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FLUX: Optimize dataset loading logic (#1038)

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madroid
2024-10-16 01:37:45 +08:00
committed by GitHub
parent 3d62b058a4
commit f491d473a3
6 changed files with 461 additions and 365 deletions
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121
flux/dreambooth.py
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@@ -1,7 +1,6 @@
# Copyright © 2024 Apple Inc.
import argparse
import json
import time
from functools import partial
from pathlib import Path
@@ -13,105 +12,8 @@ import numpy as np
from mlx.nn.utils import average_gradients
from mlx.utils import tree_flatten, tree_map, tree_reduce
from PIL import Image
from tqdm import tqdm
from flux import FluxPipeline
class FinetuningDataset:
def __init__(self, flux, args):
self.args = args
self.flux = flux
self.dataset_base = Path(args.dataset)
dataset_index = self.dataset_base / "index.json"
if not dataset_index.exists():
raise ValueError(f"'{args.dataset}' is not a valid finetuning dataset")
with open(dataset_index, "r") as f:
self.index = json.load(f)
self.latents = []
self.t5_features = []
self.clip_features = []
def _random_crop_resize(self, img):
resolution = self.args.resolution
width, height = img.size
a, b, c, d = mx.random.uniform(shape=(4,), stream=mx.cpu).tolist()
# Random crop the input image between 0.8 to 1.0 of its original dimensions
crop_size = (
max((0.8 + 0.2 * a) * width, resolution[0]),
max((0.8 + 0.2 * a) * height, resolution[1]),
)
pan = (width - crop_size[0], height - crop_size[1])
img = img.crop(
(
pan[0] * b,
pan[1] * c,
crop_size[0] + pan[0] * b,
crop_size[1] + pan[1] * c,
)
)
# Fit the largest rectangle with the ratio of resolution in the image
# rectangle.
width, height = crop_size
ratio = resolution[0] / resolution[1]
r1 = (height * ratio, height)
r2 = (width, width / ratio)
r = r1 if r1[0] <= width else r2
img = img.crop(
(
(width - r[0]) / 2,
(height - r[1]) / 2,
(width + r[0]) / 2,
(height + r[1]) / 2,
)
)
# Finally resize the image to resolution
img = img.resize(resolution, Image.LANCZOS)
return mx.array(np.array(img))
def encode_images(self):
"""Encode the images in the latent space to prepare for training."""
self.flux.ae.eval()
for sample in tqdm(self.index["data"]):
input_img = Image.open(self.dataset_base / sample["image"])
for i in range(self.args.num_augmentations):
img = self._random_crop_resize(input_img)
img = (img[:, :, :3].astype(self.flux.dtype) / 255) * 2 - 1
x_0 = self.flux.ae.encode(img[None])
x_0 = x_0.astype(self.flux.dtype)
mx.eval(x_0)
self.latents.append(x_0)
def encode_prompts(self):
"""Pre-encode the prompts so that we don't recompute them during
training (doesn't allow finetuning the text encoders)."""
for sample in tqdm(self.index["data"]):
t5_tok, clip_tok = self.flux.tokenize([sample["text"]])
t5_feat = self.flux.t5(t5_tok)
clip_feat = self.flux.clip(clip_tok).pooled_output
mx.eval(t5_feat, clip_feat)
self.t5_features.append(t5_feat)
self.clip_features.append(clip_feat)
def iterate(self, batch_size):
xs = mx.concatenate(self.latents)
t5 = mx.concatenate(self.t5_features)
clip = mx.concatenate(self.clip_features)
mx.eval(xs, t5, clip)
n_aug = self.args.num_augmentations
while True:
x_indices = mx.random.permutation(len(self.latents))
c_indices = x_indices // n_aug
for i in range(0, len(self.latents), batch_size):
x_i = x_indices[i : i + batch_size]
c_i = c_indices[i : i + batch_size]
yield xs[x_i], t5[c_i], clip[c_i]
from flux import FluxPipeline, Trainer, load_dataset
def generate_progress_images(iteration, flux, args):
@@ -157,7 +59,8 @@ def save_adapters(iteration, flux, args):
)
if __name__ == "__main__":
def setup_arg_parser():
"""Set up and return the argument parser."""
parser = argparse.ArgumentParser(
description="Finetune Flux to generate images with a specific subject"
)
@@ -247,7 +150,11 @@ if __name__ == "__main__":
)
parser.add_argument("dataset")
return parser
if __name__ == "__main__":
parser = setup_arg_parser()
args = parser.parse_args()
# Load the model and set it up for LoRA training. We use the same random
@@ -267,7 +174,7 @@ if __name__ == "__main__":
trainable_params = tree_reduce(
lambda acc, x: acc + x.size, flux.flow.trainable_parameters(), 0
)
print(f"Training {trainable_params / 1024**2:.3f}M parameters", flush=True)
print(f"Training {trainable_params / 1024 ** 2:.3f}M parameters", flush=True)
# Set up the optimizer and training steps. The steps are a bit verbose to
# support gradient accumulation together with compilation.
@@ -340,10 +247,10 @@ if __name__ == "__main__":
x, t5_feat, clip_feat, guidance, prev_grads
)
print("Create the training dataset.", flush=True)
dataset = FinetuningDataset(flux, args)
dataset.encode_images()
dataset.encode_prompts()
dataset = load_dataset(args.dataset)
trainer = Trainer(flux, dataset, args)
trainer.encode_dataset()
guidance = mx.full((args.batch_size,), args.guidance, dtype=flux.dtype)
# An initial generation to compare
@@ -352,7 +259,7 @@ if __name__ == "__main__":
grads = None
losses = []
tic = time.time()
for i, batch in zip(range(args.iterations), dataset.iterate(args.batch_size)):
for i, batch in zip(range(args.iterations), trainer.iterate(args.batch_size)):
loss, grads = step(*batch, guidance, grads, (i + 1) % args.grad_accumulate == 0)
mx.eval(loss, grads, state)
losses.append(loss.item())
@@ -361,7 +268,7 @@ if __name__ == "__main__":
toc = time.time()
peak_mem = mx.metal.get_peak_memory() / 1024**3
print(
f"Iter: {i+1} Loss: {sum(losses) / 10:.3f} "
f"Iter: {i + 1} Loss: {sum(losses) / 10:.3f} "
f"It/s: {10 / (toc - tic):.3f} "
f"Peak mem: {peak_mem:.3f} GB",
flush=True,