--- language: - en library_name: stable-audio-3 license: other license_name: stable-audio-community license_link: https://huggingface.co/stabilityai/SAME-L/blob/main/LICENSE.md base_model: stabilityai/SAME-L tags: - music - audio - autoencoder - lora - stable-audio-3 --- # SAME-L Decoder LoRA (`squeakfix_v3`) A LoRA on **SAME-L's decoder**. It changes how latents are rendered to audio, not how latents are produced, so the latent space, the DiT and every existing DiT LoRA stay bit-compatible and this stacks with them in any order. It targets the high-frequency squeaks SAME's autoencoder adds to percussion when audio goes round the encode/decode loop more than once. A continuation or a transform re-encodes its input, so a chained session compounds them. Powered by Stability AI. This is a Derivative Work of [SAME-L](https://huggingface.co/stabilityai/SAME-L) under the [Stability AI Community License](LICENSE.md); [NOTICE](NOTICE) records what it changes. No SAME-L weights are redistributed here, only the adapter. ## Listen | | stock decoder | `squeakfix_v3` | |---|---|---| | **jungle**, fresh generation |

| | **jungle**, after 3 round trips |

| | **drums**, fresh generation |

| | **drums**, after 3 round trips |

| | **neurofunk**, fresh generation |

| | **neurofunk**, after 3 round trips |

| 15 s clips, same latent and seed, decoder adapter the only variable, peak-matched to −1 dBFS, generated on `medium` with a DiT LoRA attached. These three prompts were not picked for artifact content, and the fresh-generation rows are there to show the progression rather than because much is audible in them. ### On a fresh generation | | stock decoder | `squeakfix_v3` | |---|---|---| | **glitch hop**, worst of 16 screened |

| Much subtler, and it took screening 16 candidates by tonality runaway to find a pair where it carries. What changes here is air rather than squeak count: on this pair tonality moves 0.08 dB, which is nothing, while 16-22 kHz comes back +2.4 dB. ## How this came about I do a lot of transformations and continuations using my guitar as input audio. When I first noticed these squeaks in the outputs, I mentioned that my trick was to stem separate in Ableton and then apply the erosion effect to the percussion layer. Claude used that as a guide for refining the methodology. The technical version is below. ## Usage Requires the `target: "decoder"` support on the [`decoder-lora` branch of betweentwomidnights/stable-audio-3](https://github.com/betweentwomidnights/stable-audio-3/tree/decoder-lora). It is not upstream yet, so that branch is currently the only place a decoder LoRA will load. The `target` field in the checkpoint config routes it; there is no special loading path. Ship at strength **1.0**, where it was trained and auditioned. ```bash python run_gradio.py --model medium-base --lora-ckpt-path squeakfix_v3.safetensors # stacks with a DiT LoRA, either order python run_gradio.py --model medium-base \ --lora-ckpt-path my_style.safetensors squeakfix_v3.safetensors ``` ```python from stable_audio_3 import AutoencoderModel from stable_audio_3.models.lora.loader import load_and_apply_loras from stable_audio_3.models.lora.model import set_lora_strength ae = AutoencoderModel.from_pretrained("same-l", device="cuda").autoencoder load_and_apply_loras(ae, ["squeakfix_v3.safetensors"], "autoencoder") set_lora_strength(ae.decoder, 1.0) # separate call; 0.0 is the stock decode audio = ae.decode(ae.encode(waveform)) ``` ## Numbers Four base-model generations of 2 minutes on ordinary descriptive prompts, screened from 12 candidates by tonality runaway. Each latent is decoded twice, adapter off and on, and each chain re-encodes in place, so dN is N round trips of that decoder config. The adapter is the only variable. | depth | tonality p95 | tonal frames | 16-22 kHz | SI-SDR vs own d0 | |---|---|---|---|---| | d0, fresh generation | 9.80 → **9.10** | 87 → **62** | 3.16 → **4.34** | | | d1, one continuation | 11.82 → **9.73** | 462 → **64** | 0.95 → **3.76** | 16.9 → **21.0** | | d2 | 14.79 → **10.61** | 2111 → **133** | −1.36 → **3.32** | 12.4 → **16.2** | | d3 | 17.85 → **11.74** | 4279 → **453** | −3.36 → **2.96** | 10.1 → **13.6** | The adapter is ahead at every depth, including a fresh generation. Stock tonality climbs +8.05 dB over three round trips against the adapter's +2.64, and by d3 stock carries 4279 tonal frames against 453. It is not doing this by sanding the top end. After three round trips the adapter's 16-22 kHz sits 0.2 dB from a **fresh** stock render, at unchanged RMS, while stock has lost 6.5 dB of it. Stock's 12-16 kHz meanwhile *rises* with depth: the invented energy is replacing the air. Prompt distribution matters more here than anything else. The same ladder run on terse genre-tag prompts, which are out of distribution for the base model, starts at **1649** tonal frames instead of 87. With a DiT LoRA attached, which makes those tags in-distribution again, it starts at 780. The artifact is worst where the prompt sits furthest from what the model expects, and re-encoding compounds whatever it starts with. A DiT LoRA does not change the shape: on that run stock climbs +5.79 dB against the adapter's +0.73, and the adapter ends d3 holding more 12-16 kHz and 16-22 kHz than a fresh stock render. All runs are fp16 at 120 s. ## Limits - **Decoder LoRAs trained this way stamp a comb into the output.** The un-patch is a bare reshape at `patch_size 256`, so output channel and time-position-within-patch are the same axis, and any channel-wise bias the adapter learns repeats in every patch. That is a harmonic comb at `sr/256 = 172.27 Hz`, which reads as a wrong note rather than as noise. It is inaudible in a mix and obvious once a stem separator strips the masking content, which is how it was found. `--lambda_patch 30` is what prevents it: a `v1` control trained without it sits **+10.9 dB** of comb excess over stock while v3 sits **0.8 dB below** stock, and both hold through three further round trips. On real audio rather than model output v3 measures +1.5 to +4.0 dB, so measure on generated audio. - **SAME-L only.** SAME-S shares the patch grid but its artifact sits 1-8 kHz and wants a different recipe. Loading this there will not error, it will sound wrong. - **The gain scales with re-encode depth**, so it is small for one-shot generation and compounds across chained continuations. - **The squeaks are worst on out-of-distribution prompts.** Genre-tag prompts on the base model start with 19x more tonal frames than descriptive ones. A DiT LoRA makes such tags in-distribution again and lands in between. This adapter reduces the artifact; it does not make prompt choice irrelevant. - **One checkpoint, one dataset**, step 2000, chosen on re-encode behaviour and listening. Your material may want a different one. - **Neither half of the autoencoder is bit-deterministic** (encoder 1.5 % relative, decoder 7e-3 max abs), so read any A/B as a multiple of those floors. ## Training Self-supervised, audio only, no captions or paired data. Rank 16 / alpha 16, 5.63M trainable params (~1.30 % of the decoder), fp32 training and fp16 storage, single GB10 (DGX Spark) at ~2.9 s/step on 10 s crops, under 10 GB. ```bash python scripts/train_decoder_lora.py \ --model same-l --rank 16 --steps 8000 --lr 1e-4 \ --w_real 0.4 --w_drift 0.3 --w_dit 0.3 \ --lambda_cycle 10.0 --lambda_tonal 0.3 --lambda_patch 30 \ --patch_size 256 --crop_seconds 10 --seed 0 \ --dit_latent_dir out/dit_latents \ --data_dir /path/to/audio --eval_audio /path/to/holdout.wav \ --out_dir out/declora ``` Latents come from three buckets: real audio, drifted audio (`E(D(E(x)))` at depth ≥ 1, targeted against the original so the decoder is asked to undo accumulated drift), and latents sampled from the base DiT. No discriminator, since adversarial training makes a decoder invent *more* detail. Full method, losses, ablations and traps: [Decoder LoRA workflow](https://github.com/betweentwomidnights/stable-audio-3/blob/decoder-lora/docs/workflows/decoder-lora.md). Provenance is embedded in the file's `lora_config` metadata. `v1` (identical recipe, `--lambda_patch 0`) is an internal control, not published. Trained and used by [the collabage patch](https://huggingface.co/thepatch), in production in a live `stable-audio-3` backend since 2026-08-14. The autoencoder this adapts: [SAME](https://arxiv.org/abs/2605.18613).