tintwotin/Foundation-1-Diffusers

This is the Foundation-1 weights by RoyalCities converted to Diffusers weights format.

Foundation-1

Structured text-to-sample generation for modern music production

---

Overview

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:

1. Instrument Family – what broad source category the sound belongs to
2. Sub-Family – the more specific instrument role or identity
3. Timbre Tags – the tonal, spectral, or textural character
4. FX Tags – the processing layer applied to the sound
5. 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

1. Create a subfolder inside your models directory
2. Place the model checkpoint and config file inside that folder
3. Launch the interface
4. Select the model from the UI
5. 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

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)