bucketsampler 0.1.0a1

Aspect ratio bucketing toolkit for diffusion model training. PyTorch native, DDP correct.

bucketsampler

Aspect ratio bucketing for diffusion model training (SDXL-style multi-AR batches). PyTorch native, DDP correct, zero training-framework lock-in.

Heads up: "bucket sampler" also names a length-bucketing pattern in NLP. This is the image / diffusion variant, not the sequence one.

Why

Diffusion U-Nets want a fixed (H, W) per batch. Real datasets do not. The naive options either distort (squeeze every image to a square) or throw data away (center-crop to the smallest common size). Bucketing splits images into a small set of (W, H) targets and draws each batch from a single bucket, so nothing gets squished and nothing gets dropped.

bucketsampler ships the plumbing: assignment, dataset wrapper, DDP-correct sampler, presets for SDXL / SD1.5 / NovelAI, and a CLI to inspect your data before you start training.

Install

pip install bucketsampler             # core (no torch)
pip install "bucketsampler[torch]"    # + PyTorch integration
pip install "bucketsampler[hf]"       # + HuggingFace datasets adapter
pip install "bucketsampler[analyze]"  # + HTML reports from the analyzer

30-second quickstart

from pathlib import Path
from torch.utils.data import DataLoader
from bucketsampler import (
    BucketBatchSampler,
    BucketedDataset,
    FixedBuckets,
    load_preset,
)

paths = sorted(Path("data/").glob("*.jpg"))
strategy = FixedBuckets(load_preset("sdxl"))

dataset = BucketedDataset(paths=paths, strategy=strategy)
sampler = BucketBatchSampler(dataset, batch_size=4)
loader = DataLoader(dataset, batch_sampler=sampler)

for batch in loader:
    images = batch["image"]   # [4, 3, H, W], same (H, W) within a batch
    buckets = batch["bucket"] # list[Bucket], one per sample
    # ... feed images to your VAE / U-Net / etc.

Inspect your dataset

Before you commit to a bucket set, see how your images actually distribute:

bucketsampler analyze data/ --preset sdxl
bucketsampler analyze data/ --preset sdxl --json > report.json
bucketsampler analyze data/ --preset sdxl --html report.html

The report shows readable / broken counts, AR distribution, per-bucket counts, underutilized buckets (so you know what to drop), and outliers (extreme ARs that match no bucket well, often a sign of bad data).

HuggingFace datasets

Already have a datasets.Dataset of images and captions? Skip the file-list step:

from datasets import load_dataset
from bucketsampler import BucketedDataset, FixedBuckets, load_preset

hf = load_dataset("lambdalabs/pokemon-blip-captions", split="train")

dataset = BucketedDataset.from_hf(
    hf,
    FixedBuckets(load_preset("sdxl")),
    image_column="image",
    caption_column="text",
)

The adapter accepts PIL columns (the common case), raw bytes (datasets.Image(decode=False)), and numpy / torch tensor columns. CHW vs HWC is auto-detected by the small-channel axis. Streaming (IterableDataset) is not yet supported, pass a map-style dataset.

DDP

Same sampler, two extra kwargs:

sampler = BucketBatchSampler(
    dataset,
    batch_size=4,
    num_replicas=world_size,
    rank=rank,
)
for epoch in range(num_epochs):
    sampler.set_epoch(epoch)   # required, reseeds the per-bucket shuffle
    for batch in DataLoader(dataset, batch_sampler=sampler):
        ...

All ranks yield the same number of batches per epoch and see disjoint indices, so gradient sync stays happy.

Auto-generate buckets from your dataset

Presets are reasonable defaults, but a bucket set picked from your distribution always crops less. Let bucketsampler derive one for you:

bucketsampler buckets-from-dataset data/ \
    --num 8 --target 1024 \
    --compare-to sdxl \
    --output my_buckets.toml

Sample output:

Generated 8 buckets (requested 8, k-means converged in 6 iterations):
  640x1536      AR= 0.417  cluster log-AR=-0.875  size=120
  768x1280      AR= 0.600  cluster log-AR=-0.511  size=210
  ...
Mean crop loss (auto):       3.42%
Mean crop loss (sdxl):       7.18%  (auto is -3.76 pts vs sdxl)

Wrote bucket set to my_buckets.toml

Then feed the TOML straight back into your training script:

from bucketsampler import FixedBuckets, load_from_toml

strategy = FixedBuckets(load_from_toml("my_buckets.toml"))

Or do it inline (no file involved) with the AutoBuckets convenience:

import numpy as np
from bucketsampler import AutoBuckets

# dims = (N, 2) int array of (width, height); analyzer's scan returns this
strategy = AutoBuckets.from_dims(dims, num_buckets=8, target=1024)

The algorithm is 1-D k-means on log(width / height), then snaps each cluster center to a (w, h) whose product is close to target^2 and whose dims are multiples of vae_factor (default 64). Fully deterministic for a given seed.

Custom buckets

Drop a TOML file anywhere on disk:

# my_buckets.toml
name = "my-budget"
vae_factor = 8

[[buckets]]
width  = 768
height = 768

[[buckets]]
width  = 896
height = 640

[[buckets]]
width  = 640
height = 896

from bucketsampler import FixedBuckets, load_from_toml

strategy = FixedBuckets(load_from_toml("my_buckets.toml"))

JSON is also supported via load_from_json. The bundled presets (sdxl, sd15, novelai) live in the same format.

Metadata cache

Header reads are cheap individually but expensive at 100K+ images. Cache them once:

bucketsampler build-cache data/ --output data.cache.parquet

Then reuse on every subsequent run:

from bucketsampler import BucketedDataset, FixedBuckets, MetadataCache, load_preset

cache = MetadataCache.load("data.cache.parquet")
dataset = BucketedDataset(
    paths=image_paths,
    strategy=FixedBuckets(load_preset("sdxl")),
    metadata_cache=cache,
)

Cache invalidation is automatic per row: files whose mtime has changed get re-read, new files get added, removed files are dropped. Re-run build-cache --refresh to refresh in place.

Precompute VAE latents

Move the VAE forward pass off the training hot path:

bucketsampler precompute \
    data/ \
    --vae stabilityai/sdxl-vae \
    --output latents/ \
    --preset sdxl \
    --batch-size 8 \
    --dtype bfloat16

Then train against the precomputed latents:

from bucketsampler import BucketBatchSampler, BucketedLatentDataset
from torch.utils.data import DataLoader

dataset = BucketedLatentDataset("latents/")
sampler = BucketBatchSampler(dataset, batch_size=8)
loader = DataLoader(dataset, batch_sampler=sampler)

for batch in loader:
    latents = batch["latents"]   # [B, C, H/8, W/8]
    captions = batch.get("caption")
    # ... feed latents straight to your U-Net

Custom VAE? Implement the tiny VAEEncoder protocol (downsample_factor, latent_channels, scale_factor, encode) and call precompute_latents() directly.

CLI cheatsheet

bucketsampler --help
bucketsampler version
bucketsampler presets [--json]
bucketsampler analyze <path> --preset sdxl [--json | --html report.html]
bucketsampler buckets-from-dataset <path> --num 8 --target 1024 [--output buckets.toml] [--compare-to sdxl]
bucketsampler build-cache <path> --output cache.parquet [--refresh]
bucketsampler precompute <path> --vae stabilityai/sdxl-vae --output latents/ --preset sdxl

Examples

See examples/ for runnable scripts:

• minimal_training_loop.py — full end-to-end loop
• auto_buckets_inline.py — data-driven bucket sets
• hf_dataset.py — train from a HuggingFace dataset
• precompute_latents.py — VAE precompute + latent training
• ddp_training.py — distributed training sketch

Status

All milestones shipped:

• M1 Core bucketing (Bucket, BucketSet, assignment, presets)
• M2 PyTorch integration (BucketedDataset, BucketBatchSampler, DDP)
• M3 Dataset analyzer CLI (bucketsampler analyze)
• M4 Auto-bucket generation (AutoBuckets, buckets-from-dataset)
• M5 HuggingFace datasets adapter (BucketedDataset.from_hf, map-style)
• M6 Metadata cache (MetadataCache, build-cache)
• M7 VAE latent precomputation (precompute_latents, BucketedLatentDataset)
• M8 Examples, GitHub Actions CI, PyPI release

See PLAN.md for the full roadmap.

License

MIT