
" or to a folder containing files that 🤗 Datasets can understand." | |
), | |
) | |
parser.add_argument( | |
"--dataset_config_name", | |
type=str, | |
default=None, | |
help="The config of the Dataset, leave as None if there's only one config.", | |
) | |
parser.add_argument( | |
"--train_data_dir", | |
type=str, | |
default=None, | |
help=( | |
"A folder containing the training data. Folder contents must follow the structure described in" | |
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" | |
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified." | |
), | |
) | |
parser.add_argument( | |
"--image_column", type=str, default="image", help="The column of the dataset containing an image." | |
) | |
parser.add_argument( | |
"--caption_column", | |
type=str, | |
default="text", | |
help="The column of the dataset containing a caption or a list of captions.", | |
) | |
parser.add_argument( | |
"--max_train_samples", | |
type=int, | |
default=None, | |
help=( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
), | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="sd-model-finetuned", | |
help="The output directory where the model predictions and checkpoints will be written.", | |
) | |
parser.add_argument( | |
"--cache_dir", | |
type=str, | |
default=None, | |
help="The directory where the downloaded models and datasets will be stored.", | |
) | |
parser.add_argument("--seed", type=int, default=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( | |
"--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( | |
"--random_flip", | |
action="store_true", | |
help="whether to randomly flip images horizontally", | |
) | |
parser.add_argument( | |
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." | |
) | |
parser.add_argument("--num_train_epochs", type=int, default=100) | |
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( | |
"--learning_rate", | |
type=float, | |
default=1e-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("--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( | |
"--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="no", | |
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." | |
), | |
) | |
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") | |
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 | |
# Sanity checks | |
if args.dataset_name is None and args.train_data_dir is None: | |
raise ValueError("Need either a dataset name or a training folder.") | |
return args | |
dataset_name_mapping = { | |
"lambdalabs/pokemon-blip-captions": ("image", "text"), | |
} | |
def get_params_to_save(params): | |
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params)) | |
def main(): | |
args = parse_args() | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
level=logging.INFO, | |
) | |
# Setup logging, we only want one process per machine to log things on the screen. | |
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) | |
if jax.process_index() == 0: | |
transformers.utils.logging.set_verbosity_info() | |
else: | |
transformers.utils.logging.set_verbosity_error() | |
if args.seed is not None: | |
set_seed(args.seed) | |
# Handle the repository creation | |
if jax.process_index() == 0: | |
if args.output_dir is not None: | |
os.makedirs(args.output_dir, exist_ok=True) | |
if args.push_to_hub: | |
repo_id = create_repo( | |
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token | |
).repo_id | |
# Get the datasets: you can either provide your own training and evaluation files (see below) | |
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). | |
# In distributed training, the load_dataset function guarantees that only one local process can concurrently | |
# download the dataset. | |
if args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
dataset = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
cache_dir=args.cache_dir, | |
) | |
else: | |
data_files = {} | |
if args.train_data_dir is not None: | |
data_files["train"] = os.path.join(args.train_data_dir, "**") | |
dataset = load_dataset( | |
"imagefolder", | |
data_files=data_files, | |
cache_dir=args.cache_dir, | |
) | |
# See more about loading custom images at | |
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder | |
# Preprocessing the datasets. | |
# We need to tokenize inputs and targets. | |
column_names = dataset["train"].column_names | |
# 6. Get the column names for input/target. | |
dataset_columns = dataset_name_mapping.get(args.dataset_name, None) | |
if args.image_column is None: | |
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] | |
else: | |
image_column = args.image_column | |
if image_column not in column_names: | |
raise ValueError( | |
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
if args.caption_column is None: | |
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] | |
else: | |
caption_column = args.caption_column | |
if caption_column not in column_names: | |
raise ValueError( | |
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
# Preprocessing the datasets. | |
# We need to tokenize input captions and transform the images. | |
def tokenize_captions(examples, is_train=True): | |
captions = [] | |
for caption in examples[caption_column]: | |
if isinstance(caption, str): | |
captions.append(caption) | |
elif isinstance(caption, (list, np.ndarray)): | |
# take a random caption if there are multiple | |
captions.append(random.choice(caption) if is_train else caption[0]) | |
else: | |
raise ValueError( | |
f"Caption column `{caption_column}` should contain either strings or lists of strings." | |
) | |
inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True) | |
input_ids = inputs.input_ids | |
return input_ids | |
train_transforms = transforms.Compose( | |
[ | |
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), | |
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution), | |
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x), | |
transforms.ToTensor(), | |
transforms.Normalize([0.5], [0.5]), | |
] | |
) | |
def preprocess_train(examples): | |
images = [image.convert("RGB") for image in examples[image_column]] | |
examples["pixel_values"] = [train_transforms(image) for image in images] | |
examples["input_ids"] = tokenize_captions(examples) | |
return examples | |
if jax.process_index() == 0: | |
if args.max_train_samples is not None: | |
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) | |
# Set the training transforms | |
train_dataset = dataset["train"].with_transform(preprocess_train) | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
input_ids = [example["input_ids"] for example in examples] | |
padded_tokens = tokenizer.pad( | |
{"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt" | |
) | |
batch = { | |
"pixel_values": pixel_values, | |
"input_ids": padded_tokens.input_ids, | |
} | |
batch = {k: v.numpy() for k, v in batch.items()} | |
return batch | |
total_train_batch_size = args.train_batch_size * jax.local_device_count() | |
train_dataloader = torch.utils.data.DataLoader( | |
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=total_train_batch_size, drop_last=True | |
) | |
weight_dtype = jnp.float32 | |
if args.mixed_precision == "fp16": | |
weight_dtype = jnp.float16 | |
elif args.mixed_precision == "bf16": | |
weight_dtype = jnp.bfloat16 | |
# Load models and create wrapper for stable diffusion | |
tokenizer = CLIPTokenizer.from_pretrained( | |
args.pretrained_model_name_or_path, revision=args.revision, subfolder="tokenizer" | |
) | |
text_encoder = FlaxCLIPTextModel.from_pretrained( | |
args.pretrained_model_name_or_path, revision=args.revision, subfolder="text_encoder", dtype=weight_dtype | |
) | |
vae, vae_params = FlaxAutoencoderKL.from_pretrained( | |
args.pretrained_model_name_or_path, revision=args.revision, subfolder="vae", dtype=weight_dtype | |
) | |
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained( | |
args.pretrained_model_name_or_path, revision=args.revision, subfolder="unet", dtype=weight_dtype | |
) | |
# Optimization | |
if args.scale_lr: | |
args.learning_rate = args.learning_rate * total_train_batch_size | |
constant_scheduler = optax.constant_schedule(args.learning_rate) | |
adamw = optax.adamw( | |
learning_rate=constant_scheduler, | |
b1=args.adam_beta1, | |
b2=args.adam_beta2, | |
eps=args.adam_epsilon, | |
weight_decay=args.adam_weight_decay, | |
) | |
optimizer = optax.chain( | |
optax.clip_by_global_norm(args.max_grad_norm), | |
adamw, | |
) | |
state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer) | |
noise_scheduler = FlaxDDPMScheduler( | |
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000 | |
) | |
noise_scheduler_state = noise_scheduler.create_state() | |
# Initialize our training | |
rng = jax.random.PRNGKey(args.seed) | |
train_rngs = jax.random.split(rng, jax.local_device_count()) | |
def train_step(state, text_encoder_params, vae_params, batch, train_rng): | |
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3) | |
def compute_loss(params): | |
# Convert images to latent space | |
vae_outputs = vae.apply( | |
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode | |
) | |
latents = vae_outputs.latent_dist.sample(sample_rng) | |
# (NHWC) -> (NCHW) | |
latents = jnp.transpose(latents, (0, 3, 1, 2)) | |
latents = latents * vae.config.scaling_factor | |
# Sample noise that we'll add to the latents | |
noise_rng, timestep_rng = jax.random.split(sample_rng) | |
noise = jax.random.normal(noise_rng, latents.shape) | |
# Sample a random timestep for each image | |
bsz = latents.shape[0] | |
timesteps = jax.random.randint( | |
timestep_rng, | |
(bsz,), | |
0, | |
noise_scheduler.config.num_train_timesteps, | |
) | |
# 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(noise_scheduler_state, latents, noise, timesteps) | |
# Get the text embedding for conditioning | |
encoder_hidden_states = text_encoder( | |
batch["input_ids"], | |
params=text_encoder_params, | |
train=False, | |
)[0] | |
# Predict the noise residual and compute loss | |
model_pred = unet.apply( | |
{"params": params}, noisy_latents, timesteps, encoder_hidden_states, train=True | |
).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(noise_scheduler_state, latents, noise, timesteps) | |
else: | |
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") | |
loss = (target - model_pred) ** 2 | |
loss = loss.mean() | |
return loss | |
grad_fn = jax.value_and_grad(compute_loss) | |
loss, grad = grad_fn(state.params) | |
grad = jax.lax.pmean(grad, "batch") | |
new_state = state.apply_gradients(grads=grad) | |
metrics = {"loss": loss} | |
metrics = jax.lax.pmean(metrics, axis_name="batch") | |
return new_state, metrics, new_train_rng | |
# Create parallel version of the train step | |
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) | |
# Replicate the train state on each device | |
state = jax_utils.replicate(state) | |
text_encoder_params = jax_utils.replicate(text_encoder.params) | |
vae_params = jax_utils.replicate(vae_params) | |
# Train! | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader)) | |
# Scheduler and math around the number of training steps. | |
if args.max_train_steps is None: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
logger.info("***** Running training *****") | |
logger.info(f" Num examples = {len(train_dataset)}") | |
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) = {total_train_batch_size}") | |
logger.info(f" Total optimization steps = {args.max_train_steps}") | |
global_step = 0 | |
epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0) | |
for epoch in epochs: | |
# ======================== Training ================================ | |
train_metrics = [] | |
steps_per_epoch = len(train_dataset) // total_train_batch_size | |
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False) | |
# train | |
for batch in train_dataloader: | |
batch = shard(batch) | |
state, train_metric, train_rngs = p_train_step(state, text_encoder_params, vae_params, batch, train_rngs) | |
train_metrics.append(train_metric) | |
train_step_progress_bar.update(1) | |
global_step += 1 | |
if global_step >= args.max_train_steps: | |
break | |
train_metric = jax_utils.unreplicate(train_metric) | |
train_step_progress_bar.close() | |
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})") | |
# Create the pipeline using using the trained modules and save it. | |
if jax.process_index() == 0: | |
scheduler = FlaxPNDMScheduler( | |
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True | |
) | |
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained( | |
"CompVis/stable-diffusion-safety-checker", from_pt=True | |
) | |
pipeline = FlaxStableDiffusionPipeline( | |
text_encoder=text_encoder, | |
vae=vae, | |
unet=unet, | |
tokenizer=tokenizer, | |
scheduler=scheduler, | |
safety_checker=safety_checker, | |
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"), | |
) | |
pipeline.save_pretrained( | |
args.output_dir, | |
params={ | |
"text_encoder": get_params_to_save(text_encoder_params), | |
"vae": get_params_to_save(vae_params), | |
"unet": get_params_to_save(state.params), | |
"safety_checker": safety_checker.params, | |
}, | |
) | |
if args.push_to_hub: | |
upload_folder( | |
repo_id=repo_id, | |
folder_path=args.output_dir, | |
commit_message="End of training", | |
ignore_patterns=["step_*", "epoch_*"], | |
) | |
if __name__ == "__main__": | |
main() | |