tensors

A CLI tool for working with safetensor files, CivitAI models, and stable-diffusion.cpp image generation.

Features

https://github.com/user-attachments/assets/2e7629b4-34e7-4cbc-b50e-31d7fdd30239

• Read safetensor metadata - Parse headers, count tensors, extract embedded metadata
• CivitAI integration - Search models, fetch info, identify files by hash
• Download models - Resume support, type-based default paths
• Hash verification - SHA256 computation with progress display
• Image generation - txt2img/img2img via stable-diffusion.cpp server
• Server wrapper - FastAPI wrapper for sd-server process management

Installation

# Clone and install
git clone https://github.com/aladac/tensors.git
cd tensors
uv sync

# Or install directly
uv pip install git+https://github.com/aladac/tensors.git

# With server wrapper support
pip install tensors[server]

Usage

Search CivitAI

# Search by query
tsr search "illustrious"

# Filter by type and base model
tsr search -t lora -b sdxl

# Sort by newest, limit results
tsr search -t checkpoint -s newest -n 10

Get Model Info

# Get model info by ID (shows all versions)
tsr get 12345

# Get specific version info
tsr get -v 67890

Download Models

# Download latest version of a model
tsr dl -m 12345

# Download specific version
tsr dl -v 67890

# Download by hash lookup
tsr dl -H ABC123...

# Custom output directory
tsr dl -m 12345 -o ./models

Inspect Local Files

# Read safetensor file and lookup on CivitAI
tsr info model.safetensors

# Skip CivitAI lookup
tsr info model.safetensors --skip-civitai

# Output as JSON
tsr info model.safetensors -j

# Save metadata files
tsr info model.safetensors --save-to ./metadata

Generate Images

Requires a running stable-diffusion.cpp server.

# Generate an image
tsr generate "a cat sitting on a roof"

# Custom size, steps, and output
tsr generate "sunset over mountains" -W 768 -H 512 --steps 30 -o ./output

# Multiple images with seed
tsr generate "cyberpunk city" -b 4 -s 42

# With sampler and negative prompt
tsr generate "portrait" --sampler euler_a -n "blurry, low quality"

Server Wrapper

Manage sd-server process via a REST API. Requires pip install tensors[server].

# Start the wrapper API
tsr serve

# Custom host and port
tsr serve --host 0.0.0.0 --port 9000

Configuration

# Show current config
tsr config

# Set CivitAI API key
tsr config --set-key YOUR_API_KEY

Configuration

Config file: ~/.config/tensors/config.toml

[api]
civitai_key = "your-api-key"

Or set via environment variable:

export CIVITAI_API_KEY="your-api-key"

Default Paths

Models are downloaded to XDG-compliant paths:

Type	Path
Checkpoint	~/.local/share/tensors/models/checkpoints/
LoRA	~/.local/share/tensors/models/loras/
Metadata	~/.local/share/tensors/metadata/

Search Options

Option	Values
-t, --type	checkpoint, lora, embedding, vae, controlnet, locon
-b, --base	sd15, sdxl, pony, flux, illustrious
-s, --sort	downloads, rating, newest
-n, --limit	Number of results (default: 20)

Generate Options

Option	Description
-W	Image width (default: 512)
-H	Image height (default: 512)
--steps	Sampling steps (default: 20)
--cfg-scale	CFG scale (default: 7.0)
-s	RNG seed, -1 for random (default: -1)
-b	Batch size / number of images (default: 1)
-n	Negative prompt
-o	Output directory (default: .)
--sampler	Sampler name
--scheduler	Scheduler name
--host	sd-server address (default: 127.0.0.1)
--port	sd-server port (default: 1234)

Development

# Install dev dependencies
uv sync --group dev

# Run tests
uv run pytest

# Lint and format
uv run ruff check .
uv run ruff format .

# Type check
uv run mypy tensors/

License

MIT