From 90f91adb0ebd6d04c4f4e5dd87afd08acff16c9e Mon Sep 17 00:00:00 2001 From: "Duong A. Nguyen" <38061659+duongna21@users.noreply.github.com> Date: Thu, 27 Oct 2022 19:25:04 +0700 Subject: [PATCH] [Flax] Add DreamBooth (#1001) * [Flax] Add DreamBooth * fix sample rng * style * not reuse rng * add dtype for mixed precision training * Add Flax example --- examples/dreambooth/README.md | 65 ++ examples/dreambooth/train_dreambooth_flax.py | 665 +++++++++++++++++++ 2 files changed, 730 insertions(+) create mode 100644 examples/dreambooth/train_dreambooth_flax.py diff --git a/examples/dreambooth/README.md b/examples/dreambooth/README.md index fb4bb900a9..6cae5ca2f2 100644 --- a/examples/dreambooth/README.md +++ b/examples/dreambooth/README.md @@ -58,6 +58,24 @@ accelerate launch train_dreambooth.py \ --max_train_steps=400 ``` +Or use the Flax implementation if you need a speedup + +```bash +export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" +export INSTANCE_DIR="path-to-instance-images" +export OUTPUT_DIR="path-to-save-model" + +python train_dreambooth_flax.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --instance_prompt="a photo of sks dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --max_train_steps=400 +``` + ### Training with prior-preservation loss Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data. @@ -87,6 +105,29 @@ accelerate launch train_dreambooth.py \ --max_train_steps=800 ``` +Or use the Flax implementation if you need a speedup + +```bash +export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" +export INSTANCE_DIR="path-to-instance-images" +export CLASS_DIR="path-to-class-images" +export OUTPUT_DIR="path-to-save-model" + +python train_dreambooth_flax.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --class_data_dir=$CLASS_DIR \ + --output_dir=$OUTPUT_DIR \ + --with_prior_preservation --prior_loss_weight=1.0 \ + --instance_prompt="a photo of sks dog" \ + --class_prompt="a photo of dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --learning_rate=5e-6 \ + --num_class_images=200 \ + --max_train_steps=800 +``` + ### Training on a 16GB GPU: With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU. @@ -193,6 +234,30 @@ accelerate launch train_dreambooth.py \ --max_train_steps=800 ``` +Or use the Flax implementation if you need a speedup + +```bash +export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" +export INSTANCE_DIR="path-to-instance-images" +export CLASS_DIR="path-to-class-images" +export OUTPUT_DIR="path-to-save-model" + +python train_dreambooth_flax.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --train_text_encoder \ + --instance_data_dir=$INSTANCE_DIR \ + --class_data_dir=$CLASS_DIR \ + --output_dir=$OUTPUT_DIR \ + --with_prior_preservation --prior_loss_weight=1.0 \ + --instance_prompt="a photo of sks dog" \ + --class_prompt="a photo of dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --learning_rate=2e-6 \ + --num_class_images=200 \ + --max_train_steps=800 +``` + ## Inference Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt. diff --git a/examples/dreambooth/train_dreambooth_flax.py b/examples/dreambooth/train_dreambooth_flax.py new file mode 100644 index 0000000000..d2652606b5 --- /dev/null +++ b/examples/dreambooth/train_dreambooth_flax.py @@ -0,0 +1,665 @@ +import argparse +import hashlib +import logging +import math +import os +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 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( + "--instance_data_dir", + type=str, + default=None, + required=True, + help="A folder containing the training data of instance images.", + ) + 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", + type=str, + default=None, + help="The prompt with identifier specifying the instance", + ) + parser.add_argument( + "--class_prompt", + type=str, + default=None, + help="The prompt to specify images in the same class as provided instance images.", + ) + parser.add_argument( + "--with_prior_preservation", + default=False, + action="store_true", + help="Flag to add prior preservation loss.", + ) + 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 not have enough images, 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=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", action="store_true", help="Whether to center crop images before resizing to resolution" + ) + 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( + "--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("--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="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 + + if args.instance_data_dir is None: + raise ValueError("You must specify a train data directory.") + + if args.with_prior_preservation: + if args.class_data_dir is None: + raise ValueError("You must specify a data directory for class images.") + if args.class_prompt is None: + raise ValueError("You must specify prompt for class images.") + + return args + + +class DreamBoothDataset(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + """ + + def __init__( + self, + instance_data_root, + instance_prompt, + tokenizer, + class_data_root=None, + class_prompt=None, + size=512, + center_crop=False, + ): + self.size = size + self.center_crop = center_crop + self.tokenizer = tokenizer + + self.instance_data_root = Path(instance_data_root) + if not self.instance_data_root.exists(): + raise ValueError("Instance images root doesn't exists.") + + self.instance_images_path = list(Path(instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + self.instance_prompt = instance_prompt + self._length = self.num_instance_images + + if class_data_root is not None: + self.class_data_root = Path(class_data_root) + self.class_data_root.mkdir(parents=True, exist_ok=True) + self.class_images_path = list(self.class_data_root.iterdir()) + self.num_class_images = len(self.class_images_path) + self._length = max(self.num_class_images, self.num_instance_images) + self.class_prompt = class_prompt + else: + self.class_data_root = None + + self.image_transforms = transforms.Compose( + [ + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + def __len__(self): + return self._length + + def __getitem__(self, index): + example = {} + instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) + if not instance_image.mode == "RGB": + instance_image = instance_image.convert("RGB") + example["instance_images"] = self.image_transforms(instance_image) + example["instance_prompt_ids"] = self.tokenizer( + self.instance_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + if self.class_data_root: + class_image = Image.open(self.class_images_path[index % self.num_class_images]) + if not class_image.mode == "RGB": + class_image = class_image.convert("RGB") + example["class_images"] = self.image_transforms(class_image) + example["class_prompt_ids"] = self.tokenizer( + self.class_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + return example + + +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 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) + + 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) + + rng = jax.random.PRNGKey(args.seed) + + if args.with_prior_preservation: + class_images_dir = Path(args.class_data_dir) + if not class_images_dir.exists(): + class_images_dir.mkdir(parents=True) + cur_class_images = len(list(class_images_dir.iterdir())) + + if cur_class_images < args.num_class_images: + pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, safety_checker=None + ) + pipeline.set_progress_bar_config(disable=True) + + num_new_images = args.num_class_images - cur_class_images + logger.info(f"Number of class images to sample: {num_new_images}.") + + sample_dataset = PromptDataset(args.class_prompt, num_new_images) + sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) + + for example in tqdm( + sample_dataloader, desc="Generating class images", disable=not jax.process_index() == 0 + ): + prompt_ids = pipeline.prepare_inputs(example["prompt"]) + prompt_ids = shard(prompt_ids) + p_params = jax_utils.replicate(params) + rng = jax.random.split(rng)[0] + sample_rng = jax.random.split(rng, jax.device_count()) + images = pipeline(prompt_ids, p_params, sample_rng, jit=True).images + images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) + images = pipeline.numpy_to_pil(np.array(images)) + + for i, image in enumerate(images): + hash_image = hashlib.sha1(image.tobytes()).hexdigest() + image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" + image.save(image_filename) + + del pipeline + + # Handle the repository creation + 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) + + # 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") + + train_dataset = DreamBoothDataset( + instance_data_root=args.instance_data_dir, + instance_prompt=args.instance_prompt, + class_data_root=args.class_data_dir if args.with_prior_preservation else None, + class_prompt=args.class_prompt, + tokenizer=tokenizer, + size=args.resolution, + center_crop=args.center_crop, + ) + + def collate_fn(examples): + input_ids = [example["instance_prompt_ids"] for example in examples] + pixel_values = [example["instance_images"] for example in examples] + + # Concat class and instance examples for prior preservation. + # We do this to avoid doing two forward passes. + if args.with_prior_preservation: + input_ids += [example["class_prompt_ids"] for example in examples] + pixel_values += [example["class_images"] for example in examples] + + pixel_values = torch.stack(pixel_values) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = tokenizer.pad( + {"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt" + ).input_ids + + batch = { + "input_ids": input_ids, + "pixel_values": pixel_values, + } + 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, collate_fn=collate_fn, 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 + text_encoder = FlaxCLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", dtype=weight_dtype) + vae, vae_params = FlaxAutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path, subfolder="vae", dtype=weight_dtype + ) + unet, unet_params = FlaxUNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, 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, + ) + + unet_state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer) + text_encoder_state = train_state.TrainState.create( + apply_fn=text_encoder.__call__, params=text_encoder.params, tx=optimizer + ) + + 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()) + + def train_step(unet_state, text_encoder_state, vae_params, batch, train_rng): + dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3) + + if args.train_text_encoder: + params = {"text_encoder": text_encoder_state.params, "unet": unet_state.params} + else: + params = {"unet": unet_state.params} + + 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 * 0.18215 + + # 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(latents, noise, timesteps) + + # Get the text embedding for conditioning + if args.train_text_encoder: + encoder_hidden_states = text_encoder_state.apply_fn( + batch["input_ids"], params=params["text_encoder"], dropout_rng=dropout_rng, train=True + )[0] + else: + encoder_hidden_states = text_encoder( + batch["input_ids"], params=text_encoder_state.params, train=False + )[0] + + # Predict the noise residual + unet_outputs = unet.apply( + {"params": params["unet"]}, noisy_latents, timesteps, encoder_hidden_states, train=True + ) + noise_pred = unet_outputs.sample + + if args.with_prior_preservation: + # Chunk the noise and noise_pred into two parts and compute the loss on each part separately. + noise_pred, noise_pred_prior = jnp.split(noise_pred, 2, axis=0) + noise, noise_prior = jnp.split(noise, 2, axis=0) + + # Compute instance loss + loss = (noise - noise_pred) ** 2 + loss = loss.mean() + + # Compute prior loss + prior_loss = (noise_prior - noise_pred_prior) ** 2 + prior_loss = prior_loss.mean() + + # Add the prior loss to the instance loss. + loss = loss + args.prior_loss_weight * prior_loss + else: + loss = (noise - noise_pred) ** 2 + loss = loss.mean() + + return loss + + grad_fn = jax.value_and_grad(compute_loss) + loss, grad = grad_fn(params) + grad = jax.lax.pmean(grad, "batch") + + new_unet_state = unet_state.apply_gradients(grads=grad["unet"]) + if args.train_text_encoder: + new_text_encoder_state = text_encoder_state.apply_gradients(grads=grad["text_encoder"]) + else: + new_text_encoder_state = text_encoder_state + + metrics = {"loss": loss} + metrics = jax.lax.pmean(metrics, axis_name="batch") + + return new_unet_state, new_text_encoder_state, metrics, new_train_rng + + # Create parallel version of the train step + p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0, 1)) + + # Replicate the train state on each device + unet_state = jax_utils.replicate(unet_state) + text_encoder_state = jax_utils.replicate(text_encoder_state) + 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) + unet_state, text_encoder_state, train_metric, train_rngs = p_train_step( + unet_state, text_encoder_state, 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=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"), + ) + + pipeline.save_pretrained( + args.output_dir, + params={ + "text_encoder": get_params_to_save(text_encoder_state.params), + "vae": get_params_to_save(vae_params), + "unet": get_params_to_save(unet_state.params), + "safety_checker": safety_checker.params, + }, + ) + + if args.push_to_hub: + repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) + + +if __name__ == "__main__": + main()