DreamBooth

!pip install -q -U keras_cv==0.6.0
!pip install -q -U tensorflow

import math

import keras_cv
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from imutils import paths
from tensorflow import keras

from tqdm import tqdm
import numpy as np
import hashlib
import keras_cv
import PIL
import os

class_images_dir = "class-images"
os.makedirs(class_images_dir, exist_ok=True)

model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True)

class_prompt = "a photo of dog"
num_imgs_to_generate = 200
for i in tqdm(range(num_imgs_to_generate)):
    images = model.text_to_image(
        class_prompt,
        batch_size=3,
    )
    idx = np.random.choice(len(images))
    selected_image = PIL.Image.fromarray(images[idx])
    hash_image = hashlib.sha1(selected_image.tobytes()).hexdigest()
    image_filename = os.path.join(class_images_dir, f"{hash_image}.jpg")
    selected_image.save(image_filename)

instance_images_root = tf.keras.utils.get_file(
    origin="https://hugging-face.cn/datasets/sayakpaul/sample-datasets/resolve/main/instance-images.tar.gz",
    untar=True,
)
class_images_root = tf.keras.utils.get_file(
    origin="https://hugging-face.cn/datasets/sayakpaul/sample-datasets/resolve/main/class-images.tar.gz",
    untar=True,
)

instance_image_paths = list(paths.list_images(instance_images_root))
class_image_paths = list(paths.list_images(class_images_root))

def load_images(image_paths):
    images = [np.array(keras.utils.load_img(path)) for path in image_paths]
    return images

def plot_images(images, title=None):
    plt.figure(figsize=(20, 20))
    for i in range(len(images)):
        ax = plt.subplot(1, len(images), i + 1)
        if title is not None:
            plt.title(title)
        plt.imshow(images[i])
        plt.axis("off")

plot_images(load_images(instance_image_paths[:5]))

plot_images(load_images(class_image_paths[:5]))

# Since we're using prior preservation, we need to match the number
# of instance images we're using. We just repeat the instance image paths
# to do so.
new_instance_image_paths = []
for index in range(len(class_image_paths)):
    instance_image = instance_image_paths[index % len(instance_image_paths)]
    new_instance_image_paths.append(instance_image)

# We just repeat the prompts / captions per images.
unique_id = "sks"
class_label = "dog"

instance_prompt = f"a photo of {unique_id} {class_label}"
instance_prompts = [instance_prompt] * len(new_instance_image_paths)

class_prompt = f"a photo of {class_label}"
class_prompts = [class_prompt] * len(class_image_paths)

import itertools

# The padding token and maximum prompt length are specific to the text encoder.
# If you're using a different text encoder be sure to change them accordingly.
padding_token = 49407
max_prompt_length = 77

# Load the tokenizer.
tokenizer = keras_cv.models.stable_diffusion.SimpleTokenizer()

# Method to tokenize and pad the tokens.
def process_text(caption):
    tokens = tokenizer.encode(caption)
    tokens = tokens + [padding_token] * (max_prompt_length - len(tokens))
    return np.array(tokens)


# Collate the tokenized captions into an array.
tokenized_texts = np.empty(
    (len(instance_prompts) + len(class_prompts), max_prompt_length)
)

for i, caption in enumerate(itertools.chain(instance_prompts, class_prompts)):
    tokenized_texts[i] = process_text(caption)


# We also pre-compute the text embeddings to save some memory during training.
POS_IDS = tf.convert_to_tensor([list(range(max_prompt_length))], dtype=tf.int32)
text_encoder = keras_cv.models.stable_diffusion.TextEncoder(max_prompt_length)

gpus = tf.config.list_logical_devices("GPU")

# Ensure the computation takes place on a GPU.
# Note that it's done automatically when there's a GPU present.
# This example just attempts at showing how you can do it
# more explicitly.
with tf.device(gpus[0].name):
    embedded_text = text_encoder(
        [tf.convert_to_tensor(tokenized_texts), POS_IDS], training=False
    ).numpy()

# To ensure text_encoder doesn't occupy any GPU space.
del text_encoder

resolution = 512
auto = tf.data.AUTOTUNE

augmenter = keras.Sequential(
    layers=[
        keras_cv.layers.CenterCrop(resolution, resolution),
        keras_cv.layers.RandomFlip(),
        keras.layers.Rescaling(scale=1.0 / 127.5, offset=-1),
    ]
)


def process_image(image_path, tokenized_text):
    image = tf.io.read_file(image_path)
    image = tf.io.decode_png(image, 3)
    image = tf.image.resize(image, (resolution, resolution))
    return image, tokenized_text


def apply_augmentation(image_batch, embedded_tokens):
    return augmenter(image_batch), embedded_tokens


def prepare_dict(instance_only=True):
    def fn(image_batch, embedded_tokens):
        if instance_only:
            batch_dict = {
                "instance_images": image_batch,
                "instance_embedded_texts": embedded_tokens,
            }
            return batch_dict
        else:
            batch_dict = {
                "class_images": image_batch,
                "class_embedded_texts": embedded_tokens,
            }
            return batch_dict

    return fn


def assemble_dataset(image_paths, embedded_texts, instance_only=True, batch_size=1):
    dataset = tf.data.Dataset.from_tensor_slices((image_paths, embedded_texts))
    dataset = dataset.map(process_image, num_parallel_calls=auto)
    dataset = dataset.shuffle(5, reshuffle_each_iteration=True)
    dataset = dataset.batch(batch_size)
    dataset = dataset.map(apply_augmentation, num_parallel_calls=auto)

    prepare_dict_fn = prepare_dict(instance_only=instance_only)
    dataset = dataset.map(prepare_dict_fn, num_parallel_calls=auto)
    return dataset

instance_dataset = assemble_dataset(
    new_instance_image_paths,
    embedded_text[: len(new_instance_image_paths)],
)
class_dataset = assemble_dataset(
    class_image_paths,
    embedded_text[len(new_instance_image_paths) :],
    instance_only=False,
)
train_dataset = tf.data.Dataset.zip((instance_dataset, class_dataset))

sample_batch = next(iter(train_dataset))
print(sample_batch[0].keys(), sample_batch[1].keys())

for k in sample_batch[0]:
    print(k, sample_batch[0][k].shape)

for k in sample_batch[1]:
    print(k, sample_batch[1][k].shape)

import tensorflow.experimental.numpy as tnp


class DreamBoothTrainer(tf.keras.Model):
    # Reference:
    # https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py

    def __init__(
        self,
        diffusion_model,
        vae,
        noise_scheduler,
        use_mixed_precision=False,
        prior_loss_weight=1.0,
        max_grad_norm=1.0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.diffusion_model = diffusion_model
        self.vae = vae
        self.noise_scheduler = noise_scheduler
        self.prior_loss_weight = prior_loss_weight
        self.max_grad_norm = max_grad_norm

        self.use_mixed_precision = use_mixed_precision
        self.vae.trainable = False

    def train_step(self, inputs):
        instance_batch = inputs[0]
        class_batch = inputs[1]

        instance_images = instance_batch["instance_images"]
        instance_embedded_text = instance_batch["instance_embedded_texts"]
        class_images = class_batch["class_images"]
        class_embedded_text = class_batch["class_embedded_texts"]

        images = tf.concat([instance_images, class_images], 0)
        embedded_texts = tf.concat([instance_embedded_text, class_embedded_text], 0)
        batch_size = tf.shape(images)[0]

        with tf.GradientTape() as tape:
            # Project image into the latent space and sample from it.
            latents = self.sample_from_encoder_outputs(self.vae(images, training=False))
            # Know more about the magic number here:
            # https://keras.org.cn/examples/generative/fine_tune_via_textual_inversion/
            latents = latents * 0.18215

            # Sample noise that we'll add to the latents.
            noise = tf.random.normal(tf.shape(latents))

            # Sample a random timestep for each image.
            timesteps = tnp.random.randint(
                0, self.noise_scheduler.train_timesteps, (batch_size,)
            )

            # Add noise to the latents according to the noise magnitude at each timestep
            # (this is the forward diffusion process).
            noisy_latents = self.noise_scheduler.add_noise(
                tf.cast(latents, noise.dtype), noise, timesteps
            )

            # Get the target for loss depending on the prediction type
            # just the sampled noise for now.
            target = noise  # noise_schedule.predict_epsilon == True

            # Predict the noise residual and compute loss.
            timestep_embedding = tf.map_fn(
                lambda t: self.get_timestep_embedding(t), timesteps, dtype=tf.float32
            )
            model_pred = self.diffusion_model(
                [noisy_latents, timestep_embedding, embedded_texts], training=True
            )
            loss = self.compute_loss(target, model_pred)
            if self.use_mixed_precision:
                loss = self.optimizer.get_scaled_loss(loss)

        # Update parameters of the diffusion model.
        trainable_vars = self.diffusion_model.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        if self.use_mixed_precision:
            gradients = self.optimizer.get_unscaled_gradients(gradients)
        gradients = [tf.clip_by_norm(g, self.max_grad_norm) for g in gradients]
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))

        return {m.name: m.result() for m in self.metrics}

    def get_timestep_embedding(self, timestep, dim=320, max_period=10000):
        half = dim // 2
        log_max_period = tf.math.log(tf.cast(max_period, tf.float32))
        freqs = tf.math.exp(
            -log_max_period * tf.range(0, half, dtype=tf.float32) / half
        )
        args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
        embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
        return embedding

    def sample_from_encoder_outputs(self, outputs):
        mean, logvar = tf.split(outputs, 2, axis=-1)
        logvar = tf.clip_by_value(logvar, -30.0, 20.0)
        std = tf.exp(0.5 * logvar)
        sample = tf.random.normal(tf.shape(mean), dtype=mean.dtype)
        return mean + std * sample

    def compute_loss(self, target, model_pred):
        # Chunk the noise and model_pred into two parts and compute the loss
        # on each part separately.
        # Since the first half of the inputs has instance samples and the second half
        # has class samples, we do the chunking accordingly.
        model_pred, model_pred_prior = tf.split(
            model_pred, num_or_size_splits=2, axis=0
        )
        target, target_prior = tf.split(target, num_or_size_splits=2, axis=0)

        # Compute instance loss.
        loss = self.compiled_loss(target, model_pred)

        # Compute prior loss.
        prior_loss = self.compiled_loss(target_prior, model_pred_prior)

        # Add the prior loss to the instance loss.
        loss = loss + self.prior_loss_weight * prior_loss
        return loss

    def save_weights(self, filepath, overwrite=True, save_format=None, options=None):
        # Overriding this method will allow us to use the `ModelCheckpoint`
        # callback directly with this trainer class. In this case, it will
        # only checkpoint the `diffusion_model` since that's what we're training
        # during fine-tuning.
        self.diffusion_model.save_weights(
            filepath=filepath,
            overwrite=overwrite,
            save_format=save_format,
            options=options,
        )

    def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None):
        # Similarly override `load_weights()` so that we can directly call it on
        # the trainer class object.
        self.diffusion_model.load_weights(
            filepath=filepath,
            by_name=by_name,
            skip_mismatch=skip_mismatch,
            options=options,
        )

# Comment it if you are not using a GPU having tensor cores.
tf.keras.mixed_precision.set_global_policy("mixed_float16")

use_mp = True  # Set it to False if you're not using a GPU with tensor cores.

image_encoder = keras_cv.models.stable_diffusion.ImageEncoder()
dreambooth_trainer = DreamBoothTrainer(
    diffusion_model=keras_cv.models.stable_diffusion.DiffusionModel(
        resolution, resolution, max_prompt_length
    ),
    # Remove the top layer from the encoder, which cuts off the variance and only
    # returns the mean.
    vae=tf.keras.Model(
        image_encoder.input,
        image_encoder.layers[-2].output,
    ),
    noise_scheduler=keras_cv.models.stable_diffusion.NoiseScheduler(),
    use_mixed_precision=use_mp,
)

# These hyperparameters come from this tutorial by Hugging Face:
# https://github.com/huggingface/diffusers/tree/main/examples/dreambooth
learning_rate = 5e-6
beta_1, beta_2 = 0.9, 0.999
weight_decay = (1e-2,)
epsilon = 1e-08

optimizer = tf.keras.optimizers.experimental.AdamW(
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    beta_1=beta_1,
    beta_2=beta_2,
    epsilon=epsilon,
)
dreambooth_trainer.compile(optimizer=optimizer, loss="mse")

num_update_steps_per_epoch = train_dataset.cardinality()
max_train_steps = 800
epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
print(f"Training for {epochs} epochs.")

ckpt_path = "dreambooth-unet.h5"
ckpt_callback = tf.keras.callbacks.ModelCheckpoint(
    ckpt_path,
    save_weights_only=True,
    monitor="loss",
    mode="min",
)
dreambooth_trainer.fit(train_dataset, epochs=epochs, callbacks=[ckpt_callback])

# Initialize a new Stable Diffusion model.
dreambooth_model = keras_cv.models.StableDiffusion(
    img_width=resolution, img_height=resolution, jit_compile=True
)
dreambooth_model.diffusion_model.load_weights(ckpt_path)

# Note how the unique identifier and the class have been used in the prompt.
prompt = f"A photo of {unique_id} {class_label} in a bucket"
num_imgs_to_gen = 3

images_dreamboothed = dreambooth_model.text_to_image(prompt, batch_size=num_imgs_to_gen)
plot_images(images_dreamboothed, prompt)

unet_weights = tf.keras.utils.get_file(
    origin="https://hugging-face.cn/chansung/dreambooth-dog/resolve/main/lr%409e-06-max_train_steps%40200-train_text_encoder%40True-unet.h5"
)
text_encoder_weights = tf.keras.utils.get_file(
    origin="https://hugging-face.cn/chansung/dreambooth-dog/resolve/main/lr%409e-06-max_train_steps%40200-train_text_encoder%40True-text_encoder.h5"
)

dreambooth_model.diffusion_model.load_weights(unet_weights)
dreambooth_model.text_encoder.load_weights(text_encoder_weights)

images_dreamboothed = dreambooth_model.text_to_image(prompt, batch_size=num_imgs_to_gen)
plot_images(images_dreamboothed, prompt)

images_dreamboothed = dreambooth_model.text_to_image(
    prompt, batch_size=num_imgs_to_gen, num_steps=100
)
plot_images(images_dreamboothed, prompt)