Foundation-1 is a next-generation text-to-sample model designed around musical structure. It was trained to understand instrumentation, timbre, FX, and notation as separate composable controls. This gives musicians and producers direct control over not just instrument identity, but also sonic character, phrase behavior, musical feel, and loop structure.

The result is a model built for actual production workflows: tempo-synced, key-aware, bar-aware sample generation with strong musicality, strong prompt adherence, and unusually high timbral flexibility.

Foundation-1 is designed for pure sample generation. It excels at generating coherent musical loops that stay locked to tempo and phrase length while allowing layered prompting across instrument families, timbre descriptors, FX, and notation-driven musical behavior.

What Foundation-1 Does

Generates musically coherent loops for production workflows
Understands BPM and bar count for structured loop generation
Locks to major and minor keys across western music theory
Supports enharmonic equivalents when prompting scales and keys
Separates instrument identity from timbral character
Supports timbral mixing by combining instrument and sonic descriptors
Responds to FX tags such as reverb, delay, distortion, and modulation
Uses notation-style prompt structure to encourage coherent phrasing, melodic shape, rhythmic behavior, and harmonic motion
Produces perfect loops within supported BPM / bar denominations
Understands Wet vs Dry production context — adding terms like Dry encourages minimal FX processing, while Wet or FX tags produce more processed, spatial, or effected sounds.

Why It Feels Different

Most audio models can react to broad prompt terms like “warm pad” or “bright synth.” with inconsistent results. Foundation-1 was designed to go further by treating the sound as a layered system:

Instrument Family – what broad source category the sound belongs to
Sub-Family – the more specific instrument role or identity
Timbre Tags – the tonal, spectral, or textural character
FX Tags – the processing layer applied to the sound
Notation / Structure Tags – the musical behavior of the generated phrase

This layered conditioning approach is a major reason Foundation-1 is able to deliver both high musicality and high prompt control at the same time.

Audio Showcase

Prompt	Audio
Bass, FM Bass, Medium Delay, Medium Reverb, Low Distortion, Phaser, Sub Bass, Bass, Upper Mids, Acid, Gritty, Wide, Dubstep, Thick, Silky, Warm, Rich, Overdriven, Crisp, Deep, Clean, Pitch Bend, 303, 8 Bars, 140 BPM, E minor
Sub Bass, Bass, Gritty, Small, Square, Bass, Dark, Digital, Thick, Clean, Simple, Bassline, Epic, Choppy, Melody, 4 Bars, 150 BPM, G# minor
Flute, Pizzicato, Punchy, Present, Ambient, Nasal, Melody, Epic, Airy, Slow Speed, 8 Bars, 150 BPM, E minor
High Saw, Spacey, Lead, Warm, Silky, Smooth, 303, Synth Lead, Medium Reverb, Low Distortion, Upper Mids, Mids, Pitch Bend, Arp, 8 Bars, 140 BPM, F minor
Trumpet, Warm, Complex Arp Melody, High Reverb, Low Distortion, Smooth, Silky, Texture, 8 Bars, 130 BPM, C minor
Synth, Pad, Chord Progression, Rising, Digital, Bass, Fat, Near, Wide, Silky, Warm, Focused, 8 Bars, 110 BPM, D major
Piccolo, Flute, Airy, Music Box, plucked, complex melody, 8 Bars, 140 BPM, C# minor
Synth Lead, Wavetable Bass, Low Distortion, High Reverb, Sub Bass, Upper Mids, Acid, Gritty, Wide, Thick, Silky, Warm, Rich, Overdriven, Crisp, Clean, 303, Complex, 8 Bars, 140 BPM, F minor
Fiddle, Bowed Strings, Full, Clean, Spacey, Rich, Intimate, Thick, Rolling, Arp, Fast Speed, Complex, 8 Bars, 128 BPM, B minor
Chiptune, Chord Progression, Pulse Wave, Medium Reverb, 8 Bars, 128 BPM, D minor
Kalimba, Mallet, Medium Reverb, Overdriven, Wide, Metallic, Thick, Sparkly, Upper Mids, Bright, Airy, Alternating, Chord Progression, Atmosphere, Spacey, Fast Speed, 8 Bars, 120 BPM, B minor

Core Capabilities

1. Musical Structure

Foundation-1 was trained to produce structured musical material rather than full music or generic textures. Musical Notation terms can encourage notation, chord progressions, melodies, arps, phrase direction, rhythmic density, and other musically relevant behaviors.

2. Instrument Identity

The model supports a broad instrument hierarchy spanning synths, keys, basses, bowed strings, mallets, winds, guitars, brass, vocals, and plucked strings.

3. Timbral Control

Foundation-1 is not limited to broad instrument naming. It also responds to timbral descriptors such as spectral shape, tone, width, density, texture, brightness, warmth, grit, space, and other sonic traits.

4. Timbral Mixing

Because instrument identity and timbral character were not collapsed into a single flat label, the model is especially strong at timbral hybridization and layered sonic prompting.

5. FX Prompting

The model supports a dedicated FX layer covering multiple forms of reverb, delay, distortion, phaser, and bitcrushing.

6. Loop Fidelity

Foundation-1 is built for production-ready loop generation, including BPM-aware and bar-aware structure within supported denominations.

Conditioning Architecture

Foundation-1 was trained with a layered tagging hierarchy designed to improve control, composability, and prompt clarity.

Hierarchy Overview

Major Family → broad instrument class
Sub-Family → more specific instrument role
Timbre Tags → tonal / spectral / textural descriptors
FX Tags → processing layer
Notation Tags → musical behavior and phrasing

This makes it possible to prompt at different levels of abstraction. A user can stay broad with a family-level prompt like Synth or Keys, or get more specific with terms like Synth Lead, Wavetable Bass, Grand Piano, Violin, or Trumpet, then further shape the output using timbral and FX descriptors.

Instrument Coverage

Major Families

Foundation-1 was trained across the following major instrument families:

Synth
Keys
Bass
Bowed Strings
Mallet
Wind
Guitar
Brass
Vocal
Plucked Strings

Sub-Family Coverage

Foundation-1 includes a wide sub-family layer covering a broad range of production-relevant instrument roles, including but not limited to:

Synth Lead
Synth Bass
Digital Piano
Pluck
Grand Piano
Bell
Pad
Atmosphere
Digital Strings
FM Synth
Violin
Digital Organ
Supersaw
Wavetable Bass
Rhodes Piano
Cello
Texture
Flute
Reese Bass
Wavetable Synth
Electric Bass
Marimba
Trumpet
Pan Flute
Choir
Harp
Church Organ
Acoustic Guitar
Hammond Organ
Celesta
Vibraphone
Glockenspiel
Ocarina
Clarinet
French Horn
Tuba
Oboe

Timbre System

One of Foundation-1’s main strengths is that it was not trained to treat timbre as an afterthought. Timbral character is directly represented in the prompt system, giving users control over not only what is being generated, but also how it sounds.

Representative timbre descriptors include:

Warm
Bright
Wide
Airy
Thick
Rich
Tight
Full
Gritty
Clean
Retro
Saw
Crisp
Focused
Metallic
Chiptune
Dark
303
Shiny
Analog
Present
Sparkly
Ambient
Soft
Smooth
Cold
Buzzy
Deep
Formant Vocal
Round
Punchy
Nasal
Vintage
Growl
Breathy
Glassy
Noisy
Synthetic Vox
Supersaw
Bitcrushed
Dreamy

Why This Matters

This tagging design makes prompts much more flexible. Instead of only asking for an instrument, users can shape:

tonal balance
brightness / darkness
width / intimacy
clean vs driven character
synthetic vs organic feel
transient sharpness
texture and density
spatial character

This is especially useful for producers who want to guide the output toward a specific role in a mix rather than just a generic instrument label.

For a list of used tags please see the Tag Reference Sheet.

FX Layer

Foundation-1 includes a dedicated FX descriptor layer spanning multiple common production effects.

Representative FX tags include:

Low Reverb
Medium Reverb
High Reverb
Plate Reverb
Low Delay
Medium Delay
High Delay
Ping Pong Delay
Stereo Delay
Cross Delay
Mono Delay
Low Distortion
Medium Distortion
High Distortion
Phaser
Low Phaser
Medium Phaser
High Phaser
Bitcrush
High Bitcrush

Musical Notation and Structure

Foundation-1 was trained with structured musical descriptors designed to improve phrase coherence, rhythmic intent, melodic motion, and prompt control.

These notation-style prompt terms help steer:

chord progressions
melodies
top-line layers
arpeggios
phrase direction
rhythmic density
harmonic feel
subdivision style
simple vs complex motion
sustained vs plucked behavior
melodic contour and pacing

Examples of supported structural ideas may include terms such as:

chord progression
melody
top melody
arp
triplets
simple
complex
rising
falling
strummed
sustained
catchy
epic
slow
fast

This notation layer is one of the main reasons Foundation-1 produces unusually coherent musical material instead of static or loosely related phrases. These can be mixed and matched as desired.

Tonal and Timing Support

Foundation-1 is designed for structured music production workflows and supports:

Keys and Modes

Major keys
Minor keys
Enharmonic equivalents
Western 12-tone chromatic prompting

Loop Structure

Supported bar lengths: 4 Bars, 8 Bars
Supported BPM denominations: 100 BPM, 110 BPM, 120 BPM, 128 BPM, 130 BPM, 140 BPM, 150 BPM

Prompt Structure

For best results, use rich prompts built around the model’s tags. These tags can be mixed and matched as needed. The model was trained on a structured hierarchy designed to encourage musically coherent sample generation.

Layered Prompt Structure

[Instrument Family / Sub-Family], [Timbre], [Musical Behavior / Notation], [FX], [Key], [Bars], [BPM]

Prompting Notes

Start with a clear instrument identity
Add 1–3 timbre descriptors for stronger steering
Include a notation or musical structure term for better phrase coherence
Always include Bars and BPM, which define the musical loop length
Ensure the generation duration matches the requested musical structure
The RC Stable Audio Fork automatically handles this timing alignment

Use FX and timbre tags sparingly at first, then layer more once you understand the model’s behavior.

One Prompt → Multiple Outputs

Each row below uses the exact same prompt, but a different random seed.
The timbre tags remain unchanged, so the overall sound character stays consistent while the melodic and musical content varies between generations.

Prompt	Output A	Output B	Output C
Bass, FM Bass, Medium Delay, Medium Reverb, Low Distortion, Phaser, Acid, Gritty, Wide, Dubstep, Thick, Silky, Warm, Rich, Overdriven, Crisp, Deep, Clean, Triplets, 8 Bars, 150 BPM, A minor
Gritty, Acid, Bassline, 303, Synth Lead, FM, Sub, Upper Mids, High Phaser, High Reverb, Pitch Bend, 8 Bars, 140 BPM, E minor
Kalimba, Mallet, Medium Reverb, Overdriven, Wide, Metallic, Thick, Sparkly, Upper Mids, Bright, Airy, Small, Alternating Chord Progression, Atmosphere, Spacey, Fast, 4 Bars, 120 BPM, B minor

Recommended Workflow

Foundation-1 is best used with the RC Stable Audio Fork, which is tuned around this model’s metadata and prompting structure.

It provides:

random prompt generation aligned with the training tags
automatic MIDI extraction from generated audio
automatic BPM / bar timing alignment for loop generation

Recommended Interfaces

RC Stable Audio Tools (Enhanced Fork)

Stable Audio Tools (Original Repository)

Model Files

In the folder you will find two files: the model itself and its associated config.json.

Unlike prior releases where both 32-bit and 16-bit models were provided, this release includes only the 16-bit version.

There is no quality loss, while reducing the model footprint.

Foundation_1.safetensors
model_config.json

Basic Setup for usage in the RC Enhanced Fork

Create a subfolder inside your models directory
Place the model checkpoint and config file inside that folder
Launch the interface
Select the model from the UI
Prompt with layered musical descriptors for best results

Hardware Requirements

Foundation-1 is designed to run locally on modern GPUs.

Typical VRAM usage during generation is approximately ~7 GB.
For reliable operation, a GPU with at least 8 GB of VRAM is recommended.

Generation Performance

Generation speed will vary depending on GPU model and system configuration.

On an RTX 3090, generation time is approximately ~7–8 seconds per sample.

Dataset and Training Philosophy

Foundation-1 was built around a structured sample-generation philosophy, rather than generic or genre-based audio captioning. The dataset consists entirely of hand-crafted and labeled audio, produced through a controlled augmentation pipeline.

At a high level, the training design emphasizes:

structured musical loops
instrument hierarchy
explicit timbre representation
dedicated FX descriptors
notation-aware prompt terms
strong production relevance
broad reuse for compositional workflows

This design is central to the model’s musical coherence and high degree of sonic control.

For more details on the dataset and training methodology, see the Training & Dataset Notes.

Limitations

Foundation-1 is a specialized model for music sample generation, not a general-purpose music generator.

Important notes:

It performs best when prompted using vocabulary aligned with the training design
It is optimized for sample-generation workflows, not open-ended genre captioning
Only two genre tags were included (Dubstep Growls and Chiptune waveforms), primarily to reinforce waveform behaviors
Prompt quality matters — structured layered prompts outperform vague natural language
Some timbre tags exert stronger influence than others
Certain tag combinations may require iteration to achieve the exact musical role or timbral blend desired
Percussion and drum sounds are outside the scope of this release

The model is also optimized around specific timing relationships between Bars, BPM, and generation duration.

For example:

an 8-bar loop at 100 BPM ≈ 19 seconds

If the generation duration is shorter than the musical structure implied by the prompt (for example requesting an 8-bar loop but generating only 5 seconds), the model may produce less coherent musical phrases.

The RC Stable Audio Fork automatically handles this timing alignment, making this workflow much easier.

License

This model is licensed under the Stability AI Community License. It is available for non-commercial use or limited commercial use by entities with annual revenues below USD $1M. For revenues exceeding USD $1M, please refer to the repository license file for full terms.

Companion Video

Further information on the model and design philosophy can be found in the companion video:

🎥 Watch the Foundation-1 overview and design philosophy video

Final Notes

Foundation-1 is intended as a producer-facing foundation model for structured sample generation, designed to augment music production rather than replace it.

Its goal is to let users explore sound in new ways while retaining precise control over:

what the sound is
how it behaves musically
how it sits tonally
how it feels sonically
how it fits into a production workflow

That combination of musical structure, instrument identity, timbral control, and loop fidelity is what defines the model.

Code for running the weight in Diffusers

python

import scipy
import torch
import soundfile as sf
from diffusers import StableAudioPipeline

repo_id = "tintwotin/Foundation-1-Diffusers"
pipe = StableAudioPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# define the prompts
prompt = "Bass, FM Bass, Medium Delay, Medium Reverb, Low Distortion, Phaser, Sub Bass, Bass, Upper Mids, Acid, Gritty, Wide, Dubstep, Thick, Silky, Warm, Rich, Overdriven, Crisp, Deep, Clean, Pitch Bend, 303, 8 Bars, 140 BPM, E minor"
negative_prompt = "Low quality."

# set the seed for generator
generator = torch.Generator("cuda").manual_seed(0)

# run the generation
audio = pipe(
    prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=200,
    audio_end_in_s=10.0,
    num_waveforms_per_prompt=1,
    generator=generator,
).audios

output = audio[0].T.float().cpu().numpy()
sf.write("./foundation_loop.wav", output, pipe.vae.sampling_rate)