* Add ACE-Step pipeline for text-to-music generation
Rebased on origin/main from the original pr-13095 branch (3 commits squashed).
- AceStepDiTModel: Diffusion Transformer with RoPE, GQA, sliding window,
AdaLN timestep conditioning, and cross-attention.
- AceStepConditionEncoder: fuses text / lyric / timbre into a single
cross-attention sequence.
- AceStepPipeline: text2music / cover / repaint / extract / lego / complete.
- Conversion script for the original checkpoint layout.
- Docs + tests.
* Fix ACE-Step pipeline audio quality and auto-detect turbo/base/sft variants
The PR's original inference produced low-quality audio on turbo because the
pipeline (a) mangled the SFT prompt format, (b) applied classifier-free guidance
with the wrong unconditional embedding (empty-string encoded vs. the learned
`null_condition_emb`), and (c) hardcoded turbo defaults even when loading a
base/SFT checkpoint.
Changes:
* Converter preserves `null_condition_emb` (stored under the condition encoder)
and propagates `is_turbo`/`model_version` into the transformer config so the
pipeline can route per-variant defaults.
* `AceStepConditionEncoder` registers `null_condition_emb` as a learned
parameter matching the original module.
* Pipeline auto-detects variant via `is_turbo`/`model_version` and picks
defaults that match `acestep/inference.py`:
* turbo: steps=8, shift=3.0, guidance_scale=1.0 (no CFG)
* base/SFT: steps=27, shift=1.0, guidance_scale=7.0
* Base/SFT timestep schedule uses the linear+shift transform from
`acestep/models/base/modeling_acestep_v15_base.py`; turbo still uses the
hardcoded 8-step `SHIFT_TIMESTEPS` table.
* CFG reuses the learned `null_condition_emb` and batches the
conditional+unconditional forwards into a single transformer call.
* `SFT_GEN_PROMPT` matches the newline layout in `acestep/constants.py` so the
text encoder sees the same prompt distribution it was trained on.
DiT parity vs. the original ACE-Step 1.5 turbo DiT is bit-identical
(max_abs=0.0 in fp32 eager/SDPA across 4 seed/shape cases) — see
scripts/dit_parity_test.py.
* Add ACE-Step parity test scripts
Two developer-facing parity harnesses live under scripts/:
* dit_parity_test.py — loads the same converted turbo weights into the
original AceStepDiTModel and the diffusers AceStepDiTModel, drives
identical (hidden_states, timestep, timestep_r, encoder_hidden_states,
context_latents) inputs, and asserts max-abs-diff ≤ 1e-5 in fp32
eager/SDPA. Currently passes bit-identical (max_abs=0) across four
shape/seed cases including batched + odd-length paths.
* audio_parity_jieyue.py — full end-to-end audio parity. Given the same
JSON example, runs both the original ACE-Step 1.5 pipeline and the
diffusers AceStepPipeline at matched seed/precision (bf16 + FA2 by
default) and saves side-by-side .wav files for listening verification.
Supports text2music / cover / repaint × turbo / base / sft via a
--matrix mode that writes 18 wavs named
{variant}_{task}_{official,diffusers}.wav.
* Route SFT parity to acestep-v15-sft checkpoint
On jieyue the release tree has a dedicated SFT checkpoint at
checkpoints/acestep-v15-sft with its own modeling_acestep_v15_base.py
shipped under acestep/models/sft/. Point the SFT row of the parity matrix
at that checkpoint / module so we're testing the actual SFT weights, not
the plain base ones.
* audio_parity_jieyue: fix doubled 'acestep-' in cache path; --converted-root flag
Previously the converted-pipeline cache dir was
`/tmp/acestep-<variant>-diffusers` but <variant> already starts with
"acestep-", giving `/tmp/acestep-acestep-v15-turbo-diffusers`. Drop the
prefix.
On jieyue the overlay rootfs (including /tmp) only has a few GB free; a
full turbo conversion needs ~5 GB per variant. Add --converted-root (env
ACESTEP_CONVERTED_ROOT) so the cache can live on vepfs.
* audio_parity_jieyue: two-phase matrix bootstraps cover/repaint from text2music
The ACE-Step release bundle on jieyue doesn't ship sample .wav/.mp3
files, so matrix mode had no default --src-audio and would skip
cover/repaint entirely. Run text2music first for every variant, then
reuse the TURBO official text2music output as the shared source for the
cover/repaint rows. Users can still override with --src-audio.
* audio_parity_jieyue: seed the diffusers generator on the pipeline device
The ORIGINAL ACE-Step pipeline seeds on the execution device
(`torch.Generator(device=device).manual_seed(seed)`), i.e. the CUDA RNG
stream when running on GPU. Previously the parity harness seeded the
diffusers side with a CPU generator, so even though the seed integer
matched, the two sides drew different noise from the outset and the
final outputs were essentially uncorrelated. Use the execution-device
generator on both sides for a fair comparison.
* Fix ACE-Step pipeline: switch to APG guidance + peak normalization
Two issues found after the first jieyue audio parity run:
1. The original base/SFT pipeline uses APG (Adaptive Projected Guidance,
acestep/models/common/apg_guidance.py) with a stateful momentum
buffer and norm/projection steps — NOT vanilla CFG. Using vanilla CFG
produced uncorrelated outputs vs. the reference (pearson ~0.0 on
20 s samples); this PR ports `_apg_forward` + `_APGMomentumBuffer`
and plugs them into the denoising loop when `guidance_scale > 1`.
Momentum is instantiated once per pipeline call (persists across
denoising steps) to match the reference semantics.
2. The post-VAE "anti-clipping normalization" in this pipeline was
`audio /= std * 5` with a `std<1 -> std=1` guard. The original
post-processing in
acestep/core/generation/handler/generate_music_decode.py is simple
peak normalization: `if audio.abs().max() > 1: audio /= peak`. The
std-based proxy both (a) let clips with peak < 1 leak through
unchanged (over-quiet) and (b) failed to bring clipping peaks to
exactly 1 in a bunch of base/SFT cases (observed max=1.000, std=0.200
repeatedly in the first parity run). Switch to peak normalization on
both sides.
Tested via scripts/audio_parity_jieyue.py on A800; re-run pending to
confirm the base/SFT correlation improvements.
* Fix ACE-Step chunk mask values to match the original pipeline
The DiT receives `context_latents = concat(src_latents, chunk_mask)` on the
channel dim, and was trained with chunk_mask values drawn from the three
sentinels documented in acestep/inference.py:
2.0 -> model-decided (default for text2music / cover / full-generation)
1.0 -> keep this latent frame from src_latents (repaint preserved region)
0.0 -> explicitly repaint this frame (only inside the repaint window)
Previously _build_chunk_mask returned all-1.0 for text2music (and cover /
lego), and an inverted 0/1 mask for repaint (1 inside the window, 0 outside).
Either case puts context_latents out of distribution. Switch text2music /
cover to the 2.0 sentinel and flip the repaint mask so it's 1.0 outside /
0.0 inside. Update the repaint src_latents zero-out to multiply by the new
mask (was `1 - chunk_mask`) so the zero region still lines up with the
repaint window.
* Add direct invoker for ACE-Step generate_music (ground truth)
Our earlier audio_parity_jieyue.py reconstructs the original pipeline by
calling AceStepConditionGenerationModel.generate_audio() directly, which
silently skips a lot of the real handler plumbing (conditioning masks,
silence-latent tiling, cover/repaint pre-processing, etc.). That made the
'official' wavs we saved sound wrong — flat, drone-like, not music.
This new script calls acestep.inference.generate_music end-to-end through
the real AceStepHandler, with LM + CoT explicitly disabled so we still have
a deterministic comparison. Use it to generate the ground-truth 'official'
wav for a given JSON example, then separately run the diffusers pipeline
with the same inputs and diff the two.
* run_official_generate_music: call initialize_service to bind a DiT variant
AceStepHandler() is a shell — you have to call handler.initialize_service(
project_root=..., config_path=..., device=..., use_flash_attention=..., ...)
before generate_music will work. Mirror what cli.py does at the equivalent
spot (around cli.py:1400).
* Fix silence-reference for ACE-Step timbre encoder
The root cause for the flat / drone-like outputs I was seeing (including
in my 'official' reconstruction): when no reference_audio is provided the
pipeline was feeding literal zeros to the timbre encoder. The real
handler feeds a slice of the learned `silence_latent` tensor.
The handler also transposes silence_latent on load (see
acestep/core/generation/handler/init_service_loader.py:214:
self.silence_latent = torch.load(...).transpose(1, 2)
) converting [1, 64, 15000] -> [1, 15000, 64] so that
`silence_latent[:, :750, :]` yields the expected [1, 750, 64] shape.
Changes:
* Converter: load silence_latent.pt, transpose to [1, T, C], bake into
the condition_encoder safetensors under key `silence_latent`.
(Also keeps the raw .pt file at the pipeline root for debugging.)
* AceStepConditionEncoder: register `silence_latent` as a persistent
buffer so from_pretrained loads it alongside the trained weights.
* Pipeline: when reference_audio is None, slice
`condition_encoder.silence_latent[:, :timbre_fix_frame, :]` and
broadcast across the batch instead of zeros. Emits a loud warning
(and falls back to zeros) if the buffer is all-zero — that means the
checkpoint was produced by an older converter and should be rebuilt.
* audio_parity_jieyue.py: the reference path now matches the handler's
silence-latent slicing.
Without this fix, every variant/task combo produced drone-like audio
even when my numeric DiT-forward parity claimed they were identical.
* Fix three more ACE-Step pipeline bugs I found by dumping real inputs
Instrumented the live generate_audio call in the real ACE-Step handler and
observed the exact tensors it sees — my diffusers pipeline was wrong in
three independent ways:
1. src_latents for text2music should be silence_latent tiled to
latent_length, NOT zeros. The handler fills no-target cases from
silence_latent_tiled (observed std=0.96). Zeros are OOD for the DiT
context_latents concat and produce drone-like outputs.
2. chunk_mask values cap at 1.0 (not 2.0). The handler starts with a
bool tensor (True inside the generate span, False outside); the
chunk_mask_modes=auto -> 2.0 override does NOT take effect because
the underlying tensor is bool, so setting entry = 2.0 casts to True.
After the later .to(dtype) float cast, the DiT sees 1.0/0.0 — exactly
what I observed in the captured tensor (unique values = [True]).
3. Default shift is 1.0 for ALL variants, including turbo. I was
defaulting turbo to shift=3.0 which picks a different SHIFT_TIMESTEPS
table (the 8-step schedule is keyed by shift, not variant).
Also:
* Added _silence_latent_tiled() helper that slices / tiles the learned
silence_latent (now loaded as a buffer on the condition encoder) to
the requested latent length.
* Repaint path now substitutes silence_latent (not raw zeros) inside
the repaint window — matches conditioning_masks.py.
* audio_parity_jieyue.py mirrors the same src/chunk/shift choices on
its 'original' leg for apples-to-apples parity once the buggy
reconstruction is removed from the picture.
* Add peak+loudness post-normalization to AceStepPipeline
The real pipeline normalizes audio in two stages (see
acestep/audio_utils.py:72 normalize_audio + generate_music_decode.py):
1. if peak > 1: audio /= peak (anti-clip)
2. audio *= target_amp / peak (target_amp = 10 ** (-1/20) ~ 0.891)
Step 2 is loudness normalization to -1 dBFS. Without it diffusers outputs
had peak=1.0 vs the real 0.891 — same music content (pearson was ~0.86
already), just 1.12x louder. Add step 2 after the existing anti-clip step.
* Match acestep/inference.py inference_steps=8 for ALL variants
GenerationParams.inference_steps default is 8 — turbo AND base/SFT. I had
base/SFT defaulting to 27 here, so every base/SFT parity run was comparing
a 27-step diffusers trajectory against an 8-step real trajectory. Different
number of denoising steps means different audio even at fixed seed.
This likely explains the lower base/SFT correlation in my earlier jieyue
runs (turbo was 0.86, base/SFT were 0.32-0.34). Aligning step counts
should bring base/SFT closer to turbo parity.
* Address PR #13095 review: rename classes + reuse diffusers primitives
Response to dg845's PR comments batch 1+2. DiT parity harness still bit-identical
(max_abs=0 on fp32 / SDPA across 4 shape cases).
Transformer file:
* Rename AceStepDiTModel -> AceStepTransformer1DModel (alias kept).
* Rename AceStepDiTLayer -> AceStepTransformerBlock (alias kept).
* Inherit AttentionMixin + CacheMixin on the DiT model.
* Swap in diffusers.models.normalization.RMSNorm for the hand-rolled
AceStepRMSNorm (weight-key-compatible).
* Swap the hand-rolled rotary embedding + apply_rotary for diffusers'
get_1d_rotary_pos_embed + apply_rotary_emb (use_real_unbind_dim=-2 to
match the cat-half convention ACE-Step inherits from Qwen3).
* Use get_timestep_embedding with flip_sin_to_cos=True — keeps the
(cos, sin) ordering of the original sinusoidal. State-dict-compatible.
* Drop max_position_embeddings arg from DiT config (RoPE computes freqs
per call based on seq_len); converter drops it.
* Gradient-checkpoint call now takes just the layer module (matches the
Flux2 idiom).
Pipeline modeling file (pipelines/ace_step/modeling_ace_step.py):
* Moved _pack_sequences + AceStepEncoderLayer here — they aren't used
by the DiT, so they shouldn't live in the transformer file.
* AceStepLyricEncoder + AceStepTimbreEncoder set
_supports_gradient_checkpointing = True and wrap encoder-layer calls
through the checkpointing func when enabled.
* Use diffusers RMSNorm + the RoPE helper from the transformer file
(shared single implementation).
Converter (scripts/convert_ace_step_to_diffusers.py):
* model_index.json now carries AceStepTransformer1DModel.
* Drop max_position_embeddings / use_sliding_window from the emitted
configs.
No numerical regressions: scripts/dit_parity_test.py PASSES with
max_abs=0.0 on fp32/SDPA across short, long, batched, and
padding-path shape variants.
* Address PR #13095 review: pipeline polish + converter HF-hub support
Response to dg845 review comments on the pipeline side. DiT parity still
bit-identical (max_abs=0 across 4 shape cases).
Pipeline (pipelines/ace_step/pipeline_ace_step.py):
* Add `sample_rate` + `latents_per_second` properties sourced from the
VAE config so the pipeline no longer hardcodes 48000 / 25 / 1920.
Propagates through prepare_latents, chunk_mask window math, and the
audio-duration round-trip.
* Add `do_classifier_free_guidance` property (matches LTX2 et al.).
* Add `check_inputs(...)` called from `__call__` before allocating noise.
Validates prompt type, lyrics type, task_type, step count, guidance
scale, shift, cfg interval bounds and repaint window ordering.
* Add `callback_on_step_end` + `callback_on_step_end_tensor_inputs` —
the modern callback form. The legacy `callback` / `callback_steps`
pair is kept for back-compat. Setting `pipe._interrupt = True` inside
the callback stops the loop early.
* Expose `encode_audio(audio)` as a public helper that wraps the tiled
VAE encode + (B, T, D) transpose the pipeline performs internally.
Converter (scripts/convert_ace_step_to_diffusers.py):
* Accept a Hugging Face Hub repo id for `--checkpoint_dir`; resolves it
via `huggingface_hub.snapshot_download` when the argument isn't a
local path.
Exports:
* Register `AceStepTransformer1DModel` in the top-level __init__,
models/__init__, models/transformers/__init__, and dummy_pt_objects so
`from diffusers import AceStepTransformer1DModel` works and the
pipeline loader resolves the new class name from model_index.json.
Deferred for a follow-up (commented inline in the PR): full
`Attention + AttnProcessor + dispatch_attention_fn` refactor and
`FlowMatchEulerDiscreteScheduler` migration — both would benefit from a
dedicated parity re-run and review.
* Fix stale ACE-Step 1.0-era docs / class names in the 1.5 integration
Docs and docstrings still carried a mix of 1.0 paper title, non-existent
`ACE-Step/ACE-Step-v1-5-turbo` hub id, `shift=3.0` turbo default, and
the old `AceStepDiTModel` class name. Cleaned up to match the actual
1.5 release:
* pipelines/ace_step.md: correct citation title ("ACE-Step 1.5: Pushing
the Boundaries of Open-Source Music Generation"), correct repo
(`ace-step/ACE-Step-1.5`), new variants table with real HF ids
(`Ace-Step1.5` / `acestep-v15-base` / `acestep-v15-sft`) and their
per-variant step/CFG defaults, drop the wrong `shift=3.0` tip.
* models/ace_step_transformer.md: page renamed to
`AceStepTransformer1DModel` with a short 1.5-specific description;
`AceStepDiTModel` noted as a backwards-compat alias.
* pipeline_ace_step.py: import, docstring, `Args`, and `__init__`
annotation reference `AceStepTransformer1DModel`; example model id
now `ACE-Step/Ace-Step1.5`; `_variant_defaults` docstring and the
`__call__` variant-fallback comment no longer claim `shift=3.0` /
`27 steps` — real defaults are 8 steps / shift=1.0 across all
variants, guidance=1.0 (turbo) vs 7.0 (base+sft).
* Address PR #13095 review: VAE tiling on AutoencoderOobleck + Timesteps class
Two more deferred review threads from dg845 addressed:
* Move tiled encode/decode onto AutoencoderOobleck
(https://github.com/huggingface/diffusers/pull/13095#discussion_r2785513647).
AutoencoderOobleck now carries `use_tiling` + `tile_sample_min_length` /
`tile_sample_overlap` / `tile_latent_min_length` / `tile_latent_overlap`
attributes and private `_tiled_encode` / `_tiled_decode` methods; the
existing `encode` / `_decode` dispatch to them when tiling is enabled and
the input exceeds the threshold. `AutoencoderMixin.enable_tiling()` is
already inherited.
AceStepPipeline's private `_tiled_encode` / `_tiled_decode` and the
`use_tiled_decode` `__call__` arg are gone; `__init__` now calls
`self.vae.enable_tiling()` so the long-audio memory behaviour is preserved
by default. Users can opt out with `pipe.vae.disable_tiling()`.
Note: the VAE-side tiling concatenates encoder features (h) and samples
the posterior once, instead of the old per-tile `.sample()` calls. This
is the standard diffusers pattern; numerically differs only in the
structure of the noise across tile boundaries.
* Use the Timesteps nn.Module for the sinusoid
(https://github.com/huggingface/diffusers/pull/13095#discussion_r2785420234).
`AceStepTimestepEmbedding` wraps `Timesteps(in_channels, flip_sin_to_cos=
True, downscale_freq_shift=0)` instead of calling `get_timestep_embedding`
directly — reviewer asked for the Module form.
* Address PR #13095 review: refactor AceStepAttention to Attention + AttnProcessor
Splits the monolithic AceStepAttention into the diffusers standard
Attention + AttnProcessor layout:
- AceStepAttention (torch.nn.Module, AttentionModuleMixin) holds the
to_q/to_k/to_v/to_out projections and norm_q/norm_k RMSNorms.
- AceStepAttnProcessor2_0 runs the attention dispatch through
dispatch_attention_fn so users can pick flash / sage / native backends
via model.set_attention_backend(...) or the attention_backend context
manager.
GQA (Q has 16 heads / K,V have 8) is preserved by passing enable_gqa=True
to dispatch_attention_fn instead of repeat_interleave; fusion is disabled
(_supports_qkv_fusion = False) because Q and K,V have different output
sizes.
The converter is updated to rename the six attention sub-keys
(q_proj -> to_q, k_proj -> to_k, v_proj -> to_v, o_proj -> to_out.0,
q_norm -> norm_q, k_norm -> norm_k) on both the DiT decoder path and the
condition encoder path, since AceStepLyricEncoder / AceStepTimbreEncoder
share the same AceStepAttention class.
Addresses review comments r2785433213 and r2785450463.
* Address PR #13095 review: migrate to FlowMatchEulerDiscreteScheduler
Replace the hand-rolled flow-matching Euler loop with
`FlowMatchEulerDiscreteScheduler`. ACE-Step still computes its own shifted /
turbo sigma schedule via `_get_timestep_schedule`, but now passes it to
`scheduler.set_timesteps(sigmas=...)` and delegates the ODE step to
`scheduler.step()`. The scheduler is configured with `num_train_timesteps=1`
and `shift=1.0` so `scheduler.timesteps` stays in `[0, 1]` (the convention the
DiT was trained on) and the scheduler doesn't re-shift already-shifted sigmas.
The scheduler's appended terminal `sigma=0` reproduces the old loop's
final-step "project to x0" case exactly: `prev = x + (0 - t_curr) * v`.
Parity on jieyue (seed=42, bf16 + flash-attn, turbo text2music, 8 steps):
waveform Pearson = 0.999999
spectral Pearson = 1.000000
max |diff| = 2.5e-3 (fp32 step-math vs previous bf16 step-math)
fp32 Euler-loop A/B against the hand-rolled path: max |diff| = 3.6e-7.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Address PR #13095 review: move DiT tests + drop stale test kwargs
- Move the DiT transformer tests out of the pipeline test file into a new
tests/models/transformers/test_models_transformer_ace_step.py that follows
the standard BaseModelTesterConfig + ModelTesterMixin scaffold (matches
test_models_transformer_longcat_audio_dit.py).
- Drop `max_position_embeddings` from the remaining AceStepDiTModel and
AceStepConditionEncoder test fixtures — neither constructor accepts that
argument anymore.
- Drop `use_sliding_window` from the same fixtures — also no longer a
constructor argument (the actual `sliding_window` int kwarg is kept).
- Wire `FlowMatchEulerDiscreteScheduler(num_train_timesteps=1, shift=1.0)`
into `get_dummy_components()` now that the pipeline requires it.
Resolves https://github.com/huggingface/diffusers/pull/13095#discussion_r3115653554,
r3115664850, r3115673059, r3115676580, r3115680700.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Address PR #13095 review from dg845 (2026-04-23)
Fixes 5 review threads + style:
1. Converter now builds `AceStepPipeline` in memory and calls
`save_pretrained`. Previously the hand-written `model_index.json` was
missing the `scheduler` entry — fresh converter output couldn't be loaded
by `AceStepPipeline.from_pretrained` (r3127767785). This also makes the
converter robust to future `__init__` signature changes.
2. `latent_length` uses `math.ceil(...)` instead of `int(...)` so non-integer
products (e.g. `latents_per_second=2.0, audio_duration=0.4 → 0.8`) round up
to `1` instead of truncating to `0` and crashing shape checks (r3127790939).
3. Add `_callback_tensor_inputs = ["latents"]` on `AceStepPipeline` so the
standard diffusers callback tests pick up the right tensor (r3127795954).
4. `AceStepConditionEncoder.silence_latent` no longer hard-codes the channel
dim to 64. The placeholder buffer now uses the `timbre_hidden_dim`
constructor argument, so smaller test configs with `timbre_hidden_dim != 64`
load without shape errors (r3127812932).
5. Revert `self.vae.enable_tiling()` from `AceStepPipeline.__init__`. Users can
call `pipe.vae.enable_tiling()` themselves for long-form generation; that
matches the opt-in convention used by the rest of diffusers (r3127777296).
6. `ruff check --fix` + `ruff format` over all ACE-Step sources (the style fix
dg845 asked for via `@bot /style`).
Also: converter now accepts sharded `model.safetensors.index.json` layouts
alongside the single-file `model.safetensors`, so the 5B XL turbo variant
converts without a pre-processing step.
Parity on jieyue (seed=42, bf16 + flash-attn, turbo text2music 160s, fresh
converter output loaded via `from_pretrained`):
waveform Pearson = 0.999954
spectral Pearson = 0.999977
max |a-b| bf16 = 4.3e-02 (dominated by the VAE tiling default flip)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Address PR #13095 review from yiyixuxu (2026-04-23)
Code-level (22 threads):
1. Delete 3 dev/parity scripts (`scripts/audio_parity_jieyue.py`,
`scripts/dit_parity_test.py`, `scripts/run_official_generate_music.py`)
that shouldn't have been committed.
2. Rename `AutoencoderOobleck._encode_one` → `_encode` to match the convention
used by other diffusers VAEs.
3. Delete the hard-coded `SHIFT_TIMESTEPS` / `VALID_SHIFTS` table in
`pipeline_ace_step.py`: the per-shift turbo schedules are recovered
exactly by `linspace(1, 0, N+1)[:-1]` plus the flow-match shift formula
that the non-turbo branch already uses, so a single code path covers both.
4. Drop the backwards-compat `AceStepDiTModel` / `AceStepDiTLayer` aliases
and every reference (top-level `__init__`, `models/__init__`,
`transformers/__init__`, dummy objects, tests, docs toctree, model card).
`AceStepTransformer1DModel` is the only exported name now.
5. Remove the unused `attention_mask` / `encoder_attention_mask` args from
`AceStepTransformer1DModel.forward`; the model rebuilds its masks from
the sequence shape and never consumed them.
6. In the DiT forward and both encoders, pass `None` instead of an all-zero
`full_attn_mask` / `encoder_4d_mask` to non-sliding attention layers — SDPA
dispatches to a faster kernel when the mask is None.
7. Inline the shared `_run_encoder_layers` helper directly into
`AceStepLyricEncoder.forward` / `AceStepTimbreEncoder.forward` so layer
calls are visible at the forward boundary (diffusers style).
8. Move `is_turbo` / `sample_rate` / `latents_per_second` from `@property`s
that re-read module configs each call to cached attributes populated in
`__init__` (Flux2-style), with a default-ACE-Step fallback when
`self.vae` is offloaded. Drop the now-unused `SAMPLE_RATE = 48000`
module-level constant and the three property definitions.
9. Warn + coerce `guidance_scale` to 1.0 on turbo (guidance-distilled)
checkpoints, following `pipeline_flux2_klein`. Prevents over-guided
audio when users forward their base/sft CFG settings to a turbo pipe.
10. Remove the `logger.warning(...)` paths that triggered on
`silence_latent` missing/zero — those only fired for author-side
unconverted checkpoints and tests; end users always load converted
weights where the buffer is baked in.
11. Drop the redundant `with torch.no_grad():` wrappers inside
`encode_prompt` — the pipeline's `__call__` runs under `torch.no_grad`
already.
12. Strip "reviewer comment on PR #13095" attribution comments from three
docstrings (here and everywhere).
Parity on jieyue (seed=42, bf16 + flash-attn, XL turbo 160s text2music):
waveform Pearson = 0.9747
spectral Pearson = 0.9895
The shift comes from full-attention layers switching `attn_mask=0_tensor` →
`attn_mask=None`, which dispatches to a different SDPA kernel on bf16. The
two outputs are algebraically equivalent for fp32 eager; on bf16+FA the
delta is dominated by kernel-level ULPs, well within the sampler-noise
band (ear-check on the 160s example confirms no audible regression).
Still open — AudioTokenizer/Detokenizer (deferred) + APG guider follow-up
(dims differ from `diffusers.guiders.adaptive_projected_guidance`, not a
drop-in; worth a separate PR).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Address ACE-Step audio token and APG review
* Fix ACE-Step docs CI
* Address ACE-Step pipeline cleanup review
* Fix ACE-Step flash attention sliding windows
* Add ACE-Step callback properties
* Address ACE-Step final review comments
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
* tests: add cp backend and attention backend tests.
* up
* up
* up
* fix ring for flash and flash_3
* generate.
* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* up
* up
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fix(freeu): run FFT in float32 for float16 inputs to avoid ComplexHalf
`fourier_filter` already upcasts `bfloat16` inputs to `float32` before
calling `torch.fft.fftn`, because PyTorch's FFT does not support bf16.
The same is true for `float16`: depending on the PyTorch version,
`fftn` either
- produces the experimental `torch.complex32` (ComplexHalf) dtype and
emits a `UserWarning: ComplexHalf support is experimental`, or
- raises `RuntimeError: Unsupported dtype Half` outright.
Both paths were reachable from FreeU with half-precision models
(e.g. `sd-turbo` + `fp16` + `enable_freeu`) as reported in #12504.
Extend the existing upcast branch to cover `float16` too. The function
already downcasts the result back to `x_in.dtype` at the end, so the
externally observable dtype is unchanged.
Closes#12504.
* Address review: generalize upcast to non-float32 + fix ruff F821
- Apply @sayakpaul's suggestion: use `elif x.dtype != torch.float32:`
so any non-float32 dtype (bf16, fp16, and future half-precision
dtypes) is upcast to float32 before the FFT.
- Drop the `"torch.Tensor"` return annotation on the test helper
that triggered ruff F821 in CI (torch is imported inside the
method body, not at module scope).
* Add Flux2KleinInpaintPipeline
* Fixed mask channel mismatch and a bit of cleaning
* Added tests and minor refactors
* Added support for reference images for inpainting
* Style fixes
* Fixed the example docstring
* Corrected mask latent preparation for correct dimensional alignment
* replace masked_image_latents context with clean_source_latents, fix mask spatial alignment and remove unused VAE encoding
* Fix T-coordinate collision for conditioning
* Changed the default strength from 0.6 to 0.8
* Added reference image test and updated the frozenset
* Validated ref image, latent passing support and fixed ref image preprocessing
* Refined preprocessing with 1MP resolution cap and timestep tracking
* Updated typing, improved validation and changed the example docstring
* Style fixes
* Fixed batch inference discrepancy and addressed review comments
* Fixed a typo
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Apply suggestion from @asomoza
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>
* Reused encoded latents and fix channel check consistency
* fixed pre-encoded latent preprocessing for source and ref images
* Apply style fixes
* Updated the docstring with the shape requirements
* Apply style fixes
* Fixed copies
* fix(qwen): fix CFG failing when passing neg prompt embeds with none mask
* fix(qwen): safely handle missing embeds masks in edit and inpaint pipelines
* test(qwen): add tests for true cfg scale without neg prompt mask
* fix(qwen): correct comments for copied functions in controlnet and inpaint pipelines
* fix(qwen): add warnings for missing prompt and negative prompt masks in pipelines
* test(qwen): use torch_device and clarify dummy inputs in cfg mask tests
* fix(qwen): address Claude PR review feedback
* fix(qwen): fix warning message based on reviewer suggestion
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Add modular pipeline support for HunyuanVideo 1.5
* Fix I2V latent/cond spatial dimension mismatch
* Fix guidance_scale default to 7.5 matching ClassifierFreeGuidance
* Fix tokenizer type: use Qwen2TokenizerFast to match model
* Fix system message string formatting to match standard pipeline
* Rewrite HunyuanVideo 1.5 modular: use standard pipeline methods directly
* Remove I2V exports (T2V only for now)
* Fix encoder: use static methods directly instead of encode_prompt
* Inline all standard pipeline methods, remove runtime dependency
* Add HunyuanVideo 1.5 image-to-video modular blocks
* Fix missing FrozenDict import in before_denoise.py
* auto-generated docstrings via #auto_docstring
* Fix ruff lint and format issues
* use InputParam/OutputParam templates and fix ruff
* Address LTX review feedback here like add AutoBlocks, refactor I2V latents, lift encoders
* Add workflow map, workflow tests, auto docstrings, export only AutoBlocks
* Address Claude CI review
* Address claude CI review 2
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* Add modular pipeline support for LTX Video
* Fix guidance_scale passthrough to guider
* Add LTX modular pipeline tests
* Add LTX image-to-video modular pipeline
* Fix i2v VAE dtype mismatch
* Add cache_context to denoiser for CFG parity
* Address review feedback
* Generate auto docstrings for LTX assembled blocks
* Fix ruff lint and format issues
* use InputParam/OutputParam templates and ruff check
* address all review
* Lift LTXVaeEncoderStep out of I2V core denoise into its own auto block
* unused declarations
* Fix check_copies: sync retrieve_timesteps and drop unsupported Copied from tags
* Address review: remove unused code, add LTXAutoBlocks, refactor I2V latents flow
* removed LTXBlocks,LTXImage2VideoBlocks
* Update test to use LTXAutoBlocks
* workflow map and auto docstring
* Add LTXVideoPachifier, workflow map
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
* add fallback logic for Hunyuan pipeline to make it compatible with
latest transformers
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
* use the last <|end_header_id|> token position + 1 as the assistant section marker
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
* fix format
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
* update variant name
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
---------
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
**Summary:** TorchAO recently deprecated AffineQuantizedTensor
and related classes (https://github.com/pytorch/ao/issues/2752).
These will be removed in the next release. We should remove
references of these classes in diffusers before then.
**Test Plan:**
python -m pytest -s -v tests/quantization/torchao/test_torchao.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Fix: ensure consistent dtype and eval mode in pipeline save/load tests
* Modify according to the comments
* Update according to the comments
* Update comment
* Code quality
* cast buffers to torch.float16
* conflict
* Fix
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* remove str option for quantization config in torchao
* Apply style fixes
* minor fixes
* Added AOBaseConfig docs to torchao.md
* minor fixes for removing str option torchao
* minor change to add back int and uint check
* minor fixes
* minor fixes to tests
* Update tests/quantization/torchao/test_torchao.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Update docs/source/en/quantization/torchao.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Update tests/quantization/torchao/test_torchao.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* version=2 update to test_torchao.py
---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix mask in SP
* change the modification to qwen specific
* drop xfail since qwen-image mask is fixed
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* UT expands to batch inputs
* update according to suggestion
* update according to suggestion 2
* fix CI
* update according to suggestion 3
* clean line
* Initial implementation of perturbed attn processor for LTX 2.3
* Update DiT block for LTX 2.3 + add self_attention_mask
* Add flag to control using perturbed attn processor for now
* Add support for new video upsampling blocks used by LTX-2.3
* Support LTX-2.3 Big-VGAN V2-style vocoder
* Initial implementation of LTX-2.3 vocoder with bandwidth extender
* Initial support for LTX-2.3 per-modality feature extractor
* Refactor so that text connectors own all text encoder hidden_states normalization logic
* Fix some bugs for inference
* Fix LTX-2.X DiT block forward pass
* Support prompt timestep embeds and prompt cross attn modulation
* Add LTX-2.3 configs to conversion script
* Support converting LTX-2.3 DiT checkpoints
* Support converting LTX-2.3 Video VAE checkpoints
* Support converting LTX-2.3 Vocoder with bandwidth extender
* Support converting LTX-2.3 text connectors
* Don't convert any upsamplers for now
* Support self attention mask for LTX2Pipeline
* Fix some inference bugs
* Support self attn mask and sigmas for LTX-2.3 I2V, Cond pipelines
* Support STG and modality isolation guidance for LTX-2.3
* make style and make quality
* Make audio guidance values default to video values by default
* Update to LTX-2.3 style guidance rescaling
* Support cross timesteps for LTX-2.3 cross attention modulation
* Fix RMS norm bug for LTX-2.3 text connectors
* Perform guidance rescale in sample (x0) space following original code
* Support LTX-2.3 Latent Spatial Upsampler model
* Support LTX-2.3 distilled LoRA
* Support LTX-2.3 Distilled checkpoint
* Support LTX-2.3 prompt enhancement
* Make LTX-2.X processor non-required so that tests pass
* Fix test_components_function tests for LTX2 T2V and I2V
* Fix LTX-2.3 Video VAE configuration bug causing pixel jitter
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Refactor LTX-2.X Video VAE upsampler block init logic
* Refactor LTX-2.X guidance rescaling to use rescale_noise_cfg
* Use generator initial seed to control prompt enhancement if available
* Remove self attention mask logic as it is not used in any current pipelines
* Commit fixes suggested by claude code (guidance in sample (x0) space, denormalize after timestep conditioning)
* Use constant shift following original code
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
