import argparse import logging import math import os import random from pathlib import Path from typing import Optional import numpy as np import torch import torch.utils.checkpoint from torch.utils.data import Dataset import jax import jax.numpy as jnp import optax import transformers from diffusers import ( FlaxAutoencoderKL, FlaxDDPMScheduler, FlaxPNDMScheduler, FlaxStableDiffusionPipeline, FlaxUNet2DConditionModel, ) from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker from diffusers.utils import PIL_INTERPOLATION from flax import jax_utils from flax.training import train_state from flax.training.common_utils import shard from huggingface_hub import HfFolder, Repository, whoami from PIL import Image from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed 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 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 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.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) # 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 ) # 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 * 0.18215 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(latents, noise, timesteps) encoder_hidden_states = state.apply_fn( batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True )[0] unet_outputs = unet.apply( {"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False ) noise_pred = unet_outputs.sample loss = (noise - noise_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=CLIPFeatureExtractor.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: repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) if __name__ == "__main__": main()