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mustango
/
diffusers
/examples
/research_projects
/mulit_token_textual_inversion
/textual_inversion_flax.py
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 PIL | |
import torch | |
import torch.utils.checkpoint | |
import transformers | |
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 | |
# TODO: remove and import from diffusers.utils when the new version of diffusers is released | |
from packaging import version | |
from PIL import Image | |
from torch.utils.data import Dataset | |
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 | |
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): | |
PIL_INTERPOLATION = { | |
"linear": PIL.Image.Resampling.BILINEAR, | |
"bilinear": PIL.Image.Resampling.BILINEAR, | |
"bicubic": PIL.Image.Resampling.BICUBIC, | |
"lanczos": PIL.Image.Resampling.LANCZOS, | |
"nearest": PIL.Image.Resampling.NEAREST, | |
} | |
else: | |
PIL_INTERPOLATION = { | |
"linear": PIL.Image.LINEAR, | |
"bilinear": PIL.Image.BILINEAR, | |
"bicubic": PIL.Image.BICUBIC, | |
"lanczos": PIL.Image.LANCZOS, | |
"nearest": PIL.Image.NEAREST, | |
} | |
# ------------------------------------------------------------------------------ | |
# Will error if the minimal version of diffusers is not installed. Remove at your own risks. | |
check_min_version("0.14.0.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( | |
"--tokenizer_name", | |
type=str, | |
default=None, | |
help="Pretrained tokenizer name or path if not the same as model_name", | |
) | |
parser.add_argument( | |
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data." | |
) | |
parser.add_argument( | |
"--placeholder_token", | |
type=str, | |
default=None, | |
required=True, | |
help="A token to use as a placeholder for the concept.", | |
) | |
parser.add_argument( | |
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word." | |
) | |
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'") | |
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.") | |
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=42, 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", action="store_true", help="Whether to center crop images before resizing to resolution." | |
) | |
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=5000, | |
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=True, | |
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", | |
) | |
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_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("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") | |
parser.add_argument( | |
"--use_auth_token", | |
action="store_true", | |
help=( | |
"Will use the token generated when running `huggingface-cli login` (necessary to use this script with" | |
" private models)." | |
), | |
) | |
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("--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 | |
if args.train_data_dir is None: | |
raise ValueError("You must specify a train data directory.") | |
return args | |
imagenet_templates_small = [ | |
"a photo of a {}", | |
"a rendering of a {}", | |
"a cropped photo of the {}", | |
"the photo of a {}", | |
"a photo of a clean {}", | |
"a photo of a dirty {}", | |
"a dark photo of the {}", | |
"a photo of my {}", | |
"a photo of the cool {}", | |
"a close-up photo of a {}", | |
"a bright photo of the {}", | |
"a cropped photo of a {}", | |
"a photo of the {}", | |
"a good photo of the {}", | |
"a photo of one {}", | |
"a close-up photo of the {}", | |
"a rendition of the {}", | |
"a photo of the clean {}", | |
"a rendition of a {}", | |
"a photo of a nice {}", | |
"a good photo of a {}", | |
"a photo of the nice {}", | |
"a photo of the small {}", | |
"a photo of the weird {}", | |
"a photo of the large {}", | |
"a photo of a cool {}", | |
"a photo of a small {}", | |
] | |
imagenet_style_templates_small = [ | |
"a painting in the style of {}", | |
"a rendering in the style of {}", | |
"a cropped painting in the style of {}", | |
"the painting in the style of {}", | |
"a clean painting in the style of {}", | |
"a dirty painting in the style of {}", | |
"a dark painting in the style of {}", | |
"a picture in the style of {}", | |
"a cool painting in the style of {}", | |
"a close-up painting in the style of {}", | |
"a bright painting in the style of {}", | |
"a cropped painting in the style of {}", | |
"a good painting in the style of {}", | |
"a close-up painting in the style of {}", | |
"a rendition in the style of {}", | |
"a nice painting in the style of {}", | |
"a small painting in the style of {}", | |
"a weird painting in the style of {}", | |
"a large painting in the style of {}", | |
] | |
class TextualInversionDataset(Dataset): | |
def __init__( | |
self, | |
data_root, | |
tokenizer, | |
learnable_property="object", # [object, style] | |
size=512, | |
repeats=100, | |
interpolation="bicubic", | |
flip_p=0.5, | |
set="train", | |
placeholder_token="*", | |
center_crop=False, | |
): | |
self.data_root = data_root | |
self.tokenizer = tokenizer | |
self.learnable_property = learnable_property | |
self.size = size | |
self.placeholder_token = placeholder_token | |
self.center_crop = center_crop | |
self.flip_p = flip_p | |
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)] | |
self.num_images = len(self.image_paths) | |
self._length = self.num_images | |
if set == "train": | |
self._length = self.num_images * repeats | |
self.interpolation = { | |
"linear": PIL_INTERPOLATION["linear"], | |
"bilinear": PIL_INTERPOLATION["bilinear"], | |
"bicubic": PIL_INTERPOLATION["bicubic"], | |
"lanczos": PIL_INTERPOLATION["lanczos"], | |
}[interpolation] | |
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small | |
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p) | |
def __len__(self): | |
return self._length | |
def __getitem__(self, i): | |
example = {} | |
image = Image.open(self.image_paths[i % self.num_images]) | |
if not image.mode == "RGB": | |
image = image.convert("RGB") | |
placeholder_string = self.placeholder_token | |
text = random.choice(self.templates).format(placeholder_string) | |
example["input_ids"] = self.tokenizer( | |
text, | |
padding="max_length", | |
truncation=True, | |
max_length=self.tokenizer.model_max_length, | |
return_tensors="pt", | |
).input_ids[0] | |
# default to score-sde preprocessing | |
img = np.array(image).astype(np.uint8) | |
if self.center_crop: | |
crop = min(img.shape[0], img.shape[1]) | |
( | |
h, | |
w, | |
) = ( | |
img.shape[0], | |
img.shape[1], | |
) | |
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2] | |
image = Image.fromarray(img) | |
image = image.resize((self.size, self.size), resample=self.interpolation) | |
image = self.flip_transform(image) | |
image = np.array(image).astype(np.uint8) | |
image = (image / 127.5 - 1.0).astype(np.float32) | |
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1) | |
return example | |
def resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng): | |
if model.config.vocab_size == new_num_tokens or new_num_tokens is None: | |
return | |
model.config.vocab_size = new_num_tokens | |
params = model.params | |
old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"] | |
old_num_tokens, emb_dim = old_embeddings.shape | |
initializer = jax.nn.initializers.normal() | |
new_embeddings = initializer(rng, (new_num_tokens, emb_dim)) | |
new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings) | |
new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id]) | |
params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings | |
model.params = params | |
return model | |
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() | |
if args.seed is not None: | |
set_seed(args.seed) | |
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 | |
# 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, | |
) | |
# 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() | |
# Load the tokenizer and add the placeholder token as a additional special token | |
if args.tokenizer_name: | |
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) | |
elif args.pretrained_model_name_or_path: | |
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") | |
# Add the placeholder token in tokenizer | |
num_added_tokens = tokenizer.add_tokens(args.placeholder_token) | |
if num_added_tokens == 0: | |
raise ValueError( | |
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" | |
" `placeholder_token` that is not already in the tokenizer." | |
) | |
# Convert the initializer_token, placeholder_token to ids | |
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) | |
# Check if initializer_token is a single token or a sequence of tokens | |
if len(token_ids) > 1: | |
raise ValueError("The initializer token must be a single token.") | |
initializer_token_id = token_ids[0] | |
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) | |
# Load models and create wrapper for stable diffusion | |
text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") | |
vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae") | |
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet") | |
# Create sampling rng | |
rng = jax.random.PRNGKey(args.seed) | |
rng, _ = jax.random.split(rng) | |
# Resize the token embeddings as we are adding new special tokens to the tokenizer | |
text_encoder = resize_token_embeddings( | |
text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng | |
) | |
original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"] | |
train_dataset = TextualInversionDataset( | |
data_root=args.train_data_dir, | |
tokenizer=tokenizer, | |
size=args.resolution, | |
placeholder_token=args.placeholder_token, | |
repeats=args.repeats, | |
learnable_property=args.learnable_property, | |
center_crop=args.center_crop, | |
set="train", | |
) | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
input_ids = torch.stack([example["input_ids"] for example in examples]) | |
batch = {"pixel_values": pixel_values, "input_ids": 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, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn | |
) | |
# Optimization | |
if args.scale_lr: | |
args.learning_rate = args.learning_rate * total_train_batch_size | |
constant_scheduler = optax.constant_schedule(args.learning_rate) | |
optimizer = optax.adamw( | |
learning_rate=constant_scheduler, | |
b1=args.adam_beta1, | |
b2=args.adam_beta2, | |
eps=args.adam_epsilon, | |
weight_decay=args.adam_weight_decay, | |
) | |
def create_mask(params, label_fn): | |
def _map(params, mask, label_fn): | |
for k in params: | |
if label_fn(k): | |
mask[k] = "token_embedding" | |
else: | |
if isinstance(params[k], dict): | |
mask[k] = {} | |
_map(params[k], mask[k], label_fn) | |
else: | |
mask[k] = "zero" | |
mask = {} | |
_map(params, mask, label_fn) | |
return mask | |
def zero_grads(): | |
# from https://github.com/deepmind/optax/issues/159#issuecomment-896459491 | |
def init_fn(_): | |
return () | |
def update_fn(updates, state, params=None): | |
return jax.tree_util.tree_map(jnp.zeros_like, updates), () | |
return optax.GradientTransformation(init_fn, update_fn) | |
# Zero out gradients of layers other than the token embedding layer | |
tx = optax.multi_transform( | |
{"token_embedding": optimizer, "zero": zero_grads()}, | |
create_mask(text_encoder.params, lambda s: s == "token_embedding"), | |
) | |
state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx) | |
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 | |
train_rngs = jax.random.split(rng, jax.local_device_count()) | |
# Define gradient train step fn | |
def train_step(state, vae_params, unet_params, batch, train_rng): | |
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3) | |
def compute_loss(params): | |
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 | |
noise_rng, timestep_rng = jax.random.split(sample_rng) | |
noise = jax.random.normal(noise_rng, latents.shape) | |
bsz = latents.shape[0] | |
timesteps = jax.random.randint( | |
timestep_rng, | |
(bsz,), | |
0, | |
noise_scheduler.config.num_train_timesteps, | |
) | |
noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps) | |
encoder_hidden_states = state.apply_fn( | |
batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True | |
)[0] | |
# Predict the noise residual and compute loss | |
model_pred = unet.apply( | |
{"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False | |
).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) | |
# Keep the token embeddings fixed except the newly added embeddings for the concept, | |
# as we only want to optimize the concept embeddings | |
token_embeds = original_token_embeds.at[placeholder_token_id].set( | |
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id] | |
) | |
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds | |
metrics = {"loss": loss} | |
metrics = jax.lax.pmean(metrics, axis_name="batch") | |
return new_state, metrics, new_train_rng | |
# Create parallel version of the train and eval step | |
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) | |
# Replicate the train state on each device | |
state = jax_utils.replicate(state) | |
vae_params = jax_utils.replicate(vae_params) | |
unet_params = jax_utils.replicate(unet_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=f"Epoch ... (1/{args.num_train_epochs})", 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, vae_params, unet_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(state.params), | |
"vae": get_params_to_save(vae_params), | |
"unet": get_params_to_save(unet_params), | |
"safety_checker": safety_checker.params, | |
}, | |
) | |
# Also save the newly trained embeddings | |
learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][ | |
placeholder_token_id | |
] | |
learned_embeds_dict = {args.placeholder_token: learned_embeds} | |
jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict) | |
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() | |