datasety 0.40.0

CLI tool for dataset preparation: resize, align, caption, shuffle, synthetic, mask, filter, degrade, and character generation.

datasety

CLI tool for dataset preparation — resize, caption, align, shuffle, synthetic editing, masking, degradation, character generation, LoRA training, audio TTS datasets, upload to HuggingFace, and multi-step workflows.

Full documentation →

Installation

pip install datasety                 # core (resize, align, shuffle, degrade)
pip install datasety[caption]        # + Florence-2 captioning
pip install datasety[synthetic]      # + image editing (FLUX, Qwen, SDXL)
pip install datasety[mask]           # + segmentation masks (SAM 3, CLIPSeg)
pip install datasety[filter]         # + content filtering (CLIP, NudeNet)
pip install datasety[character]      # + character dataset generation
pip install datasety[workflow]       # + YAML workflow support
pip install datasety[train]          # + LoRA training (FLUX, Qwen) & TTS (Piper)
pip install datasety[audio]          # + TTS audio datasets (YouTube, VAD, Piper)
pip install datasety[upload]         # + upload to HuggingFace Hub
pip install datasety[all]            # everything

---

Commands

resize — Resize & Crop Images

Batch resize images to exact dimensions with configurable crop positions.

datasety resize --input ./raw --output ./resized --resolution 768x1024 --crop-position top

Options

Option	Description	Default
--input, -i	Input directory	required*
--output, -o	Output directory	required*
--input-image	Single input image (alternative to dir mode)
--output-image	Single output image (use with --input-image)
--resolution, -r	Target resolution (WIDTHxHEIGHT)
--megapixel	Target megapixel count (e.g., 0.5, 1.0)
--aspect-ratio	Aspect ratio W:H (e.g., 1:1, 16:9)
--crop-position	top, center, bottom, left, right	center
--input-format	Comma-separated input formats	jpg,jpeg,png,webp
--output-format	jpg, png, webp	jpg
--output-name-numbers	Rename output files to 1.jpg, 2.jpg, ...	off
--upscale	Upscale images smaller than target	off
--min-resolution	Skip images below this size (e.g., 256x256)
--workers	Parallel workers for processing	1
--recursive, -R	Search input directory recursively	off
--progress	Show tqdm progress bar	off
--dry-run	Preview without modifying files	off

# Single image
datasety resize --input-image photo.jpg --output-image resized.jpg -r 512x512

# Batch with sequential numbering
datasety resize -i ./photos -o ./dataset -r 1024x1024 --output-name-numbers --crop-position top

Full documentation →

---

caption — Generate Image Captions

Generate captions using Florence-2 (local) or OpenAI-compatible vision APIs.

datasety caption --input ./images --output ./captions --trigger-word "[trigger]"

Options

Option	Description	Default
--input, -i	Input directory	required*
--output, -o	Output directory for .txt files	required*
--input-image	Single input image
--output-caption	Single output .txt path
--device	auto, cpu, cuda, mps	auto
--trigger-word	Text to prepend to each caption
--prompt	Florence-2 task prompt	<MORE_DETAILED_CAPTION>
--model	HF model name or API model ID
--num-beams	Beam search width (1 = greedy)	3
--florence-2-base	Use Florence-2-base (0.23B, faster)	default
--florence-2-large	Use Florence-2-large (0.77B, more accurate)
--llm-api	Use OpenAI-compatible vision API
--max-tokens	Max response tokens (API mode)	300
--temperature	Temperature (API mode)	0.3
--skip-existing	Skip images that already have a .txt file	off
--append	Append text to existing captions
--prepend	Prepend text to existing captions
--recursive, -R	Search input directory recursively	off
--progress	Show tqdm progress bar	off
--dry-run	Preview without processing	off

# Florence-2 with trigger word
datasety caption -i ./dataset -o ./dataset --trigger-word "photo of sks person," --device cuda

# OpenAI vision API (supports OPENAI_MODEL env var)
datasety caption -i ./images -o ./captions --llm-api --model gpt-5-nano

Full documentation →

---

align — Align Control/Target Pairs

Match dimensions, enforce multiples of 32, and unify formats for control/target training pairs. Includes a built-in web server for visual comparison with a compare slider, caption editing, and pair management.

datasety align --target ./target --control ./control --dry-run

Options

Option	Description	Default
--target, -t	Target images directory	required
--control, -c	Control images directory	required
--multiple-of	Align dimensions to this multiple	32
--output-format	Convert all images: jpg, png, webp	keep original
--recursive, -R	Search input directories recursively	off
--dry-run	Preview changes without modifying files	off

# Preview, then apply
datasety align -t ./target -c ./control --dry-run
datasety align -t ./target -c ./control --output-format jpg

Visual comparison: use datasety server -i ./target --control ./control to browse and compare aligned pairs in the browser.

Full documentation →

---

shuffle — Random Caption Generation

Generate random captions by picking one variant from each text group.

datasety shuffle -i ./images -o ./captions \
    --group "A photo of a person.|Portrait of someone." \
    --group "Remove the hat.|Take off the hat."

Options

Option	Description	Default
--input, -i	Input directory containing images	required
--output, -o	Output directory for .txt files	required
--group, -g	Inline |-separated, .txt file, or URL	required
--separator	Separator between groups	" "
--seed	Random seed for reproducibility
--dry-run	Preview captions without writing	off
--show-distribution	Show caption distribution after generation	off

# Mix file, URL, and inline sources
datasety shuffle -i ./images -o ./captions \
    --group subjects.txt \
    --group "ending A|ending B" \
    --seed 42 --show-distribution

Full documentation →

---

synthetic — Synthetic Image Editing

Generate synthetic variations using image editing models (FLUX.2-klein FP8, FLUX.2-klein-9b-kv, Qwen-Image-Edit-2511, SDXL, LongCat, HunyuanImage). The default model FLUX.2-klein-4b-fp8 requires no HuggingFace token and fits in ~5 GB VRAM.

datasety synthetic --input ./images --output ./synthetic --prompt "add a winter hat" --steps 4

Options

Option	Description	Default
--input, -i	Input directory	required*
--output, -o	Output directory	required*
--input-image	Single input image
--output-image	Single output image
--prompt, -p	Edit instruction	required
--model	Model (auto-detects family or API model)	black-forest-labs/FLUX.2-klein-4b-fp8
--image-api	Use OpenAI-compatible API for generation	off
--api-aspect-ratio	Aspect ratio for --image-api (e.g. 16:9, 9:16, 1:1)	auto
--api-image-size	Resolution for --image-api: 0.5K, 1K, 2K, 4K	1K
--weights	Fine-tuned weights file
--lora	LoRA adapter (repeatable, :WEIGHT)
--device	auto, cpu, cuda, mps	auto
--cpu-offload	Force CPU offload	auto
--steps	Inference steps	4
--cfg-scale	Guidance scale	2.5
--true-cfg-scale	True CFG (Qwen only)	4.0
--negative-prompt	Negative prompt	" "
--num-images	Images per input	1
--seed	Random seed
--gguf	GGUF path/URL for quantized loading
--strength	Img2img strength (SDXL/FLUX.2, 0.0-1.0)	0.7
--recursive, -R	Search input directory recursively	off
--output-format	png, jpg, webp	png
--skip-existing	Skip images with existing output	off
--batch-size	Flush GPU memory every N images	0 (off)
--progress	Show tqdm progress bar	off
--dry-run	Preview without loading models	off

# Single image edit
datasety synthetic --input-image photo.jpg --output-image edited.png \
    --prompt "add sunglasses" --steps 4

# Cloud API — FLUX.2-flex (no GPU needed)
OPENAI_API_KEY=sk-... OPENAI_BASE_URL=https://openrouter.ai/api/v1 \
  datasety synthetic -i ./images -o ./synthetic \
  --prompt "add a winter hat" --image-api --model black-forest-labs/flux.2-flex \
  --api-aspect-ratio 1:1

# Cloud API — Gemini 2.5 Flash (text+image, supports image-to-image)
OPENAI_API_KEY=sk-... OPENAI_BASE_URL=https://openrouter.ai/api/v1 \
  datasety synthetic -i ./images -o ./synthetic \
  --prompt "transform into oil painting style" \
  --model google/gemini-2.5-flash-image --image-api \
  --api-aspect-ratio 3:4 --api-image-size 2K

# FLUX.2-klein-9b-kv (KV-cache, faster multi-reference, ~29 GB VRAM)
datasety synthetic -i ./images -o ./synthetic \
    --model "black-forest-labs/FLUX.2-klein-9b-kv" \
    --prompt "add sunglasses" --steps 4

# Qwen-Image-Edit-2511 with LoRA
datasety synthetic -i ./dataset -o ./synthetic \
    --model "Qwen/Qwen-Image-Edit-2511" \
    --lora "adapter.safetensors:0.8" \
    --prompt "add a red scarf" --steps 40

Full documentation →

---

mask — Text-Prompted Segmentation Masks

Generate binary masks from images using text keywords. Supports SAM 3, SAM 2, and CLIPSeg.

datasety mask --input ./dataset --output ./masks --keywords "face,hair" --device cuda

Options

Option	Description	Default
--input, -i	Input directory	required*
--output, -o	Output directory for masks	required*
--input-image	Single input image
--output-image	Single output mask
--keywords, -k	Comma-separated keywords	required
--model	sam3, sam2, clipseg	sam3
--device	auto, cpu, cuda, mps	auto
--threshold	Confidence threshold (0.0-1.0)	0.3
--padding	Pixels to expand mask (dilation)	0
--blur	Gaussian blur radius for edges	0
--invert	Invert mask colors	off
--naming	folder or suffix (_mask)	folder
--output-format	png, jpg, webp	png
--skip-existing	Skip images with existing masks	off
--dry-run	Preview detections without saving	off
--recursive, -R	Search input directory recursively	off
--progress	Show tqdm progress bar	off

# CLIPSeg (lightweight, no extra deps)
datasety mask -i ./dataset -o ./masks -k "face" --model clipseg --threshold 0.5

# SAM 2 with mask refinement
datasety mask -i ./dataset -o ./masks -k "hat,glasses" --model sam2 --padding 5 --blur 3

Full documentation →

---

filter — Filter Dataset by Content

Filter, curate, or clean datasets based on image content. Use CLIP for arbitrary text queries or NudeNet for NSFW label detection.

datasety filter --input ./dataset --output ./rejected --query "leg,male face" --action move

Options

Option	Description	Default
--input, -i	Input directory	required
--output, -o	Output directory for matched/rejected images
--query, -q	Comma-separated text queries (CLIP)
--labels, -l	Comma-separated NudeNet labels
--model	clip, nudenet	clip
--action	move, copy, delete, keep	move
--threshold	Confidence threshold (0.0-1.0)	0.5
--device	auto, cpu, cuda, mps	auto
--confirm	Required for destructive actions (delete, keep)	off
--preserve-structure	Keep subfolder hierarchy in output (with --recursive)	off
--invert	Invert match logic (act on non-matches)	off
--log	Write CSV log of all decisions to this path
--dry-run	Preview detections without modifying files	off
--recursive, -R	Search input directory recursively	off
--progress	Show tqdm progress bar	off

# Move images containing legs or male faces to a reject folder
datasety filter -i ./dataset -o ./rejected --query "leg,male face" --action move

# Delete NSFW images using NudeNet labels
datasety filter -i ./dataset --labels "FEMALE_BREAST_EXPOSED,MALE_GENITALIA_EXPOSED" \
    --action delete --model nudenet --threshold 0.6 --confirm

# Keep only images with "hat and socks", move the rest out
datasety filter -i ./dataset -o ./rejected --query "hat and socks" --action keep

# Dry-run to preview what would be filtered
datasety filter -i ./dataset --query "blurry,low quality" --action delete --dry-run -R

# Write a decision log for review
datasety filter -i ./dataset -o ./rejected --query "outdoor" --action copy --log filter_log.csv

Full documentation →

---

inspect — Dataset Statistics

Scan a dataset directory and report image count, resolution distribution, format breakdown, file sizes, caption coverage, and optionally detect duplicate images via perceptual hashing.

datasety inspect --input ./dataset --duplicates

Options

Option	Description	Default
--input, -i	Input directory	required
--duplicates	Detect duplicate/near-duplicate images	off
--json	Export report as JSON to this path
--csv	Export per-image data as CSV to this path
--recursive, -R	Search input directory recursively	off

# Full report with duplicate detection
datasety inspect -i ./dataset --duplicates

# Export report to JSON
datasety inspect -i ./dataset --json report.json

# Export per-image data to CSV
datasety inspect -i ./dataset --csv images.csv -R

Full documentation →

---

server — Dataset Management Dashboard

Start a universal web server for managing your entire dataset from the browser. Browse images in a gallery, edit and create captions, delete or compare images, view statistics, upload new images, and detect duplicates — all in one interface.

datasety server --input ./dataset

Options

Option	Description	Default
--input, -i	Dataset directory to manage	required
--control, -c	Control images directory (enables Pairs tab)
--port	Port for the web server	8080
--recursive, -R	Search directories recursively for images	off
--duplicates	Pre-compute perceptual hashes for duplicate detection	off

# Start the dashboard on the default port
datasety server -i ./dataset

# With duplicate detection pre-computed
datasety server -i ./dataset --duplicates --port 9000

# Pairs comparison (align workflow)
datasety server -i ./target --control ./control

The dashboard provides:

• Gallery — thumbnail grid with sorting and filtering; click any image for the detail panel (caption editor, file info, delete)
• Compare — drag-slider side-by-side comparison for any two images
• Pairs (with --control) — compare control/target pairs with a drag slider; edit captions for both sides; delete pairs; arrow-key navigation
• Stats — live dataset overview: image count, total size, caption coverage, format and orientation breakdown
• Upload — drag images into the browser or use the Upload button to add images to the dataset
• Keyboard navigation — arrow keys to move through gallery or pairs, Ctrl+S to save, T to toggle theme, ? for help

---

degrade — Image Degradation

Create degraded versions of images for upscale/enhance training. Pure Pillow, no extra dependencies.

datasety degrade --input ./originals --output ./dataset --type random --intensity-range 0.2-0.8 --paired

Options

Option	Description	Default
--input, -i	Input directory	required*
--output, -o	Output directory	required*
--input-image	Single input image
--output-image	Single output image
--type, -t	Degradation type(s), repeatable	random
--intensity	Global intensity (0.0-1.0)	0.5
--intensity-range	Random range MIN-MAX
--chain	Apply multiple types sequentially	off
--num-variants	Variants per input image	1
--paired	Create control/ + target/ subdirs	off
--seed	Random seed
--output-format	png, jpg, webp	png
--skip-existing	Skip images with existing output	off
--workers	Parallel workers for processing	1
--progress	Show tqdm progress bar	off
--dry-run	Preview without writing files	off

Degradation types: lowres, oversharpen, noise, blur, jpeg, motion-blur, pixelate, color-bands, upscale-sim, random

# Chain specific degradations for paired output
datasety degrade -i ./images -o ./dataset --type jpeg --type noise --chain --paired --seed 42

# Multiple random variants per image
datasety degrade -i ./images -o ./degraded --type random --num-variants 3 --intensity-range 0.3-0.8

Full documentation →

---

character — Character Dataset Generation

Generate character datasets using LLM-generated prompts + text-to-image (FLUX.2-klein local or cloud API).

datasety character --output ./dataset --llm-ollama qwen3.5:4b --num-images 20

Options

Option	Description	Default
--reference, -r	Reference face image(s) (optional, prompt context)
--output, -o	Output directory	required
--num-images, -n	Number of images to generate	10
--model	Model for generation (local HF or API model ID)	black-forest-labs/FLUX.2-klein-4b-fp8
--gguf	GGUF path/URL for quantized loading
--image-api	Use OpenAI-compatible API for image generation	off
--api-aspect-ratio	Aspect ratio for --image-api (e.g. 9:16, 1:1)	derived from --width/--height
--api-image-size	Resolution for --image-api: 0.5K, 1K, 2K, 4K
--character-description	Text description of the character
--style	Style guidance (e.g., photorealistic)
--prompts-only	Only generate prompts, skip images	off
--prompts-file	Load prompts from file instead of LLM
--llm-api	Use OpenAI-compatible API for prompts
--llm-ollama MODEL	Use local Ollama server for prompts
--llm-gguf PATH	Use local GGUF model for prompts
--llm-model REPO	Use HuggingFace model for prompts
--device	auto, cpu, cuda, mps	auto
--steps	Inference steps	4
--cfg-scale	Guidance scale	4.0
--seed	Random seed
--height	Output image height	1024
--width	Output image width	1024
--output-format	png, jpg, webp	png
--batch-size	Flush GPU memory every N images	0 (off)
--dry-run	Preview prompts without generating images	off

# Generate with local pipeline + Ollama prompts
datasety character -o ./dataset --llm-ollama qwen3.5:4b --num-images 20

# Cloud API for images (no GPU needed)
OPENAI_API_KEY=sk-... OPENAI_BASE_URL=https://openrouter.ai/api/v1 \
  datasety character -o ./dataset --prompts-file prompts.txt \
  --image-api --model black-forest-labs/flux.2-flex --api-aspect-ratio 2:3

# Preview prompts only
datasety character -o ./dataset --llm-api --prompts-only

Full documentation →

---

sweep — Parameter Grid Search

Generate workflow YAML files with parameter grid combinations for synthetic editing. Computes the Cartesian product of sweep parameters.

datasety sweep -i ./images -o ./sweep_output -p "add a winter hat" --steps 4,8,16 --cfg-scale 1.0,2.5,5.0

Options

Option	Description	Default
--input, -i	Input images directory	required
--output, -o	Base output directory	required
--prompt, -p	Edit prompt	required
--steps	Comma-separated step values to sweep
--cfg-scale	Comma-separated CFG values to sweep
--true-cfg-scale	Comma-separated true CFG values to sweep
--strength	Comma-separated strength values to sweep
--lora	Comma-separated LoRA specs to sweep
--model	Comma-separated model names to sweep
--seed	Random seed (passed through)
--output-file	Output YAML path	sweep.yaml
--run	Generate and immediately execute	off

# Generate YAML, inspect, then run
datasety sweep -i ./images -o ./sweep -p "add sunglasses" --steps 4,8,16 --cfg-scale 1.0,2.5
datasety workflow -f sweep.yaml

# Generate and run immediately
datasety sweep -i ./images -o ./sweep -p "add a hat" --steps 4,8 --cfg-scale 2.0,3.0 --run

Full documentation →

---

train — LoRA Fine-Tuning & TTS Training

Train a LoRA adapter for image generation models (FLUX, SDXL, Qwen) or a TTS voice model (Piper). The mode is auto-detected from --family (flux/sdxl/qwen) or --backend (piper/coqui/f5-tts).

Image parameters (--family flux/sdxl/qwen): --lr, --lora-rank, --lora-alpha, --image-size, --optimizer, --lr-scheduler, etc.

Audio parameters (--backend piper): --sample-rate, --batch-size, --accelerator, --devices, --test-text.

# Image: FLUX.2-klein LoRA (~8 GB VRAM)
datasety train --input ./dataset --output lora.safetensors --family flux --steps 500 --lr 1e-4 --lora-rank 16

# Audio: Piper TTS (auto-downloads base model, auto-installs Piper, multi-GPU, voice watcher)
datasety train -i ./tts_dataset -o ./tts_output --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --devices auto --test-text "Hello world"

Image (LoRA) Options

Option	Description	Default
--family	Model family: flux, sdxl, qwen	auto-detected
--model, -m	HuggingFace repo ID (base model)	black-forest-labs/FLUX.2-klein-base-4B
--output, -o	Output .safetensors path	lora.safetensors
--steps	Training steps	100
--lr	Learning rate	1e-4
--lora-rank	LoRA rank	16
--lora-alpha	LoRA alpha	16.0
--lora-dropout	LoRA dropout rate	0.0
--image-size	Training resolution (square crop)	512
--device	auto, cpu, cuda, mps	auto
--seed	Random seed	42
--save-every	Save checkpoint every N steps	end only
--resume	Resume from a .safetensors checkpoint
--validation-split	Fraction for validation (0.0–0.5)
--timestep-type	Timestep sampling: sigmoid, lognorm, linear	sigmoid
--caption-dropout	Probability of dropping caption	0.05
--gradient-checkpointing	Enable gradient checkpointing (saves VRAM)	off
--optimizer	adamw or adamw8bit (requires bitsandbytes)	adamw
--lr-scheduler	LR schedule: constant, cosine, linear	constant
--lr-warmup-steps	Linear warmup steps	0
--gradient-accumulation-steps	Accumulate gradients over N steps	1
--min-snr-gamma	Min-SNR-γ for SDXL (recommended: 5.0)	disabled
--noise-offset	Per-channel noise offset for SDXL (recommended: 0.05–0.1)	0.0

Audio (TTS) Options

Option	Description	Default
--backend	TTS backend: piper (coqui, f5-tts planned)	piper
--model	Piper base model (repo_id:subfolder or local path)	(required)
--output, -o	Output directory for .ckpt checkpoints	(required)
--steps	Training epochs	100
--sample-rate	Audio sample rate in Hz	22050
--batch-size	Training batch size	32
--accelerator	PyTorch Lightning accelerator: auto, gpu, cpu	auto
--devices	Number of GPUs: auto, 1, 2, -1 (all)	auto
--test-text	Background inference text to test checkpoints
--seed	Random seed	42

Full documentation →

---

audio — Build TTS Audio Datasets

Build TTS (Text-to-Speech) audio datasets from video or audio files. Supports YouTube URLs, direct media URLs, local files, and text files containing lists of paths. Extracts audio, transcribes with faster-whisper, performs deep text cleaning, and outputs Piper/LJSpeech-compatible datasets.

datasety audio --input ./video.mp4 --output ./dataset
datasety audio --input ./clips/ --output ./dataset
datasety audio --input "https://www.youtube.com/watch?v=..." --output ./dataset --language uk

Options

Option	Description	Default
--input, -i	Input: local file, URL, dir, or .txt list. Append ?start=X&end=Y to slice	required
--output, -o	Output directory for the dataset	required
--sample-rate	Output audio sample rate in Hz	22050
--demucs	Enable Demucs vocal isolation	false
--demucs-model	Demucs model name	htdemucs
--whisper-model	Faster-Whisper model: tiny, base, small, medium, large-v3	base
--language	Language code (e.g., en, es, fr, uk). Auto-detected if omitted	(auto)
--device	Device: auto, cpu, cuda, mps	auto
--vad	Enable voice activity detection (VAD) to filter non-speech	false
--min-duration	Minimum segment duration in seconds	1.5
--max-duration	Maximum segment duration in seconds	30.0
--merge-gap	Merge segments closer than this many seconds	0.0 (off)
--normalize-numbers	Expand digits into words	false
--no-clean-text	Disable special character stripping	false
--phoneme-map	Path to config.json/phonemes.json to filter bad text
--workers	Number of parallel file workers (default: 1)	1
--keep-temp	Keep temporary audio files at this path
--resume	Resume a previous run (skip existing chunks, append to CSV)	false
--overwrite	Overwrite existing output directory	false
--dry-run	Print pipeline steps without executing	false
--verbose, -V	Print detailed progress messages	false

# Process a list of URLs from a text file, dropping unsupported characters
datasety audio --input urls.txt --output ./dataset --phoneme-map phonemes.json

# Extract a specific 40-second slice from a YouTube video
datasety audio --input "https://youtube.com/watch?v=...?start=50&end=90" -o ./dataset

# Local video with vocal isolation and high-quality transcription
datasety audio --input ./video.mp4 --output ./dataset --demucs --whisper-model large-v3

# Parallel processing of multiple files
datasety audio --input ./videos/ --output ./dataset --workers 4

Full documentation →

---

upload — Upload to HuggingFace Hub

Upload datasets and model adapters to HuggingFace Hub. Auto-detects type (audio, image, video, document, model, generic) from directory structure and generates HF-compliant README dataset cards with YAML frontmatter.

datasety upload --path ./tts_dataset --repo-id user/my-voice --type audio
datasety upload --path ./lora_output --repo-id user/klein-lora --type model
datasety upload --path ./dataset --repo-id user/my-dataset --dry-run

Options

Option	Description	Default
--path, -p	Path to the dataset or model directory to upload	required
--repo-id, -r	HuggingFace repo ID (e.g. username/my-dataset). Derived from dir name if omitted	(derived)
--type, -t	Dataset or model type	auto
--private	Make the repository private	false
--token	HuggingFace API token (or set HF_TOKEN env var)	HF_TOKEN
--force	Force regenerate README.md if it already exists	false
--dry-run	Show what would be uploaded without uploading	false
--metadata	Extra YAML key: value pairs for dataset card frontmatter
--yes, -y	Skip all confirmation prompts	false
--verbose, -V	Print detailed progress messages	false

# Upload a TTS dataset (auto-generates README with TTS task card)
datasety upload --path ./tts_dataset --repo-id your-username/my-voice --private

# Upload a LoRA adapter
datasety upload --path ./lora.safetensors --repo-id your-username/klein-lora --type model

# Dry-run to verify what will be uploaded
datasety upload --path ./dataset --repo-id user/dataset --dry-run --verbose

# With extra metadata
datasety upload --path ./dataset --repo-id user/dataset \
    --metadata 'license:cc-by-4.0 language: [en,fr]'

Full documentation →

---

workflow — Multi-Step Pipelines

Run multi-step datasety pipelines from YAML or JSON files with dry-run validation.

datasety workflow --file datasety.yaml --dry-run

Options

Option	Description	Default
--file, -f	Path to workflow file	auto-detect
--dry-run	Validate steps without executing	off

Create datasety.yaml:

steps:
  - command: resize
    args:
      input: ./raw
      output: ./resized
      resolution: 768x1024
  - command: caption
    args:
      input: ./resized
      output: ./resized
      llm-api: true
      model: gpt-5-nano

# Validate first, then execute
datasety workflow --dry-run
datasety workflow

Full documentation →

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License

MIT