From c9cd15223d09102b8fb691d256f44fbbf073f14a Mon Sep 17 00:00:00 2001 From: Jinxiaolong1129 Date: Tue, 19 Nov 2024 16:03:11 -0500 Subject: [PATCH 1/9] rebuttal update --- flux-dev.png | Bin 0 -> 1071640 bytes flux-test.py | 43 + img1.png | Bin 0 -> 36669 bytes img2.png | Bin 0 -> 72114 bytes latte_spatial_average_mse_plot.pdf | Bin 0 -> 13682 bytes latte_spatial_block_mse_plot.pdf | Bin 0 -> 14708 bytes latte_temporal_average_mse_plot.pdf | Bin 0 -> 13681 bytes latte_temporal_block_mse_plot.pdf | Bin 0 -> 14734 bytes open_sora_plan_spatial_average_mse_plot.pdf | Bin 0 -> 14615 bytes open_sora_plan_spatial_block_mse_plot.pdf | Bin 0 -> 16953 bytes open_sora_plan_temporal_average_mse_plot.pdf | Bin 0 -> 14580 bytes open_sora_plan_temporal_block_mse_plot.pdf | Bin 0 -> 17114 bytes open_sora_spatial_average_mse_plot.pdf | Bin 0 -> 13320 bytes open_sora_spatial_block_mse_plot.pdf | Bin 0 -> 14276 bytes open_sora_temporal_average_mse_plot.pdf | Bin 0 -> 13428 bytes open_sora_temporal_block_mse_plot.pdf | Bin 0 -> 14311 bytes sample_latte_mse.txt | 562 + sample_opensora_mse.txt | 382 + sample_opensora_plan_mse.txt | 11218 ++++++++++++++++ taming/modules/autoencoder/lpips/vgg.pth | Bin 0 -> 7289 bytes .../models/transformers/flux_transformer.py | 574 + videosys/pipelines/flux/__init__.py | 3 + videosys/pipelines/flux/pipeline_flux_pab.py | 886 ++ 23 files changed, 13668 insertions(+) create mode 100644 flux-dev.png create mode 100644 flux-test.py create mode 100644 img1.png create mode 100644 img2.png create mode 100644 latte_spatial_average_mse_plot.pdf create mode 100644 latte_spatial_block_mse_plot.pdf create mode 100644 latte_temporal_average_mse_plot.pdf create mode 100644 latte_temporal_block_mse_plot.pdf create mode 100644 open_sora_plan_spatial_average_mse_plot.pdf create mode 100644 open_sora_plan_spatial_block_mse_plot.pdf create mode 100644 open_sora_plan_temporal_average_mse_plot.pdf create mode 100644 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Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead. + deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message) +[10/01/24 15:05:16] INFO colossalai - colossalai - INFO: + /home/jin509/anaconda3/envs/opendit/lib/python3.10/ + site-packages/colossalai/initialize.py:60 launch + INFO colossalai - colossalai - INFO: Distributed + environment is initialized, world size: 1 + +Init SkipManager: + steps=50 + cross_broadcast=True, cross_threshold=[100, 800], cross_gap=6 + spatial_broadcast=True, spatial_threshold=[100, 800], spatial_gap=2, spatial_layer_range=[0, 28] + temporal_broadcast=True, temporal_threshold=[100, 800], temporal_gap=3 + diffusion_skip=False, diffusion_timestep_respacing=None + +Some weights of the model checkpoint were not used when initializing LatteT2V_mse: + ['caption_projection.y_embedding'] +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. + +Downloading shards: 0%| | 0/4 [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 + +Loading checkpoint shards: 0%| | 0/2 [00:00 +[rank0]: main(args) +[rank0]: File "/home/jin509/OpenDiT/sc/home/jin509/anaconda3/envs/opendit/lib/python3.10/site-packages/torch/nn/modules/conv.py:605: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/ + 3%|▎ | 1/30 [00:09<04:27, 9.23s/it]Time step 992, Spatial MSE: [(0, 0.0009266708511859179), (1, 0.001964372815564275), (2, 0.020445087924599648), (3, 0.021416611969470978), (4, 0.024798057973384857), (5, 0.05009352043271065), (6, 0.04161224141716957), (7, 0.07172810286283493), (8, 0.08762899786233902), (9, 0.2350788116455078), (10, 0.3339252173900604), (11, 0.38562941551208496), (12, 0.4068857431411743), (13, 0.3412085473537445), (14, 0.7005358338356018), (15, 0.6336411237716675), (16, 1.2084649801254272), (17, 0.6472557783126831), (18, 0.44402867555618286), (19, 0.4843830466270447), (20, 0.9968878626823425), (21, 0.7619172930717468), (22, 0.621554434299469), (23, 1.4418096542358398), (24, 7.050262451171875), (25, 6.062499523162842), (26, 5.6432647705078125), (27, 0.6295115351676941)], Temporal MSE: [(0, 4.37132730439771e-05), (1, 0.0004547384742181748), (2, 0.0013936946634203196), (3, 0.0020310101099312305), (4, 0.002069223904982209), (5, 0.007184454705566168), (6, 0.004656512290239334), (7, 0.008510898798704147), (8, 0.007425728254020214), (9, 0.013835558667778969), (10, 0.024980736896395683), (11, 0.031195254996418953), (12, 0.04029916226863861), (13, 0.023357659578323364), (14, 0.09227907657623291), (15, 0.14006251096725464), (16, 0.17229478061199188), (17, 0.08008652925491333), (18, 0.08565345406532288), (19, 0.09104886651039124), (20, 0.0826355516910553), (21, 0.14083971083164215), (22, 0.43182292580604553), (23, 0.42485642433166504), (24, 0.19641447067260742), (25, 0.11965048313140869), (26, 0.6062396764755249), (27, 0.8362858891487122)] + + 7%|▋ | 2/30 [00:21<05:14, 11.24s/it]Time step 984, Spatial MSE: [(0, 0.0004006043600384146), (1, 0.0015674657188355923), (2, 0.006948538590222597), (3, 0.005912716966122389), (4, 0.0072502014227211475), (5, 0.008970772847533226), (6, 0.014967601746320724), (7, 0.025083597749471664), (8, 0.032312605530023575), (9, 0.07322235405445099), (10, 0.1464146226644516), (11, 0.13587170839309692), (12, 0.14334699511528015), (13, 0.14076267182826996), (14, 0.2820338308811188), (15, 0.36522674560546875), (16, 0.36476871371269226), (17, 0.2858518660068512), (18, 0.3078519105911255), (19, 0.29815563559532166), (20, 0.29479435086250305), (21, 0.379610151052475), (22, 0.3598935306072235), (23, 0.41742438077926636), (24, 1.4148459434509277), (25, 1.1107865571975708), (26, 0.8324218392372131), (27, 0.13442839682102203)], Temporal MSE: [(0, 7.0442597461806145e-06), (1, 0.00017014547483995557), (2, 0.0002885904104914516), (3, 0.00048401099047623575), (4, 0.0005046654841862619), (5, 0.001635013846680522), (6, 0.001332456129603088), (7, 0.006680983118712902), (8, 0.0048481253907084465), (9, 0.006023965310305357), (10, 0.011178079061210155), (11, 0.012565084733068943), (12, 0.01089107058942318), (13, 0.009719246998429298), (14, 0.05527784302830696), (15, 0.08385003358125687), (16, 0.05810687318444252), (17, 0.038452163338661194), (18, 0.040270425379276276), (19, 0.048138607293367386), (20, 0.06920678168535233), (21, 0.08912437409162521), (22, 0.18188022077083588), (23, 0.17255337536334991), (24, 0.08186456561088562), (25, 0.05199260264635086), (26, 0.11436077207326889), (27, 0.08013878762722015)] + + 10%|█ | 3/30 [00:35<05:27, 12.14s/it]Time step 976, Spatial MSE: [(0, 0.0001727823109831661), (1, 0.0006860928260721266), (2, 0.002079337602481246), (3, 0.0015498434659093618), (4, 0.0018771053291857243), (5, 0.00224782875739038), (6, 0.004517590161412954), (7, 0.008605698123574257), (8, 0.009618696756660938), (9, 0.01589665748178959), (10, 0.03976340591907501), (11, 0.03559447079896927), (12, 0.027149515226483345), (13, 0.028175532817840576), (14, 0.09069004654884338), (15, 0.05815749242901802), (16, 0.09089938551187515), (17, 0.06441894918680191), (18, 0.06571715325117111), (19, 0.06271632015705109), (20, 0.07174547761678696), (21, 0.06651832163333893), (22, 0.0719655454158783), (23, 0.07704268395900726), (24, 0.20802593231201172), (25, 0.1995108425617218), (26, 0.12982037663459778), (27, 0.04146185144782066)], Temporal MSE: [(0, 3.0767628231842536e-06), (1, 4.991464447812177e-05), (2, 0.00011014417395927012), (3, 0.00017054576892405748), (4, 0.0001959232467925176), (5, 0.0005941847921349108), (6, 0.0005628261133097112), (7, 0.0024047265760600567), (8, 0.0015033873496577144), (9, 0.0020875507034361362), (10, 0.0032916245982050896), (11, 0.0031261141411960125), (12, 0.002398868091404438), (13, 0.002356247277930379), (14, 0.010932584293186665), (15, 0.015843834728002548), (16, 0.015390238724648952), (17, 0.011027290485799313), (18, 0.010268963873386383), (19, 0.010804057121276855), (20, 0.02292189933359623), (21, 0.032459937036037445), (22, 0.0368894524872303), (23, 0.033287856727838516), (24, 0.016354206949472427), (25, 0.007656225934624672), (26, 0.01898087002336979), (27, 0.018119309097528458)] + + 13%|█▎ | 4/30 [00:48<05:29, 12.68s/it]Time step 968, Spatial MSE: [(0, 0.00010960254439851269), (1, 0.00041812710696831346), (2, 0.0014024536358192563), (3, 0.0006026348564773798), (4, 0.0009371120249852538), (5, 0.0011854877229779959), (6, 0.002584119327366352), (7, 0.004766265861690044), (8, 0.004756585229188204), (9, 0.0074585494585335255), (10, 0.015636006370186806), (11, 0.016651278361678123), (12, 0.014092809520661831), (13, 0.01447084080427885), (14, 0.05387013405561447), (15, 0.04038003087043762), (16, 0.046666257083415985), (17, 0.04488399997353554), (18, 0.05084751546382904), (19, 0.04385882988572121), (20, 0.035080019384622574), (21, 0.04347064346075058), (22, 0.05180162936449051), (23, 0.048293210566043854), (24, 0.09765525907278061), (25, 0.08819663524627686), (26, 0.062382277101278305), (27, 0.03582541644573212)], Temporal MSE: [(0, 1.7567582517585834e-06), (1, 2.2844798877486028e-05), (2, 8.26762116048485e-05), (3, 8.782563963904977e-05), (4, 0.00013262232823763043), (5, 0.0003562266065273434), (6, 0.00031596660846844316), (7, 0.001102547044865787), (8, 0.0008168087806552649), (9, 0.0010255518136546016), (10, 0.0012601652415469289), (11, 0.0013915509916841984), (12, 0.0011155422544106841), (13, 0.0011673681437969208), (14, 0.0056514739990234375), (15, 0.008906176313757896), (16, 0.008293483406305313), (17, 0.007517124991863966), (18, 0.0074993004091084), (19, 0.008325088769197464), (20, 0.012617958709597588), (21, 0.024936256930232048), (22, 0.019689854234457016), (23, 0.019675038754940033), (24, 0.011224239133298397), (25, 0.004602625500410795), (26, 0.009749721735715866), (27, 0.008444169536232948)] + + 17%|█▋ | 5/30 [01:02<05:26, 13.04s/it]Time step 960, Spatial MSE: [(0, 7.807557994965464e-05), (1, 0.00046529382234439254), (2, 0.0010989962611347437), (3, 0.000597073114477098), (4, 0.0006496207788586617), (5, 0.0008811640436761081), (6, 0.0019633930642157793), (7, 0.0032501493114978075), (8, 0.0034625132102519274), (9, 0.005906999576836824), (10, 0.014647521078586578), (11, 0.014449208974838257), (12, 0.013379567302763462), (13, 0.011510612443089485), (14, 0.05505785346031189), (15, 0.041723039001226425), (16, 0.05323343351483345), (17, 0.04218742251396179), (18, 0.05014714598655701), (19, 0.04473369941115379), (20, 0.039304886013269424), (21, 0.03324873745441437), (22, 0.04593143239617348), (23, 0.051350899040699005), (24, 0.07500309497117996), (25, 0.064060740172863), (26, 0.037272218614816666), (27, 0.019546428695321083)], Temporal MSE: [(0, 1.539536242489703e-06), (1, 1.2849155609728768e-05), (2, 6.214666063897312e-05), (3, 7.150188321247697e-05), (4, 0.0001291336229769513), (5, 0.00030068730120547116), (6, 0.00033650416298769414), (7, 0.0012281473027542233), (8, 0.0006525879725813866), (9, 0.0008248192607425153), (10, 0.0014843086246401072), (11, 0.0014821819495409727), (12, 0.0012269431026652455), (13, 0.00128587381914258), (14, 0.006934084463864565), (15, 0.009905199520289898), (16, 0.00912797637283802), (17, 0.008717631921172142), (18, 0.007950297556817532), (19, 0.008846523240208626), (20, 0.011944395489990711), (21, 0.021406568586826324), (22, 0.014286103658378124), (23, 0.015795107930898666), (24, 0.009501827880740166), (25, 0.003874744288623333), (26, 0.006433569360524416), (27, 0.004826321266591549)] + + 20%|██ | 6/30 [01:17<05:26, 13.62s/it]Time step 952, Spatial MSE: [(0, 7.491833821404725e-05), (1, 0.00033736717887222767), (2, 0.0007922904915176332), (3, 0.0003180203784722835), (4, 0.00040328802424483), (5, 0.0005527707980945706), (6, 0.0012780980905517936), (7, 0.002014124533161521), (8, 0.0023985544685274363), (9, 0.0038997442461550236), (10, 0.010480476543307304), (11, 0.01181764341890812), (12, 0.01249717827886343), (13, 0.011255301535129547), (14, 0.05175250023603439), (15, 0.041526660323143005), (16, 0.05169491097331047), (17, 0.0411551296710968), (18, 0.05044740438461304), (19, 0.048104241490364075), (20, 0.042700596153736115), (21, 0.041923388838768005), (22, 0.051699504256248474), (23, 0.053591035306453705), (24, 0.06777022778987885), (25, 0.059386447072029114), (26, 0.03717577084898949), (27, 0.017370106652379036)], Temporal MSE: [(0, 1.155103177552519e-06), (1, 8.407658242504112e-06), (2, 4.7233508666977286e-05), (3, 4.1629573388490826e-05), (4, 6.580636545550078e-05), (5, 0.00013343969476409256), (6, 0.0001995993370655924), (7, 0.0007717765402048826), (8, 0.0004202634154353291), (9, 0.000504080846440047), (10, 0.0010434846626594663), (11, 0.0013803160982206464), (12, 0.0012947970535606146), (13, 0.0016057944158092141), (14, 0.006894640624523163), (15, 0.0109937135130167), (16, 0.009676747024059296), (17, 0.008619545958936214), (18, 0.007001226767897606), (19, 0.008823255077004433), (20, 0.015195740386843681), (21, 0.02463427744805813), (22, 0.01486996840685606), (23, 0.01656477153301239), (24, 0.010339288972318172), (25, 0.0030757852364331484), (26, 0.0057394192554056644), (27, 0.004841858521103859)] + + 23%|██▎ | 7/30 [01:31<05:22, 14.00s/it]Time step 940, Spatial MSE: [(0, 7.24489291314967e-05), (1, 0.000313686701701954), (2, 0.0007126562995836139), (3, 0.000358480989234522), (4, 0.0006416052929125726), (5, 0.0008064563735388219), (6, 0.0019163156393915415), (7, 0.0037589105777442455), (8, 0.0032417860347777605), (9, 0.00538463331758976), (10, 0.014927765354514122), (11, 0.01713404804468155), (12, 0.015686118975281715), (13, 0.014841865748167038), (14, 0.05118165537714958), (15, 0.03923684358596802), (16, 0.052187684923410416), (17, 0.044145792722702026), (18, 0.0442981980741024), (19, 0.04453035071492195), (20, 0.03709688037633896), (21, 0.0405033640563488), (22, 0.0443095825612545), (23, 0.04004473239183426), (24, 0.06612729281187057), (25, 0.08519932627677917), (26, 0.047783732414245605), (27, 0.04844006150960922)], Temporal MSE: [(0, 1.3332016806089086e-06), (1, 9.380561095895246e-06), (2, 6.583630602108315e-05), (3, 4.633643402485177e-05), (4, 0.0001370745012536645), (5, 0.0001841498160501942), (6, 0.00034363308805041015), (7, 0.00146920804399997), (8, 0.0007672221399843693), (9, 0.0008992550428956747), (10, 0.0018642748473212123), (11, 0.0024451951030641794), (12, 0.002077569719403982), (13, 0.0021917736157774925), (14, 0.009085124358534813), (15, 0.012444879859685898), (16, 0.011418365873396397), (17, 0.009851033799350262), (18, 0.0071867844089865685), (19, 0.008445629850029945), (20, 0.015931081026792526), (21, 0.01938108541071415), (22, 0.015059318393468857), (23, 0.018062695860862732), (24, 0.010663241147994995), (25, 0.004695422947406769), (26, 0.01068099495023489), (27, 0.011487695388495922)] + + 27%|██▋ | 8/30 [01:45<05:06, 13.95s/it]Time step 932, Spatial MSE: [(0, 7.119955989765003e-05), (1, 0.00028675212524831295), (2, 0.0004625353030860424), (3, 0.00026933461776934564), (4, 0.00022043999342713505), (5, 0.0003872669185511768), (6, 0.0008228204678744078), (7, 0.0011802359949797392), (8, 0.001794639159925282), (9, 0.0030920079443603754), (10, 0.008025375194847584), (11, 0.008147034794092178), (12, 0.007810922339558601), (13, 0.007287824060767889), (14, 0.01993078924715519), (15, 0.01634838618338108), (16, 0.02202003076672554), (17, 0.018556207418441772), (18, 0.01736723631620407), (19, 0.016203269362449646), (20, 0.01625337265431881), (21, 0.015932129696011543), (22, 0.016816506162285805), (23, 0.016324644908308983), (24, 0.023658620193600655), (25, 0.026683300733566284), (26, 0.020539240911602974), (27, 0.00960638839751482)], Temporal MSE: [(0, 1.1134844726257143e-06), (1, 4.394074039737461e-06), (2, 3.581352939363569e-05), (3, 2.9611974241561256e-05), (4, 6.0331090935505927e-05), (5, 9.05815904843621e-05), (6, 0.0001576998911332339), (7, 0.0004814566345885396), (8, 0.0002851993776857853), (9, 0.0004079692007508129), (10, 0.0009376517264172435), (11, 0.0012169694527983665), (12, 0.0007450192933902144), (13, 0.0009936566930264235), (14, 0.0035727820359170437), (15, 0.004670949187129736), (16, 0.004847222939133644), (17, 0.004173140041530132), (18, 0.0034396275877952576), (19, 0.004211949184536934), (20, 0.008950996212661266), (21, 0.008040103130042553), (22, 0.0057085612788796425), (23, 0.0069803413935005665), (24, 0.004266250412911177), (25, 0.001727919327095151), (26, 0.004005072172731161), (27, 0.0022541380021721125)] + + 30%|███ | 9/30 [01:57<04:39, 13.30s/it]Time step 920, Spatial MSE: [(0, 3.0278890335466713e-05), (1, 0.0003708008152898401), (2, 0.000609726004768163), (3, 0.00015430247003678232), (4, 7.25817953934893e-05), (5, 0.00011898245429620147), (6, 0.00015545588394161314), (7, 0.0006153929862193763), (8, 0.0001837576855905354), (9, 0.00033650349359959364), (10, 0.0003101854817941785), (11, 0.0002864672860596329), (12, 0.000436040892964229), (13, 0.0005159039865247905), (14, 0.0007050050189718604), (15, 0.0013454710133373737), (16, 0.0034384452737867832), (17, 0.00132788997143507), (18, 0.0024471620563417673), (19, 0.0016935461899265647), (20, 0.0016078249318525195), (21, 0.0020757552701979876), (22, 0.0017879079096019268), (23, 0.002910945098847151), (24, 0.005708375014364719), (25, 0.014250732026994228), (26, 0.011190512217581272), (27, 0.013866313733160496)], Temporal MSE: [(0, 7.933289225547924e-07), (1, 3.539646286299103e-06), (2, 1.396911102347076e-05), (3, 1.4117569662630558e-05), (4, 1.383861672366038e-05), (5, 1.8226814063382335e-05), (6, 2.0563125872286037e-05), (7, 6.425527681130916e-05), (8, 2.5504721634206362e-05), (9, 3.890625885105692e-05), (10, 7.93784492998384e-05), (11, 8.266719669336453e-05), (12, 7.661200652364641e-05), (13, 7.066615216899663e-05), (14, 0.00024042761651799083), (15, 0.000641595630440861), (16, 0.0005099154659546912), (17, 0.000582410953938961), (18, 0.000759028538595885), (19, 0.0007954341126605868), (20, 0.0015391969354823232), (21, 0.0009067552746273577), (22, 0.000733073044102639), (23, 0.000899129721801728), (24, 0.0005714813596569002), (25, 0.0007526343688368797), (26, 0.0015172931598499417), (27, 0.001010350533761084)] + + 33%|███▎ | 10/30 [02:10<04:23, 13.16s/it]Time step 908, Spatial MSE: [(0, 0.000166091529536061), (1, 0.0003747760783880949), (2, 0.0007242324063554406), (3, 0.0002913876960519701), (4, 0.0004052725271321833), (5, 0.0007809372036717832), (6, 0.0013191973557695746), (7, 0.001998354448005557), (8, 0.001514422008767724), (9, 0.0021812294144183397), (10, 0.008486056700348854), (11, 0.0062818448059260845), (12, 0.005737566389143467), (13, 0.006873187609016895), (14, 0.021637286990880966), (15, 0.009712382219731808), (16, 0.02292553335428238), (17, 0.009379059076309204), (18, 0.009110985323786736), (19, 0.0113415801897645), (20, 0.007648908067494631), (21, 0.013542670756578445), (22, 0.01163298636674881), (23, 0.010621827095746994), (24, 0.020733660086989403), (25, 0.04453950375318527), (26, 0.016548767685890198), (27, 0.01849394477903843)], Temporal MSE: [(0, 2.794477268253104e-06), (1, 4.581743723974796e-06), (2, 3.950664904550649e-05), (3, 8.736427844269201e-05), (4, 4.8264166252920404e-05), (5, 8.814658940536901e-05), (6, 0.00011145344615215436), (7, 0.0005860329838469625), (8, 0.0003770626208279282), (9, 0.0004321820742916316), (10, 0.0011011934839189053), (11, 0.0009614612208679318), (12, 0.0009641729411669075), (13, 0.0006906198104843497), (14, 0.0031227811705321074), (15, 0.004269621334969997), (16, 0.005705099552869797), (17, 0.003135703969746828), (18, 0.0019377312855795026), (19, 0.003500736551359296), (20, 0.0031091910786926746), (21, 0.005281544290482998), (22, 0.0062146857380867004), (23, 0.0054703098721802235), (24, 0.003534231334924698), (25, 0.003630605759099126), (26, 0.005111976992338896), (27, 0.003539988538250327)] + + 37%|███▋ | 11/30 [02:22<04:03, 12.83s/it]Time step 892, Spatial MSE: [(0, 3.1562474759994075e-05), (1, 0.0004946729750372469), (2, 0.0006658314960077405), (3, 0.000179580194526352), (4, 6.353826029226184e-05), (5, 0.0001127565119531937), (6, 0.00013921920617576689), (7, 0.0004576848295982927), (8, 0.000183205702342093), (9, 0.00037068245001137257), (10, 0.0002721789642237127), (11, 0.0003467546193860471), (12, 0.000334849173668772), (13, 0.0006264886469580233), (14, 0.0007569960434921086), (15, 0.0011909744935110211), (16, 0.005695628933608532), (17, 0.001277548843063414), (18, 0.0020686520729213953), (19, 0.0026272526010870934), (20, 0.001960916444659233), (21, 0.0035630566999316216), (22, 0.002265702001750469), (23, 0.0038038240745663643), (24, 0.0075464555993676186), (25, 0.012239932082593441), (26, 0.012617518194019794), (27, 0.010119365528225899)], Temporal MSE: [(0, 7.803503763170738e-07), (1, 3.4286010759387864e-06), (2, 1.3833458069711924e-05), (3, 1.4055321116757113e-05), (4, 1.3875684999220539e-05), (5, 1.826144944061525e-05), (6, 1.760348277457524e-05), (7, 5.911258631385863e-05), (8, 3.007375016750302e-05), (9, 3.579455733415671e-05), (10, 7.133802137104794e-05), (11, 7.646727317478508e-05), (12, 6.882995512569323e-05), (13, 7.182767876656726e-05), (14, 0.0002595437108539045), (15, 0.0006668344722129405), (16, 0.0006494440021924675), (17, 0.0006806309102103114), (18, 0.0003749497118405998), (19, 0.0012199671473354101), (20, 0.0028366846963763237), (21, 0.001109774922952056), (22, 0.0009198836633004248), (23, 0.0008319175685755908), (24, 0.0007375697605311871), (25, 0.0007241280400194228), (26, 0.001117958570830524), (27, 0.0010280871065333486)] + + 40%|████ | 12/30 [02:35<03:50, 12.80s/it]Time step 880, Spatial MSE: [(0, 0.0001565396087244153), (1, 0.0004383042687550187), (2, 0.0010288938647136092), (3, 0.0013283160515129566), (4, 0.0015410494524985552), (5, 0.002939558820798993), (6, 0.006559786852449179), (7, 0.013643840327858925), (8, 0.019947422668337822), (9, 0.029497215524315834), (10, 0.06092239171266556), (11, 0.04634528607130051), (12, 0.0423140823841095), (13, 0.04301172494888306), (14, 0.10755492001771927), (15, 0.10528909415006638), (16, 0.12610721588134766), (17, 0.105226069688797), (18, 0.11548751592636108), (19, 0.09860125929117203), (20, 0.08985016494989395), (21, 0.109283447265625), (22, 0.10421805083751678), (23, 0.11107815057039261), (24, 0.12677446007728577), (25, 0.14348818361759186), (26, 0.09171685576438904), (27, 0.035610221326351166)], Temporal MSE: [(0, 3.4129964205931174e-06), (1, 2.6403267838759348e-05), (2, 0.0005167841445654631), (3, 0.0002953198563773185), (4, 0.0007880820776335895), (5, 0.0010375302517786622), (6, 0.002322909887880087), (7, 0.0072181387804448605), (8, 0.004693426191806793), (9, 0.004820769652724266), (10, 0.009817460551857948), (11, 0.0082520991563797), (12, 0.006198963616043329), (13, 0.00824382621794939), (14, 0.025844065472483635), (15, 0.023077165707945824), (16, 0.02906443178653717), (17, 0.02679441124200821), (18, 0.02478397823870182), (19, 0.027072835713624954), (20, 0.05012089014053345), (21, 0.043510209769010544), (22, 0.0406271331012249), (23, 0.04704733565449715), (24, 0.02452198788523674), (25, 0.012475733645260334), (26, 0.01619626022875309), (27, 0.014487802051007748)] + + 43%|████▎ | 13/30 [02:50<03:48, 13.46s/it]Time step 864, Spatial MSE: [(0, 2.1253097656881437e-05), (1, 0.0004509810241870582), (2, 0.0006706462590955198), (3, 0.0001910636929096654), (4, 5.8955003623850644e-05), (5, 8.922728738980368e-05), (6, 0.00013940807548351586), (7, 0.000306577974697575), (8, 0.0001272544905077666), (9, 0.00021790602477267385), (10, 0.0002997590054292232), (11, 0.0002331809519091621), (12, 0.00028539905906654894), (13, 0.0007960890070535243), (14, 0.0009494886035099626), (15, 0.001088725053705275), (16, 0.012812197208404541), (17, 0.0023349567782133818), (18, 0.00262839556671679), (19, 0.004491232801228762), (20, 0.0034521848428994417), (21, 0.01177708338946104), (22, 0.011136227287352085), (23, 0.005021822173148394), (24, 0.007523537613451481), (25, 0.013837520964443684), (26, 0.013823186047375202), (27, 0.013094465248286724)], Temporal MSE: [(0, 5.85074587888812e-07), (1, 2.79014830084634e-06), (2, 9.691505511000287e-06), (3, 1.0694362572394311e-05), (4, 1.0602625479805283e-05), (5, 1.5236108083627187e-05), (6, 1.641056951484643e-05), (7, 3.44781692547258e-05), (8, 2.130493703589309e-05), (9, 2.8190204830025323e-05), (10, 5.309881817083806e-05), (11, 5.093762592878193e-05), (12, 4.8210215027211234e-05), (13, 5.69220537727233e-05), (14, 0.00018976445426233113), (15, 0.00038246490294113755), (16, 0.0008302379865199327), (17, 0.0005874696071259677), (18, 0.00023357986356131732), (19, 0.0016056963941082358), (20, 0.00392140494659543), (21, 0.00332340644672513), (22, 0.0036402822006493807), (23, 0.0008880640380084515), (24, 0.0007989564328454435), (25, 0.0006922585307620466), (26, 0.0009500714368186891), (27, 0.0017826681723818183)] + + 47%|████▋ | 14/30 [03:02<03:32, 13.27s/it]Time step 848, Spatial MSE: [(0, 0.00017113560170400888), (1, 0.0003777665551751852), (2, 0.000611045747064054), (3, 0.00026007829001173377), (4, 0.0003515677235554904), (5, 0.000936049094889313), (6, 0.0013610507594421506), (7, 0.00271411775611341), (8, 0.0014726497465744615), (9, 0.0022885138168931007), (10, 0.0075419810600578785), (11, 0.0051611303351819515), (12, 0.0041068256832659245), (13, 0.006730644032359123), (14, 0.018491236492991447), (15, 0.00918427761644125), (16, 0.02304554171860218), (17, 0.010018658824265003), (18, 0.010798759758472443), (19, 0.007549011614173651), (20, 0.01036981400102377), (21, 0.013733034022152424), (22, 0.010510589927434921), (23, 0.009913116693496704), (24, 0.02256060764193535), (25, 0.03866446763277054), (26, 0.019281556829810143), (27, 0.010074338875710964)], Temporal MSE: [(0, 2.579607325969846e-06), (1, 4.896719019598095e-06), (2, 1.850565240602009e-05), (3, 4.823458948521875e-05), (4, 6.788596510887146e-05), (5, 9.026257612276822e-05), (6, 0.00017863856919575483), (7, 0.0007853050483390689), (8, 0.0004914527526125312), (9, 0.0006033811951056123), (10, 0.0014630402438342571), (11, 0.0009929442312568426), (12, 0.0010876705637201667), (13, 0.0007078839698806405), (14, 0.003197432728484273), (15, 0.004735473543405533), (16, 0.005333151668310165), (17, 0.003768413793295622), (18, 0.0023877988569438457), (19, 0.003006890881806612), (20, 0.007924558594822884), (21, 0.004285102244466543), (22, 0.0033695686142891645), (23, 0.004176243674010038), (24, 0.003218691097572446), (25, 0.002489968203008175), (26, 0.003998082131147385), (27, 0.0031125491950660944)] + + 50%|█████ | 15/30 [03:15<03:14, 12.97s/it]Time step 828, Spatial MSE: [(0, 2.0768125978065655e-05), (1, 0.0004684971063397825), (2, 0.0008269042591564357), (3, 0.00021666144311893731), (4, 4.878716936218552e-05), (5, 8.766615792410448e-05), (6, 8.712094859220088e-05), (7, 0.00030313554452732205), (8, 0.00013233757636044174), (9, 0.00021844296134077013), (10, 0.00021372217452153563), (11, 0.00019262993009760976), (12, 0.00031742561259306967), (13, 0.000685334496665746), (14, 0.0008486410370096564), (15, 0.0008501310367137194), (16, 0.012655056081712246), (17, 0.002193685621023178), (18, 0.002470605308189988), (19, 0.0022541095968335867), (20, 0.0016490845009684563), (21, 0.0033304484095424414), (22, 0.002262155059725046), (23, 0.0038744534831494093), (24, 0.006712699308991432), (25, 0.01417556218802929), (26, 0.01380118727684021), (27, 0.01735474355518818)], Temporal MSE: [(0, 5.410924472926126e-07), (1, 1.8230061868962366e-06), (2, 1.0023208233178593e-05), (3, 1.0213394489255734e-05), (4, 1.1067387276852969e-05), (5, 1.961219822987914e-05), (6, 1.731942393234931e-05), (7, 4.0963881474453956e-05), (8, 2.6990097467205487e-05), (9, 2.9983671993250027e-05), (10, 4.812782208318822e-05), (11, 4.5729466364718974e-05), (12, 6.264648254727945e-05), (13, 5.6013021094258875e-05), (14, 0.00018032619846053421), (15, 0.0002907713642343879), (16, 0.0008071522461250424), (17, 0.0004837704764213413), (18, 0.0002430728345643729), (19, 0.0010923242662101984), (20, 0.0024590056855231524), (21, 0.001119504333473742), (22, 0.0008426139829680324), (23, 0.0009170311386696994), (24, 0.0007523293024860322), (25, 0.0007326326449401677), (26, 0.0008318649488501251), (27, 0.0016341014998033643)] + + 53%|█████▎ | 16/30 [03:26<02:54, 12.49s/it]Time step 808, Spatial MSE: [(0, 0.0001313002430833876), (1, 0.0007753408281132579), (2, 0.0009951923275366426), (3, 0.0010990797309204936), (4, 0.0013706553727388382), (5, 0.0023668543435633183), (6, 0.005181950516998768), (7, 0.011468369513750076), (8, 0.012089247815310955), (9, 0.019939806312322617), (10, 0.02391577884554863), (11, 0.0244075208902359), (12, 0.024241123348474503), (13, 0.026893485337495804), (14, 0.06506456434726715), (15, 0.05987035855650902), (16, 0.0816321149468422), (17, 0.07080021500587463), (18, 0.07576829940080643), (19, 0.0615769661962986), (20, 0.0616426095366478), (21, 0.07588231563568115), (22, 0.0723707526922226), (23, 0.08498157560825348), (24, 0.10147371143102646), (25, 0.12545621395111084), (26, 0.10565073788166046), (27, 0.025943078100681305)], Temporal MSE: [(0, 3.4809363569365814e-06), (1, 2.023650085902773e-05), (2, 0.00019268039613962173), (3, 0.00015279327635653317), (4, 0.0006598342442885041), (5, 0.0006747707375325263), (6, 0.0017167661571875215), (7, 0.004244884941726923), (8, 0.0027651789132505655), (9, 0.002558621810749173), (10, 0.004168764688074589), (11, 0.005007236264646053), (12, 0.004036130383610725), (13, 0.0059482925571501255), (14, 0.013143647462129593), (15, 0.014659900218248367), (16, 0.01843615621328354), (17, 0.019507890567183495), (18, 0.018453117460012436), (19, 0.02200586162507534), (20, 0.044608283787965775), (21, 0.03203544765710831), (22, 0.03188671916723251), (23, 0.033351585268974304), (24, 0.019846027716994286), (25, 0.010180240496993065), (26, 0.012179628014564514), (27, 0.015378889627754688)] + + 57%|█████▋ | 17/30 [03:39<02:42, 12.48s/it]Time step 788, Spatial MSE: [(0, 1.9996856281068176e-05), (1, 0.0004174018686171621), (2, 0.0009710431331768632), (3, 0.0002426371502224356), (4, 5.1379796786932275e-05), (5, 8.192604582291096e-05), (6, 8.28506745165214e-05), (7, 0.00048047243035398424), (8, 0.00016842529294081032), (9, 0.00018134461424779147), (10, 0.00019793979299720377), (11, 0.00021615333389490843), (12, 0.0002790499711409211), (13, 0.0006541052716784179), (14, 0.0007757642888464034), (15, 0.0008155793184414506), (16, 0.00962698645889759), (17, 0.0017816451145336032), (18, 0.008240523748099804), (19, 0.002674764720723033), (20, 0.0016076762694865465), (21, 0.002734120236709714), (22, 0.002384481020271778), (23, 0.0036700027994811535), (24, 0.006473197136074305), (25, 0.016536463052034378), (26, 0.014746373519301414), (27, 0.010785500518977642)], Temporal MSE: [(0, 6.095596631894296e-07), (1, 1.7775822698240518e-06), (2, 1.0030090379586909e-05), (3, 9.421170034329407e-06), (4, 1.1032554539269768e-05), (5, 1.8726985217654146e-05), (6, 2.067418427031953e-05), (7, 3.922918040188961e-05), (8, 2.487794336047955e-05), (9, 3.689961522468366e-05), (10, 5.29991157236509e-05), (11, 4.8925703595159575e-05), (12, 4.40092699136585e-05), (13, 6.0844373365398496e-05), (14, 0.0001686570030869916), (15, 0.00024069730716291815), (16, 0.0006050564697943628), (17, 0.00034417022834531963), (18, 0.0016561088850721717), (19, 0.0021060998551547527), (20, 0.0044553023763000965), (21, 0.0015133452834561467), (22, 0.0007238549296744168), (23, 0.0007844484644010663), (24, 0.00072258006548509), (25, 0.0009173303842544556), (26, 0.0008268046658486128), (27, 0.001270268694497645)] + + 60%|██████ | 18/30 [03:49<02:23, 11.95s/it]Time step 764, Spatial MSE: [(0, 0.0001739939907565713), (1, 0.0005653250264003873), (2, 0.0006500092567875981), (3, 0.00024405041767749935), (4, 0.0002481791016180068), (5, 0.0006369146285578609), (6, 0.0010216127848252654), (7, 0.0020966825541108847), (8, 0.0012228336418047547), (9, 0.0019496919121593237), (10, 0.006415071431547403), (11, 0.002732878550887108), (12, 0.002936282427981496), (13, 0.005146042909473181), (14, 0.014319978654384613), (15, 0.009339949116110802), (16, 0.01947435550391674), (17, 0.010968352667987347), (18, 0.00944012776017189), (19, 0.007470014039427042), (20, 0.006638163235038519), (21, 0.013845189474523067), (22, 0.009651326574385166), (23, 0.015227344818413258), (24, 0.025558695197105408), (25, 0.0517430417239666), (26, 0.025806942954659462), (27, 0.010244214907288551)], Temporal MSE: [(0, 2.8239571747690206e-06), (1, 5.298953510646243e-06), (2, 1.8808632376021706e-05), (3, 3.352563362568617e-05), (4, 5.018154843128286e-05), (5, 4.992877438780852e-05), (6, 0.00013047349057160318), (7, 0.00044853397412225604), (8, 0.00036019456456415355), (9, 0.0006847358890809119), (10, 0.0012587911915034056), (11, 0.0008726285886950791), (12, 0.0010623912094160914), (13, 0.0008349120034836233), (14, 0.0035863330122083426), (15, 0.004406739957630634), (16, 0.004771044012159109), (17, 0.0035079100634902716), (18, 0.0021481977310031652), (19, 0.0029709753580391407), (20, 0.005583662539720535), (21, 0.0050081596709787846), (22, 0.0037304596044123173), (23, 0.004166325554251671), (24, 0.006380094680935144), (25, 0.0017733604181557894), (26, 0.004458120558410883), (27, 0.003936386667191982)] + + 63%|██████▎ | 19/30 [04:01<02:09, 11.82s/it]Time step 736, Spatial MSE: [(0, 3.051040584978182e-05), (1, 0.0006828628829680383), (2, 0.0013059234479442239), (3, 0.0003826994798146188), (4, 7.307783380383626e-05), (5, 0.00011109431216027588), (6, 0.00012714217882603407), (7, 0.0003822876897174865), (8, 0.00015498620632570237), (9, 0.0002932060742750764), (10, 0.00023028776922728866), (11, 0.00025902711786329746), (12, 0.0003665829135570675), (13, 0.0013445507502183318), (14, 0.001660036388784647), (15, 0.0015610693953931332), (16, 0.027822380885481834), (17, 0.00535346707329154), (18, 0.005048776976764202), (19, 0.002935066120699048), (20, 0.0024998860899358988), (21, 0.005627908743917942), (22, 0.003132651327177882), (23, 0.005776533391326666), (24, 0.00880763866007328), (25, 0.02313026413321495), (26, 0.021273111924529076), (27, 0.011191981844604015)], Temporal MSE: [(0, 8.631332661934721e-07), (1, 2.4697558274056064e-06), (2, 1.6058780602179468e-05), (3, 1.2987357877136674e-05), (4, 1.691110446699895e-05), (5, 2.208548175985925e-05), (6, 3.062097675865516e-05), (7, 5.207387948757969e-05), (8, 3.5937238862970844e-05), (9, 4.962996536050923e-05), (10, 6.711410969728604e-05), (11, 6.138189928606153e-05), (12, 6.093388583394699e-05), (13, 9.568176028551534e-05), (14, 0.0002765251265373081), (15, 0.0006256261840462685), (16, 0.0011422355892136693), (17, 0.0010410486720502377), (18, 0.000499480520375073), (19, 0.0015471010701730847), (20, 0.004914985969662666), (21, 0.0020136258099228144), (22, 0.001007667277008295), (23, 0.0010949020506814122), (24, 0.0010681032435968518), (25, 0.0015015698736533523), (26, 0.001200889004394412), (27, 0.002031149575486779)] + + 67%|██████▋ | 20/30 [04:12<01:55, 11.57s/it]Time step 708, Spatial MSE: [(0, 8.947241440182552e-05), (1, 0.0014442092506214976), (2, 0.0016536642797291279), (3, 0.0012749363668262959), (4, 0.0013861419865861535), (5, 0.003656977554783225), (6, 0.004278410691767931), (7, 0.008878720924258232), (8, 0.010859200730919838), (9, 0.01936345361173153), (10, 0.021565988659858704), (11, 0.020473724231123924), (12, 0.02212996408343315), (13, 0.02470269799232483), (14, 0.05475478619337082), (15, 0.08255961537361145), (16, 0.09678848832845688), (17, 0.08712811768054962), (18, 0.09137756377458572), (19, 0.08130092173814774), (20, 0.08882882446050644), (21, 0.12055446952581406), (22, 0.10286549478769302), (23, 0.1133175939321518), (24, 0.1256517767906189), (25, 0.17337289452552795), (26, 0.15135088562965393), (27, 0.025682909414172173)], Temporal MSE: [(0, 5.519706064660568e-06), (1, 2.0226350898155943e-05), (2, 0.00011726695811375976), (3, 0.00019328310736455023), (4, 0.0007062568911351264), (5, 0.0010233721695840359), (6, 0.0016671051271259785), (7, 0.0034161247313022614), (8, 0.0022517696488648653), (9, 0.002702794736251235), (10, 0.0043049887754023075), (11, 0.005053940694779158), (12, 0.004272962920367718), (13, 0.005766340997070074), (14, 0.017053278163075447), (15, 0.018507450819015503), (16, 0.023474078625440598), (17, 0.025985684245824814), (18, 0.02627132087945938), (19, 0.031505439430475235), (20, 0.07690846920013428), (21, 0.049760352820158005), (22, 0.0385311059653759), (23, 0.0330667644739151), (24, 0.027942325919866562), (25, 0.017935816198587418), (26, 0.023853113874793053), (27, 0.02849547564983368)] + + 70%|███████ | 21/30 [04:24<01:47, 11.91s/it]Time step 676, Spatial MSE: [(0, 3.398603439563885e-05), (1, 0.0009418388362973928), (2, 0.0016788395587354898), (3, 0.0005214047851040959), (4, 9.509054507361725e-05), (5, 0.00014514852955471724), (6, 0.0001562717225169763), (7, 0.00039523010491393507), (8, 0.00018550772801972926), (9, 0.00022087340767029673), (10, 0.0002591387019492686), (11, 0.00031329988269135356), (12, 0.0003644375246949494), (13, 0.0015917276032269), (14, 0.001987458672374487), (15, 0.0018048147903755307), (16, 0.029330555349588394), (17, 0.005563876125961542), (18, 0.004907253198325634), (19, 0.002416034694761038), (20, 0.001782983192242682), (21, 0.0038816812448203564), (22, 0.0032934790942817926), (23, 0.006269518751651049), (24, 0.00858910009264946), (25, 0.030752815306186676), (26, 0.026852816343307495), (27, 0.013719729147851467)], Temporal MSE: [(0, 1.1196406148883398e-06), (1, 3.2727948564570397e-06), (2, 1.7604030290385708e-05), (3, 1.6166410205187276e-05), (4, 2.1656011085724458e-05), (5, 3.349818507558666e-05), (6, 4.462359720491804e-05), (7, 7.05463025951758e-05), (8, 4.5111119106877595e-05), (9, 6.861510337330401e-05), (10, 9.780010441318154e-05), (11, 7.702975563006476e-05), (12, 6.435053364839405e-05), (13, 0.0001129509910242632), (14, 0.00040910992538556457), (15, 0.0005931564955972135), (16, 0.0013831114629283547), (17, 0.0009678892092779279), (18, 0.00038275509723462164), (19, 0.0014548269100487232), (20, 0.0037984633818268776), (21, 0.001372152823023498), (22, 0.0010510471183806658), (23, 0.00130612647626549), (24, 0.0012207975378260016), (25, 0.002398915123194456), (26, 0.0013950763968750834), (27, 0.0027708872221410275)] + + 73%|███████▎ | 22/30 [04:35<01:32, 11.58s/it]Time step 636, Spatial MSE: [(0, 0.00010581436072243378), (1, 0.0010746546322479844), (2, 0.001348278601653874), (3, 0.00042596831917762756), (4, 0.0003612462605815381), (5, 0.0008891221368685365), (6, 0.001278855255804956), (7, 0.004088866990059614), (8, 0.002318138489499688), (9, 0.002742898417636752), (10, 0.012293591164052486), (11, 0.00587344029918313), (12, 0.0048633781261742115), (13, 0.008814406581223011), (14, 0.015482199378311634), (15, 0.014058682136237621), (16, 0.03841095045208931), (17, 0.016362883150577545), (18, 0.017609847709536552), (19, 0.014142442494630814), (20, 0.012421335093677044), (21, 0.02545698918402195), (22, 0.015502517111599445), (23, 0.02643137238919735), (24, 0.05164335295557976), (25, 0.13007506728172302), (26, 0.06535496562719345), (27, 0.03176114335656166)], Temporal MSE: [(0, 3.030781726920395e-06), (1, 7.433970040437998e-06), (2, 2.840403249138035e-05), (3, 5.5830587371019647e-05), (4, 0.0001536029885755852), (5, 0.00010337927233194932), (6, 0.00042081461288034916), (7, 0.0009984427597373724), (8, 0.0009532331023365259), (9, 0.001290150685235858), (10, 0.0037916461005806923), (11, 0.0018536103889346123), (12, 0.002190419239923358), (13, 0.001283389632590115), (14, 0.004317632410675287), (15, 0.006815032102167606), (16, 0.00803695060312748), (17, 0.006663689389824867), (18, 0.00494504114612937), (19, 0.00581274414435029), (20, 0.006610107142478228), (21, 0.009485944174230099), (22, 0.0054342192597687244), (23, 0.005883398465812206), (24, 0.007714760489761829), (25, 0.0042768181301653385), (26, 0.008761639706790447), (27, 0.007847518660128117)] + + 77%|███████▋ | 23/30 [04:47<01:21, 11.58s/it]Time step 596, Spatial MSE: [(0, 4.532820821623318e-05), (1, 0.0012697149068117142), (2, 0.0026726489886641502), (3, 0.0008644910412840545), (4, 0.00014471853501163423), (5, 0.0002451776817906648), (6, 0.00025704895961098373), (7, 0.0004658098623622209), (8, 0.0003028618230018765), (9, 0.0003328206657897681), (10, 0.00040391794755123556), (11, 0.00043149286648258567), (12, 0.0005331034772098064), (13, 0.002368920249864459), (14, 0.002873383229598403), (15, 0.0022860837634652853), (16, 0.034925639629364014), (17, 0.0066292607225477695), (18, 0.006255300249904394), (19, 0.005422188900411129), (20, 0.005002870224416256), (21, 0.015467767603695393), (22, 0.013875368051230907), (23, 0.014592807739973068), (24, 0.015152509324252605), (25, 0.053807228803634644), (26, 0.04890967905521393), (27, 0.02486397698521614)], Temporal MSE: [(0, 2.4516411940567195e-06), (1, 5.4632159844913986e-06), (2, 2.804043651849497e-05), (3, 2.4359036615351215e-05), (4, 3.8862868677824736e-05), (5, 5.1212977268733084e-05), (6, 7.978030043886974e-05), (7, 0.00011493574129417539), (8, 8.035930659389123e-05), (9, 0.00011468896991573274), (10, 0.00013909385597798973), (11, 0.00011380104842828587), (12, 0.00010954460594803095), (13, 0.0002001309476327151), (14, 0.0004150574095547199), (15, 0.0007404027273878455), (16, 0.001805810141377151), (17, 0.0014183998573571444), (18, 0.0007339560543186963), (19, 0.0026246372144669294), (20, 0.009386817924678326), (21, 0.0048860423266887665), (22, 0.0029657927807420492), (23, 0.002149159088730812), (24, 0.002111193025484681), (25, 0.004166485741734505), (26, 0.002345779910683632), (27, 0.0062392232939600945)] + + 80%|████████ | 24/30 [04:58<01:08, 11.41s/it]Time step 548, Spatial MSE: [(0, 0.00013728807971347123), (1, 0.0034570051357150078), (2, 0.004381751641631126), (3, 0.0021973643451929092), (4, 0.0022276174277067184), (5, 0.006139347329735756), (6, 0.006999131757766008), (7, 0.012428359128534794), (8, 0.014475786127150059), (9, 0.019120879471302032), (10, 0.026021679863333702), (11, 0.021926041692495346), (12, 0.025191225111484528), (13, 0.02840343676507473), (14, 0.05875236541032791), (15, 0.08263640850782394), (16, 0.10584316402673721), (17, 0.08259575068950653), (18, 0.10062895715236664), (19, 0.07674916833639145), (20, 0.08904480934143066), (21, 0.13438646495342255), (22, 0.1357794553041458), (23, 0.15761342644691467), (24, 0.21509681642055511), (25, 0.4206281900405884), (26, 0.3684559166431427), (27, 0.09144807606935501)], Temporal MSE: [(0, 1.2098555089323781e-05), (1, 3.545716390362941e-05), (2, 0.00021494664542842656), (3, 0.0003508132940623909), (4, 0.0009529016679152846), (5, 0.0017854513134807348), (6, 0.0028814144898205996), (7, 0.004224872216582298), (8, 0.003218242432922125), (9, 0.004630588926374912), (10, 0.006141536869108677), (11, 0.006099562626332045), (12, 0.006503389682620764), (13, 0.007797462400048971), (14, 0.019469639286398888), (15, 0.02213555946946144), (16, 0.027572467923164368), (17, 0.02839605323970318), (18, 0.027166126295924187), (19, 0.032851316034793854), (20, 0.08352890610694885), (21, 0.0707312673330307), (22, 0.04326946660876274), (23, 0.04913196340203285), (24, 0.07363944500684738), (25, 0.038216665387153625), (26, 0.044808026403188705), (27, 0.07945781946182251)] + + 83%|████████▎ | 25/30 [05:12<01:00, 12.13s/it]Time step 492, Spatial MSE: [(0, 8.113567309919745e-05), (1, 0.00255853240378201), (2, 0.0042618499137461185), (3, 0.001644059899263084), (4, 0.00025920916232280433), (5, 0.0005421401001513004), (6, 0.0005596745177172124), (7, 0.001248164800927043), (8, 0.000731190200895071), (9, 0.0005315342568792403), (10, 0.0006668765563517809), (11, 0.0007810075767338276), (12, 0.0012203881051391363), (13, 0.002941736252978444), (14, 0.0035038066562265158), (15, 0.0025722747668623924), (16, 0.04408378154039383), (17, 0.007283768150955439), (18, 0.018267951905727386), (19, 0.005971826612949371), (20, 0.007549780886620283), (21, 0.02306612953543663), (22, 0.012064807116985321), (23, 0.02166447788476944), (24, 0.029864491894841194), (25, 0.1139032393693924), (26, 0.10925944149494171), (27, 0.044018395245075226)], Temporal MSE: [(0, 3.94315020457725e-06), (1, 9.63428283284884e-06), (2, 5.371234510676004e-05), (3, 4.9649774155113846e-05), (4, 8.526949386578053e-05), (5, 0.00012013500963803381), (6, 0.0002013923804042861), (7, 0.0003145684895571321), (8, 0.00016479214536957443), (9, 0.0002406966086709872), (10, 0.00028097149333916605), (11, 0.00021083789761178195), (12, 0.00020930419850628823), (13, 0.00038883567322045565), (14, 0.000633863965049386), (15, 0.0009607013780623674), (16, 0.0021348653826862574), (17, 0.0018638077890500426), (18, 0.002831835299730301), (19, 0.005988954566419125), (20, 0.02087218686938286), (21, 0.00821774359792471), (22, 0.0029924476984888315), (23, 0.0036574460100382566), (24, 0.004947854671627283), (25, 0.009124444797635078), (26, 0.005246873013675213), (27, 0.01478618010878563)] + + 87%|████████▋ | 26/30 [05:23<00:47, 11.82s/it]Time step 426, Spatial MSE: [(0, 0.0003876523405779153), (1, 0.005344975274056196), (2, 0.005945901852101088), (3, 0.002249107463285327), (4, 0.0017296592704951763), (5, 0.0030563983600586653), (6, 0.005927606951445341), (7, 0.009417667053639889), (8, 0.009751967154443264), (9, 0.011639303527772427), (10, 0.019708232954144478), (11, 0.014637772925198078), (12, 0.01706935092806816), (13, 0.017143376171588898), (14, 0.035237010568380356), (15, 0.04483021795749664), (16, 0.07286158204078674), (17, 0.045884765684604645), (18, 0.05783654376864433), (19, 0.05086248368024826), (20, 0.058750223368406296), (21, 0.1085190623998642), (22, 0.10265008360147476), (23, 0.1675163358449936), (24, 0.2711549401283264), (25, 0.6334402561187744), (26, 0.5022031664848328), (27, 0.16474348306655884)], Temporal MSE: [(0, 1.6588030121056363e-05), (1, 3.972842387156561e-05), (2, 0.000220050715142861), (3, 0.00033533255918882787), (4, 0.0006761503173038363), (5, 0.0009588069515302777), (6, 0.0028602806851267815), (7, 0.003618288552388549), (8, 0.0019045354565605521), (9, 0.0036367871798574924), (10, 0.004268106073141098), (11, 0.0036924616433680058), (12, 0.0034717863891273737), (13, 0.0045830486342310905), (14, 0.01278496254235506), (15, 0.014777989126741886), (16, 0.01905887946486473), (17, 0.01641751639544964), (18, 0.016157489269971848), (19, 0.021492280066013336), (20, 0.03836370259523392), (21, 0.057860132306814194), (22, 0.03508758172392845), (23, 0.03660421445965767), (24, 0.08140912652015686), (25, 0.02951396442949772), (26, 0.07016880065202713), (27, 0.10540783405303955)] + + 90%|█████████ | 27/30 [05:35<00:35, 11.86s/it]Time step 350, Spatial MSE: [(0, 0.00019871034601237625), (1, 0.0070135025307536125), (2, 0.00771060585975647), (3, 0.002313601318746805), (4, 0.0009564069914631546), (5, 0.0022267932072281837), (6, 0.0032613237854093313), (7, 0.00605567405000329), (8, 0.0052472250536084175), (9, 0.005127204582095146), (10, 0.010227181017398834), (11, 0.007311658468097448), (12, 0.009771804325282574), (13, 0.008744411170482635), (14, 0.01742197759449482), (15, 0.02076227404177189), (16, 0.025425804778933525), (17, 0.02074321173131466), (18, 0.024773839861154556), (19, 0.03627246618270874), (20, 0.04234285280108452), (21, 0.08232623338699341), (22, 0.099664106965065), (23, 0.13940010964870453), (24, 0.2141021490097046), (25, 0.4569512605667114), (26, 0.4134604036808014), (27, 0.22672314941883087)], Temporal MSE: [(0, 1.3914655028202105e-05), (1, 4.3324620492057875e-05), (2, 0.00017592091171536595), (3, 0.0002820659428834915), (4, 0.0005348958075046539), (5, 0.0007085184333845973), (6, 0.002215431071817875), (7, 0.0025531838182359934), (8, 0.0012827381724491715), (9, 0.002573395147919655), (10, 0.0029354251455515623), (11, 0.002121780999004841), (12, 0.0021294825710356236), (13, 0.002701766090467572), (14, 0.006806845776736736), (15, 0.006269821431487799), (16, 0.008632400073111057), (17, 0.00857488252222538), (18, 0.007867248728871346), (19, 0.01267306413501501), (20, 0.040209993720054626), (21, 0.05439985916018486), (22, 0.024917403236031532), (23, 0.02948216162621975), (24, 0.08077776432037354), (25, 0.024985404685139656), (26, 0.0590444914996624), (27, 0.11138077080249786)] + + 93%|█████████▎| 28/30 [05:48<00:24, 12.23s/it]Time step 258, Spatial MSE: [(0, 0.00039188945083878934), (1, 0.011899636127054691), (2, 0.012392991222441196), (3, 0.0043101548217237), (4, 0.0017210636287927628), (5, 0.004150171298533678), (6, 0.0064783296547830105), (7, 0.01449836790561676), (8, 0.01061449944972992), (9, 0.009617363102734089), (10, 0.02018958143889904), (11, 0.013235235586762428), (12, 0.01923728920519352), (13, 0.01600763574242592), (14, 0.032172735780477524), (15, 0.03868183493614197), (16, 0.04703903943300247), (17, 0.03551596775650978), (18, 0.050682272762060165), (19, 0.054921701550483704), (20, 0.07226093858480453), (21, 0.1614818572998047), (22, 0.16675850749015808), (23, 0.2735297679901123), (24, 0.4537988305091858), (25, 1.0557820796966553), (26, 0.8891826868057251), (27, 0.4954376816749573)], Temporal MSE: [(0, 3.3296011679340154e-05), (1, 8.676163270138204e-05), (2, 0.0003381789429113269), (3, 0.0005134356906637549), (4, 0.001046127756126225), (5, 0.0015677555929869413), (6, 0.004459516145288944), (7, 0.005949531216174364), (8, 0.0027791571337729692), (9, 0.004923786967992783), (10, 0.005803010892122984), (11, 0.004456839989870787), (12, 0.00411896500736475), (13, 0.004752294160425663), (14, 0.011521080508828163), (15, 0.011594102717936039), (16, 0.01372089609503746), (17, 0.013760014437139034), (18, 0.013914760202169418), (19, 0.024110592901706696), (20, 0.06254348158836365), (21, 0.10929884761571884), (22, 0.04981252923607826), (23, 0.05866857245564461), (24, 0.1814156025648117), (25, 0.06894156336784363), (26, 0.14540983736515045), (27, 0.23570774495601654)] + + 97%|█████████▋| 29/30 [06:01<00:12, 12.56s/it]Time step 143, Spatial MSE: [(0, 0.0008617169805802405), (1, 0.023438412696123123), (2, 0.023562854155898094), (3, 0.00886488240212202), (4, 0.003879025112837553), (5, 0.009579367004334927), (6, 0.014395623467862606), (7, 0.03449651971459389), (8, 0.023026417940855026), (9, 0.020763475447893143), (10, 0.039708688855171204), (11, 0.031206533312797546), (12, 0.043770089745521545), (13, 0.03170138597488403), (14, 0.07529879361391068), (15, 0.07608652859926224), (16, 0.0806422084569931), (17, 0.06656362861394882), (18, 0.09458597004413605), (19, 0.11650088429450989), (20, 0.17913199961185455), (21, 0.3029972016811371), (22, 0.3384348154067993), (23, 0.5880626440048218), (24, 1.2596023082733154), (25, 2.668867588043213), (26, 2.0173895359039307), (27, 1.1363914012908936)], Temporal MSE: [(0, 8.939651161199436e-05), (1, 0.00020733063865918666), (2, 0.0007518675993196666), (3, 0.0010208627209067345), (4, 0.0027272128500044346), (5, 0.0037651262246072292), (6, 0.011691391468048096), (7, 0.012575628235936165), (8, 0.005996612831950188), (9, 0.007503945380449295), (10, 0.010732823051512241), (11, 0.008282795548439026), (12, 0.0072469995357096195), (13, 0.0079015688970685), (14, 0.02048412710428238), (15, 0.022296415641903877), (16, 0.025562744587659836), (17, 0.02889304794371128), (18, 0.0273185633122921), (19, 0.04908120259642601), (20, 0.1802283525466919), (21, 0.2679189145565033), (22, 0.08796325325965881), (23, 0.13426680862903595), (24, 0.43247997760772705), (25, 0.290322870016098), (26, 0.34836429357528687), (27, 0.4321860373020172)] + +100%|██████████| 30/30 [06:14<00:00, 12.57s/it] +100%|██████████| 30/30 [06:14<00:00, 12.48s/it] +/home/jin509/anaconda3/envs/opendit/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.) + return F.conv2d(input, weight, bias, self.stride, +[2024-10-01 14:58:51] Prompt: sunset over the sea. aesthetic score: 6.5. +Saved to ./samples/opensora/sample_pab/Sunset over the sea..mp4 +Saved video to ./samples/opensora/sample_pab/Sunset over the sea..mp4 +Saved spatial MLP MSE outputs to ./samples/opensora/sample_pab/Sunset over the sea._spatial_mlp_mse.pt +Saved temporal MLP MSE outputs to ./samples/opensora/sample_pab/Sunset over the sea._temporal_mlp_mse.pt +timesteps: [tensor([1000.], device='cuda:0'), tensor([992.9313], device='cuda:0'), tensor([985.4678], device='cuda:0'), tensor([977.5753], device='cuda:0'), tensor([969.2160], device='cuda:0'), tensor([960.3470], device='cuda:0'), tensor([950.9203], device='cuda:0'), tensor([940.8815], device='cuda:0'), tensor([930.1691], device='cuda:0'), tensor([918.7130], device='cuda:0'), tensor([906.4327], device='cuda:0'), tensor([893.2362], device='cuda:0'), tensor([879.0170], device='cuda:0'), tensor([863.6513], device='cuda:0'), tensor([846.9945], device='cuda:0'), tensor([828.8770], device='cuda:0'), tensor([809.0977], device='cuda:0'), tensor([787.4170], device='cuda:0'), tensor([763.5468], device='cuda:0'), tensor([737.1379], device='cuda:0'), tensor([707.7626], device='cuda:0'), tensor([674.8911], device='cuda:0'), tensor([637.8601], device='cuda:0'), tensor([595.8265], device='cuda:0'), tensor([547.7032], device='cuda:0'), tensor([492.0635], device='cuda:0'), tensor([426.9973], device='cuda:0'), tensor([349.8871], device='cuda:0'), tensor([257.0481], device='cuda:0'), tensor([143.1210], device='cuda:0')] + + 0%| | 0/30 [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin +from diffusers.models.attention import FeedForward +from diffusers.models.attention_processor import ( + Attention, + AttentionProcessor, + FluxAttnProcessor2_0, + FusedFluxAttnProcessor2_0, +) +from diffusers.models.embeddings import ( + CombinedTimestepGuidanceTextProjEmbeddings, + CombinedTimestepTextProjEmbeddings, + FluxPosEmbed, +) +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle +from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers +from diffusers.utils.torch_utils import maybe_allow_in_graph + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@maybe_allow_in_graph +class FluxSingleTransformerBlock(nn.Module): + r""" + A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3. + + Reference: https://arxiv.org/abs/2403.03206 + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the + processing of `context` conditions. + """ + + def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0): + super().__init__() + self.mlp_hidden_dim = int(dim * mlp_ratio) + + self.norm = AdaLayerNormZeroSingle(dim) + self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim) + self.act_mlp = nn.GELU(approximate="tanh") + self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim) + + processor = FluxAttnProcessor2_0() + self.attn = Attention( # TODO: Add support for `cross_attention_dim` in the future. + query_dim=dim, + cross_attention_dim=None, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + bias=True, + processor=processor, + qk_norm="rms_norm", + eps=1e-6, + pre_only=True, + ) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: torch.FloatTensor, + image_rotary_emb=None, + joint_attention_kwargs=None, + ): + residual = hidden_states + norm_hidden_states, gate = self.norm(hidden_states, emb=temb) + mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states)) + joint_attention_kwargs = joint_attention_kwargs or {} + attn_output = self.attn( + hidden_states=norm_hidden_states, + image_rotary_emb=image_rotary_emb, + **joint_attention_kwargs, + ) + + hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2) + gate = gate.unsqueeze(1) + hidden_states = gate * self.proj_out(hidden_states) + hidden_states = residual + hidden_states + if hidden_states.dtype == torch.float16: + hidden_states = hidden_states.clip(-65504, 65504) + + return hidden_states + + +@maybe_allow_in_graph +class FluxTransformerBlock(nn.Module): + r""" + A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3. + + Reference: https://arxiv.org/abs/2403.03206 + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the + processing of `context` conditions. + """ + + def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6): + super().__init__() + + self.norm1 = AdaLayerNormZero(dim) + + self.norm1_context = AdaLayerNormZero(dim) + + if hasattr(F, "scaled_dot_product_attention"): + processor = FluxAttnProcessor2_0() + else: + raise ValueError( + "The current PyTorch version does not support the `scaled_dot_product_attention` function." + ) + self.attn = Attention( + query_dim=dim, + cross_attention_dim=None, + added_kv_proj_dim=dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + context_pre_only=False, + bias=True, + processor=processor, + qk_norm=qk_norm, + eps=eps, + ) + + self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate") + + self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate") + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor, + temb: torch.FloatTensor, + image_rotary_emb=None, + joint_attention_kwargs=None, + ): + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb) + + norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context( + encoder_hidden_states, emb=temb + ) + joint_attention_kwargs = joint_attention_kwargs or {} + # Attention. # TODO: Add support for `cross_attention_dim` in the future. + attn_output, context_attn_output = self.attn( + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + **joint_attention_kwargs, + ) + + # Process attention outputs for the `hidden_states`. + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = hidden_states + attn_output + + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + ff_output = self.ff(norm_hidden_states) + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = hidden_states + ff_output + + # Process attention outputs for the `encoder_hidden_states`. + + context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output + encoder_hidden_states = encoder_hidden_states + context_attn_output + + norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states) + norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] + + context_ff_output = self.ff_context(norm_encoder_hidden_states) + encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output + if encoder_hidden_states.dtype == torch.float16: + encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) + + return encoder_hidden_states, hidden_states + + +class FluxTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin): + """ + The Transformer model introduced in Flux. + + Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ + + Parameters: + patch_size (`int`): Patch size to turn the input data into small patches. + in_channels (`int`, *optional*, defaults to 16): The number of channels in the input. + num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use. + num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use. + attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head. + num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention. + joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`. + guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings. + """ + + _supports_gradient_checkpointing = True + _no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"] + + @register_to_config + def __init__( + self, + patch_size: int = 1, + in_channels: int = 64, + num_layers: int = 19, + num_single_layers: int = 38, + attention_head_dim: int = 128, + num_attention_heads: int = 24, + joint_attention_dim: int = 4096, + pooled_projection_dim: int = 768, + guidance_embeds: bool = False, + axes_dims_rope: Tuple[int] = (16, 56, 56), + ): + super().__init__() + self.out_channels = in_channels + self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim + + self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope) + + text_time_guidance_cls = ( + CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings + ) + self.time_text_embed = text_time_guidance_cls( + embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim + ) + + self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim) + self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + FluxTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.config.num_attention_heads, + attention_head_dim=self.config.attention_head_dim, + ) + for i in range(self.config.num_layers) + ] + ) + + self.single_transformer_blocks = nn.ModuleList( + [ + FluxSingleTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.config.num_attention_heads, + attention_head_dim=self.config.attention_head_dim, + ) + for i in range(self.config.num_single_layers) + ] + ) + + self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6) + self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True) + + self.gradient_checkpointing = False + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor() + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0 + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + self.set_attn_processor(FusedFluxAttnProcessor2_0()) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is 🧪 experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + pooled_projections: torch.Tensor = None, + timestep: torch.LongTensor = None, + img_ids: torch.Tensor = None, + txt_ids: torch.Tensor = None, + guidance: torch.Tensor = None, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + controlnet_block_samples=None, + controlnet_single_block_samples=None, + return_dict: bool = True, + controlnet_blocks_repeat: bool = False, + ) -> Union[torch.FloatTensor, Transformer2DModelOutput]: + """ + The [`FluxTransformer2DModel`] forward method. + + Args: + hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`): + Input `hidden_states`. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`): + Conditional embeddings (embeddings computed from the input conditions such as prompts) to use. + pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected + from the embeddings of input conditions. + timestep ( `torch.LongTensor`): + Used to indicate denoising step. + block_controlnet_hidden_states: (`list` of `torch.Tensor`): + A list of tensors that if specified are added to the residuals of transformer blocks. + joint_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain + tuple. + + Returns: + If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a + `tuple` where the first element is the sample tensor. + """ + if joint_attention_kwargs is not None: + joint_attention_kwargs = joint_attention_kwargs.copy() + lora_scale = joint_attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + else: + if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None: + logger.warning( + "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective." + ) + hidden_states = self.x_embedder(hidden_states) + + timestep = timestep.to(hidden_states.dtype) * 1000 + if guidance is not None: + guidance = guidance.to(hidden_states.dtype) * 1000 + else: + guidance = None + temb = ( + self.time_text_embed(timestep, pooled_projections) + if guidance is None + else self.time_text_embed(timestep, guidance, pooled_projections) + ) + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + if txt_ids.ndim == 3: + logger.warning( + "Passing `txt_ids` 3d torch.Tensor is deprecated." + "Please remove the batch dimension and pass it as a 2d torch Tensor" + ) + txt_ids = txt_ids[0] + if img_ids.ndim == 3: + logger.warning( + "Passing `img_ids` 3d torch.Tensor is deprecated." + "Please remove the batch dimension and pass it as a 2d torch Tensor" + ) + img_ids = img_ids[0] + + ids = torch.cat((txt_ids, img_ids), dim=0) + image_rotary_emb = self.pos_embed(ids) + + for index_block, block in enumerate(self.transformer_blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb, + **ckpt_kwargs, + ) + + else: + encoder_hidden_states, hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, + image_rotary_emb=image_rotary_emb, + joint_attention_kwargs=joint_attention_kwargs, + ) + + # controlnet residual + if controlnet_block_samples is not None: + interval_control = len(self.transformer_blocks) / len(controlnet_block_samples) + interval_control = int(np.ceil(interval_control)) + # For Xlabs ControlNet. + if controlnet_blocks_repeat: + hidden_states = ( + hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)] + ) + else: + hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control] + + hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) + + for index_block, block in enumerate(self.single_transformer_blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + temb, + image_rotary_emb, + **ckpt_kwargs, + ) + + else: + hidden_states = block( + hidden_states=hidden_states, + temb=temb, + image_rotary_emb=image_rotary_emb, + joint_attention_kwargs=joint_attention_kwargs, + ) + + # controlnet residual + if controlnet_single_block_samples is not None: + interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples) + interval_control = int(np.ceil(interval_control)) + hidden_states[:, encoder_hidden_states.shape[1] :, ...] = ( + hidden_states[:, encoder_hidden_states.shape[1] :, ...] + + controlnet_single_block_samples[index_block // interval_control] + ) + + hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...] + + hidden_states = self.norm_out(hidden_states, temb) + output = self.proj_out(hidden_states) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) diff --git a/videosys/pipelines/flux/__init__.py b/videosys/pipelines/flux/__init__.py new file mode 100644 index 00000000..fd0a154e --- /dev/null +++ b/videosys/pipelines/flux/__init__.py @@ -0,0 +1,3 @@ +from .pipeline_flux_pab import FluxConfig, FluxPABConfig, FluxPipeline + +__all__ = ["FluxConfig", "FluxPipeline", "FluxPABConfig"] diff --git a/videosys/pipelines/flux/pipeline_flux_pab.py b/videosys/pipelines/flux/pipeline_flux_pab.py new file mode 100644 index 00000000..b7bb2d05 --- /dev/null +++ b/videosys/pipelines/flux/pipeline_flux_pab.py @@ -0,0 +1,886 @@ +# Adapted from Flux + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# Flux: +# -------------------------------------------------------- + +import inspect +import os +import re +from typing import Any, Callable, Dict, List, Optional, Union + +import numpy as np +import torch +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import FluxLoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.models import AutoencoderKL +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from diffusers.utils import USE_PEFT_BACKEND, replace_example_docstring, scale_lora_layers, unscale_lora_layers +from diffusers.utils.torch_utils import randn_tensor +from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast + +from videosys.core.pab_mgr import PABConfig, set_pab_manager +from videosys.core.pipeline import VideoSysPipeline +from videosys.models.transformers.flux_transformer import FluxTransformer2DModel +from videosys.utils.logging import logger + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import FluxPipeline + + >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) + >>> pipe.to("cuda") + >>> prompt = "A cat holding a sign that says hello world" + >>> # Depending on the variant being used, the pipeline call will slightly vary. + >>> # Refer to the pipeline documentation for more details. + >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0] + >>> image.save("flux.png") + ``` +""" + + +def calculate_shift( + image_seq_len, + base_seq_len: int = 256, + max_seq_len: int = 4096, + base_shift: float = 0.5, + max_shift: float = 1.16, +): + m = (max_shift - base_shift) / (max_seq_len - base_seq_len) + b = base_shift - m * base_seq_len + mu = image_seq_len * m + b + return mu + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + r""" + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class FluxPABConfig(PABConfig): + def __init__( + self, + spatial_broadcast: bool = True, + spatial_threshold: list = [100, 800], + spatial_range: int = 2, + temporal_broadcast: bool = True, + temporal_threshold: list = [100, 800], + temporal_range: int = 3, + cross_broadcast: bool = True, + cross_threshold: list = [100, 800], + cross_range: int = 6, + mlp_broadcast: bool = True, + mlp_spatial_broadcast_config: dict = { + 720: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 640: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 560: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 480: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 400: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + }, + mlp_temporal_broadcast_config: dict = { + 720: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 640: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 560: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 480: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + 400: {"block": [0, 1, 2, 3, 4], "skip_count": 2}, + }, + ): + super().__init__( + spatial_broadcast=spatial_broadcast, + spatial_threshold=spatial_threshold, + spatial_range=spatial_range, + temporal_broadcast=temporal_broadcast, + temporal_threshold=temporal_threshold, + temporal_range=temporal_range, + cross_broadcast=cross_broadcast, + cross_threshold=cross_threshold, + cross_range=cross_range, + mlp_broadcast=mlp_broadcast, + mlp_spatial_broadcast_config=mlp_spatial_broadcast_config, + mlp_temporal_broadcast_config=mlp_temporal_broadcast_config, + ) + + +class FluxConfig: + def __init__( + self, + model_path: str = "maxin-cn/Flux-1", + # ======= distributed ======= + num_gpus: int = 1, + # ======= vae ======== + enable_vae_temporal_decoder: bool = True, + # ======= scheduler ======== + beta_start: float = 0.0001, + beta_end: float = 0.02, + beta_schedule: str = "linear", + variance_type: str = "learned_range", + # ======= memory ======= + cpu_offload: bool = False, + # ======= pab ======== + enable_pab: bool = False, + pab_config: PABConfig = FluxPABConfig(), + ): + self.model_path = model_path + # self.vae = vae + + # vae = AutoencoderKL(config.vae) + # text_encoder = CLIPTextModel(config.text_encoder) + # tokenizer = CLIPTokenizer(config.tokenizer) + # text_encoder_2 = T5EncoderModel(config.text_encoder_2) + # tokenizer_2 = T5TokenizerFast(config.tokenizer_2) + + # self.pipeline_cls = FluxPipeline + # ======= distributed ======= + self.num_gpus = num_gpus + # ======= vae ======== + self.enable_vae_temporal_decoder = enable_vae_temporal_decoder + # ======= memory ======== + self.cpu_offload = cpu_offload + # ======= scheduler ======== + self.beta_start = beta_start + self.beta_end = beta_end + self.beta_schedule = beta_schedule + self.variance_type = variance_type + # ======= pab ======== + self.enable_pab = enable_pab + self.pab_config = pab_config + + +class FluxPipeline(VideoSysPipeline): + r""" + Pipeline for text-to-image generation using PixArt-Alpha. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. PixArt-Alpha uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the + [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`Transformer2DModel`]): + A text conditioned `Transformer2DModel` to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + """ + bad_punct_regex = re.compile( + r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}" + ) # noqa + + # _optional_components = ["tokenizer", "text_encoder", "vae", "transformer", "scheduler"] + # model_cpu_offload_seq = "text_encoder->transformer->vae" + _callback_tensor_inputs = ["latents"] + + def __init__( + self, + # model + scheduler: FlowMatchEulerDiscreteScheduler, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + text_encoder_2: T5EncoderModel, + tokenizer_2: T5TokenizerFast, + # transformer: FluxTransformer2DModel, + config: FluxConfig = FluxConfig(), + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float16, + ): + super().__init__() + self._config = config + print("Loading FluxPipeline modules") + + # set the components + # scheduler = FlowMatchEulerDiscreteScheduler( + # num_train_timesteps= 1000, + # shift= 3.0, + # use_dynamic_shifting=True, + # base_shift = 0.5, + # max_shift = 1.15, + # base_image_seq_len= 256, + # max_image_seq_len = 4096, + # ) + + os.path.join(os.path.dirname(text_encoder.name_or_path), "transformer") + + transformer = FluxTransformer2DModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + torch_dtype=torch.bfloat16, + low_cpu_mem_usage=True, + offload_state_dict=False, + ).to("cuda:1") + + # pab + if config.enable_pab: + set_pab_manager(config.pab_config) + + print("Registering FluxPipeline modules") + self.register_modules( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + transformer=transformer, + scheduler=scheduler, + ) + + # cpu offload + # if config.cpu_offload: + # self.enable_model_cpu_offload() + # else: + # self.set_eval_and_device(device, text_encoder, vae, transformer) + + # model setting + self.vae_scale_factor = ( + 2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16 + ) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.tokenizer_max_length = ( + self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77 + ) + self.default_sample_size = 64 + + # parallel + # self._set_parallel() + + def _get_t5_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + num_images_per_prompt: int = 1, + max_sequence_length: int = 512, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer_2) + + text_inputs = self.tokenizer_2( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + return_length=False, + return_overflowing_tokens=False, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because `max_sequence_length` is set to " + f" {max_sequence_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0] + + dtype = self.text_encoder_2.dtype + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + _, seq_len, _ = prompt_embeds.shape + + # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + return prompt_embeds + + def _get_clip_prompt_embeds( + self, + prompt: Union[str, List[str]], + num_images_per_prompt: int = 1, + device: Optional[torch.device] = None, + ): + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer_max_length, + truncation=True, + return_overflowing_tokens=False, + return_length=False, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer_max_length} tokens: {removed_text}" + ) + prompt_embeds = self.text_encoder(text_input_ids.to(device), output_hidden_states=False) + + # Use pooled output of CLIPTextModel + prompt_embeds = prompt_embeds.pooler_output + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt) + prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1) + + return prompt_embeds + + def encode_prompt( + self, + prompt: Union[str, List[str]], + prompt_2: Union[str, List[str]], + device: Optional[torch.device] = None, + num_images_per_prompt: int = 1, + prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + max_sequence_length: int = 512, + lora_scale: Optional[float] = None, + ): + r""" + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + used in all text-encoders + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + lora_scale (`float`, *optional*): + A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + """ + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if self.text_encoder is not None and USE_PEFT_BACKEND: + scale_lora_layers(self.text_encoder, lora_scale) + if self.text_encoder_2 is not None and USE_PEFT_BACKEND: + scale_lora_layers(self.text_encoder_2, lora_scale) + + prompt = [prompt] if isinstance(prompt, str) else prompt + + if prompt_embeds is None: + prompt_2 = prompt_2 or prompt + prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 + + # We only use the pooled prompt output from the CLIPTextModel + pooled_prompt_embeds = self._get_clip_prompt_embeds( + prompt=prompt, + device=device, + num_images_per_prompt=num_images_per_prompt, + ) + prompt_embeds = self._get_t5_prompt_embeds( + prompt=prompt_2, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + ) + + if self.text_encoder is not None: + if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder_2, lora_scale) + + dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype + text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype) + + return prompt_embeds, pooled_prompt_embeds, text_ids + + def check_inputs( + self, + prompt, + prompt_2, + height, + width, + prompt_embeds=None, + pooled_prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + max_sequence_length=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + print(f"Checking FluxPipeline inputs callback_on_step_end_tensor_inputs: {callback_on_step_end_tensor_inputs}") + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt_2 is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): + raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") + + if prompt_embeds is not None and pooled_prompt_embeds is None: + raise ValueError( + "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." + ) + + if max_sequence_length is not None and max_sequence_length > 512: + raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}") + + @staticmethod + def _prepare_latent_image_ids(batch_size, height, width, device, dtype): + latent_image_ids = torch.zeros(height // 2, width // 2, 3) + latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None] + latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :] + + latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape + + latent_image_ids = latent_image_ids.reshape( + latent_image_id_height * latent_image_id_width, latent_image_id_channels + ) + + return latent_image_ids.to(device=device, dtype=dtype) + + @staticmethod + def _pack_latents(latents, batch_size, num_channels_latents, height, width): + latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2) + latents = latents.permute(0, 2, 4, 1, 3, 5) + latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4) + + return latents + + @staticmethod + def _unpack_latents(latents, height, width, vae_scale_factor): + batch_size, num_patches, channels = latents.shape + + height = height // vae_scale_factor + width = width // vae_scale_factor + + latents = latents.view(batch_size, height, width, channels // 4, 2, 2) + latents = latents.permute(0, 3, 1, 4, 2, 5) + + latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2) + + return latents + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + """ + self.vae.enable_tiling() + + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + height = 2 * (int(height) // self.vae_scale_factor) + width = 2 * (int(width) // self.vae_scale_factor) + + shape = (batch_size, num_channels_latents, height, width) + + if latents is not None: + latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype) + return latents.to(device=device, dtype=dtype), latent_image_ids + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width) + + latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype) + + return latents, latent_image_ids + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def joint_attention_kwargs(self): + return self._joint_attention_kwargs + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def interrupt(self): + return self._interrupt + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + prompt_2: Optional[Union[str, List[str]]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 28, + timesteps: List[int] = None, + guidance_scale: float = 3.5, + num_images_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + max_sequence_length: int = 512, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + will be used instead + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. This is set to 1024 by default for the best results. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. This is set to 1024 by default for the best results. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + guidance_scale (`float`, *optional*, defaults to 7.0): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple. + joint_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. + + Examples: + + Returns: + [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict` + is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated + images. + """ + print("Running FluxPipeline Generation") + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + height, + width, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + max_sequence_length=max_sequence_length, + ) + + self._guidance_scale = guidance_scale + self._joint_attention_kwargs = joint_attention_kwargs + self._interrupt = False + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + lora_scale = self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None + ( + prompt_embeds, + pooled_prompt_embeds, + text_ids, + ) = self.encode_prompt( + prompt=prompt, + prompt_2=prompt_2, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + device=device, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + lora_scale=lora_scale, + ) + + # 4. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels // 4 + latents, latent_image_ids = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 5. Prepare timesteps + sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) + image_seq_len = latents.shape[1] + mu = calculate_shift( + image_seq_len, + self.scheduler.config.base_image_seq_len, + self.scheduler.config.max_image_seq_len, + self.scheduler.config.base_shift, + self.scheduler.config.max_shift, + ) + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, + num_inference_steps, + device, + timesteps, + sigmas, + mu=mu, + ) + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + self._num_timesteps = len(timesteps) + + # handle guidance + if self.transformer.config.guidance_embeds: + guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) + guidance = guidance.expand(latents.shape[0]) + else: + guidance = None + + # 6. Denoising loop + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latents.shape[0]).to(latents.dtype) + + noise_pred = self.transformer( + hidden_states=latents, + timestep=timestep / 1000, + guidance=guidance, + pooled_projections=pooled_prompt_embeds, + encoder_hidden_states=prompt_embeds, + txt_ids=text_ids, + img_ids=latent_image_ids, + joint_attention_kwargs=self.joint_attention_kwargs, + return_dict=False, + )[0] + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + if output_type == "latent": + image = latents + + else: + latents = self._unpack_latents(latents, height, width, self.vae_scale_factor) + latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor + image = self.vae.decode(latents, return_dict=False)[0] + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return FluxPipelineOutput(images=image) From 1097188cb02682ade7054c9ae490abfa0f8f1a48 Mon Sep 17 00:00:00 2001 From: Jinxiaolong1129 Date: Tue, 19 Nov 2024 16:29:59 -0500 Subject: [PATCH 2/9] update --- videosys/pipelines/flux/pipeline_flux_pab.py | 61 ++++++++++++++------ 1 file changed, 42 insertions(+), 19 deletions(-) diff --git a/videosys/pipelines/flux/pipeline_flux_pab.py b/videosys/pipelines/flux/pipeline_flux_pab.py index b7bb2d05..6ccf4c9b 100644 --- a/videosys/pipelines/flux/pipeline_flux_pab.py +++ b/videosys/pipelines/flux/pipeline_flux_pab.py @@ -239,34 +239,51 @@ class FluxPipeline(VideoSysPipeline): def __init__( self, # model - scheduler: FlowMatchEulerDiscreteScheduler, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - text_encoder_2: T5EncoderModel, - tokenizer_2: T5TokenizerFast, - # transformer: FluxTransformer2DModel, + scheduler: FlowMatchEulerDiscreteScheduler = None, + vae: AutoencoderKL = None, + text_encoder: CLIPTextModel = None, + tokenizer: CLIPTokenizer = None, + text_encoder_2: T5EncoderModel = None, + tokenizer_2: T5TokenizerFast = None, + transformer: FluxTransformer2DModel = None, config: FluxConfig = FluxConfig(), - device: Optional[torch.device] = None, + device: torch.device = torch.device("cuda"), dtype: torch.dtype = torch.float16, ): super().__init__() self._config = config print("Loading FluxPipeline modules") - # set the components - # scheduler = FlowMatchEulerDiscreteScheduler( - # num_train_timesteps= 1000, - # shift= 3.0, - # use_dynamic_shifting=True, - # base_shift = 0.5, - # max_shift = 1.15, - # base_image_seq_len= 256, - # max_image_seq_len = 4096, - # ) + # init + if tokenizer is None: + tokenizer = CLIPTokenizer.from_pretrained(config.model, subfolder="tokenizer", torch_dtype=dtype) - os.path.join(os.path.dirname(text_encoder.name_or_path), "transformer") + if tokenizer_2 is None: + tokenizer_2 = T5TokenizerFast.from_pretrained(config.model, subfolder="tokenizer_2", torch_dtype=dtype) + + if text_encoder is None: + text_encoder = CLIPTextModel.from_pretrained(config.model, subfolder="text_encoder", torch_dtype=dtype) + + if text_encoder_2 is None: + text_encoder_2 = T5EncoderModel.from_pretrained(config.model, subfolder="text_encoder_2", torch_dtype=dtype) + + if vae is None: + vae = AutoencoderKL.from_pretrained(config.model, subfolder="vae", torch_dtype=dtype) + + if transformer is None: + transformer = FluxTransformer2DModel.from_pretrained( + config.model, subfolder="transformer", torch_dtype=dtype + ) + if scheduler is None: + scheduler = FlowMatchEulerDiscreteScheduler( + beta_start=config.beta_start, + beta_end=config.beta_end, + beta_schedule=config.beta_schedule, + variance_type=config.variance_type, + ) + + os.path.join(os.path.dirname(text_encoder.name_or_path), "transformer") transformer = FluxTransformer2DModel.from_pretrained( "black-forest-labs/FLUX.1-dev", subfolder="transformer", @@ -275,6 +292,12 @@ def __init__( offload_state_dict=False, ).to("cuda:1") + # cpu offload + if config.cpu_offload: + self.enable_model_cpu_offload() + else: + self.set_eval_and_device(self._device, vae, transformer, text_encoder) + # pab if config.enable_pab: set_pab_manager(config.pab_config) From 657d2147786133cae7870ea54ee6f5dbc4309718 Mon Sep 17 00:00:00 2001 From: Jinxiaolong1129 Date: Tue, 19 Nov 2024 16:54:01 -0500 Subject: [PATCH 3/9] update --- flux-test.py | 36 +++++------ videosys/pipelines/flux/pipeline_flux_pab.py | 63 ++++++++++---------- 2 files changed, 49 insertions(+), 50 deletions(-) diff --git a/flux-test.py b/flux-test.py index 51107e58..c0fd586d 100644 --- a/flux-test.py +++ b/flux-test.py @@ -1,31 +1,31 @@ import os import torch -from diffusers.models.transformers import FluxTransformer2DModel os.environ["CUDA_VISIBLE_DEVICES"] = "5,6" -# from videosys.pipelines.flux.pipeline_flux_pab import FluxPipeline, FluxConfig -# pipe = FluxPipeline.from_pretrained( +from videosys.pipelines.flux.pipeline_flux_pab import FluxConfig, FluxPipeline + +pipe = FluxPipeline( + # "black-forest-labs/FLUX.1-dev", + # torch_dtype=torch.bfloat16, + # device_map="balanced", + config=FluxConfig() +) + +# transformer = FluxTransformer2DModel.from_pretrained( # "black-forest-labs/FLUX.1-dev", +# subfolder="transformer", # torch_dtype=torch.bfloat16, -# device_map="balanced", -# config = FluxConfig() -# ) +# low_cpu_mem_usage=True, +# offload_state_dict=False, +# ).to("cuda:1") -transformer = FluxTransformer2DModel.from_pretrained( - "black-forest-labs/FLUX.1-dev", - subfolder="transformer", - torch_dtype=torch.bfloat16, - low_cpu_mem_usage=True, - offload_state_dict=False, -).to("cuda:1") +# from diffusers import FluxPipeline -from diffusers import FluxPipeline - -pipe = FluxPipeline.from_pretrained( - "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="balanced" # 自动分配到多个 GPU -) +# pipe = FluxPipeline.from_pretrained( +# "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="balanced" # 自动分配到多个 GPU +# ) # pipe.enable_model_cpu_offload() prompt = "A cat holding a sign that says hello world" diff --git a/videosys/pipelines/flux/pipeline_flux_pab.py b/videosys/pipelines/flux/pipeline_flux_pab.py index 6ccf4c9b..bb115627 100644 --- a/videosys/pipelines/flux/pipeline_flux_pab.py +++ b/videosys/pipelines/flux/pipeline_flux_pab.py @@ -8,7 +8,6 @@ # -------------------------------------------------------- import inspect -import os import re from typing import Any, Callable, Dict, List, Optional, Union @@ -164,7 +163,7 @@ def __init__( class FluxConfig: def __init__( self, - model_path: str = "maxin-cn/Flux-1", + model_path: str = "black-forest-labs/FLUX.1-dev", # ======= distributed ======= num_gpus: int = 1, # ======= vae ======== @@ -238,65 +237,65 @@ class FluxPipeline(VideoSysPipeline): def __init__( self, - # model - scheduler: FlowMatchEulerDiscreteScheduler = None, - vae: AutoencoderKL = None, - text_encoder: CLIPTextModel = None, - tokenizer: CLIPTokenizer = None, - text_encoder_2: T5EncoderModel = None, - tokenizer_2: T5TokenizerFast = None, - transformer: FluxTransformer2DModel = None, + # model components + scheduler: Optional[FlowMatchEulerDiscreteScheduler] = None, + vae: Optional[AutoencoderKL] = None, + text_encoder: Optional[CLIPTextModel] = None, + tokenizer: Optional[CLIPTokenizer] = None, + text_encoder_2: Optional[T5EncoderModel] = None, + tokenizer_2: Optional[T5TokenizerFast] = None, + transformer: Optional[FluxTransformer2DModel] = None, config: FluxConfig = FluxConfig(), - device: torch.device = torch.device("cuda"), + device: torch.device = torch.device("cuda:0"), dtype: torch.dtype = torch.float16, ): super().__init__() self._config = config + self._device = device print("Loading FluxPipeline modules") # init if tokenizer is None: - tokenizer = CLIPTokenizer.from_pretrained(config.model, subfolder="tokenizer", torch_dtype=dtype) + tokenizer = CLIPTokenizer.from_pretrained(config.model_path, subfolder="tokenizer", torch_dtype=dtype) if tokenizer_2 is None: - tokenizer_2 = T5TokenizerFast.from_pretrained(config.model, subfolder="tokenizer_2", torch_dtype=dtype) + tokenizer_2 = T5TokenizerFast.from_pretrained(config.model_path, subfolder="tokenizer_2", torch_dtype=dtype) if text_encoder is None: - text_encoder = CLIPTextModel.from_pretrained(config.model, subfolder="text_encoder", torch_dtype=dtype) + text_encoder = CLIPTextModel.from_pretrained(config.model_path, subfolder="text_encoder", torch_dtype=dtype) if text_encoder_2 is None: - text_encoder_2 = T5EncoderModel.from_pretrained(config.model, subfolder="text_encoder_2", torch_dtype=dtype) + text_encoder_2 = T5EncoderModel.from_pretrained( + config.model_path, subfolder="text_encoder_2", torch_dtype=dtype + ) if vae is None: - vae = AutoencoderKL.from_pretrained(config.model, subfolder="vae", torch_dtype=dtype) + vae = AutoencoderKL.from_pretrained(config.model_path, subfolder="vae", torch_dtype=dtype) if transformer is None: transformer = FluxTransformer2DModel.from_pretrained( - config.model, subfolder="transformer", torch_dtype=dtype + config.model_path, subfolder="transformer", torch_dtype=dtype ) if scheduler is None: - scheduler = FlowMatchEulerDiscreteScheduler( - beta_start=config.beta_start, - beta_end=config.beta_end, - beta_schedule=config.beta_schedule, - variance_type=config.variance_type, - ) + scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.model_path, subfolder="scheduler") - os.path.join(os.path.dirname(text_encoder.name_or_path), "transformer") - transformer = FluxTransformer2DModel.from_pretrained( - "black-forest-labs/FLUX.1-dev", - subfolder="transformer", - torch_dtype=torch.bfloat16, - low_cpu_mem_usage=True, - offload_state_dict=False, - ).to("cuda:1") + # os.path.join(os.path.dirname(text_encoder.name_or_path), "transformer") + # transformer = FluxTransformer2DModel.from_pretrained( + # "black-forest-labs/FLUX.1-dev", + # subfolder="transformer", + # torch_dtype=torch.bfloat16, + # low_cpu_mem_usage=True, + # offload_state_dict=False, + # ).to("cuda:1") # cpu offload if config.cpu_offload: self.enable_model_cpu_offload() else: - self.set_eval_and_device(self._device, vae, transformer, text_encoder) + self.set_eval_and_device( + self._device, vae, transformer, text_encoder, text_encoder_2, tokenizer, tokenizer_2 + ) # pab if config.enable_pab: From d30a20e449504fa83437ddf3f9b821744d9ffe0d Mon Sep 17 00:00:00 2001 From: jinxiaolong1129 Date: Wed, 20 Nov 2024 08:36:00 +0800 Subject: [PATCH 4/9] keep update --- calculate_FID.py | 81 + examples/flux/sample.py | 87 + examples/open_sora_plan/sample.py | 2 +- flux-dev.png | Bin 1071640 -> 0 bytes idx_caption.txt | 10000 ++++++++++++++++ .../models/transformers/flux_transformer.py | 71 +- videosys/pipelines/flux/pipeline_flux_pab.py | 90 +- 7 files changed, 10276 insertions(+), 55 deletions(-) create mode 100644 calculate_FID.py create mode 100644 examples/flux/sample.py delete mode 100644 flux-dev.png create mode 100644 idx_caption.txt diff --git a/calculate_FID.py b/calculate_FID.py new file mode 100644 index 00000000..b7d7af2c --- /dev/null +++ b/calculate_FID.py @@ -0,0 +1,81 @@ +import os +import requests +import zipfile +from PIL import Image +from pytorch_fid import fid_score +import torch +import argparse + + +# Paths +COCO_DOWNLOAD_URL = "http://images.cocodataset.org/zips/val2017.zip" +COCO_DIR = "coco_dataset" +REAL_IMAGES_DIR = os.path.join(COCO_DIR, "real_images") + + +def download_coco(output_dir): + os.makedirs(output_dir, exist_ok=True) + coco_zip_path = os.path.join(output_dir, "val2017.zip") + + # Download MS-COCO validation set + print("Downloading MS-COCO validation images...") + with requests.get(COCO_DOWNLOAD_URL, stream=True) as r: + r.raise_for_status() + with open(coco_zip_path, "wb") as f: + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + + print("Extracting images...") + with zipfile.ZipFile(coco_zip_path, "r") as zip_ref: + zip_ref.extractall(output_dir) + + os.rename(os.path.join(output_dir, "val2017"), REAL_IMAGES_DIR) + os.remove(coco_zip_path) + print(f"MS-COCO images downloaded and extracted to {REAL_IMAGES_DIR}") + +# Function to preprocess images (resize to 299x299) +def preprocess_images(input_dir, output_dir, target_size=(299, 299)): + os.makedirs(output_dir, exist_ok=True) + for img_name in os.listdir(input_dir): + img_path = os.path.join(input_dir, img_name) + output_path = os.path.join(output_dir, img_name) + with Image.open(img_path) as img: + img = img.convert("RGB").resize(target_size) + img.save(output_path) + print(f"Preprocessed images saved to {output_dir}") + +# Function to calculate FID +def calculate_fid(real_dir, generated_dir): + print("Calculating FID score...") + fid_value = fid_score.calculate_fid_given_paths( + [real_dir, generated_dir], + batch_size=50, # Adjust based on your hardware + device="cuda" if torch.cuda.is_available() else "cpu", + dims=2048, # Default feature dimensions for Inception-v3 pool3 + ) + print(f"FID Score: {fid_value}") + return fid_value + + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Calculate FID for generated images.") + parser.add_argument( + "generated_images_dir", + type=str, + help="Path to the directory containing generated images." + ) + args = parser.parse_args() + + GENERATED_IMAGES_DIR = args.generated_images_dir + if not os.path.exists(REAL_IMAGES_DIR): + download_coco(COCO_DIR) + preprocessed_coco_dir = os.path.join(COCO_DIR, "preprocessed_real_images") + preprocess_images(REAL_IMAGES_DIR, preprocessed_coco_dir) + + if not os.path.exists(GENERATED_IMAGES_DIR): + raise ValueError("Generated images directory does not exist. Add your images to 'generated_images/'.") + + print(f"Calculating FID for {GENERATED_IMAGES_DIR}...") + calculate_fid(preprocessed_coco_dir, GENERATED_IMAGES_DIR) diff --git a/examples/flux/sample.py b/examples/flux/sample.py new file mode 100644 index 00000000..adca2066 --- /dev/null +++ b/examples/flux/sample.py @@ -0,0 +1,87 @@ +from videosys.pipelines.flux.pipeline_flux_pab import FluxConfig, FluxPipeline, FluxPABConfig +import torch + +def run_base(): + # change num_gpus for multi-gpu inference + # sampling parameters are defined in the config + + # config = OpenSoraConfig(num_sampling_steps=30, cfg_scale=7.0, num_gpus=1) + # engine = VideoSysEngine(config) + config = FluxConfig() + + pipe = FluxPipeline( + config = config + ) + + prompts = [ + "Sunset over the sea.", + "A group of people wading around a large body of water.", + "A cabinet towels a toilet and a sink.", + "A few boys read comic books together outside.", + "There is a man and a young girl snowboarding.", + "A male snowboarder sits with his board on a snowy hill.", + "A woman wearing a skirt shows off her tattoos.", + "A large dog is tied up to a fire hydrant.", + "A man making a pizza in a kitchen." + ] + + # Generate and save images + for prompt in prompts: + image = pipe( + prompt, + height=1024, + width=1024, + guidance_scale=3.5, + num_inference_steps=50, + max_sequence_length=512, + generator=torch.Generator("cuda:0").manual_seed(0) + ).images[0] + pipe.save_image(image, f"./outputs/flux/{prompt}.png") + + +def run_pab(): + pab_config = FluxPABConfig( + spatial_broadcast=True, + spatial_threshold=[100, 930], + spatial_range=5, + temporal_broadcast=False, + cross_broadcast=True, + cross_threshold=[100, 930], + cross_range=5, + mlp_broadcast=True + ) + config = FluxConfig( + enable_pab=True, + pab_config=pab_config) + pipe = FluxPipeline( + config = config + ) + + prompts = [ + "Sunset over the sea.", + "A group of people wading around a large body of water.", + "A cabinet towels a toilet and a sink.", + "A few boys read comic books together outside.", + "There is a man and a young girl snowboarding.", + "A male snowboarder sits with his board on a snowy hill.", + "A woman wearing a skirt shows off her tattoos.", + "A large dog is tied up to a fire hydrant.", + "A man making a pizza in a kitchen." + ] + + for prompt in prompts: + image = pipe( + prompt, + height=1024, + width=1024, + guidance_scale=3.5, + num_inference_steps=50, + max_sequence_length=512, + generator=torch.Generator("cuda:0").manual_seed(0) + ).images[0] + pipe.save_image(image, f"./outputs/flux-pab/{prompt.replace(' ', '_')}.png") + +if __name__ == "__main__": + run_base() + # run_low_mem() + run_pab() diff --git a/examples/open_sora_plan/sample.py b/examples/open_sora_plan/sample.py index f98a8016..d81a7eca 100644 --- a/examples/open_sora_plan/sample.py +++ b/examples/open_sora_plan/sample.py @@ -15,7 +15,7 @@ def run_base(): guidance_scale=7.5, num_inference_steps=100, seed=-1, - ).video[0] + ).images[0] 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