0 j/UCSSKrSSKrSSKrSSKJrJrJrJrJr SSK r SSK J r \R"\ 5rSS1r/SQr/SQr/SQ/S Q/S QS.rS3S \S \\S \\S\\4SjjrS4S\S\\S\4SjjrSrSrS5S\ R4S\ R4S\\S4S\\\\\4S\S \S \\S \\S\S\\ R4S\S\S\S \\ R4S!\\ R4S"\\\\\4S#\S$\S%\S&\S'\S(\S)\S*\S+\S,\S-\S.\\ R4S/\\ R4S0\S1\S\\\44@S2jjrg)6N)DictListOptionalTupleUnion)tqdmeulerheun)?@@)r .袋????۶m۶m?竪?ى؉??UUUUUU?%I$I??tE]t??皙??333333??qq??)r rrrrrrr )r rrrrrrr)r rrrrrrrshift timesteps infer_stepsreturnc^SnUb[U5nU(a+USS:Xa"UR5 U(a USS:XaM"[U5S:a[R ST5 OW[U5S:a$[R S[U55 USSnUVs/sHn[ [ U4SjS 9PM nnUnUcRUbOUS:aI[U5Vs/sH nS Xr- - PM nnTS :wa!UVs/sHnTU-S TS - U--- PM nnUnUc:Tn [ [U4S jS 9mU T:wa[R S U T5 [TnU$s snfs snfs snf) aCompute the timestep schedule for diffusion sampling. When ``infer_steps`` is provided and ``timesteps`` is None, a continuous linspace schedule is generated (matching the PyTorch base-model behaviour). The legacy lookup-table path (8-step ``SHIFT_TIMESTEPS``) is used only when neither ``timesteps`` nor ``infer_steps`` is supplied. Args: shift: Diffusion timestep shift (applied via ``shift*t / (1+(shift-1)*t)``). timesteps: Optional custom list of timesteps. infer_steps: Number of diffusion steps. When given, overrides the fixed 8-step lookup table. Returns: List of timestep values (descending, without trailing 0). Nrz 20; truncatingc[X- 5$Nabs)xts V/mnt/workspace/acestep.cpp/tests/../../ACE-Step-1.5/acestep/models/mlx/dit_generate.py'get_timestep_schedule..Ts c!%jkeyr c >[UT- 5$r*r+)r-r!s r/r0r1_s AIr2z.shift=%.2f rounded to nearest valid shift=%.1f) listpoplenloggerwarningminVALID_TIMESTEPSrange VALID_SHIFTSSHIFT_TIMESTEPS) r!r"r#t_schedule_listts_listr.mappediraworiginal_shifts ` r/get_timestep_schedulerF3sf*Oy/'"+* KKM'"+* w? U " NNK^]b c)%0 !\BFs!EE7Eguidance_scalemomentum_statenorm_thresholdcSSKJn SnX- nUbXsRSS5-nXsS'US:aKURXw-R USS95nUR UR U5XHS-- 5n Xy-nXRX-R USS95S-- n Xz-R USS9U -n X{- n XS- U --$)uAPG (Adaptive Projected Guidance) in pure MLX — mirrors the PyTorch ``apg_forward``. Projection is performed along axis 1 (the time/sequence dimension) to match the PyTorch implementation which calls ``apg_forward(..., dims=[1])``. rNr'runningTaxiskeepdimsg:0yE>)mlx.corecoregetsqrtsumminimum ones_like) pred_cond pred_uncondrGrHrImx proj_axisdiff diff_norm scale_factorv1parallel orthogonals r/_mlx_apg_forwardr`gsI  "D!((A66$(y!GGT[--9t-LM zz",,y"9>Y]M];^_ " ggy499ySW9XY\`` aB I=BHJ *j8 88r2clSSKJn X4-SU- U--nURUSS9nURXpU5$)zEReplace non-repaint regions of *xt* with noised source latents (MLX).rNr r&rM)rOrP expand_dimswhere)xt clean_srcmaskt_nextnoiserXztms r/_mlx_repaint_step_injectionrls= 3<94 4B t"%A 88A2 r2cDSSKJn UR[R5R 5nUS::a,UR URU5SS9nX`-SU- U--$URupx[U5Hn X)n U R5(dU R5(dM3[R"U 5Sn [U 5S:XaM][U S5[U S5S-p[X- S5nX- S:a%[R "SSX- S-5SSXYX24'[#X-U5nX- S:dM[R "SSX- S-5SSXYX24'M UR URU5SS9nX`-SU- U--$)z@Blend generated latents with source at repaint boundaries (MLX).rNr&rbr r')rOrPastypenpfloat32copyrcarrayshaper=allanynonzeror8intmaxlinspacer;)x_genrfmask_np cf_framesrXsoftrkBTbrowidxleftrightfsfes r/_mlx_repaint_boundary_blendrs} >>"** % * * ,DA~ NN288D>N 3yC!Gy000 ==DA 1Xj 7799CGGII jjoa  s8q= #a&k3s2w " Aq"*q. A!B GDEH  rxx~B/A 9a9, ,,r2encoder_hidden_states_npcontext_latents_npsrc_latents_shape.seed infer_methodnull_condition_emb_npcfg_interval_startcfg_interval_endaudio_cover_strength"encoder_hidden_states_non_cover_npcontext_latents_non_cover_np retake_seedretake_variance compile_model disable_tqdm sampler_modevelocity_norm_thresholdvelocity_ema_factor dcw_enableddcw_mode dcw_scalerdcw_high_scaler dcw_waveletrepaint_mask_npclean_src_latents_nprepaint_crossfade_framesrepaint_injection_ratioc ^^ ^ ^ ^^^X^Y^Z^[^\^]^^^_^`^aSSKJm^ SSKJn U[;a[ SUS[S35eUS:Hm`TS:maTS:m_T`(a1US :Xa[ RS 5 O[ RS 5 Ta(a[ RS T5 T_(a[ RS T5 0n![R"5n"T^RU5n#T^RU5n$UbT^RU5OSn%UbT^RU5OSn&USL=(a USLn'U'(aT^RU5OSn(U'(aT^RU5OSn)USm[USmYUSmXT S:=(a U SLm\T\(aT^RU 5OSn*T\(aT^RU*U#R5n+T^RU#U+/SS9n#T^RU$U$/SS9n$U%b.T^RU*U%R5n,T^RU%U,/SS9n%U&bT^RU&U&/SS9n&T\(a0OSm]UXUYU[U^4Sjn-U-"U5n.US:aQU-"U5n/U[RS- -n0[R "U05U.-[R""U05U/--n.[%XgUS9n1['U15n2[)U2U -5n3SmZU(a-U4Sjn4T^R+U45mZ[ RS5 T`(aSn6O TZcU "5OSn6U.n7Sn8UZU\UU^U`4Sjn9U[U U U\U U]4Sjn:U^U_UaUU4Sjn;[R"5n U=(a US:g=(d US:H=(a US:gn?U?(a(US:XaSOS US!3n@[ RS"UUUUU@5 [3[5U25S#US$9GHnAU1UAnBUAU3:aU=(dS%n=U%bU%n#U&n$U6bU "5n6T\(aT^RU7U7/SS9OU7nCU9"UCWBU#U$U65unDn6T^R7UD5 U:"UDUB5nDU;"UDU7U85nDU7nEUDnFWAU2S- :Xa0T^R9T[SS4WB5nGU7WDUG-- n7T^R7U75 GO3U1WAS-nHT`(aUS&:XaWBWH- nIT^R9T[SS4UI5nJU7WDUJ-- nKT^R7UK5 T\(aT^RWKUK/SS9OWKnLU9"ULWHU#U$U65unMn6T^R7UM5 U:"UMUH5nMU;"UMWKWD5nMS'UDUM--nNU7UNWJ-- n7UNnDOyUS :XaQT^R9T[SS4WB5nGU7WDUG-- nOT^R:R=U7R5nPWHUP-SUH- UO--n7O"WBWH- nIT^R9T[SS4UI5nJU7WDUJ-- n7T^R7U75 U?(a;T^R9T[SS4WB5nQWEWFUQ-- nRU>"U7URUBS%UUUUS(9n7T^R7U75 WDn8U'(dGM5[?UU2-5nSWAUS:dGMLWAU2S- :aU1WAS-OSnT[AU7U)U(UTU.5n7T^R7U75 GM U'(a%US:a[CU7U)UU5n7T^R7U75 [R"5nU[R"5nVUUU<- U!S)'U!S)[EU2S5- U!S*'UVU"- U!S+'UU!S,'[FR"U75nWUWU!S-.$![,a!n5[ RSU55 Sn5A5GNSn5A5ff=f).uRun the complete MLX diffusion loop with optional CFG guidance. This is the core generation function. It accepts numpy arrays (converted from PyTorch tensors by the handler) and returns numpy arrays that the handler converts back to PyTorch. Args: mlx_decoder: ``MLXDiTDecoder`` instance with loaded weights. encoder_hidden_states_np: [B, enc_L, D] from prepare_condition (numpy). context_latents_np: [B, T, C] from prepare_condition (numpy). src_latents_shape: shape tuple [B, T, 64] for noise generation. seed: random seed (int, list[int], or None). infer_method: "ode" or "sde". shift: timestep shift factor. timesteps: optional custom timestep list. infer_steps: number of diffusion steps. guidance_scale: CFG guidance strength (>1.0 enables CFG). null_condition_emb_np: [1, 1, D] null condition embedding for CFG. cfg_interval_start: timestep ratio below which CFG is disabled. cfg_interval_end: timestep ratio above which CFG is disabled. audio_cover_strength: cover strength (0-1). encoder_hidden_states_non_cover_np: optional [B, enc_L, D] for non-cover. context_latents_non_cover_np: optional [B, T, C] for non-cover. compile_model: If True, compile the decoder step with ``mx.compile``. disable_tqdm: If True, suppress the diffusion progress bar. sampler_mode: Sampler algorithm — ``"euler"`` (first-order, default) or ``"heun"`` (second-order predictor-corrector for cleaner output). velocity_norm_threshold: Clamp velocity prediction L2 norm relative to input norm at each step. 0 disables (default). Values around 1.5–3.0 reduce outlier artefacts. velocity_ema_factor: Blend current velocity prediction with the previous step's prediction via EMA (``vt = (1-f)*vt + f*prev``). 0 disables (default). Values around 0.05–0.2 smooth the trajectory. Returns: Dict with ``"target_latents"`` (numpy) and ``"time_costs"`` dict. rNr')MLXCrossAttentionCachezUnsupported sampler_mode 'z'. Expected one of .r sdez[MLX-DiT] Heun sampler is not supported with SDE inference method. Falling back to Euler for SDE steps. Use infer_method='ode' for Heun.zF[MLX-DiT] Using Heun (second-order) sampler for higher-quality output.z:[MLX-DiT] Velocity norm clamping enabled (threshold=%.2f).z7[MLX-DiT] Velocity EMA smoothing enabled (factor=%.3f).rnr rbc8>UcTRRTTT45$[U[5(a/nUHnUbUS:a/UR TRRSTT455 M;TRR [ U55nUR TRRSTT4US95 M TRUSS9$TRR [ U55nTRRTTT4US9$)Nrr'r3rb)randomnormal isinstancer6appendr4rx concatenate)_seedpartssr4CrbszrXs r/ _draw_noise+mlx_generate_diffusion.._draw_noise)s =99##S!QK0 0 eT " "E9ALL!1!11a)!<=))--A/CLL!1!11a)!1!EF  >>%a>0 0iimmCJ'yya 55r2r )r#c  >T"XUX4SSS9upVU$)NF hidden_statestimestep timestep_rencoder_hidden_statescontext_latentscache use_cache)rer.trencctxvt_ mlx_decoders r/ _raw_step)mlx_generate_diffusion.._raw_stepIs# &)eEB Ir2z4[MLX-DiT] Diffusion step compiled with mx.compile().z:[MLX-DiT] mx.compile() failed (%s); using uncompiled path.c >T RURS4U5nTb T"XXRU5U4$T "UUUUUUT(+=(a T (+S9updXd4$)zSingle model evaluation helper.rr)fullrt) x_inputt_valrctx_in step_cachet_arrvt_out_compiled_stepdo_cfgrrXuse_heuns r/ _model_eval+mlx_generate_diffusion.._model_evalcsoq)+U3  %!'%fEzQ Q(!"%"!z2(l !!r2cj>T(dU$USTnUTSnTUs=::aT::aO U$[X#TT 5$U$)zApply CFG guidance if enabled.N)r`) vt_raw current_t_valrVrWrrrrrGrHs r/ _apply_cfg*mlx_generate_diffusion.._apply_cfgssLM4CL STl  B2B B$INN[ [r2c,>T(asTRX-RSSS95nTRX-RSSS95S-nTRTRU5T U-US-- 5nX-nT(aUbST - U-T U--nU$)z/Apply optional norm clamping and EMA smoothing.)r'rnTrLg|=r )rRrSrTrU) vt_guided xt_current prev_velocityvt_normxt_normscalerXuse_emause_norm_clamprrs r/_apply_stabilisation4mlx_generate_diffusion.._apply_stabilisation}s ggy499vPT9UVGggz6;;RV;WX[``GJJ W%(72wGE")I }022i?BUXeBeeIr2F) apply_mlx_dcwdoublehaarzMLX-native Haarztorch bridge ()zW[MLX-DiT] DCW enabled (mode=%s, scaler=%.3f, high_scaler=%.3f, wavelet=%s, backend=%s).zMLX DiT diffusion)descdisableToder)t_currenabledmodescaler high_scalerwaveletdiffusion_time_costdiffusion_per_step_time_costtotal_time_costr)target_latents time_costs)$rOrP dit_modelrVALID_SAMPLER_MODES ValueErrorr9r:infotimers broadcast_tortrmathpicossinrFr8rxcompile Exception%acestep.models.mlx.dcw_correction_mlxrrr=evalrrrroundrlrryrp)brrrrrrr!r"r#rGrrrrrrrrrrrrrrrrrrrrrrrr total_startenc_hsr enc_hs_ncctx_nc do_repaintrepaint_mask_mx clean_src_mx null_cond null_expandednull_expanded_ncrri retake_noisev_radr@ num_steps cover_stepsrexcrreprev_vtrrr diff_start_switched_to_non_coverr dcw_active_backendstep_idx current_tx_inrxt_before_stepvt_for_denoiset_unsqnext_tdtdt_arr xt_predictedx_in2vt2vt_avg pred_clean new_noiset_unsq_ddenoisedinjection_cutofft_afterdiff_end total_end result_nprrrrrrHrXrrrsb` ` `` `` @@@@@@@@@@r/mlx_generate_diffusionr0sN1..5l^CVWjVkklmnnv%H,q0N!A%G 5 NNX  KK` a PRij MObcJ))+K XX. /F ((% &C@b@n;r<r?floatr6rxrFr`rlrndarraystrboolobjectr0rr2r/r<s( 55   8 $'  < K (  $!%1 1~1#1 %[ 1p&* 99TN 9  9B->-1 $!%26 #!"%?C9=37 %(!$!,015$&%(Af jjf fS#X f 5d3i( ) f  f f~f#ff$BJJ/fff f)1(<f #+2::"6!f"%T#Y/0#f$%f&'f()f*+f,#-f./f01f23f45f67f89f:bjj);f<#2::.=f>"?f@#AfB #v+Cfr2