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Gemma text encoder modeli GPU'ya yüklenmeye çalışırken bellek dolmuş.\n\nSorunu çözmek için birkaç seçenek var:\n\n**Çözünürlüğü düşürün** — video çözünürlüğünü daha küçük bir değere ayarlayın (örneğin 512x320 veya 480x272). Bu, VRAM kullanımını önemli ölçüde azaltır.\n\n**Frame sayısını azaltın** — daha kısa bir video üretin. Frame sayısını yarıya indirmek bellek ihtiyacını da düşürür.\n\n**MMGP offload profilini değiştirin** — LTX-Desktop-WanGP'de MMGP offload seviyesini daha agresif bir değere (örneğin profile 2 veya 3) ayarlayarak modellerin CPU/RAM'e taşınmasını sağlayabilirsiniz. Bu daha yavaş olur ama VRAM sorununu çözer.\n\nHangi GPU'yu kullanıyorsunuz ve ne kadar VRAM'ınız var? Ayrıca şu an hangi çözünürlük ve frame sayısıyla deniyorsunuz? Bu bilgilerle daha spesifik bir ayar önerebilirim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":14938,"cache_read_input_tokens":15590,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":14938},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"8fae5162-3071-4a7e-a8d0-c700206ec302","uuid":"d51e9ed9-2941-417c-862d-8238e7eefff2","_audit_timestamp":"2026-04-19T21:58:30.771Z","_audit_hmac":"5e418a55bdb4c28880d5808df2957986342de7ddb16be5b38ed7a774579af7b1"} {"type":"result","subtype":"success","is_error":false,"api_error_status":null,"duration_ms":17143,"duration_api_ms":18208,"num_turns":1,"result":"Bu hata, video üretimi sırasında **VRAM (GPU belleği) yetersizliği** nedeniyle oluşuyor. 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PYTHON=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\backend\\.venv\\Scripts\\python.exe\"\r\nset SCRIPT=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\wangp_video_uret.py\"\r\n\r\nset IMAGE=\r\nset AUDIO=\r\nset FACEIMG=\r\n\r\necho.\r\necho ============================================================\r\necho WANGP MUZIK VIDEO URETICI\r\necho RTX 4090 Optimize - Hizli ve Kaliteli\r\necho ============================================================\r\necho.\r\n\r\nREM --- Surukle-birak ile gelen dosyalari ayikla ---\r\nfor %%A in (%*) do (\r\n set \"FILE=%%~A\"\r\n set \"EXT=%%~xA\"\r\n\r\n if /i \"!EXT!\"==\".png\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".jpg\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".jpeg\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".webp\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".bmp\" set \"IMAGE=%%~A\"\r\n\r\n if /i \"!EXT!\"==\".mp3\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".wav\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".flac\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".m4a\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".ogg\" set \"AUDIO=%%~A\"\r\n)\r\n\r\nREM --- Dosya bulunamadiysa sor ---\r\nif \"%IMAGE%\"==\"\" (\r\n echo [!] Resim dosyasi bulunamadi.\r\n echo Resim ve sarki dosyalarini bu BAT'in ustune surukleyin.\r\n echo.\r\n echo VEYA asagiya elle yazin:\r\n echo.\r\n set /p IMAGE=\" Resim yolu (png/jpg): \"\r\n)\r\nif \"%AUDIO%\"==\"\" (\r\n echo.\r\n echo [!] Sarki dosyasi bulunamadi.\r\n set /p AUDIO=\" Sarki yolu (mp3/wav): \"\r\n)\r\nif \"%IMAGE%\"==\"\" (\r\n echo HATA: Resim dosyasi gerekli!\r\n pause\r\n exit /b 1\r\n)\r\nif \"%AUDIO%\"==\"\" (\r\n echo HATA: Sarki dosyasi gerekli!\r\n pause\r\n exit /b 1\r\n)\r\n\r\necho.\r\necho Resim : %IMAGE%\r\necho Sarki : %AUDIO%\r\necho.\r\n\r\nREM ============================================================\r\nREM PROFIL SECIMI\r\nREM ============================================================\r\necho ------------------------------------------------------------\r\necho PROFIL SEC - RTX 4090 24GB + 64GB RAM Icin Optimize\r\necho ------------------------------------------------------------\r\necho.\r\necho *** LTX-2.3 - ONERILEN - Hizli, Gercekci, Lip-Sync ***\r\necho.\r\necho 1) LTX2 HIZLI - LTX2 22B Distilled 480p, 10s\r\necho ~35sn/segment ONERILEN - en hizli, iyi kalite\r\necho.\r\necho 2) LTX2 HD - LTX2 22B 720p, 10s\r\necho ~90sn/segment 720p HD, en kaliteli\r\necho.\r\necho 3) LTX2 KALITE++ - LTX2 22B 720p, perturbation+phases\r\necho ~120sn/segment Tum LTX2 ozellikler acik, profesyonel\r\necho.\r\necho *** WAN2.1 IMAGE2VIDEO - Farkli Stil [LIP-SYNC YOK!] ***\r\necho.\r\necho 4) WAN2 HIZLI - Wan2.1 I2V 480p, 10s\r\necho ~80sn/segment Wan2 stili, LIP-SYNC DESTEKLEMIYOR\r\necho.\r\necho 5) WAN2 KALITE - Wan2.1 I2V 480p, 15s\r\necho ~150sn/segment Uzun sahneler, LIP-SYNC DESTEKLEMIYOR\r\necho.\r\necho 6) OZEL AYARLAR - Tum parametreleri elle gir\r\necho.\r\necho ------------------------------------------------------------\r\necho.\r\nset /p PROFIL=\" Profil secin [1-6, Enter=1]: \"\r\nif \"%PROFIL%\"==\"\" set PROFIL=1\r\n\r\nREM --- Varsayilan degerler (LTX2 Distilled) ---\r\nset MODEL=ltx2_22B_distilled\r\nset RESOLUTION=832x480\r\nset VIDEOLENGTH=241\r\nset STEPS=20\r\nset GUIDANCE=3.0\r\nset FLOWSHIFT=5.0\r\nset SOLVER=euler\r\nset SEGDUR=10\r\nset FPS=24\r\nset \"NEG_PROMPT=blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, cartoon, animated, watermark, text, logo, static, frozen, jerky motion\"\r\nset SLIDINGWIN=0\r\nset SLIDINGOVERLAP=1\r\nset PERTURBATION=0\r\nset SELFREFINER=0\r\nset REPEAT=1\r\nset SEED=-1\r\nset LORA=\r\nset LORAWEIGHT=1.0\r\nset ENDIMAGE=\r\nset AUDIOGUIDE=\r\nset AUDIOSCALE=\r\nset ALTGUIDANCE=\r\nset ALTSCALE=\r\nset EMBEDDEDGUIDE=\r\nset GUIDANCEPHASES=\r\nset FILMGRAIN=0\r\nset TEMPORALUP=\r\nset SPATIALUP=\r\nset NOLIPSYNC=0\r\nset NOVOCALISOL=0\r\n\r\nREM --- Profil ayarlari (RTX 4090 24GB optimize) ---\r\nREM Profil 1: LTX2 HIZLI (varsayilan degerler zaten LTX2 Distilled)\r\nif \"%PROFIL%\"==\"1\" (\r\n set MODEL=ltx2_22B_distilled\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=241\r\n set STEPS=20\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nREM Profil 2: LTX2 HD\r\nif \"%PROFIL%\"==\"2\" (\r\n set MODEL=ltx2_22B\r\n set RESOLUTION=1280x720\r\n set VIDEOLENGTH=241\r\n set STEPS=30\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n set GUIDANCEPHASES=2\r\n set ALTGUIDANCE=3.0\r\n set ALTSCALE=0.7\r\n)\r\nREM Profil 3: LTX2 KALITE++\r\nif \"%PROFIL%\"==\"3\" (\r\n set MODEL=ltx2_22B\r\n set RESOLUTION=1280x720\r\n set VIDEOLENGTH=241\r\n set STEPS=40\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n set GUIDANCEPHASES=2\r\n set ALTGUIDANCE=3.0\r\n set ALTSCALE=0.7\r\n set PERTURBATION=2\r\n set AUDIOSCALE=1.0\r\n)\r\nREM Profil 4: WAN2 HIZLI\r\nif \"%PROFIL%\"==\"4\" (\r\n set MODEL=i2v\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=161\r\n set STEPS=30\r\n set GUIDANCE=5.0\r\n set FLOWSHIFT=7.0\r\n set SEGDUR=10\r\n set FPS=16\r\n set SOLVER=unipc\r\n)\r\nREM Profil 5: WAN2 KALITE\r\nif \"%PROFIL%\"==\"5\" (\r\n set MODEL=i2v\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=241\r\n set STEPS=30\r\n set GUIDANCE=5.0\r\n set FLOWSHIFT=7.0\r\n set SEGDUR=15\r\n set FPS=16\r\n set SOLVER=unipc\r\n)\r\n\r\nif not \"%PROFIL%\"==\"6\" goto :PROMPT_AYAR\r\n\r\nREM ============================================================\r\nREM OZEL AYARLAR (Profil 6)\r\nREM ============================================================\r\n\r\necho.\r\necho --- MODEL SECIMI ---\r\necho *** YUKLU MODELLER ***\r\necho 1) i2v - Wan2.1 I2V 480p (Varsayilan)\r\necho 2) ltx2_22B - LTX-2 22B (En kaliteli)\r\necho 3) ltx2_22B_distilled - LTX-2 22B Distilled (Hizli)\r\necho.\r\necho *** INDIRILMESI GEREKEN (otomatik indirir) ***\r\necho 4) i2v_720p - Wan2.1 I2V 720p\r\necho 5) i2v_2_2 - Wan2.1 2.2 I2V (Yeni versiyon)\r\necho 6) t2v - Wan2.1 Text2Video\r\necho 7) hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\r\necho 8) Elle gir (model ID)\r\necho.\r\nset /p MODELCHOICE=\" Model (1-8, Enter=1): \"\r\nif \"%MODELCHOICE%\"==\"\" set MODELCHOICE=1\r\nif \"%MODELCHOICE%\"==\"1\" set MODEL=i2v\r\nif \"%MODELCHOICE%\"==\"2\" (\r\n set MODEL=ltx2_22B\r\n set FLOWSHIFT=5.0\r\n set GUIDANCE=3.0\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nif \"%MODELCHOICE%\"==\"3\" (\r\n set MODEL=ltx2_22B_distilled\r\n set FLOWSHIFT=5.0\r\n set GUIDANCE=3.0\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nif \"%MODELCHOICE%\"==\"4\" (\r\n set MODEL=i2v_720p\r\n set RESOLUTION=1280x720\r\n)\r\nif \"%MODELCHOICE%\"==\"5\" set MODEL=i2v_2_2\r\nif \"%MODELCHOICE%\"==\"6\" set MODEL=t2v\r\nif \"%MODELCHOICE%\"==\"7\" set MODEL=hunyuan_1_5_i2v\r\nif \"%MODELCHOICE%\"==\"8\" (\r\n set /p MODEL=\" Model ID (orn: i2v_2_2): \"\r\n)\r\n\r\necho.\r\necho --- COZUNURLUK ---\r\necho 1) 832x480 - 480p 16:9 (Varsayilan - Hizli)\r\necho 2) 480x832 - 480p 9:16 (Dikey/Shorts)\r\necho 3) 1280x720 - 720p 16:9 (HD)\r\necho 4) 720x1280 - 720p 9:16 (HD Dikey)\r\necho 5) 960x544 - 540p 16:9\r\necho 6) Elle gir (WIDTHxHEIGHT)\r\necho.\r\nset /p RESCHOICE=\" Cozunurluk (1-6, Enter=1): \"\r\nif \"%RESCHOICE%\"==\"\" set RESCHOICE=1\r\nif \"%RESCHOICE%\"==\"1\" set RESOLUTION=832x480\r\nif \"%RESCHOICE%\"==\"2\" set RESOLUTION=480x832\r\nif \"%RESCHOICE%\"==\"3\" set RESOLUTION=1280x720\r\nif \"%RESCHOICE%\"==\"4\" set RESOLUTION=720x1280\r\nif \"%RESCHOICE%\"==\"5\" set RESOLUTION=960x544\r\nif \"%RESCHOICE%\"==\"6\" (\r\n set /p RESOLUTION=\" Cozunurluk (WxH): \"\r\n)\r\n\r\necho.\r\necho --- SEGMENT SURESI / FRAME SAYISI ---\r\necho 16fps: 81 frame=5s, 121=7.5s, 161=10s, 241=15s\r\necho 24fps: 81 frame=3.4s, 121=5s, 161=6.7s, 241=10s\r\necho.\r\necho 1) 81 frame (~5s @16fps) - Varsayilan, hizli\r\necho 2) 121 frame (~7.5s) - Orta sure\r\necho 3) 161 frame (~10s) - Uzun\r\necho 4) 241 frame (~15s) - Cok uzun\r\necho 5) Elle gir (4n+1 olmali: 81,121,161,201,241...)\r\necho.\r\nset /p FRAMECHOICE=\" Frame sayisi (1-5, Enter=1): \"\r\nif \"%FRAMECHOICE%\"==\"\" set FRAMECHOICE=1\r\nif \"%FRAMECHOICE%\"==\"1\" set VIDEOLENGTH=81\r\nif \"%FRAMECHOICE%\"==\"2\" set VIDEOLENGTH=121\r\nif \"%FRAMECHOICE%\"==\"3\" set VIDEOLENGTH=161\r\nif \"%FRAMECHOICE%\"==\"4\" set VIDEOLENGTH=241\r\nif \"%FRAMECHOICE%\"==\"5\" (\r\n set /p VIDEOLENGTH=\" Frame sayisi (4n+1): \"\r\n)\r\n\r\nREM Segment suresini frame sayisindan otomatik hesapla\r\nset /a SEGDUR=!VIDEOLENGTH! / !FPS!\r\n\r\necho.\r\necho --- INFERENCE STEPS ---\r\necho 1) 20 - Hizli (test/deneme)\r\necho 2) 30 - Dengeli (Varsayilan)\r\necho 3) 40 - Yuksek kalite\r\necho 4) 50 - Maksimum kalite (yavas)\r\necho 5) Elle gir\r\necho.\r\nset /p STEPCHOICE=\" Steps (1-5, Enter=2): \"\r\nif \"%STEPCHOICE%\"==\"\" set STEPCHOICE=2\r\nif \"%STEPCHOICE%\"==\"1\" set STEPS=20\r\nif \"%STEPCHOICE%\"==\"2\" set STEPS=30\r\nif \"%STEPCHOICE%\"==\"3\" set STEPS=40\r\nif \"%STEPCHOICE%\"==\"4\" set STEPS=50\r\nif \"%STEPCHOICE%\"==\"5\" (\r\n set /p STEPS=\" Steps (1-100): \"\r\n)\r\n\r\necho.\r\necho --- GUIDANCE SCALE ---\r\necho Dusuk = serbest/yaratici, Yuksek = prompt'a bagli\r\necho.\r\necho 1) 3.0 - Serbest (LTX2 icin ideal)\r\necho 2) 5.0 - Dengeli (Wan2 icin ideal, Varsayilan)\r\necho 3) 7.0 - Prompt'a yakin\r\necho 4) Elle gir\r\necho.\r\nset /p GCHOICE=\" Guidance (1-4, Enter=2): \"\r\nif \"%GCHOICE%\"==\"\" set GCHOICE=2\r\nif \"%GCHOICE%\"==\"1\" set GUIDANCE=3.0\r\nif \"%GCHOICE%\"==\"2\" set GUIDANCE=5.0\r\nif \"%GCHOICE%\"==\"3\" set GUIDANCE=7.0\r\nif \"%GCHOICE%\"==\"4\" (\r\n set /p GUIDANCE=\" Guidance Scale: \"\r\n)\r\n\r\necho.\r\necho --- FLOW SHIFT ---\r\necho 1) 5.0 - LTX2 varsayilan\r\necho 2) 7.0 - Wan2 varsayilan\r\necho 3) 10.0 - Daha dinamik\r\necho 4) Elle gir\r\necho.\r\nset /p FSCHOICE=\" Flow Shift (1-4, Enter=2): \"\r\nif \"%FSCHOICE%\"==\"\" set FSCHOICE=2\r\nif \"%FSCHOICE%\"==\"1\" set FLOWSHIFT=5.0\r\nif \"%FSCHOICE%\"==\"2\" set FLOWSHIFT=7.0\r\nif \"%FSCHOICE%\"==\"3\" set FLOWSHIFT=10.0\r\nif \"%FSCHOICE%\"==\"4\" (\r\n set /p FLOWSHIFT=\" Flow Shift (1-25): \"\r\n)\r\n\r\necho.\r\necho --- SOLVER ---\r\necho 1) unipc - Varsayilan (Wan2 icin iyi)\r\necho 2) euler - LTX2 icin ideal\r\necho.\r\nset /p SOLCHOICE=\" Solver (1/2, Enter=1): \"\r\nif \"%SOLCHOICE%\"==\"\" set SOLCHOICE=1\r\nif \"%SOLCHOICE%\"==\"1\" set SOLVER=unipc\r\nif \"%SOLCHOICE%\"==\"2\" set SOLVER=euler\r\n\r\necho.\r\necho --- SEED ---\r\necho -1 = her seferinde rastgele (Varsayilan)\r\necho Sabit sayi = tekrarlanabilir sonuc\r\necho.\r\nset /p SEED=\" Seed (-1=rastgele, Enter=-1): \"\r\nif \"%SEED%\"==\"\" set SEED=-1\r\n\r\necho.\r\necho --- GELISMIS AYARLAR (istege bagli, Enter=atla) ---\r\n\r\necho.\r\necho Perturbation (Detay Cesitliligi)\r\necho 0=Kapali 1=Hafif 2=Normal 3=Guclu 4=Max\r\nset /p PERTURBATION=\" Perturbation (0-4, Enter=0): \"\r\nif \"%PERTURBATION%\"==\"\" set PERTURBATION=0\r\n\r\necho.\r\necho Self-Refiner (Coklu Gecis Kalite Artirma)\r\necho 0=Kapali 1=P1-Norm 2=P2-Norm\r\nset /p SELFREFINER=\" Self-Refiner (0-2, Enter=0): \"\r\nif \"%SELFREFINER%\"==\"\" set SELFREFINER=0\r\n\r\necho.\r\necho Guidance Phases (Coklu Faz Rehberlik)\r\necho Bos=varsayilan 1=Tek faz 2=Iki faz 3=Uc faz\r\nset /p GUIDANCEPHASES=\" Guidance Phases (Enter=varsayilan): \"\r\n\r\necho.\r\necho Film Grain (Film Tane Efekti)\r\necho 0=Kapali, 0.1-1.0=Yogunluk\r\nset /p FILMGRAIN=\" Film Grain (0-1, Enter=0): \"\r\nif \"%FILMGRAIN%\"==\"\" set FILMGRAIN=0\r\n\r\necho.\r\necho LoRA Dosyasi (bos=kullanma)\r\nset /p LORA=\" LoRA dosya adi (Enter=yok): \"\r\nif not \"%LORA%\"==\"\" (\r\n set /p LORAWEIGHT=\" LoRA agirligi (Enter=1.0): \"\r\n if \"!LORAWEIGHT!\"==\"\" set LORAWEIGHT=1.0\r\n)\r\n\r\necho.\r\necho Bitis Gorseli (son sahne icin, bos=kullanma)\r\nset /p ENDIMAGE=\" Bitis gorseli yolu (Enter=yok): \"\r\n\r\n:PROMPT_AYAR\r\n\r\nREM ============================================================\r\nREM SEGMENT SURESI AYARI\r\nREM ============================================================\r\necho.\r\necho ------------------------------------------------------------\r\necho SEGMENT SURESI - Her parca kac saniye olsun?\r\necho ------------------------------------------------------------\r\necho Simdi ayarli: %SEGDUR% saniye / segment\r\necho Model: %MODEL% FPS: %FPS% Frames: %VIDEOLENGTH%\r\necho.\r\necho Uzun segment = daha az gecis, daha akici klip\r\necho Kisa segment = daha fazla sahne degisimi\r\necho.\r\necho 1) 5 saniye - 81 frame Wan / 121 frame LTX - Hizli\r\necho 2) 10 saniye - 161 frame Wan / 241 frame LTX - Dengeli\r\necho 3) 15 saniye - 241 frame Wan / 361 frame LTX - Uzun\r\necho 4) 20 saniye - 321 frame Wan / 481 frame LTX - Cok uzun\r\necho 5) Elle gir\r\necho Enter = Mevcut ayari koru [%SEGDUR%s]\r\necho.\r\nset /p SEGCHOICE=\" Segment suresi [1-5, Enter=mevcut]: \"\r\nif \"%SEGCHOICE%\"==\"\" goto :SEG_DONE\r\nif \"%SEGCHOICE%\"==\"1\" set SEGDUR=5\r\nif \"%SEGCHOICE%\"==\"2\" set SEGDUR=10\r\nif \"%SEGCHOICE%\"==\"3\" set SEGDUR=15\r\nif \"%SEGCHOICE%\"==\"4\" set SEGDUR=20\r\nif \"%SEGCHOICE%\"==\"5\" set /p SEGDUR=\" Segment suresi [saniye]: \"\r\nREM Frame sayisini FPS'e gore hesapla (4n+1 formatina yuvarla)\r\nset /a VIDEOLENGTH=!SEGDUR! * !FPS! + 1\r\nREM 4n+1 formatina yuvarla\r\nset /a _REMAINDER=(!VIDEOLENGTH! - 1) %% 4\r\nif not \"!_REMAINDER!\"==\"0\" set /a VIDEOLENGTH=!VIDEOLENGTH! + 4 - !_REMAINDER!\r\necho Ayarlandi: %SEGDUR%s = !VIDEOLENGTH! frame\r\n:SEG_DONE\r\n\r\nREM ============================================================\r\nREM PROMPT GIRISI (Tum profiller icin ortak)\r\nREM ============================================================\r\necho.\r\necho --- PROMPT ---\r\necho Video icin aciklama girin.\r\necho Enter = Varsayilan muzik klibi prompt'u\r\necho.\r\nset /p VIDPROMPT=\" Prompt: \"\r\nif \"%VIDPROMPT%\"==\"\" set \"VIDPROMPT=cinematic music video, single person performing with deep emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera push, person singing with expressive lip movements matching the music, background music playing\"\r\n\r\necho.\r\necho --- NEGATIF PROMPT ---\r\necho Enter = Varsayilan negatif prompt\r\necho.\r\nset /p NEG=\" Negatif Prompt (Enter=varsayilan): \"\r\nif not \"%NEG%\"==\"\" set \"NEG_PROMPT=%NEG%\"\r\n\r\nREM ============================================================\r\nREM FACE SWAP\r\nREM ============================================================\r\necho.\r\necho --- FACE SWAP ---\r\necho 1) Kapali (Varsayilan)\r\necho 2) Acik - Yuz resmi sec\r\necho.\r\nset /p FSWCHOICE=\" Face Swap (1/2, Enter=1): \"\r\nif \"%FSWCHOICE%\"==\"\" set FSWCHOICE=1\r\nset FACE_SWAP_ARG=\r\nif \"%FSWCHOICE%\"==\"2\" (\r\n echo.\r\n set /p FACEIMG=\" Yuz resmi yolu (png/jpg): \"\r\n if not \"!FACEIMG!\"==\"\" set \"FACE_SWAP_ARG=--face-swap \"!FACEIMG!\"\"\r\n)\r\n\r\nREM ============================================================\r\nREM LIP-SYNC AYARLARI\r\nREM ============================================================\r\necho.\r\necho ------------------------------------------------------------\r\necho LIP-SYNC - Dudak Senkronizasyonu\r\necho ------------------------------------------------------------\r\necho.\r\necho 1) LIP-SYNC ACIK - Dudaklar muzige uyar - ONERILEN\r\necho 2) LIP-SYNC KAPALI - Ses sadece son videoya eklenir\r\necho.\r\nset /p LIPSYNC_CHOICE=\" Lip-sync secin [1-2, Enter=1]: \"\r\nif \"%LIPSYNC_CHOICE%\"==\"\" set LIPSYNC_CHOICE=1\r\nif \"%LIPSYNC_CHOICE%\"==\"2\" set NOLIPSYNC=1\r\nif \"%NOLIPSYNC%\"==\"1\" goto :SKIP_VOCAL\r\necho.\r\necho Vokal izolasyonu arka plan muzigini ayirir,\r\necho lip-sync icin daha temiz ses saglar.\r\necho.\r\necho 1) VOKAL IZOLASYONU ACIK - ONERILEN\r\necho 2) VOKAL IZOLASYONU KAPALI\r\necho.\r\nset /p VOCAL_CHOICE=\" Secin [1-2, Enter=1]: \"\r\nif \"%VOCAL_CHOICE%\"==\"\" set VOCAL_CHOICE=1\r\nif \"%VOCAL_CHOICE%\"==\"2\" set NOVOCALISOL=1\r\n:SKIP_VOCAL\r\n\r\nREM ============================================================\r\nREM OZET VE BASLATMA\r\nREM ============================================================\r\necho.\r\necho ============================================================\r\necho AYARLAR OZETI\r\necho ============================================================\r\necho Resim : %IMAGE%\r\necho Sarki : %AUDIO%\r\necho Model : %MODEL%\r\necho Cozunurluk: %RESOLUTION%\r\necho Frames : %VIDEOLENGTH% (%SEGDUR%s segment)\r\necho Steps : %STEPS%\r\necho Guidance : %GUIDANCE%\r\necho Flow Shift: %FLOWSHIFT%\r\necho Solver : %SOLVER%\r\necho Seed : %SEED%\r\necho FPS : %FPS%\r\necho Prompt : %VIDPROMPT:~0,50%...\r\nif not \"%LORA%\"==\"\" echo LoRA : %LORA% (x%LORAWEIGHT%)\r\nif not \"%PERTURBATION%\"==\"0\" echo Perturbat.: %PERTURBATION%\r\nif not \"%SELFREFINER%\"==\"0\" echo Refiner : %SELFREFINER%\r\nif not \"%GUIDANCEPHASES%\"==\"\" echo G.Phases : %GUIDANCEPHASES%\r\nif not \"%FILMGRAIN%\"==\"0\" echo Film Grain: %FILMGRAIN%\r\nif not \"%FACEIMG%\"==\"\" echo Face Swap : %FACEIMG%\r\nif not \"%ENDIMAGE%\"==\"\" echo End Image : %ENDIMAGE%\r\nif \"%NOLIPSYNC%\"==\"1\" echo Lip-Sync : KAPALI\r\nif \"%NOLIPSYNC%\"==\"0\" echo Lip-Sync : ACIK\r\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"0\" echo Vokal Izol: ACIK\r\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"1\" echo Vokal Izol: KAPALI\r\necho ============================================================\r\necho NOT: WanGP'nin localhost:7860'da acik olmasi gerekir!\r\necho ============================================================\r\necho.\r\necho Baslatmak icin Enter'a basin, iptal icin Ctrl+C...\r\npause >nul\r\n\r\nREM --- Komut olustur ---\r\nset CMD=%PYTHON% %SCRIPT% \"%AUDIO%\" \"%IMAGE%\"\r\nset CMD=%CMD% --model %MODEL%\r\nset CMD=%CMD% --resolution %RESOLUTION%\r\nset CMD=%CMD% --video-length %VIDEOLENGTH%\r\nset CMD=%CMD% --steps %STEPS%\r\nset CMD=%CMD% --guidance-scale %GUIDANCE%\r\nset CMD=%CMD% --flow-shift %FLOWSHIFT%\r\nset CMD=%CMD% --solver %SOLVER%\r\nset CMD=%CMD% --seed %SEED%\r\nset CMD=%CMD% --fps %FPS%\r\nset CMD=%CMD% --segment-duration %SEGDUR%\r\nset CMD=%CMD% --prompt \"%VIDPROMPT%\"\r\nset CMD=%CMD% --negative-prompt \"%NEG_PROMPT%\"\r\n\r\nif not \"%LORA%\"==\"\" (\r\n set CMD=!CMD! --lora \"%LORA%\" --lora-weight %LORAWEIGHT%\r\n)\r\nif not \"%PERTURBATION%\"==\"0\" (\r\n set CMD=!CMD! --perturbation %PERTURBATION%\r\n)\r\nif not \"%SELFREFINER%\"==\"0\" (\r\n set CMD=!CMD! --self-refiner %SELFREFINER%\r\n)\r\nif not \"%GUIDANCEPHASES%\"==\"\" (\r\n set CMD=!CMD! --guidance-phases %GUIDANCEPHASES%\r\n)\r\nif not \"%FILMGRAIN%\"==\"0\" (\r\n set CMD=!CMD! --film-grain %FILMGRAIN%\r\n)\r\nif not \"%ENDIMAGE%\"==\"\" (\r\n set CMD=!CMD! --image-end \"%ENDIMAGE%\"\r\n)\r\nif not \"%FACEIMG%\"==\"\" (\r\n set CMD=!CMD! --face-swap \"%FACEIMG%\"\r\n)\r\nif \"%NOLIPSYNC%\"==\"1\" (\r\n set CMD=!CMD! --no-lip-sync\r\n)\r\nif \"%NOVOCALISOL%\"==\"1\" (\r\n set CMD=!CMD! --no-vocal-isolation\r\n)\r\n\r\necho.\r\necho [*] Calistiriliyor...\r\necho.\r\n%CMD%\r\n\r\necho.\r\necho ============================================================\r\necho Islem tamamlandi!\r\necho ============================================================\r\necho.\r\npause","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"705002e9-5f74-4429-8920-fd801eb7945b","timestamp":"2026-04-19T21:59:53.025Z","tool_use_result":{"stdout":"@echo off\r\ntitle WanGP Muzik Video Uretici\r\nsetlocal enabledelayedexpansion\r\n\r\nREM ── CUDA OOM FIX: Bellek parcalanmasini onle ──\r\nset PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\r\n\r\nset PYTHON=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\backend\\.venv\\Scripts\\python.exe\"\r\nset SCRIPT=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\wangp_video_uret.py\"\r\n\r\nset IMAGE=\r\nset AUDIO=\r\nset FACEIMG=\r\n\r\necho.\r\necho ============================================================\r\necho WANGP MUZIK VIDEO URETICI\r\necho RTX 4090 Optimize - Hizli ve Kaliteli\r\necho ============================================================\r\necho.\r\n\r\nREM --- Surukle-birak ile gelen dosyalari ayikla ---\r\nfor %%A in (%*) do (\r\n set \"FILE=%%~A\"\r\n set \"EXT=%%~xA\"\r\n\r\n if /i \"!EXT!\"==\".png\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".jpg\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".jpeg\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".webp\" set \"IMAGE=%%~A\"\r\n if /i \"!EXT!\"==\".bmp\" set \"IMAGE=%%~A\"\r\n\r\n if /i \"!EXT!\"==\".mp3\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".wav\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".flac\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".m4a\" set \"AUDIO=%%~A\"\r\n if /i \"!EXT!\"==\".ogg\" set \"AUDIO=%%~A\"\r\n)\r\n\r\nREM --- Dosya bulunamadiysa sor ---\r\nif \"%IMAGE%\"==\"\" (\r\n echo [!] Resim dosyasi bulunamadi.\r\n echo Resim ve sarki dosyalarini bu BAT'in ustune surukleyin.\r\n echo.\r\n echo VEYA asagiya elle yazin:\r\n echo.\r\n set /p IMAGE=\" Resim yolu (png/jpg): \"\r\n)\r\nif \"%AUDIO%\"==\"\" (\r\n echo.\r\n echo [!] Sarki dosyasi bulunamadi.\r\n set /p AUDIO=\" Sarki yolu (mp3/wav): \"\r\n)\r\nif \"%IMAGE%\"==\"\" (\r\n echo HATA: Resim dosyasi gerekli!\r\n pause\r\n exit /b 1\r\n)\r\nif \"%AUDIO%\"==\"\" (\r\n echo HATA: Sarki dosyasi gerekli!\r\n pause\r\n exit /b 1\r\n)\r\n\r\necho.\r\necho Resim : %IMAGE%\r\necho Sarki : %AUDIO%\r\necho.\r\n\r\nREM ============================================================\r\nREM PROFIL SECIMI\r\nREM ============================================================\r\necho ------------------------------------------------------------\r\necho PROFIL SEC - RTX 4090 24GB + 64GB RAM Icin Optimize\r\necho ------------------------------------------------------------\r\necho.\r\necho *** LTX-2.3 - ONERILEN - Hizli, Gercekci, Lip-Sync ***\r\necho.\r\necho 1) LTX2 HIZLI - LTX2 22B Distilled 480p, 10s\r\necho ~35sn/segment ONERILEN - en hizli, iyi kalite\r\necho.\r\necho 2) LTX2 HD - LTX2 22B 720p, 10s\r\necho ~90sn/segment 720p HD, en kaliteli\r\necho.\r\necho 3) LTX2 KALITE++ - LTX2 22B 720p, perturbation+phases\r\necho ~120sn/segment Tum LTX2 ozellikler acik, profesyonel\r\necho.\r\necho *** WAN2.1 IMAGE2VIDEO - Farkli Stil [LIP-SYNC YOK!] ***\r\necho.\r\necho 4) WAN2 HIZLI - Wan2.1 I2V 480p, 10s\r\necho ~80sn/segment Wan2 stili, LIP-SYNC DESTEKLEMIYOR\r\necho.\r\necho 5) WAN2 KALITE - Wan2.1 I2V 480p, 15s\r\necho ~150sn/segment Uzun sahneler, LIP-SYNC DESTEKLEMIYOR\r\necho.\r\necho 6) OZEL AYARLAR - Tum parametreleri elle gir\r\necho.\r\necho ------------------------------------------------------------\r\necho.\r\nset /p PROFIL=\" Profil secin [1-6, Enter=1]: \"\r\nif \"%PROFIL%\"==\"\" set PROFIL=1\r\n\r\nREM --- Varsayilan degerler (LTX2 Distilled) ---\r\nset MODEL=ltx2_22B_distilled\r\nset RESOLUTION=832x480\r\nset VIDEOLENGTH=241\r\nset STEPS=20\r\nset GUIDANCE=3.0\r\nset FLOWSHIFT=5.0\r\nset SOLVER=euler\r\nset SEGDUR=10\r\nset FPS=24\r\nset \"NEG_PROMPT=blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, cartoon, animated, watermark, text, logo, static, frozen, jerky motion\"\r\nset SLIDINGWIN=0\r\nset SLIDINGOVERLAP=1\r\nset PERTURBATION=0\r\nset SELFREFINER=0\r\nset REPEAT=1\r\nset SEED=-1\r\nset LORA=\r\nset LORAWEIGHT=1.0\r\nset ENDIMAGE=\r\nset AUDIOGUIDE=\r\nset AUDIOSCALE=\r\nset ALTGUIDANCE=\r\nset ALTSCALE=\r\nset EMBEDDEDGUIDE=\r\nset GUIDANCEPHASES=\r\nset FILMGRAIN=0\r\nset TEMPORALUP=\r\nset SPATIALUP=\r\nset NOLIPSYNC=0\r\nset NOVOCALISOL=0\r\n\r\nREM --- Profil ayarlari (RTX 4090 24GB optimize) ---\r\nREM Profil 1: LTX2 HIZLI (varsayilan degerler zaten LTX2 Distilled)\r\nif \"%PROFIL%\"==\"1\" (\r\n set MODEL=ltx2_22B_distilled\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=241\r\n set STEPS=20\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nREM Profil 2: LTX2 HD\r\nif \"%PROFIL%\"==\"2\" (\r\n set MODEL=ltx2_22B\r\n set RESOLUTION=1280x720\r\n set VIDEOLENGTH=241\r\n set STEPS=30\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n set GUIDANCEPHASES=2\r\n set ALTGUIDANCE=3.0\r\n set ALTSCALE=0.7\r\n)\r\nREM Profil 3: LTX2 KALITE++\r\nif \"%PROFIL%\"==\"3\" (\r\n set MODEL=ltx2_22B\r\n set RESOLUTION=1280x720\r\n set VIDEOLENGTH=241\r\n set STEPS=40\r\n set GUIDANCE=3.0\r\n set FLOWSHIFT=5.0\r\n set SEGDUR=10\r\n set FPS=24\r\n set SOLVER=euler\r\n set GUIDANCEPHASES=2\r\n set ALTGUIDANCE=3.0\r\n set ALTSCALE=0.7\r\n set PERTURBATION=2\r\n set AUDIOSCALE=1.0\r\n)\r\nREM Profil 4: WAN2 HIZLI\r\nif \"%PROFIL%\"==\"4\" (\r\n set MODEL=i2v\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=161\r\n set STEPS=30\r\n set GUIDANCE=5.0\r\n set FLOWSHIFT=7.0\r\n set SEGDUR=10\r\n set FPS=16\r\n set SOLVER=unipc\r\n)\r\nREM Profil 5: WAN2 KALITE\r\nif \"%PROFIL%\"==\"5\" (\r\n set MODEL=i2v\r\n set RESOLUTION=832x480\r\n set VIDEOLENGTH=241\r\n set STEPS=30\r\n set GUIDANCE=5.0\r\n set FLOWSHIFT=7.0\r\n set SEGDUR=15\r\n set FPS=16\r\n set SOLVER=unipc\r\n)\r\n\r\nif not \"%PROFIL%\"==\"6\" goto :PROMPT_AYAR\r\n\r\nREM ============================================================\r\nREM OZEL AYARLAR (Profil 6)\r\nREM ============================================================\r\n\r\necho.\r\necho --- MODEL SECIMI ---\r\necho *** YUKLU MODELLER ***\r\necho 1) i2v - Wan2.1 I2V 480p (Varsayilan)\r\necho 2) ltx2_22B - LTX-2 22B (En kaliteli)\r\necho 3) ltx2_22B_distilled - LTX-2 22B Distilled (Hizli)\r\necho.\r\necho *** INDIRILMESI GEREKEN (otomatik indirir) ***\r\necho 4) i2v_720p - Wan2.1 I2V 720p\r\necho 5) i2v_2_2 - Wan2.1 2.2 I2V (Yeni versiyon)\r\necho 6) t2v - Wan2.1 Text2Video\r\necho 7) hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\r\necho 8) Elle gir (model ID)\r\necho.\r\nset /p MODELCHOICE=\" Model (1-8, Enter=1): \"\r\nif \"%MODELCHOICE%\"==\"\" set MODELCHOICE=1\r\nif \"%MODELCHOICE%\"==\"1\" set MODEL=i2v\r\nif \"%MODELCHOICE%\"==\"2\" (\r\n set MODEL=ltx2_22B\r\n set FLOWSHIFT=5.0\r\n set GUIDANCE=3.0\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nif \"%MODELCHOICE%\"==\"3\" (\r\n set MODEL=ltx2_22B_distilled\r\n set FLOWSHIFT=5.0\r\n set GUIDANCE=3.0\r\n set FPS=24\r\n set SOLVER=euler\r\n)\r\nif \"%MODELCHOICE%\"==\"4\" (\r\n set MODEL=i2v_720p\r\n set RESOLUTION=1280x720\r\n)\r\nif \"%MODELCHOICE%\"==\"5\" set MODEL=i2v_2_2\r\nif \"%MODELCHOICE%\"==\"6\" set MODEL=t2v\r\nif \"%MODELCHOICE%\"==\"7\" set MODEL=hunyuan_1_5_i2v\r\nif \"%MODELCHOICE%\"==\"8\" (\r\n set /p MODEL=\" Model ID (orn: i2v_2_2): \"\r\n)\r\n\r\necho.\r\necho --- COZUNURLUK ---\r\necho 1) 832x480 - 480p 16:9 (Varsayilan - Hizli)\r\necho 2) 480x832 - 480p 9:16 (Dikey/Shorts)\r\necho 3) 1280x720 - 720p 16:9 (HD)\r\necho 4) 720x1280 - 720p 9:16 (HD Dikey)\r\necho 5) 960x544 - 540p 16:9\r\necho 6) Elle gir (WIDTHxHEIGHT)\r\necho.\r\nset /p RESCHOICE=\" Cozunurluk (1-6, Enter=1): \"\r\nif \"%RESCHOICE%\"==\"\" set RESCHOICE=1\r\nif \"%RESCHOICE%\"==\"1\" set RESOLUTION=832x480\r\nif \"%RESCHOICE%\"==\"2\" set RESOLUTION=480x832\r\nif \"%RESCHOICE%\"==\"3\" set RESOLUTION=1280x720\r\nif \"%RESCHOICE%\"==\"4\" set RESOLUTION=720x1280\r\nif \"%RESCHOICE%\"==\"5\" set RESOLUTION=960x544\r\nif \"%RESCHOICE%\"==\"6\" (\r\n set /p RESOLUTION=\" Cozunurluk (WxH): \"\r\n)\r\n\r\necho.\r\necho --- SEGMENT SURESI / FRAME SAYISI ---\r\necho 16fps: 81 frame=5s, 121=7.5s, 161=10s, 241=15s\r\necho 24fps: 81 frame=3.4s, 121=5s, 161=6.7s, 241=10s\r\necho.\r\necho 1) 81 frame (~5s @16fps) - Varsayilan, hizli\r\necho 2) 121 frame (~7.5s) - Orta sure\r\necho 3) 161 frame (~10s) - Uzun\r\necho 4) 241 frame (~15s) - Cok uzun\r\necho 5) Elle gir (4n+1 olmali: 81,121,161,201,241...)\r\necho.\r\nset /p FRAMECHOICE=\" Frame sayisi (1-5, Enter=1): \"\r\nif \"%FRAMECHOICE%\"==\"\" set FRAMECHOICE=1\r\nif \"%FRAMECHOICE%\"==\"1\" set VIDEOLENGTH=81\r\nif \"%FRAMECHOICE%\"==\"2\" set VIDEOLENGTH=121\r\nif \"%FRAMECHOICE%\"==\"3\" set VIDEOLENGTH=161\r\nif \"%FRAMECHOICE%\"==\"4\" set VIDEOLENGTH=241\r\nif \"%FRAMECHOICE%\"==\"5\" (\r\n set /p VIDEOLENGTH=\" Frame sayisi (4n+1): \"\r\n)\r\n\r\nREM Segment suresini frame sayisindan otomatik hesapla\r\nset /a SEGDUR=!VIDEOLENGTH! / !FPS!\r\n\r\necho.\r\necho --- INFERENCE STEPS ---\r\necho 1) 20 - Hizli (test/deneme)\r\necho 2) 30 - Dengeli (Varsayilan)\r\necho 3) 40 - Yuksek kalite\r\necho 4) 50 - Maksimum kalite (yavas)\r\necho 5) Elle gir\r\necho.\r\nset /p STEPCHOICE=\" Steps (1-5, Enter=2): \"\r\nif \"%STEPCHOICE%\"==\"\" set STEPCHOICE=2\r\nif \"%STEPCHOICE%\"==\"1\" set STEPS=20\r\nif \"%STEPCHOICE%\"==\"2\" set STEPS=30\r\nif \"%STEPCHOICE%\"==\"3\" set STEPS=40\r\nif \"%STEPCHOICE%\"==\"4\" set STEPS=50\r\nif \"%STEPCHOICE%\"==\"5\" (\r\n set /p STEPS=\" Steps (1-100): \"\r\n)\r\n\r\necho.\r\necho --- GUIDANCE SCALE ---\r\necho Dusuk = serbest/yaratici, Yuksek = prompt'a bagli\r\necho.\r\necho 1) 3.0 - Serbest (LTX2 icin ideal)\r\necho 2) 5.0 - Dengeli (Wan2 icin ideal, Varsayilan)\r\necho 3) 7.0 - Prompt'a yakin\r\necho 4) Elle gir\r\necho.\r\nset /p GCHOICE=\" Guidance (1-4, Enter=2): \"\r\nif \"%GCHOICE%\"==\"\" set GCHOICE=2\r\nif \"%GCHOICE%\"==\"1\" set GUIDANCE=3.0\r\nif \"%GCHOICE%\"==\"2\" set GUIDANCE=5.0\r\nif \"%GCHOICE%\"==\"3\" set GUIDANCE=7.0\r\nif \"%GCHOICE%\"==\"4\" (\r\n set /p GUIDANCE=\" Guidance Scale: \"\r\n)\r\n\r\necho.\r\necho --- FLOW SHIFT ---\r\necho 1) 5.0 - LTX2 varsayilan\r\necho 2) 7.0 - Wan2 varsayilan\r\necho 3) 10.0 - Daha dinamik\r\necho 4) Elle gir\r\necho.\r\nset /p FSCHOICE=\" Flow Shift (1-4, Enter=2): \"\r\nif \"%FSCHOICE%\"==\"\" set FSCHOICE=2\r\nif \"%FSCHOICE%\"==\"1\" set FLOWSHIFT=5.0\r\nif \"%FSCHOICE%\"==\"2\" set FLOWSHIFT=7.0\r\nif \"%FSCHOICE%\"==\"3\" set FLOWSHIFT=10.0\r\nif \"%FSCHOICE%\"==\"4\" (\r\n set /p FLOWSHIFT=\" Flow Shift (1-25): \"\r\n)\r\n\r\necho.\r\necho --- SOLVER ---\r\necho 1) unipc - Varsayilan (Wan2 icin iyi)\r\necho 2) euler - LTX2 icin ideal\r\necho.\r\nset /p SOLCHOICE=\" Solver (1/2, Enter=1): \"\r\nif \"%SOLCHOICE%\"==\"\" set SOLCHOICE=1\r\nif \"%SOLCHOICE%\"==\"1\" set SOLVER=unipc\r\nif \"%SOLCHOICE%\"==\"2\" set SOLVER=euler\r\n\r\necho.\r\necho --- SEED ---\r\necho -1 = her seferinde rastgele (Varsayilan)\r\necho Sabit sayi = tekrarlanabilir sonuc\r\necho.\r\nset /p SEED=\" Seed (-1=rastgele, Enter=-1): \"\r\nif \"%SEED%\"==\"\" set SEED=-1\r\n\r\necho.\r\necho --- GELISMIS AYARLAR (istege bagli, Enter=atla) ---\r\n\r\necho.\r\necho Perturbation (Detay Cesitliligi)\r\necho 0=Kapali 1=Hafif 2=Normal 3=Guclu 4=Max\r\nset /p PERTURBATION=\" Perturbation (0-4, Enter=0): \"\r\nif \"%PERTURBATION%\"==\"\" set PERTURBATION=0\r\n\r\necho.\r\necho Self-Refiner (Coklu Gecis Kalite Artirma)\r\necho 0=Kapali 1=P1-Norm 2=P2-Norm\r\nset /p SELFREFINER=\" Self-Refiner (0-2, Enter=0): \"\r\nif \"%SELFREFINER%\"==\"\" set SELFREFINER=0\r\n\r\necho.\r\necho Guidance Phases (Coklu Faz Rehberlik)\r\necho Bos=varsayilan 1=Tek faz 2=Iki faz 3=Uc faz\r\nset /p GUIDANCEPHASES=\" Guidance Phases (Enter=varsayilan): \"\r\n\r\necho.\r\necho Film Grain (Film Tane Efekti)\r\necho 0=Kapali, 0.1-1.0=Yogunluk\r\nset /p FILMGRAIN=\" Film Grain (0-1, Enter=0): \"\r\nif \"%FILMGRAIN%\"==\"\" set FILMGRAIN=0\r\n\r\necho.\r\necho LoRA Dosyasi (bos=kullanma)\r\nset /p LORA=\" LoRA dosya adi (Enter=yok): \"\r\nif not \"%LORA%\"==\"\" (\r\n set /p LORAWEIGHT=\" LoRA agirligi (Enter=1.0): \"\r\n if \"!LORAWEIGHT!\"==\"\" set LORAWEIGHT=1.0\r\n)\r\n\r\necho.\r\necho Bitis Gorseli (son sahne icin, bos=kullanma)\r\nset /p ENDIMAGE=\" Bitis gorseli yolu (Enter=yok): \"\r\n\r\n:PROMPT_AYAR\r\n\r\nREM ============================================================\r\nREM SEGMENT SURESI AYARI\r\nREM ============================================================\r\necho.\r\necho ------------------------------------------------------------\r\necho SEGMENT SURESI - Her parca kac saniye olsun?\r\necho ------------------------------------------------------------\r\necho Simdi ayarli: %SEGDUR% saniye / segment\r\necho Model: %MODEL% FPS: %FPS% Frames: %VIDEOLENGTH%\r\necho.\r\necho Uzun segment = daha az gecis, daha akici klip\r\necho Kisa segment = daha fazla sahne degisimi\r\necho.\r\necho 1) 5 saniye - 81 frame Wan / 121 frame LTX - Hizli\r\necho 2) 10 saniye - 161 frame Wan / 241 frame LTX - Dengeli\r\necho 3) 15 saniye - 241 frame Wan / 361 frame LTX - Uzun\r\necho 4) 20 saniye - 321 frame Wan / 481 frame LTX - Cok uzun\r\necho 5) Elle gir\r\necho Enter = Mevcut ayari koru [%SEGDUR%s]\r\necho.\r\nset /p SEGCHOICE=\" Segment suresi [1-5, Enter=mevcut]: \"\r\nif \"%SEGCHOICE%\"==\"\" goto :SEG_DONE\r\nif \"%SEGCHOICE%\"==\"1\" set SEGDUR=5\r\nif \"%SEGCHOICE%\"==\"2\" set SEGDUR=10\r\nif \"%SEGCHOICE%\"==\"3\" set SEGDUR=15\r\nif \"%SEGCHOICE%\"==\"4\" set SEGDUR=20\r\nif \"%SEGCHOICE%\"==\"5\" set /p SEGDUR=\" Segment suresi [saniye]: \"\r\nREM Frame sayisini FPS'e gore hesapla (4n+1 formatina yuvarla)\r\nset /a VIDEOLENGTH=!SEGDUR! * !FPS! + 1\r\nREM 4n+1 formatina yuvarla\r\nset /a _REMAINDER=(!VIDEOLENGTH! - 1) %% 4\r\nif not \"!_REMAINDER!\"==\"0\" set /a VIDEOLENGTH=!VIDEOLENGTH! + 4 - !_REMAINDER!\r\necho Ayarlandi: %SEGDUR%s = !VIDEOLENGTH! frame\r\n:SEG_DONE\r\n\r\nREM ============================================================\r\nREM PROMPT GIRISI (Tum profiller icin ortak)\r\nREM ============================================================\r\necho.\r\necho --- PROMPT ---\r\necho Video icin aciklama girin.\r\necho Enter = Varsayilan muzik klibi prompt'u\r\necho.\r\nset /p VIDPROMPT=\" Prompt: \"\r\nif \"%VIDPROMPT%\"==\"\" set \"VIDPROMPT=cinematic music video, single person performing with deep emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera push, person singing with expressive lip movements matching the music, background music playing\"\r\n\r\necho.\r\necho --- NEGATIF PROMPT ---\r\necho Enter = Varsayilan negatif prompt\r\necho.\r\nset /p NEG=\" Negatif Prompt (Enter=varsayilan): \"\r\nif not \"%NEG%\"==\"\" set \"NEG_PROMPT=%NEG%\"\r\n\r\nREM ============================================================\r\nREM FACE SWAP\r\nREM ============================================================\r\necho.\r\necho --- FACE SWAP ---\r\necho 1) Kapali (Varsayilan)\r\necho 2) Acik - Yuz resmi sec\r\necho.\r\nset /p FSWCHOICE=\" Face Swap (1/2, Enter=1): \"\r\nif \"%FSWCHOICE%\"==\"\" set FSWCHOICE=1\r\nset FACE_SWAP_ARG=\r\nif \"%FSWCHOICE%\"==\"2\" (\r\n echo.\r\n set /p FACEIMG=\" Yuz resmi yolu (png/jpg): \"\r\n if not \"!FACEIMG!\"==\"\" set \"FACE_SWAP_ARG=--face-swap \"!FACEIMG!\"\"\r\n)\r\n\r\nREM ============================================================\r\nREM LIP-SYNC AYARLARI\r\nREM ============================================================\r\necho.\r\necho ------------------------------------------------------------\r\necho LIP-SYNC - Dudak Senkronizasyonu\r\necho ------------------------------------------------------------\r\necho.\r\necho 1) LIP-SYNC ACIK - Dudaklar muzige uyar - ONERILEN\r\necho 2) LIP-SYNC KAPALI - Ses sadece son videoya eklenir\r\necho.\r\nset /p LIPSYNC_CHOICE=\" Lip-sync secin [1-2, Enter=1]: \"\r\nif \"%LIPSYNC_CHOICE%\"==\"\" set LIPSYNC_CHOICE=1\r\nif \"%LIPSYNC_CHOICE%\"==\"2\" set NOLIPSYNC=1\r\nif \"%NOLIPSYNC%\"==\"1\" goto :SKIP_VOCAL\r\necho.\r\necho Vokal izolasyonu arka plan muzigini ayirir,\r\necho lip-sync icin daha temiz ses saglar.\r\necho.\r\necho 1) VOKAL IZOLASYONU ACIK - ONERILEN\r\necho 2) VOKAL IZOLASYONU KAPALI\r\necho.\r\nset /p VOCAL_CHOICE=\" Secin [1-2, Enter=1]: \"\r\nif \"%VOCAL_CHOICE%\"==\"\" set VOCAL_CHOICE=1\r\nif \"%VOCAL_CHOICE%\"==\"2\" set NOVOCALISOL=1\r\n:SKIP_VOCAL\r\n\r\nREM ============================================================\r\nREM OZET VE BASLATMA\r\nREM ============================================================\r\necho.\r\necho ============================================================\r\necho AYARLAR OZETI\r\necho ============================================================\r\necho Resim : %IMAGE%\r\necho Sarki : %AUDIO%\r\necho Model : %MODEL%\r\necho Cozunurluk: %RESOLUTION%\r\necho Frames : %VIDEOLENGTH% (%SEGDUR%s segment)\r\necho Steps : %STEPS%\r\necho Guidance : %GUIDANCE%\r\necho Flow Shift: %FLOWSHIFT%\r\necho Solver : %SOLVER%\r\necho Seed : %SEED%\r\necho FPS : %FPS%\r\necho Prompt : %VIDPROMPT:~0,50%...\r\nif not \"%LORA%\"==\"\" echo LoRA : %LORA% (x%LORAWEIGHT%)\r\nif not \"%PERTURBATION%\"==\"0\" echo Perturbat.: %PERTURBATION%\r\nif not \"%SELFREFINER%\"==\"0\" echo Refiner : %SELFREFINER%\r\nif not \"%GUIDANCEPHASES%\"==\"\" echo G.Phases : %GUIDANCEPHASES%\r\nif not \"%FILMGRAIN%\"==\"0\" echo Film Grain: %FILMGRAIN%\r\nif not \"%FACEIMG%\"==\"\" echo Face Swap : %FACEIMG%\r\nif not \"%ENDIMAGE%\"==\"\" echo End Image : %ENDIMAGE%\r\nif \"%NOLIPSYNC%\"==\"1\" echo Lip-Sync : KAPALI\r\nif \"%NOLIPSYNC%\"==\"0\" echo Lip-Sync : ACIK\r\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"0\" echo Vokal Izol: ACIK\r\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"1\" echo Vokal Izol: KAPALI\r\necho ============================================================\r\necho NOT: WanGP'nin localhost:7860'da acik olmasi gerekir!\r\necho ============================================================\r\necho.\r\necho Baslatmak icin Enter'a basin, iptal icin Ctrl+C...\r\npause >nul\r\n\r\nREM --- Komut olustur ---\r\nset CMD=%PYTHON% %SCRIPT% \"%AUDIO%\" \"%IMAGE%\"\r\nset CMD=%CMD% --model %MODEL%\r\nset CMD=%CMD% --resolution %RESOLUTION%\r\nset CMD=%CMD% --video-length %VIDEOLENGTH%\r\nset CMD=%CMD% --steps %STEPS%\r\nset CMD=%CMD% --guidance-scale %GUIDANCE%\r\nset CMD=%CMD% --flow-shift %FLOWSHIFT%\r\nset CMD=%CMD% --solver %SOLVER%\r\nset CMD=%CMD% --seed %SEED%\r\nset CMD=%CMD% --fps %FPS%\r\nset CMD=%CMD% --segment-duration %SEGDUR%\r\nset CMD=%CMD% --prompt \"%VIDPROMPT%\"\r\nset CMD=%CMD% --negative-prompt \"%NEG_PROMPT%\"\r\n\r\nif not \"%LORA%\"==\"\" (\r\n set CMD=!CMD! --lora \"%LORA%\" --lora-weight %LORAWEIGHT%\r\n)\r\nif not \"%PERTURBATION%\"==\"0\" (\r\n set CMD=!CMD! --perturbation %PERTURBATION%\r\n)\r\nif not \"%SELFREFINER%\"==\"0\" (\r\n set CMD=!CMD! --self-refiner %SELFREFINER%\r\n)\r\nif not \"%GUIDANCEPHASES%\"==\"\" (\r\n set CMD=!CMD! --guidance-phases %GUIDANCEPHASES%\r\n)\r\nif not \"%FILMGRAIN%\"==\"0\" (\r\n set CMD=!CMD! --film-grain %FILMGRAIN%\r\n)\r\nif not \"%ENDIMAGE%\"==\"\" (\r\n set CMD=!CMD! --image-end \"%ENDIMAGE%\"\r\n)\r\nif not \"%FACEIMG%\"==\"\" (\r\n set CMD=!CMD! --face-swap \"%FACEIMG%\"\r\n)\r\nif \"%NOLIPSYNC%\"==\"1\" (\r\n set CMD=!CMD! --no-lip-sync\r\n)\r\nif \"%NOVOCALISOL%\"==\"1\" (\r\n set CMD=!CMD! --no-vocal-isolation\r\n)\r\n\r\necho.\r\necho [*] Calistiriliyor...\r\necho.\r\n%CMD%\r\n\r\necho.\r\necho ============================================================\r\necho Islem tamamlandi!\r\necho ============================================================\r\necho.\r\npause","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T21:59:53.269Z","_audit_hmac":"51273db077aadf2126d43bbe1ef5ea3d41e59568cab8eae3c07c4af796a1adff"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013vEcU2ooMUqUAgCC1d2EoS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DFxKDREPBu3c4c8K2nYdZZ","name":"Bash","input":{"command":"cat /sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py","description":"Read WanGP Python script"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":677,"cache_read_input_tokens":29004,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":677},"output_tokens":64,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"447deeb1-4d10-4394-a456-556e46e157fc","_audit_timestamp":"2026-04-19T21:59:53.271Z","_audit_hmac":"73b3841cdd828dbddf32c3c97ca12029001d4a7608226074590df2ca8841347f"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DFxKDREPBu3c4c8K2nYdZZ","type":"tool_result","content":"\nOutput too large (57.8KB). Full output saved to: /sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt\n\nPreview (first 2KB):\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nWanGP Müzik Video Üretici\n==========================\nWanGP'nin Python API'sini kullanarak şarkı + görsel girişinden\notomatik uzun müzik videosu üretir.\n\nPipeline:\n1. Şarkıyı segmentlere böl (ffmpeg)\n2. Her segment için WanGP API ile video üret\n - image_start: giriş görseli (ilk segment) veya önceki videonun son karesi\n - audio_guide: o segmente karşılık gelen ses parçası (lip-sync için)\n3. Her videonun son karesini bir sonraki videonun giriş görseli olarak kullan\n4. Tüm videoları birleştir (ffmpeg)\n5. Orijinal sesi tam olarak ekle (ffmpeg)\n\nKullanım:\n python wangp_video_uret.py [seçenekler]\n\n veya sürükle-bırak ile WanGP-Video-Uret.bat kullanın.\n\nDesteklenen Modeller (örnekler):\n i2v - Wan2.1 Image2Video 480p 14B\n i2v_720p - Wan2.1 Image2Video 720p 14B\n i2v_2_2 - Wan2.1 2.2 Image2Video\n t2v - Wan2.1 Text2Video 14B\n t2v_2_2 - Wan2.1 2.2 Text2Video\n ltx2_22B - LTX-2 22B\n ltx2_22B_distilled - LTX-2 22B Distilled (hızlı)\n ltx2_19B - LTX-2 19B\n hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\n hunyuan_1_5_t2v - HunyuanVideo 1.5 T2V\n ... ve daha fazlası (defaults/ klasöründeki tüm modeller)\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport time\nimport shutil\nimport argparse\nimport subprocess\nfrom pathlib import Path\nfrom datetime import datetime\n\n# ============================================================================\n# AYARLAR\n# ============================================================================\nWANGP_ROOT = Path(__file__).parent / \"LTX-Desktop-WanGP\" / \"LTX-Desktop-WanGP\" / \"Wan2GP\"\nWANGP_PYTHON = WANGP_ROOT / \"env_venv\" / \"Scripts\" / \"python.exe\"\nFFMPEG = \"ffmpeg\"\nFFPROBE = \"ffprobe\"\n\n# Varsayılan değerler\nDEFAULT_MODEL = \"i2v\"\nDEFAULT_RESOLUTION = \"832x480\"\n...\n","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"17d41b72-ecf7-483a-9d3f-eb7b35bed1e4","timestamp":"2026-04-19T21:59:53.331Z","tool_use_result":{"stdout":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nWanGP Müzik Video Üretici\n==========================\nWanGP'nin Python API'sini kullanarak şarkı + görsel girişinden\notomatik uzun müzik videosu üretir.\n\nPipeline:\n1. Şarkıyı segmentlere böl (ffmpeg)\n2. Her segment için WanGP API ile video üret\n - image_start: giriş görseli (ilk segment) veya önceki videonun son karesi\n - audio_guide: o segmente karşılık gelen ses parçası (lip-sync için)\n3. Her videonun son karesini bir sonraki videonun giriş görseli olarak kullan\n4. Tüm videoları birleştir (ffmpeg)\n5. Orijinal sesi tam olarak ekle (ffmpeg)\n\nKullanım:\n python wangp_video_uret.py [seçenekler]\n\n veya sürükle-bırak ile WanGP-Video-Uret.bat kullanın.\n\nDesteklenen Modeller (örnekler):\n i2v - Wan2.1 Image2Video 480p 14B\n i2v_720p - Wan2.1 Image2Video 720p 14B\n i2v_2_2 - Wan2.1 2.2 Image2Video\n t2v - Wan2.1 Text2Video 14B\n t2v_2_2 - Wan2.1 2.2 Text2Video\n ltx2_22B - LTX-2 22B\n ltx2_22B_distilled - LTX-2 22B Distilled (hızlı)\n ltx2_19B - LTX-2 19B\n hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\n hunyuan_1_5_t2v - HunyuanVideo 1.5 T2V\n ... ve daha fazlası (defaults/ klasöründeki tüm modeller)\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport time\nimport shutil\nimport argparse\nimport subprocess\nfrom pathlib import Path\nfrom datetime import datetime\n\n# ============================================================================\n# AYARLAR\n# ============================================================================\nWANGP_ROOT = Path(__file__).parent / \"LTX-Desktop-WanGP\" / \"LTX-Desktop-WanGP\" / \"Wan2GP\"\nWANGP_PYTHON = WANGP_ROOT / \"env_venv\" / \"Scripts\" / \"python.exe\"\nFFMPEG = \"ffmpeg\"\nFFPROBE = \"ffprobe\"\n\n# Varsayılan değerler\nDEFAULT_MODEL = \"i2v\"\nDEFAULT_RESOLUTION = \"832x480\"\nDEFAULT_PROMPT = \"[VISUAL] cinematic music video, single person performing with emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens flare, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera movement [SPEECH] person singing with expressive lip movements matching the music [SOUND] background music playing\"\nDEFAULT_NEGATIVE_PROMPT = \"blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, clone, cartoon, animated, watermark, text, logo, static, frozen, jerky motion, ugly\"\nDEFAULT_GUIDANCE_SCALE = 5.0\nDEFAULT_STEPS = 30\nDEFAULT_SEED = -1 # rastgele\nDEFAULT_FLOW_SHIFT = 7.0\nDEFAULT_SOLVER = \"unipc\"\nDEFAULT_SEGMENT_DURATION = 5 # saniye (WanGP frame sayısıyla çalışır, fps'e göre hesaplanır)\nDEFAULT_FPS = 16 # Wan modelleri genelde 16fps\nDEFAULT_VIDEO_LENGTH = 81 # frame (81 frame = ~5s @ 16fps)\nDEFAULT_SLIDING_WINDOW_SIZE = 0 # 0 = kapalı\nDEFAULT_SLIDING_WINDOW_OVERLAP = 1\n\n# FaceFusion ayarları\nFACEFUSION_DIR = Path(__file__).parent / \"FaceFusion\"\nFACEFUSION_VENV_PYTHON = FACEFUSION_DIR / \"venv\" / \"Scripts\" / \"python.exe\"\n\n\n# ============================================================================\n# SES ENERJİSİ ANALİZİ VE PROMPT VARYASYONU\n# ============================================================================\n\nENERGY_PROMPT_TEMPLATES = {\n \"silence\": {\n \"prefix\": \"dark ambient atmosphere, minimal movement, \",\n \"suffix\": \", slow fade, contemplative mood, still camera\"\n },\n \"low\": {\n \"prefix\": \"gentle and calm scene, soft lighting, \",\n \"suffix\": \", subtle motion, intimate mood, slow camera pan\"\n },\n \"medium\": {\n \"prefix\": \"dynamic scene with moderate energy, \",\n \"suffix\": \", fluid motion, engaging atmosphere, steady camera movement\"\n },\n \"high\": {\n \"prefix\": \"intense and energetic scene, dramatic lighting, \",\n \"suffix\": \", fast motion, powerful atmosphere, dynamic camera angles\"\n },\n \"climax\": {\n \"prefix\": \"epic climactic moment, explosive visual energy, \",\n \"suffix\": \", rapid motion, overwhelming atmosphere, sweeping cinematic camera\"\n },\n}\n\n\ndef analyze_audio_energy(audio_path, segment_boundaries):\n \"\"\"Her segment için ses enerji seviyesini analiz eder (RMS loudness).\"\"\"\n rms_values = []\n for seg_start, seg_end in segment_boundaries:\n cmd = [\n FFPROBE, \"-v\", \"error\",\n \"-f\", \"lavfi\",\n \"-i\", f\"amovie={str(audio_path)}:sp={seg_start}:ep={seg_end},astats=metadata=1:reset=0\",\n \"-show_entries\", \"frame_tags=lavfi.astats.Overall.RMS_level\",\n \"-of\", \"default=noprint_wrappers=1:nokey=1\"\n ]\n try:\n result = subprocess.run(cmd, capture_output=True, text=True, timeout=15)\n values = []\n for line in result.stdout.strip().split('\\n'):\n line = line.strip()\n if line and line != '-inf':\n try:\n values.append(float(line))\n except ValueError:\n pass\n rms_values.append(sum(values) / len(values) if values else -60.0)\n except Exception:\n rms_values.append(-60.0)\n\n if not rms_values:\n return [\"medium\"] * len(segment_boundaries)\n\n min_rms = min(rms_values)\n max_rms = max(rms_values)\n rms_range = max_rms - min_rms if max_rms > min_rms else 1.0\n\n levels = []\n for rms in rms_values:\n if rms <= -50:\n levels.append(\"silence\")\n else:\n norm = (rms - min_rms) / rms_range\n if norm < 0.2:\n levels.append(\"low\")\n elif norm < 0.5:\n levels.append(\"medium\")\n elif norm < 0.8:\n levels.append(\"high\")\n else:\n levels.append(\"climax\")\n return levels\n\n\ndef build_dynamic_prompt(base_prompt, energy_level):\n \"\"\"Enerji seviyesine göre prompt'u zenginleştirir.\"\"\"\n template = ENERGY_PROMPT_TEMPLATES.get(energy_level, ENERGY_PROMPT_TEMPLATES[\"medium\"])\n return template[\"prefix\"] + base_prompt + template[\"suffix\"]\n\n\n# ============================================================================\n# YARDIMCI FONKSİYONLAR\n# ============================================================================\n\ndef get_available_models():\n \"\"\"defaults/ klasöründeki tüm kullanılabilir modelleri listeler.\"\"\"\n defaults_dir = WANGP_ROOT / \"defaults\"\n models = {}\n if defaults_dir.exists():\n for f in sorted(defaults_dir.glob(\"*.json\")):\n if f.name == \"ReadMe.txt\":\n continue\n model_id = f.stem\n try:\n with open(f, \"r\", encoding=\"utf-8\") as fh:\n data = json.load(fh)\n name = data.get(\"model\", {}).get(\"name\", model_id)\n desc = data.get(\"model\", {}).get(\"description\", \"\")\n arch = data.get(\"model\", {}).get(\"architecture\", model_id)\n models[model_id] = {\"name\": name, \"description\": desc, \"architecture\": arch}\n except Exception:\n models[model_id] = {\"name\": model_id, \"description\": \"\", \"architecture\": model_id}\n return models\n\n\ndef get_available_loras():\n \"\"\"loras/ klasöründeki tüm LoRA dosyalarını listeler.\"\"\"\n loras_dir = WANGP_ROOT / \"loras\"\n loras = []\n if loras_dir.exists():\n for f in loras_dir.rglob(\"*.safetensors\"):\n loras.append(str(f.relative_to(loras_dir)))\n for f in loras_dir.rglob(\"*.pt\"):\n loras.append(str(f.relative_to(loras_dir)))\n return sorted(loras)\n\n\ndef get_audio_duration(audio_path):\n \"\"\"Ses dosyasının süresini saniye olarak döndürür.\"\"\"\n cmd = [\n FFPROBE, \"-v\", \"error\",\n \"-show_entries\", \"format=duration\",\n \"-of\", \"default=noprint_wrappers=1:nokey=1\",\n str(audio_path)\n ]\n result = subprocess.run(cmd, capture_output=True, text=True)\n return float(result.stdout.strip())\n\n\ndef split_audio_into_segments(audio_path, output_dir, segment_dur, total_duration):\n \"\"\"Ses dosyasını segmentlere böler.\"\"\"\n segments = []\n num_segments = int(total_duration // segment_dur)\n remainder = total_duration % segment_dur\n if remainder >= 3.0:\n num_segments += 1\n\n print(f\"[*] Ses {num_segments} parçaya bölünüyor ({segment_dur}s)...\")\n\n for i in range(num_segments):\n seg_start = i * segment_dur\n seg_end = min((i + 1) * segment_dur, total_duration)\n seg_actual_dur = seg_end - seg_start\n\n seg_audio = output_dir / f\"audio_segment_{i:03d}.wav\"\n\n cmd = [\n FFMPEG, \"-y\",\n \"-i\", str(audio_path),\n \"-ss\", str(seg_start),\n \"-t\", str(seg_actual_dur),\n \"-acodec\", \"pcm_s16le\",\n \"-ar\", \"44100\",\n str(seg_audio)\n ]\n subprocess.run(cmd, capture_output=True, check=True)\n\n segments.append((seg_audio, seg_start, seg_end, seg_actual_dur))\n print(f\" Segment {i+1}/{num_segments}: {seg_start:.1f}s - {seg_end:.1f}s ({seg_actual_dur:.1f}s)\")\n\n return segments\n\n\ndef extract_last_frame(video_path, output_path, skip_last_frames=4):\n \"\"\"Videonun son karesini çıkarır (son N kare atlanır, bulanık olabilir).\"\"\"\n cmd = [\n FFPROBE, \"-v\", \"error\",\n \"-show_entries\", \"stream=r_frame_rate,duration\",\n \"-select_streams\", \"v:0\",\n \"-of\", \"default=noprint_wrappers=1\",\n str(video_path)\n ]\n result = subprocess.run(cmd, capture_output=True, text=True)\n duration = 0\n fps = 24\n for line in result.stdout.strip().split('\\n'):\n if line.startswith('duration='):\n duration = float(line.split('=')[1])\n elif line.startswith('r_frame_rate='):\n fr = line.split('=')[1]\n if '/' in fr:\n num, den = fr.split('/')\n fps = float(num) / float(den)\n else:\n fps = float(fr)\n\n # Son N kareyi atla (son kareler genelde bulanık/bozuk olur)\n skip_seconds = skip_last_frames / fps\n seek_time = max(0, duration - skip_seconds - 0.05)\n\n cmd = [\n FFMPEG, \"-y\",\n \"-ss\", str(seek_time),\n \"-i\", str(video_path),\n \"-frames:v\", \"1\",\n \"-q:v\", \"1\",\n str(output_path)\n ]\n subprocess.run(cmd, capture_output=True, check=True)\n print(f\" Son kare çıkarıldı: {output_path.name}\")\n\n\ndef extract_best_frame(video_path, output_path, num_candidates=8):\n \"\"\"Videonun son birkaç karesini analiz edip en kaliteli olanı seçer.\n\n Son kareler bazen bozuk/bulanık olabiliyor (halüsinasyon, blur).\n Bu fonksiyon son N kareyi çıkarıp Laplacian varyansı ile\n en keskin (en az bulanık) olanı seçer.\n\n Args:\n video_path: Video dosyası yolu\n output_path: Seçilen karenin kaydedileceği yol\n num_candidates: Analiz edilecek son kare sayısı (varsayılan: 8)\n\n Returns:\n True başarılı, False başarısız (fallback olarak son kare kullanılır)\n \"\"\"\n import tempfile\n\n # Video süresini ve FPS'i al\n cmd = [\n FFPROBE, \"-v\", \"error\",\n \"-show_entries\", \"stream=r_frame_rate,duration\",\n \"-select_streams\", \"v:0\",\n \"-of\", \"default=noprint_wrappers=1\",\n str(video_path)\n ]\n result = subprocess.run(cmd, capture_output=True, text=True)\n duration = 0\n fps = 24\n for line in result.stdout.strip().split('\\n'):\n if line.startswith('duration='):\n try:\n duration = float(line.split('=')[1])\n except ValueError:\n pass\n elif line.startswith('r_frame_rate='):\n fr = line.split('=')[1]\n try:\n if '/' in fr:\n num, den = fr.split('/')\n fps = float(num) / float(den)\n else:\n fps = float(fr)\n except ValueError:\n pass\n\n if duration <= 0:\n print(f\" [BEST FRAME] Video süresi alınamadı, fallback: son kare\")\n extract_last_frame(video_path, output_path)\n return False\n\n # Son N kareyi çıkar\n skip_last = 2\n frame_interval = 1.0 / fps\n candidates_dir = tempfile.mkdtemp(prefix=\"best_frame_\")\n\n candidate_files = []\n for i in range(num_candidates):\n frame_offset = skip_last + num_candidates - 1 - i\n seek_time = max(0, duration - (frame_offset * frame_interval) - 0.01)\n\n candidate_path = os.path.join(candidates_dir, f\"candidate_{i:02d}.jpg\")\n cmd = [\n FFMPEG, \"-y\",\n \"-ss\", str(seek_time),\n \"-i\", str(video_path),\n \"-frames:v\", \"1\",\n \"-q:v\", \"1\",\n candidate_path\n ]\n r = subprocess.run(cmd, capture_output=True)\n if r.returncode == 0 and os.path.exists(candidate_path) and os.path.getsize(candidate_path) > 0:\n candidate_files.append(candidate_path)\n\n if not candidate_files:\n print(f\" [BEST FRAME] Aday kare çıkarılamadı, fallback: son kare\")\n shutil.rmtree(candidates_dir, ignore_errors=True)\n extract_last_frame(video_path, output_path)\n return False\n\n # Laplacian varyansı yaklaşımı: komşu piksel farkları\n best_score = -1\n best_file = candidate_files[-1]\n\n for cf in candidate_files:\n try:\n cmd = [FFMPEG, \"-y\", \"-i\", cf, \"-pix_fmt\", \"gray\", \"-f\", \"rawvideo\", \"pipe:1\"]\n r = subprocess.run(cmd, capture_output=True)\n if r.returncode != 0 or len(r.stdout) < 100:\n continue\n\n raw = r.stdout\n total_diff = 0\n count = 0\n for j in range(0, len(raw) - 1):\n diff = abs(raw[j] - raw[j + 1])\n total_diff += diff * diff\n count += 1\n\n score = total_diff / max(count, 1)\n\n if score > best_score:\n best_score = score\n best_file = cf\n except Exception:\n continue\n\n shutil.copy2(best_file, str(output_path))\n shutil.rmtree(candidates_dir, ignore_errors=True)\n\n print(f\" [BEST FRAME] {len(candidate_files)} aday analiz edildi, en keskin kare seçildi (skor: {best_score:.0f})\")\n return True\n\n\ndef face_swap_image(source_face_path, target_image_path, output_path):\n \"\"\"FaceFusion ile bir resimdeki yüzü değiştirir.\"\"\"\n if not FACEFUSION_VENV_PYTHON.exists():\n print(f\" HATA: FaceFusion kurulu değil!\")\n return False\n\n ff_script = FACEFUSION_DIR / \"facefusion\" / \"facefusion.py\"\n if not ff_script.exists():\n ff_script = FACEFUSION_DIR / \"facefusion\" / \"run.py\"\n if not ff_script.exists():\n print(f\" HATA: FaceFusion script bulunamadı!\")\n return False\n\n source_abs = str(Path(source_face_path).resolve())\n target_abs = str(Path(target_image_path).resolve())\n output_abs = str(Path(output_path).resolve())\n\n env = os.environ.copy()\n nvidia_pkgs = FACEFUSION_DIR / \"venv\" / \"Lib\" / \"site-packages\" / \"nvidia\"\n extra_paths = [str(FACEFUSION_DIR / \"venv\" / \"Scripts\")]\n if nvidia_pkgs.exists():\n for d in nvidia_pkgs.iterdir():\n if d.is_dir():\n for sub in (\"bin\", \"lib\"):\n sd = d / sub\n if sd.exists():\n extra_paths.append(str(sd))\n cuda_path = r\"C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\"\n if os.path.exists(cuda_path):\n extra_paths.append(cuda_path)\n env[\"PATH\"] = \";\".join(extra_paths) + \";\" + env.get(\"PATH\", \"\")\n\n cmd = [\n str(FACEFUSION_VENV_PYTHON),\n str(ff_script),\n \"headless-run\",\n \"--source-paths\", source_abs,\n \"--target-path\", target_abs,\n \"--output-path\", output_abs,\n \"--processors\", \"face_swapper\",\n \"--execution-providers\", \"cuda\",\n \"--face-selector-mode\", \"one\",\n \"--face-detector-score\", \"0.5\",\n \"--output-image-quality\", \"95\"\n ]\n\n print(f\" [FACE SWAP] Uygulanıyor...\")\n try:\n result = subprocess.run(\n cmd, capture_output=True, text=True, timeout=120,\n cwd=str(FACEFUSION_DIR / \"facefusion\"), env=env\n )\n if Path(output_abs).exists():\n print(f\" [FACE SWAP] Başarılı!\")\n return True\n else:\n print(f\" [FACE SWAP] HATA: Çıkış dosyası oluşmadı!\")\n return False\n except subprocess.TimeoutExpired:\n print(f\" [FACE SWAP] HATA: Zaman aşımı!\")\n return False\n except Exception as e:\n print(f\" [FACE SWAP] HATA: {e}\")\n return False\n\n\ndef concatenate_videos(video_list, output_path):\n \"\"\"Videoları birleştirir.\"\"\"\n list_file = output_path.parent / \"concat_list.txt\"\n with open(list_file, \"w\", encoding=\"utf-8\") as f:\n for v in video_list:\n escaped = str(v).replace(\"'\", \"'\\\\''\")\n f.write(f\"file '{escaped}'\\n\")\n\n cmd = [\n FFMPEG, \"-y\",\n \"-f\", \"concat\",\n \"-safe\", \"0\",\n \"-i\", str(list_file),\n \"-c\", \"copy\",\n str(output_path)\n ]\n subprocess.run(cmd, capture_output=True, check=True)\n list_file.unlink(missing_ok=True)\n print(f\" Videolar birleştirildi: {output_path.name}\")\n\n\ndef add_audio_to_video(video_path, audio_path, output_path):\n \"\"\"Videoya orijinal tam sesi ekler.\"\"\"\n cmd = [\n FFMPEG, \"-y\",\n \"-i\", str(video_path),\n \"-i\", str(audio_path),\n \"-c:v\", \"copy\",\n \"-c:a\", \"aac\",\n \"-b:a\", \"192k\",\n \"-map\", \"0:v:0\",\n \"-map\", \"1:a:0\",\n \"-shortest\",\n str(output_path)\n ]\n subprocess.run(cmd, capture_output=True, check=True)\n print(f\" Orijinal ses eklendi: {output_path.name}\")\n\n\ndef duration_to_frames(duration_sec, fps):\n \"\"\"Süreyi frame sayısına çevirir (WanGP frame bazlı çalışır).\"\"\"\n frames = int(duration_sec * fps)\n # WanGP genelde 4n+1 frame bekler (81, 121, 161, 201, 241...)\n # En yakın geçerli değere yuvarla\n frames = max(17, frames)\n remainder = (frames - 1) % 4\n if remainder != 0:\n frames = frames + (4 - remainder)\n return frames\n\n\ndef generate_all_segments(manifest_path, wangp_root, tasks_dir, segments_dir, frames_dir,\n face_swap_source, timeout=3600):\n \"\"\"WanGP Python API ile tum segmentleri TEK SESSION'da uretir.\n\n Model 1 kere yuklenir. Her segment uretildikten sonra son kare\n cikarilir ve sonraki segmentin image_start'i guncellenir.\n\n Args:\n manifest_path: Tum segment task'larinin listesini iceren JSON\n wangp_root: WanGP ana dizini\n tasks_dir: Her segmentin output dizinlerinin bulundugu ust klasor\n segments_dir: Final segment videolarinin kopyalanacagi klasor\n frames_dir: Cikarilan karelerin kaydedilecegi klasor\n face_swap_source: Yuz degistirme icin kaynak gorsel (None = yok)\n timeout: Toplam max bekleme suresi (saniye)\n\n Returns:\n list: Basarili segment video yollari listesi\n \"\"\"\n # Runner script'i yaz\n runner_script = tasks_dir / \"_wangp_batch_runner.py\"\n runner_code = r'''#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"WanGP Batch Runner - tek session'da tum segmentleri uretir.\n\nKullanim:\n python _wangp_batch_runner.py \n\nModel BIR KERE yuklenir, tum segmentler sirayla uretilir.\nHer segment icin run_task() cagirilir (senkron, basit).\n\"\"\"\nimport sys, os, json, time, glob, subprocess, shutil, traceback, gc\nfrom pathlib import Path\n\n# ── CUDA OOM FIX: Bellek parcalanmasini onle ──\nos.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n\nFFMPEG = \"ffmpeg\"\nFFPROBE = \"ffprobe\"\n\ndef find_biggest_mp4(directory):\n \"\"\"Dizindeki en buyuk mp4 dosyasini bul.\"\"\"\n mp4s = glob.glob(os.path.join(str(directory), \"*.mp4\"))\n if not mp4s:\n # Alt dizinlere de bak (WanGP bazen alt klasore kaydeder)\n mp4s = glob.glob(os.path.join(str(directory), \"**\", \"*.mp4\"), recursive=True)\n if not mp4s:\n return None\n mp4s.sort(key=os.path.getsize, reverse=True)\n return mp4s[0]\n\ndef extract_frame(video_path, output_path, skip_last=4):\n \"\"\"Videonun son karesini cikar (son N kare atlanir). Fallback olarak kullanilir.\"\"\"\n cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n \"-select_streams\", \"v:0\", \"-of\", \"default=noprint_wrappers=1\", str(video_path)]\n r = subprocess.run(cmd, capture_output=True, text=True)\n dur, fps = 0, 24\n for line in r.stdout.strip().split(\"\\n\"):\n if line.startswith(\"duration=\"): dur = float(line.split(\"=\")[1])\n elif line.startswith(\"r_frame_rate=\"):\n fr = line.split(\"=\")[1]\n fps = float(fr.split(\"/\")[0]) / float(fr.split(\"/\")[1]) if \"/\" in fr else float(fr)\n seek = max(0, dur - skip_last / fps - 0.05)\n cmd = [FFMPEG, \"-y\", \"-ss\", str(seek), \"-i\", str(video_path),\n \"-frames:v\", \"1\", \"-q:v\", \"1\", str(output_path)]\n subprocess.run(cmd, capture_output=True, check=True)\n\ndef extract_best_frame(video_path, output_path, num_candidates=8):\n \"\"\"Son birkaç kareyi analiz edip en keskin olanini sec.\"\"\"\n import tempfile\n cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n \"-select_streams\", \"v:0\", \"-of\", \"default=noprint_wrappers=1\", str(video_path)]\n r = subprocess.run(cmd, capture_output=True, text=True)\n dur, fps = 0, 24\n for line in r.stdout.strip().split(\"\\n\"):\n if line.startswith(\"duration=\"):\n try: dur = float(line.split(\"=\")[1])\n except: pass\n elif line.startswith(\"r_frame_rate=\"):\n fr = line.split(\"=\")[1]\n try: fps = float(fr.split(\"/\")[0]) / float(fr.split(\"/\")[1]) if \"/\" in fr else float(fr)\n except: pass\n if dur <= 0:\n extract_frame(video_path, output_path)\n return\n skip_last = 2\n fi = 1.0 / fps\n cdir = tempfile.mkdtemp(prefix=\"bf_\")\n cands = []\n for i in range(num_candidates):\n off = skip_last + num_candidates - 1 - i\n st = max(0, dur - off * fi - 0.01)\n cp = os.path.join(cdir, f\"c_{i:02d}.jpg\")\n r2 = subprocess.run([FFMPEG, \"-y\", \"-ss\", str(st), \"-i\", str(video_path),\n \"-frames:v\", \"1\", \"-q:v\", \"1\", cp], capture_output=True)\n if r2.returncode == 0 and os.path.exists(cp) and os.path.getsize(cp) > 0:\n cands.append(cp)\n if not cands:\n shutil.rmtree(cdir, ignore_errors=True)\n extract_frame(video_path, output_path)\n return\n best_s, best_f = -1, cands[-1]\n for cf in cands:\n try:\n r3 = subprocess.run([FFMPEG, \"-y\", \"-i\", cf, \"-pix_fmt\", \"gray\",\n \"-f\", \"rawvideo\", \"pipe:1\"], capture_output=True)\n if r3.returncode != 0 or len(r3.stdout) < 100: continue\n raw = r3.stdout\n td, cnt = 0, 0\n for j in range(len(raw)-1):\n d = abs(raw[j]-raw[j+1]); td += d*d; cnt += 1\n sc = td / max(cnt, 1)\n if sc > best_s: best_s, best_f = sc, cf\n except: continue\n shutil.copy2(best_f, str(output_path))\n shutil.rmtree(cdir, ignore_errors=True)\n print(f\" [BEST FRAME] {len(cands)} aday -> en keskin secildi (skor: {best_s:.0f})\")\n\ndef main():\n if len(sys.argv) < 3:\n print(\"Kullanim: _wangp_batch_runner.py \")\n sys.exit(1)\n\n wangp_root = sys.argv[1]\n manifest_path = sys.argv[2]\n\n # WanGP'yi import edebilmek icin path ekle\n if wangp_root not in sys.path:\n sys.path.insert(0, wangp_root)\n os.chdir(wangp_root)\n\n with open(manifest_path, \"r\", encoding=\"utf-8\") as f:\n manifest = json.load(f)\n\n tasks = manifest[\"tasks\"]\n frames_dir = manifest[\"frames_dir\"]\n segments_dir = manifest[\"segments_dir\"]\n num_tasks = len(tasks)\n\n print(f\"\\n{'='*60}\")\n print(f\"[RUNNER] {num_tasks} segment uretilecek (TEK SESSION)\")\n print(f\"[RUNNER] Model 1 kere yuklenecek, RAM'de kalacak\")\n print(f\"{'='*60}\")\n\n # WanGP API'yi import et\n try:\n from shared.api import WanGPSession\n except ImportError as e:\n print(f\"[RUNNER] HATA: WanGP API import edilemedi: {e}\")\n print(f\"[RUNNER] wangp_root: {wangp_root}\")\n print(f\"[RUNNER] sys.path: {sys.path[:3]}\")\n sys.exit(1)\n\n # TEK SESSION AC - model 1 kere yuklenir\n # output_dir olarak ilk segmentin dizinini ver\n first_output_dir = tasks[0][\"output_dir\"]\n os.makedirs(first_output_dir, exist_ok=True)\n\n # ── CUDA OOM FIX: Video uretiminden once GPU bellegini temizle ──\n # Audio separation modeli GPU'da bellek parcalanmasina neden olabilir\n try:\n import torch\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n torch.cuda.synchronize()\n gc.collect()\n free_mem = torch.cuda.mem_get_info()[0] / 1024**3\n total_mem = torch.cuda.mem_get_info()[1] / 1024**3\n print(f\"[RUNNER] GPU bellek: {free_mem:.1f}/{total_mem:.1f} GiB bos (cache temizlendi)\")\n except Exception as e:\n print(f\"[RUNNER] GPU bellek temizleme uyarisi: {e}\")\n\n print(f\"[RUNNER] WanGPSession aciliyor...\")\n print(f\"[RUNNER] Output: {first_output_dir}\")\n\n try:\n session = WanGPSession(\n root=wangp_root,\n output_dir=first_output_dir,\n console_output=True,\n )\n except Exception as e:\n print(f\"[RUNNER] HATA: Session acilamadi: {e}\")\n traceback.print_exc()\n sys.exit(1)\n\n print(f\"[RUNNER] Session acildi, model yuklendi!\\n\")\n\n results = []\n total_start = time.time()\n\n for idx, task_info in enumerate(tasks):\n seg_output_dir = task_info[\"output_dir\"]\n task_params = dict(task_info[\"params\"]) # kopya al\n task_params[\"repeat_generation\"] = 1\n\n # Output dizinini guncelle\n os.makedirs(seg_output_dir, exist_ok=True)\n try:\n session._output_dir = Path(seg_output_dir)\n except Exception:\n pass # eger _output_dir yoksa sorun degil, WanGP kendi dizinine kaydeder\n\n print(f\"\\n{'='*60}\")\n print(f\"[SEGMENT {idx+1}/{num_tasks}]\")\n print(f\" Output : {seg_output_dir}\")\n print(f\" Image : {task_params.get('image_start', 'N/A')}\")\n print(f\" Audio : {os.path.basename(task_params.get('audio_source', 'N/A'))}\")\n if task_params.get('audio_guide'):\n print(f\" LipSync: ACIK\")\n print(f\"{'='*60}\")\n\n # Mevcut mp4'leri kaydet (sonra yenileri bulmak icin)\n existing_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n # WanGP bazen farkli dizine de kaydedebilir - session output_dir'i de kontrol et\n wangp_save_dir = seg_output_dir\n existing_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n\n seg_start = time.time()\n\n # ── CUDA OOM FIX: Her segment oncesi GPU cache temizle ──\n try:\n import torch\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n gc.collect()\n except Exception:\n pass\n\n try:\n # run_task = submit_task + result bekle (senkron, basit)\n print(f\" [*] Video uretiliyor (run_task)...\")\n result = session.run_task(task_params)\n seg_elapsed = time.time() - seg_start\n\n print(f\" [*] Task tamamlandi ({seg_elapsed:.1f}s)\")\n print(f\" [*] Basarili: {result.success}\")\n print(f\" [*] Dosyalar: {len(result.generated_files) if result.generated_files else 0}\")\n if result.errors:\n for err in result.errors:\n print(f\" [HATA] {err}\")\n\n # Video bul - 4 kademeli arama\n video_path = None\n\n # Yontem 1: API sonucu (generated_files)\n if result.success and result.generated_files:\n for f in result.generated_files:\n fstr = str(f)\n if fstr.endswith(\".mp4\") and os.path.exists(fstr):\n video_path = fstr\n print(f\" [BULUNDU] API sonucu: {os.path.basename(fstr)}\")\n break\n\n # Yontem 2: Segment output dizininde yeni mp4\n if not video_path:\n current_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n new_mp4 = current_mp4 - existing_mp4\n if new_mp4:\n video_path = sorted(new_mp4, key=os.path.getsize, reverse=True)[0]\n print(f\" [BULUNDU] Yeni mp4: {os.path.basename(video_path)}\")\n\n # Yontem 3: Session'in ilk output dizininde yeni mp4\n if not video_path and str(first_output_dir) != seg_output_dir:\n current_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n new_mp4_save = current_mp4_save - existing_mp4_save\n if new_mp4_save:\n video_path = sorted(new_mp4_save, key=os.path.getsize, reverse=True)[0]\n print(f\" [BULUNDU] Session dir: {os.path.basename(video_path)}\")\n\n # Yontem 4: En buyuk mp4 (segment dir veya ilk dir)\n if not video_path:\n video_path = find_biggest_mp4(seg_output_dir)\n if not video_path and str(first_output_dir) != seg_output_dir:\n video_path = find_biggest_mp4(first_output_dir)\n if video_path:\n print(f\" [BULUNDU] Fallback: {os.path.basename(video_path)}\")\n\n if video_path:\n size_mb = os.path.getsize(video_path) / 1024 / 1024\n print(f\" VIDEO: {os.path.basename(video_path)} ({size_mb:.1f}MB, {seg_elapsed:.1f}s)\")\n\n # Segment klasorune kopyala\n seg_video = os.path.jo","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false,"persistedOutputPath":"/sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt","persistedOutputSize":59166},"_audit_timestamp":"2026-04-19T21:59:53.335Z","_audit_hmac":"c9978bd26fc5784e87f4fc2c8c1d266f9952369ffb52a755b6f8a336f3b1689c"} {"type":"system","subtype":"status","status":"requesting","uuid":"c5c76fed-6acb-478d-b5b5-61c456c7f072","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T21:59:53.340Z","_audit_hmac":"70a5d4413c2d70fbefcb074e93f6bcca7ff0f2448a630a1e2e8a457c78f4571b"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_016z7daBA7XwaZ2Da4fQbDvT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018ZTZxqNXjjbJPPn7A74pLW","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt","offset":0,"limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8832,"cache_read_input_tokens":29681,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8832},"output_tokens":73,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"d830cea9-5bc7-43ae-b9a3-5267f5ddc585","_audit_timestamp":"2026-04-19T22:00:02.121Z","_audit_hmac":"85cc8fb941e6114970b6521bd97f08598bfda5b3ab90c78447f81d9f6db09917"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018ZTZxqNXjjbJPPn7A74pLW","type":"tool_result","content":"0\t#!/usr/bin/env python3\n1\t# -*- coding: utf-8 -*-\n2\t\"\"\"\n3\tWanGP Müzik Video Üretici\n4\t==========================\n5\tWanGP'nin Python API'sini kullanarak şarkı + görsel girişinden\n6\totomatik uzun müzik videosu üretir.\n7\t\n8\tPipeline:\n9\t1. Şarkıyı segmentlere böl (ffmpeg)\n10\t2. Her segment için WanGP API ile video üret\n11\t - image_start: giriş görseli (ilk segment) veya önceki videonun son karesi\n12\t - audio_guide: o segmente karşılık gelen ses parçası (lip-sync için)\n13\t3. Her videonun son karesini bir sonraki videonun giriş görseli olarak kullan\n14\t4. Tüm videoları birleştir (ffmpeg)\n15\t5. Orijinal sesi tam olarak ekle (ffmpeg)\n16\t\n17\tKullanım:\n18\t python wangp_video_uret.py [seçenekler]\n19\t\n20\t veya sürükle-bırak ile WanGP-Video-Uret.bat kullanın.\n21\t\n22\tDesteklenen Modeller (örnekler):\n23\t i2v - Wan2.1 Image2Video 480p 14B\n24\t i2v_720p - Wan2.1 Image2Video 720p 14B\n25\t i2v_2_2 - Wan2.1 2.2 Image2Video\n26\t t2v - Wan2.1 Text2Video 14B\n27\t t2v_2_2 - Wan2.1 2.2 Text2Video\n28\t ltx2_22B - LTX-2 22B\n29\t ltx2_22B_distilled - LTX-2 22B Distilled (hızlı)\n30\t ltx2_19B - LTX-2 19B\n31\t hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\n32\t hunyuan_1_5_t2v - HunyuanVideo 1.5 T2V\n33\t ... ve daha fazlası (defaults/ klasöründeki tüm modeller)\n34\t\"\"\"\n35\t\n36\timport os\n37\timport sys\n38\timport json\n39\timport time\n40\timport shutil\n41\timport argparse\n42\timport subprocess\n43\tfrom pathlib import Path\n44\tfrom datetime import datetime\n45\t\n46\t# ============================================================================\n47\t# AYARLAR\n48\t# ============================================================================\n49\tWANGP_ROOT = Path(__file__).parent / \"LTX-Desktop-WanGP\" / \"LTX-Desktop-WanGP\" / \"Wan2GP\"\n50\tWANGP_PYTHON = WANGP_ROOT / \"env_venv\" / \"Scripts\" / \"python.exe\"\n51\tFFMPEG = \"ffmpeg\"\n52\tFFPROBE = \"ffprobe\"\n53\t\n54\t# Varsayılan değerler\n55\tDEFAULT_MODEL = \"i2v\"\n56\tDEFAULT_RESOLUTION = \"832x480\"\n57\tDEFAULT_PROMPT = \"[VISUAL] cinematic music video, single person performing with emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens flare, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera movement [SPEECH] person singing with expressive lip movements matching the music [SOUND] background music playing\"\n58\tDEFAULT_NEGATIVE_PROMPT = \"blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, clone, cartoon, animated, watermark, text, logo, static, frozen, jerky motion, ugly\"\n59\tDEFAULT_GUIDANCE_SCALE = 5.0\n60\tDEFAULT_STEPS = 30\n61\tDEFAULT_SEED = -1 # rastgele\n62\tDEFAULT_FLOW_SHIFT = 7.0\n63\tDEFAULT_SOLVER = \"unipc\"\n64\tDEFAULT_SEGMENT_DURATION = 5 # saniye (WanGP frame sayısıyla çalışır, fps'e göre hesaplanır)\n65\tDEFAULT_FPS = 16 # Wan modelleri genelde 16fps\n66\tDEFAULT_VIDEO_LENGTH = 81 # frame (81 frame = ~5s @ 16fps)\n67\tDEFAULT_SLIDING_WINDOW_SIZE = 0 # 0 = kapalı\n68\tDEFAULT_SLIDING_WINDOW_OVERLAP = 1\n69\t\n70\t# FaceFusion ayarları\n71\tFACEFUSION_DIR = Path(__file__).parent / \"FaceFusion\"\n72\tFACEFUSION_VENV_PYTHON = FACEFUSION_DIR / \"venv\" / \"Scripts\" / \"python.exe\"\n73\t\n74\t\n75\t# ============================================================================\n76\t# SES ENERJİSİ ANALİZİ VE PROMPT VARYASYONU\n77\t# ============================================================================\n78\t\n79\tENERGY_PROMPT_TEMPLATES = {\n80\t \"silence\": {\n81\t \"prefix\": \"dark ambient atmosphere, minimal movement, \",\n82\t \"suffix\": \", slow fade, contemplative mood, still camera\"\n83\t },\n84\t \"low\": {\n85\t \"prefix\": \"gentle and calm scene, soft lighting, \",\n86\t \"suffix\": \", subtle motion, intimate mood, slow camera pan\"\n87\t },\n88\t \"medium\": {\n89\t \"prefix\": \"dynamic scene with moderate energy, \",\n90\t \"suffix\": \", fluid motion, engaging atmosphere, steady camera movement\"\n91\t },\n92\t \"high\": {\n93\t \"prefix\": \"intense and energetic scene, dramatic lighting, \",\n94\t \"suffix\": \", fast motion, powerful atmosphere, dynamic camera angles\"\n95\t },\n96\t \"climax\": {\n97\t \"prefix\": \"epic climactic moment, explosive visual energy, \",\n98\t \"suffix\": \", rapid motion, overwhelming atmosphere, sweeping cinematic camera\"\n99\t },"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3cc82ff9-c95a-4a61-9b18-b30e399f1de6","timestamp":"2026-04-19T22:00:02.145Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt","content":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nWanGP Müzik Video Üretici\n==========================\nWanGP'nin Python API'sini kullanarak şarkı + görsel girişinden\notomatik uzun müzik videosu üretir.\n\nPipeline:\n1. Şarkıyı segmentlere böl (ffmpeg)\n2. Her segment için WanGP API ile video üret\n - image_start: giriş görseli (ilk segment) veya önceki videonun son karesi\n - audio_guide: o segmente karşılık gelen ses parçası (lip-sync için)\n3. Her videonun son karesini bir sonraki videonun giriş görseli olarak kullan\n4. Tüm videoları birleştir (ffmpeg)\n5. Orijinal sesi tam olarak ekle (ffmpeg)\n\nKullanım:\n python wangp_video_uret.py [seçenekler]\n\n veya sürükle-bırak ile WanGP-Video-Uret.bat kullanın.\n\nDesteklenen Modeller (örnekler):\n i2v - Wan2.1 Image2Video 480p 14B\n i2v_720p - Wan2.1 Image2Video 720p 14B\n i2v_2_2 - Wan2.1 2.2 Image2Video\n t2v - Wan2.1 Text2Video 14B\n t2v_2_2 - Wan2.1 2.2 Text2Video\n ltx2_22B - LTX-2 22B\n ltx2_22B_distilled - LTX-2 22B Distilled (hızlı)\n ltx2_19B - LTX-2 19B\n hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\n hunyuan_1_5_t2v - HunyuanVideo 1.5 T2V\n ... ve daha fazlası (defaults/ klasöründeki tüm modeller)\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport time\nimport shutil\nimport argparse\nimport subprocess\nfrom pathlib import Path\nfrom datetime import datetime\n\n# ============================================================================\n# AYARLAR\n# ============================================================================\nWANGP_ROOT = Path(__file__).parent / \"LTX-Desktop-WanGP\" / \"LTX-Desktop-WanGP\" / \"Wan2GP\"\nWANGP_PYTHON = WANGP_ROOT / \"env_venv\" / \"Scripts\" / \"python.exe\"\nFFMPEG = \"ffmpeg\"\nFFPROBE = \"ffprobe\"\n\n# Varsayılan değerler\nDEFAULT_MODEL = \"i2v\"\nDEFAULT_RESOLUTION = \"832x480\"\nDEFAULT_PROMPT = \"[VISUAL] cinematic music video, single person performing with emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens flare, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera movement [SPEECH] person singing with expressive lip movements matching the music [SOUND] background music playing\"\nDEFAULT_NEGATIVE_PROMPT = \"blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, clone, cartoon, animated, watermark, text, logo, static, frozen, jerky motion, ugly\"\nDEFAULT_GUIDANCE_SCALE = 5.0\nDEFAULT_STEPS = 30\nDEFAULT_SEED = -1 # rastgele\nDEFAULT_FLOW_SHIFT = 7.0\nDEFAULT_SOLVER = \"unipc\"\nDEFAULT_SEGMENT_DURATION = 5 # saniye (WanGP frame sayısıyla çalışır, fps'e göre hesaplanır)\nDEFAULT_FPS = 16 # Wan modelleri genelde 16fps\nDEFAULT_VIDEO_LENGTH = 81 # frame (81 frame = ~5s @ 16fps)\nDEFAULT_SLIDING_WINDOW_SIZE = 0 # 0 = kapalı\nDEFAULT_SLIDING_WINDOW_OVERLAP = 1\n\n# FaceFusion ayarları\nFACEFUSION_DIR = Path(__file__).parent / \"FaceFusion\"\nFACEFUSION_VENV_PYTHON = FACEFUSION_DIR / \"venv\" / \"Scripts\" / \"python.exe\"\n\n\n# ============================================================================\n# SES ENERJİSİ ANALİZİ VE PROMPT VARYASYONU\n# ============================================================================\n\nENERGY_PROMPT_TEMPLATES = {\n \"silence\": {\n \"prefix\": \"dark ambient atmosphere, minimal movement, \",\n \"suffix\": \", slow fade, contemplative mood, still camera\"\n },\n \"low\": {\n \"prefix\": \"gentle and calm scene, soft lighting, \",\n \"suffix\": \", subtle motion, intimate mood, slow camera pan\"\n },\n \"medium\": {\n \"prefix\": \"dynamic scene with moderate energy, \",\n \"suffix\": \", fluid motion, engaging atmosphere, steady camera movement\"\n },\n \"high\": {\n \"prefix\": \"intense and energetic scene, dramatic lighting, \",\n \"suffix\": \", fast motion, powerful atmosphere, dynamic camera angles\"\n },\n \"climax\": {\n \"prefix\": \"epic climactic moment, explosive visual energy, \",\n \"suffix\": \", rapid motion, overwhelming atmosphere, sweeping cinematic camera\"\n },","numLines":100,"startLine":0,"totalLines":1431}},"_audit_timestamp":"2026-04-19T22:00:02.184Z","_audit_hmac":"a6602d5972a1892badc75805b57d66202da6cdca3fd314e7da0435a06419b8fd"} {"type":"system","subtype":"status","status":"requesting","uuid":"30437a0b-831a-437e-8d8e-1ba4093d4a2b","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:02.204Z","_audit_hmac":"9a45edb624f1c9658a4910f2bb17decb1ffb39ed3727473093151930e27cee3e"} {"type":"user","uuid":"dca14d0a-a6a5-49f6-a560-ec0aa9286471","session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"message":{"role":"user","content":"cpu rame taşıyalım bence"},"_audit_timestamp":"2026-04-19T22:00:05.926Z","_audit_hmac":"7b3abe52da1e2c2bd0d073d0b8e111ef0ae4a2702872528c4422160e06105fd8"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015of4ovFecGssN83drajYBq","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi Python scriptinin WanGP API'ye nasıl bağlandığını ve video üretim kısmını kontrol edeyim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1770,"cache_read_input_tokens":38513,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1770},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"7f421612-8b33-452a-8e6e-436970fc2b5f","_audit_timestamp":"2026-04-19T22:00:06.318Z","_audit_hmac":"555aea5b3051f58741d0fe9d99fdc39088d0e07a77bc27dc3dc0c99488051bf2"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015of4ovFecGssN83drajYBq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kgj1MNnbwZ7Z13EXiowj7L","name":"Grep","input":{"pattern":"def generate_video|def _generate|api.*generate|requests\\.post|CUDA|out of memory|OOM|error|timeout","path":"/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py","output_mode":"content","-n":true},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1770,"cache_read_input_tokens":38513,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1770},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"1c4deef0-50d2-48ed-97e4-d0fcdcbc104b","_audit_timestamp":"2026-04-19T22:00:08.132Z","_audit_hmac":"13b9ed2b583ae5a3d1f1b431ba75d7f9919f65503d17104c18da6b39626cca2b"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Kgj1MNnbwZ7Z13EXiowj7L","type":"tool_result","content":"109: FFPROBE, \"-v\", \"error\",\n116: result = subprocess.run(cmd, capture_output=True, text=True, timeout=15)\n199: FFPROBE, \"-v\", \"error\",\n245: FFPROBE, \"-v\", \"error\",\n300: FFPROBE, \"-v\", \"error\",\n356: shutil.rmtree(candidates_dir, ignore_errors=True)\n388: shutil.rmtree(candidates_dir, ignore_errors=True)\n421: cuda_path = r\"C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\"\n443: cmd, capture_output=True, text=True, timeout=120,\n512: face_swap_source, timeout=3600):\n525: timeout: Toplam max bekleme suresi (saniye)\n545:# ── CUDA OOM FIX: Bellek parcalanmasini onle ──\n546:os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n564: cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n581: cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n609: shutil.rmtree(cdir, ignore_errors=True)\n626: shutil.rmtree(cdir, ignore_errors=True)\n669: # ── CUDA OOM FIX: Video uretiminden once GPU bellegini temizle ──\n731: # ── CUDA OOM FIX: Her segment oncesi GPU cache temizle ──\n749: if result.errors:\n750: for err in result.errors:\n872: # ── CUDA OOM FIX: expandable_segments ortam degiskeni ──\n874: runner_env[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n879: timeout=timeout,\n885: print(f\" HATA: Toplam zaman asimi ({timeout}s)!\")\n1057: out_group.add_argument(\"--timeout\", type=int, default=600,\n1101: parser.error(\"Ses dosyası ve görsel gerekli! Kullanım: wangp_video_uret.py \")\n1377: total_timeout = args.timeout * num_segments + 300 # ekstra 5dk tampon\n1385: timeout=total_timeout,"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"33ac51ed-059e-48b9-a737-7f7d7c13fe1f","timestamp":"2026-04-19T22:00:08.150Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"109: FFPROBE, \"-v\", \"error\",\n116: result = subprocess.run(cmd, capture_output=True, text=True, timeout=15)\n199: FFPROBE, \"-v\", \"error\",\n245: FFPROBE, \"-v\", \"error\",\n300: FFPROBE, \"-v\", \"error\",\n356: shutil.rmtree(candidates_dir, ignore_errors=True)\n388: shutil.rmtree(candidates_dir, ignore_errors=True)\n421: cuda_path = r\"C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\"\n443: cmd, capture_output=True, text=True, timeout=120,\n512: face_swap_source, timeout=3600):\n525: timeout: Toplam max bekleme suresi (saniye)\n545:# ── CUDA OOM FIX: Bellek parcalanmasini onle ──\n546:os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n564: cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n581: cmd = [FFPROBE, \"-v\", \"error\", \"-show_entries\", \"stream=r_frame_rate,duration\",\n609: shutil.rmtree(cdir, ignore_errors=True)\n626: shutil.rmtree(cdir, ignore_errors=True)\n669: # ── CUDA OOM FIX: Video uretiminden once GPU bellegini temizle ──\n731: # ── CUDA OOM FIX: Her segment oncesi GPU cache temizle ──\n749: if result.errors:\n750: for err in result.errors:\n872: # ── CUDA OOM FIX: expandable_segments ortam degiskeni ──\n874: runner_env[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n879: timeout=timeout,\n885: print(f\" HATA: Toplam zaman asimi ({timeout}s)!\")\n1057: out_group.add_argument(\"--timeout\", type=int, default=600,\n1101: parser.error(\"Ses dosyası ve görsel gerekli! Kullanım: wangp_video_uret.py \")\n1377: total_timeout = args.timeout * num_segments + 300 # ekstra 5dk tampon\n1385: timeout=total_timeout,","numLines":29},"_audit_timestamp":"2026-04-19T22:00:08.192Z","_audit_hmac":"5ba684155f7b5cc0efd944f7d1b694d27a0e8ad7d582af7fd40a2910e13c470e"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"allowed","overageResetsAt":1777593600,"isUsingOverage":false},"uuid":"48d090e1-9cfa-45dc-9408-d7e453eb7b84","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:08.214Z","_audit_hmac":"af457633cdd19a4afe6b83e9f3e6e623785281116421ae73fd0faf167fd38914"} {"type":"user","message":{"role":"user","content":"cpu rame taşıyalım bence"},"session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"uuid":"dca14d0a-a6a5-49f6-a560-ec0aa9286471","timestamp":"2026-04-19T22:00:08.220Z","isReplay":true,"_audit_timestamp":"2026-04-19T22:00:08.221Z","_audit_hmac":"f80ca025b9cefb11f697494e5e1f58661ce8627f346fdc74bafa5384d2ab779b"} {"type":"system","subtype":"status","status":"requesting","uuid":"75de18d4-ae21-4af6-885d-fda828571d6a","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:08.227Z","_audit_hmac":"d332608f3711c404cfb75950705fac035c80db098c9cbbb05a8c144d4c80e281"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01BgMsNTDzDUEA9LSzd3krKQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TRRdDTwsEwwfKJvRzZb4Ju","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt","offset":830,"limit":200},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":924,"cache_read_input_tokens":40283,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":924},"output_tokens":60,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"f956165b-5619-479b-ae43-9bf2b383c9e8","_audit_timestamp":"2026-04-19T22:00:12.229Z","_audit_hmac":"97800b3fae6ca8cd7c66b2214621cebae1a6af26c71caf98c997584af09ed995"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TRRdDTwsEwwfKJvRzZb4Ju","type":"tool_result","content":"830\t print(f\" Fallback video bulundu: {os.path.basename(fallback)}\")\n831\t results.append({\"index\": idx, \"success\": True, \"video\": seg_video, \"elapsed\": seg_elapsed})\n832\t else:\n833\t results.append({\"index\": idx, \"success\": False, \"elapsed\": seg_elapsed})\n834\t\n835\t # Session kapat\n836\t print(f\"\\n[RUNNER] Session kapatiliyor...\")\n837\t try:\n838\t session.close()\n839\t except Exception:\n840\t pass\n841\t\n842\t total_elapsed = time.time() - total_start\n843\t success_count = sum(1 for r in results if r[\"success\"])\n844\t print(f\"\\n{'='*60}\")\n845\t print(f\"[RUNNER] TAMAMLANDI: {success_count}/{num_tasks} basarili ({total_elapsed:.1f}s)\")\n846\t print(f\"{'='*60}\")\n847\t\n848\t # Sonuclari kaydet\n849\t result_path = manifest_path.replace(\".json\", \"_results.json\")\n850\t with open(result_path, \"w\", encoding=\"utf-8\") as f:\n851\t json.dump({\"results\": results, \"total_elapsed\": total_elapsed}, f, indent=2)\n852\t\n853\t print(f\"[RUNNER] Sonuclar: {result_path}\")\n854\t\n855\tif __name__ == \"__main__\":\n856\t main()\n857\t'''\n858\t with open(runner_script, \"w\", encoding=\"utf-8\") as f:\n859\t f.write(runner_code)\n860\t\n861\t # Manifest zaten caller (main) tarafindan yazilmis olmali\n862\t # Burada sadece runner'i calistiriyoruz\n863\t\n864\t # WanGP Python ile runner'i calistir\n865\t python_exe = str(WANGP_PYTHON)\n866\t if not Path(python_exe).exists():\n867\t python_exe = sys.executable\n868\t\n869\t print(f\"\\n[*] WanGP Batch Runner baslatiliyor (tek session)...\")\n870\t print(f\" Model 1 kere yuklenecek, tum segmentler sirayla uretilecek\\n\")\n871\t\n872\t # ── CUDA OOM FIX: expandable_segments ortam degiskeni ──\n873\t runner_env = os.environ.copy()\n874\t runner_env[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n875\t\n876\t try:\n877\t proc = subprocess.run(\n878\t [python_exe, str(runner_script), str(wangp_root), str(manifest_path)],\n879\t timeout=timeout,\n880\t capture_output=False,\n881\t cwd=str(wangp_root),\n882\t env=runner_env\n883\t )\n884\t except subprocess.TimeoutExpired:\n885\t print(f\" HATA: Toplam zaman asimi ({timeout}s)!\")\n886\t except Exception as e:\n887\t print(f\" HATA: {e}\")\n888\t\n889\t # Sonuclari oku\n890\t results_path = str(manifest_path).replace(\".json\", \"_results.json\")\n891\t video_segments = []\n892\t if Path(results_path).exists():\n893\t with open(results_path, \"r\", encoding=\"utf-8\") as f:\n894\t data = json.load(f)\n895\t for r in data.get(\"results\", []):\n896\t if r.get(\"success\") and r.get(\"video\") and Path(r[\"video\"]).exists():\n897\t video_segments.append(Path(r[\"video\"]))\n898\t else:\n899\t # Fallback: segments dizininde mp4 ara\n900\t import glob\n901\t for mp4 in sorted(glob.glob(str(segments_dir / \"segment_*.mp4\"))):\n902\t video_segments.append(Path(mp4))\n903\t\n904\t return video_segments\n905\t\n906\t\n907\t# ============================================================================\n908\t# ANA PIPELINE\n909\t# ============================================================================\n910\t\n911\tdef main():\n912\t parser = argparse.ArgumentParser(\n913\t description=\"WanGP Müzik Video Üretici\",\n914\t formatter_class=argparse.RawDescriptionHelpFormatter,\n915\t epilog=\"\"\"\n916\tÖrnek Kullanımlar:\n917\t python wangp_video_uret.py sarki.mp3 gorsel.jpg\n918\t python wangp_video_uret.py sarki.mp3 gorsel.jpg --model i2v_720p --resolution 1280x720\n919\t python wangp_video_uret.py sarki.mp3 gorsel.jpg --model ltx2_22B --steps 40 --guidance 3.0\n920\t python wangp_video_uret.py sarki.mp3 gorsel.jpg --lora \"my_lora.safetensors\" --lora-weight 0.8\n921\t python wangp_video_uret.py sarki.mp3 gorsel.jpg --face-swap yuz.jpg\n922\t\n923\tModelleri Listele:\n924\t python wangp_video_uret.py --list-models\n925\t python wangp_video_uret.py --list-loras\n926\t \"\"\"\n927\t )\n928\t\n929\t # Pozisyonel argümanlar\n930\t parser.add_argument(\"audio\", nargs=\"?\", help=\"Giriş ses dosyası (mp3, wav, flac, vb.)\")\n931\t parser.add_argument(\"image\", nargs=\"?\", help=\"Giriş görseli (jpg, png)\")\n932\t\n933\t # Model ve temel ayarlar\n934\t model_group = parser.add_argument_group(\"Model Ayarları\")\n935\t model_group.add_argument(\"--model\", \"-m\", default=DEFAULT_MODEL,\n936\t help=f\"Video üretim modeli (varsayılan: {DEFAULT_MODEL})\")\n937\t model_group.add_argument(\"--resolution\", \"-r\", default=DEFAULT_RESOLUTION,\n938\t help=f\"Çözünürlük WIDTHxHEIGHT formatında (varsayılan: {DEFAULT_RESOLUTION})\")\n939\t model_group.add_argument(\"--video-length\", type=int, default=DEFAULT_VIDEO_LENGTH,\n940\t help=f\"Segment başına frame sayısı (varsayılan: {DEFAULT_VIDEO_LENGTH}). 4n+1 olmalı (81,121,161,...)\")\n941\t model_group.add_argument(\"--fps\", type=int, default=DEFAULT_FPS,\n942\t help=f\"Video FPS (varsayılan: {DEFAULT_FPS})\")\n943\t\n944\t # Prompt ayarları\n945\t prompt_group = parser.add_argument_group(\"Prompt Ayarları\")\n946\t prompt_group.add_argument(\"--prompt\", \"-p\", default=DEFAULT_PROMPT,\n947\t help=\"Video üretim prompt'u\")\n948\t prompt_group.add_argument(\"--negative-prompt\", \"--neg\", default=DEFAULT_NEGATIVE_PROMPT,\n949\t help=\"Negatif prompt (istenmeyen öğeler)\")\n950\t prompt_group.add_argument(\"--prompt-enhancer\", type=int, default=1, choices=[0, 1],\n951\t help=\"Prompt geliştirici (0=kapalı, 1=açık)\")\n952\t prompt_group.add_argument(\"--auto-prompt\", action=\"store_true\",\n953\t help=\"Ses enerjisi analizine göre her segmente otomatik farklı prompt oluştur. \"\n954\t \"Sessiz bölümlerde sakin, enerjik bölümlerde dinamik prompt kullanılır.\")\n955\t\n956\t # Inference ayarları\n957\t inf_group = parser.add_argument_group(\"Inference Ayarları\")\n958\t inf_group.add_argument(\"--steps\", \"-n\", type=int, default=DEFAULT_STEPS,\n959\t help=f\"Inference adım sayısı (varsayılan: {DEFAULT_STEPS})\")\n960\t inf_group.add_argument(\"--guidance-scale\", \"-g\", type=float, default=DEFAULT_GUIDANCE_SCALE,\n961\t help=f\"Guidance scale / CFG (varsayılan: {DEFAULT_GUIDANCE_SCALE})\")\n962\t inf_group.add_argument(\"--guidance2-scale\", type=float, default=None,\n963\t help=\"İkinci faz guidance scale (opsiyonel)\")\n964\t inf_group.add_argument(\"--guidance3-scale\", type=float, default=None,\n965\t help=\"Üçüncü faz guidance scale (opsiyonel)\")\n966\t inf_group.add_argument(\"--guidance-phases\", type=int, default=None, choices=[1, 2, 3],\n967\t help=\"Guidance faz sayısı (1-3)\")\n968\t inf_group.add_argument(\"--embedded-guidance\", type=float, default=None,\n969\t help=\"Embedded guidance scale (modele bağlı)\")\n970\t inf_group.add_argument(\"--alt-guidance\", type=float, default=None,\n971\t help=\"Alternatif guidance scale\")\n972\t inf_group.add_argument(\"--alt-scale\", type=float, default=None,\n973\t help=\"Alternatif scale (0.0-1.0)\")\n974\t inf_group.add_argument(\"--flow-shift\", type=float, default=DEFAULT_FLOW_SHIFT,\n975\t help=f\"Flow shift (varsayılan: {DEFAULT_FLOW_SHIFT})\")\n976\t inf_group.add_argument(\"--solver\", default=DEFAULT_SOLVER,\n977\t help=f\"Sampling algoritması (varsayılan: {DEFAULT_SOLVER}). Seçenekler: euler, unipc\")\n978\t inf_group.add_argument(\"--seed\", type=int, default=DEFAULT_SEED,\n979\t help=f\"Seed (-1=rastgele, varsayılan: {DEFAULT_SEED})\")\n980\t inf_group.add_argument(\"--denoising-strength\", type=float, default=None,\n981\t help=\"Denoising gücü (0.0-1.0)\")\n982\t\n983\t # Audio guidance ve lip-sync\n984\t audio_group = parser.add_argument_group(\"Audio Guidance ve Lip-Sync Ayarları\")\n985\t audio_group.add_argument(\"--audio-guidance-scale\", type=float, default=None,\n986\t help=\"Audio guidance scale (LTX2 varsayılan: 7.0)\")\n987\t audio_group.add_argument(\"--audio-scale\", type=float, default=None,\n988\t help=\"Audio scale (0.0-1.0)\")\n989\t audio_group.add_argument(\"--audio-prompt-type\", default=None,\n990\t help=\"Audio prompt tipi bayrakları: A=tek kişi lip-sync, V=vokal ayır, N=ses normalize, X=iki kişi otomatik, B=iki ayrı ses\")\n991\t audio_group.add_argument(\"--no-lip-sync\", action=\"store_true\",\n992\t help=\"Lip-sync'i devre dışı bırak (sesi sadece son videoya ekle, görüntüyü etkilemesin)\")\n993\t audio_group.add_argument(\"--no-vocal-isolation\", action=\"store_true\",\n994\t help=\"Vokal izolasyonu yapma (V bayrağını kaldır)\")\n995\t\n996\t # LoRA ayarları\n997\t lora_group = parser.add_argument_group(\"LoRA Ayarları\")\n998\t lora_group.add_argument(\"--lora\", action=\"append\", default=None,\n999\t help=\"LoRA dosyası (birden fazla --lora ile eklenebilir)\")\n1000\t lora_group.add_argument(\"--lora-weight\", default=\"1.0\",\n1001\t help=\"LoRA ağırlıkları (boşlukla ayrılmış, örn: '0.8 1.0')\")\n1002\t\n1003\t # Sliding window (uzun video)\n1004\t sw_group = parser.add_argument_group(\"Sliding Window Ayarları (Uzun Video)\")\n1005\t sw_group.add_argument(\"--sliding-window-size\", type=int, default=DEFAULT_SLIDING_WINDOW_SIZE,\n1006\t help=\"Sliding window frame boyutu (0=kapalı)\")\n1007\t sw_group.add_argument(\"--sliding-window-overlap\", type=int, default=DEFAULT_SLIDING_WINDOW_OVERLAP,\n1008\t help=\"Sliding window overlap\")\n1009\t sw_group.add_argument(\"--sliding-window-color-correction\", type=float, default=0.0,\n1010\t help=\"Renk düzeltme gücü (0.0-1.0)\")\n1011\t sw_group.add_argument(\"--sliding-window-overlap-noise\", type=int, default=0,\n1012\t help=\"Overlap noise (0-150)\")\n1013\t\n1014\t # Gelişmiş ayarlar\n1015\t adv_group = parser.add_argument_group(\"Gelişmiş Ayarlar\")\n1016\t adv_group.add_argument(\"--perturbation\", type=int, default=0, choices=[0, 1, 2, 3, 4],\n1017\t help=\"Perturbation modu (0=kapalı)\")\n1018\t adv_group.add_argument(\"--perturbation-layers\", type=int, nargs=\"+\", default=None,\n1019\t help=\"Perturbation katmanları (örn: 28)\")\n1020\t adv_group.add_argument(\"--perturbation-start\", type=int, default=0,\n1021\t help=\"Perturbation başlangıç yüzdesi (0-100)\")\n1022\t adv_group.add_argument(\"--perturbation-end\", type=int, default=100,\n1023\t help=\"Perturbation bitiş yüzdesi (0-100)\")\n1024\t adv_group.add_argument(\"--apg\", type=int, default=0,\n1025\t help=\"Adaptive Prompt Guidance modu\")\n1026\t adv_group.add_argument(\"--cfg-star\", type=int, default=0,\n1027\t help=\"CFG* scheduling modu\")\n1028\t adv_group.add_argument(\"--nag-scale\", type=float, default=None,\n1029\t help=\"Negative Attention Guidance scale\")"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"7b13227e-2d8d-4576-8781-c64e4d75ec93","timestamp":"2026-04-19T22:00:12.233Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/.claude/projects/-sessions-gifted-clever-bardeen/432fa85e-5db5-4830-9283-9556969b5029/tool-results/bxcf02f3h.txt","content":" print(f\" Fallback video bulundu: {os.path.basename(fallback)}\")\n results.append({\"index\": idx, \"success\": True, \"video\": seg_video, \"elapsed\": seg_elapsed})\n else:\n results.append({\"index\": idx, \"success\": False, \"elapsed\": seg_elapsed})\n\n # Session kapat\n print(f\"\\n[RUNNER] Session kapatiliyor...\")\n try:\n session.close()\n except Exception:\n pass\n\n total_elapsed = time.time() - total_start\n success_count = sum(1 for r in results if r[\"success\"])\n print(f\"\\n{'='*60}\")\n print(f\"[RUNNER] TAMAMLANDI: {success_count}/{num_tasks} basarili ({total_elapsed:.1f}s)\")\n print(f\"{'='*60}\")\n\n # Sonuclari kaydet\n result_path = manifest_path.replace(\".json\", \"_results.json\")\n with open(result_path, \"w\", encoding=\"utf-8\") as f:\n json.dump({\"results\": results, \"total_elapsed\": total_elapsed}, f, indent=2)\n\n print(f\"[RUNNER] Sonuclar: {result_path}\")\n\nif __name__ == \"__main__\":\n main()\n'''\n with open(runner_script, \"w\", encoding=\"utf-8\") as f:\n f.write(runner_code)\n\n # Manifest zaten caller (main) tarafindan yazilmis olmali\n # Burada sadece runner'i calistiriyoruz\n\n # WanGP Python ile runner'i calistir\n python_exe = str(WANGP_PYTHON)\n if not Path(python_exe).exists():\n python_exe = sys.executable\n\n print(f\"\\n[*] WanGP Batch Runner baslatiliyor (tek session)...\")\n print(f\" Model 1 kere yuklenecek, tum segmentler sirayla uretilecek\\n\")\n\n # ── CUDA OOM FIX: expandable_segments ortam degiskeni ──\n runner_env = os.environ.copy()\n runner_env[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n\n try:\n proc = subprocess.run(\n [python_exe, str(runner_script), str(wangp_root), str(manifest_path)],\n timeout=timeout,\n capture_output=False,\n cwd=str(wangp_root),\n env=runner_env\n )\n except subprocess.TimeoutExpired:\n print(f\" HATA: Toplam zaman asimi ({timeout}s)!\")\n except Exception as e:\n print(f\" HATA: {e}\")\n\n # Sonuclari oku\n results_path = str(manifest_path).replace(\".json\", \"_results.json\")\n video_segments = []\n if Path(results_path).exists():\n with open(results_path, \"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\n for r in data.get(\"results\", []):\n if r.get(\"success\") and r.get(\"video\") and Path(r[\"video\"]).exists():\n video_segments.append(Path(r[\"video\"]))\n else:\n # Fallback: segments dizininde mp4 ara\n import glob\n for mp4 in sorted(glob.glob(str(segments_dir / \"segment_*.mp4\"))):\n video_segments.append(Path(mp4))\n\n return video_segments\n\n\n# ============================================================================\n# ANA PIPELINE\n# ============================================================================\n\ndef main():\n parser = argparse.ArgumentParser(\n description=\"WanGP Müzik Video Üretici\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=\"\"\"\nÖrnek Kullanımlar:\n python wangp_video_uret.py sarki.mp3 gorsel.jpg\n python wangp_video_uret.py sarki.mp3 gorsel.jpg --model i2v_720p --resolution 1280x720\n python wangp_video_uret.py sarki.mp3 gorsel.jpg --model ltx2_22B --steps 40 --guidance 3.0\n python wangp_video_uret.py sarki.mp3 gorsel.jpg --lora \"my_lora.safetensors\" --lora-weight 0.8\n python wangp_video_uret.py sarki.mp3 gorsel.jpg --face-swap yuz.jpg\n\nModelleri Listele:\n python wangp_video_uret.py --list-models\n python wangp_video_uret.py --list-loras\n \"\"\"\n )\n\n # Pozisyonel argümanlar\n parser.add_argument(\"audio\", nargs=\"?\", help=\"Giriş ses dosyası (mp3, wav, flac, vb.)\")\n parser.add_argument(\"image\", nargs=\"?\", help=\"Giriş görseli (jpg, png)\")\n\n # Model ve temel ayarlar\n model_group = parser.add_argument_group(\"Model Ayarları\")\n model_group.add_argument(\"--model\", \"-m\", default=DEFAULT_MODEL,\n help=f\"Video üretim modeli (varsayılan: {DEFAULT_MODEL})\")\n model_group.add_argument(\"--resolution\", \"-r\", default=DEFAULT_RESOLUTION,\n help=f\"Çözünürlük WIDTHxHEIGHT formatında (varsayılan: {DEFAULT_RESOLUTION})\")\n model_group.add_argument(\"--video-length\", type=int, default=DEFAULT_VIDEO_LENGTH,\n help=f\"Segment başına frame sayısı (varsayılan: {DEFAULT_VIDEO_LENGTH}). 4n+1 olmalı (81,121,161,...)\")\n model_group.add_argument(\"--fps\", type=int, default=DEFAULT_FPS,\n help=f\"Video FPS (varsayılan: {DEFAULT_FPS})\")\n\n # Prompt ayarları\n prompt_group = parser.add_argument_group(\"Prompt Ayarları\")\n prompt_group.add_argument(\"--prompt\", \"-p\", default=DEFAULT_PROMPT,\n help=\"Video üretim prompt'u\")\n prompt_group.add_argument(\"--negative-prompt\", \"--neg\", default=DEFAULT_NEGATIVE_PROMPT,\n help=\"Negatif prompt (istenmeyen öğeler)\")\n prompt_group.add_argument(\"--prompt-enhancer\", type=int, default=1, choices=[0, 1],\n help=\"Prompt geliştirici (0=kapalı, 1=açık)\")\n prompt_group.add_argument(\"--auto-prompt\", action=\"store_true\",\n help=\"Ses enerjisi analizine göre her segmente otomatik farklı prompt oluştur. \"\n \"Sessiz bölümlerde sakin, enerjik bölümlerde dinamik prompt kullanılır.\")\n\n # Inference ayarları\n inf_group = parser.add_argument_group(\"Inference Ayarları\")\n inf_group.add_argument(\"--steps\", \"-n\", type=int, default=DEFAULT_STEPS,\n help=f\"Inference adım sayısı (varsayılan: {DEFAULT_STEPS})\")\n inf_group.add_argument(\"--guidance-scale\", \"-g\", type=float, default=DEFAULT_GUIDANCE_SCALE,\n help=f\"Guidance scale / CFG (varsayılan: {DEFAULT_GUIDANCE_SCALE})\")\n inf_group.add_argument(\"--guidance2-scale\", type=float, default=None,\n help=\"İkinci faz guidance scale (opsiyonel)\")\n inf_group.add_argument(\"--guidance3-scale\", type=float, default=None,\n help=\"Üçüncü faz guidance scale (opsiyonel)\")\n inf_group.add_argument(\"--guidance-phases\", type=int, default=None, choices=[1, 2, 3],\n help=\"Guidance faz sayısı (1-3)\")\n inf_group.add_argument(\"--embedded-guidance\", type=float, default=None,\n help=\"Embedded guidance scale (modele bağlı)\")\n inf_group.add_argument(\"--alt-guidance\", type=float, default=None,\n help=\"Alternatif guidance scale\")\n inf_group.add_argument(\"--alt-scale\", type=float, default=None,\n help=\"Alternatif scale (0.0-1.0)\")\n inf_group.add_argument(\"--flow-shift\", type=float, default=DEFAULT_FLOW_SHIFT,\n help=f\"Flow shift (varsayılan: {DEFAULT_FLOW_SHIFT})\")\n inf_group.add_argument(\"--solver\", default=DEFAULT_SOLVER,\n help=f\"Sampling algoritması (varsayılan: {DEFAULT_SOLVER}). Seçenekler: euler, unipc\")\n inf_group.add_argument(\"--seed\", type=int, default=DEFAULT_SEED,\n help=f\"Seed (-1=rastgele, varsayılan: {DEFAULT_SEED})\")\n inf_group.add_argument(\"--denoising-strength\", type=float, default=None,\n help=\"Denoising gücü (0.0-1.0)\")\n\n # Audio guidance ve lip-sync\n audio_group = parser.add_argument_group(\"Audio Guidance ve Lip-Sync Ayarları\")\n audio_group.add_argument(\"--audio-guidance-scale\", type=float, default=None,\n help=\"Audio guidance scale (LTX2 varsayılan: 7.0)\")\n audio_group.add_argument(\"--audio-scale\", type=float, default=None,\n help=\"Audio scale (0.0-1.0)\")\n audio_group.add_argument(\"--audio-prompt-type\", default=None,\n help=\"Audio prompt tipi bayrakları: A=tek kişi lip-sync, V=vokal ayır, N=ses normalize, X=iki kişi otomatik, B=iki ayrı ses\")\n audio_group.add_argument(\"--no-lip-sync\", action=\"store_true\",\n help=\"Lip-sync'i devre dışı bırak (sesi sadece son videoya ekle, görüntüyü etkilemesin)\")\n audio_group.add_argument(\"--no-vocal-isolation\", action=\"store_true\",\n help=\"Vokal izolasyonu yapma (V bayrağını kaldır)\")\n\n # LoRA ayarları\n lora_group = parser.add_argument_group(\"LoRA Ayarları\")\n lora_group.add_argument(\"--lora\", action=\"append\", default=None,\n help=\"LoRA dosyası (birden fazla --lora ile eklenebilir)\")\n lora_group.add_argument(\"--lora-weight\", default=\"1.0\",\n help=\"LoRA ağırlıkları (boşlukla ayrılmış, örn: '0.8 1.0')\")\n\n # Sliding window (uzun video)\n sw_group = parser.add_argument_group(\"Sliding Window Ayarları (Uzun Video)\")\n sw_group.add_argument(\"--sliding-window-size\", type=int, default=DEFAULT_SLIDING_WINDOW_SIZE,\n help=\"Sliding window frame boyutu (0=kapalı)\")\n sw_group.add_argument(\"--sliding-window-overlap\", type=int, default=DEFAULT_SLIDING_WINDOW_OVERLAP,\n help=\"Sliding window overlap\")\n sw_group.add_argument(\"--sliding-window-color-correction\", type=float, default=0.0,\n help=\"Renk düzeltme gücü (0.0-1.0)\")\n sw_group.add_argument(\"--sliding-window-overlap-noise\", type=int, default=0,\n help=\"Overlap noise (0-150)\")\n\n # Gelişmiş ayarlar\n adv_group = parser.add_argument_group(\"Gelişmiş Ayarlar\")\n adv_group.add_argument(\"--perturbation\", type=int, default=0, choices=[0, 1, 2, 3, 4],\n help=\"Perturbation modu (0=kapalı)\")\n adv_group.add_argument(\"--perturbation-layers\", type=int, nargs=\"+\", default=None,\n help=\"Perturbation katmanları (örn: 28)\")\n adv_group.add_argument(\"--perturbation-start\", type=int, default=0,\n help=\"Perturbation başlangıç yüzdesi (0-100)\")\n adv_group.add_argument(\"--perturbation-end\", type=int, default=100,\n help=\"Perturbation bitiş yüzdesi (0-100)\")\n adv_group.add_argument(\"--apg\", type=int, default=0,\n help=\"Adaptive Prompt Guidance modu\")\n adv_group.add_argument(\"--cfg-star\", type=int, default=0,\n help=\"CFG* scheduling modu\")\n adv_group.add_argument(\"--nag-scale\", type=float, default=None,\n help=\"Negative Attention Guidance scale\")","numLines":200,"startLine":830,"totalLines":1431}},"_audit_timestamp":"2026-04-19T22:00:12.274Z","_audit_hmac":"5c4bbc7a10d27888693970fc33e6c1cebeb1e20d403c045d263651ff02eedfba"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"0bec3bec-8bee-47d8-abb0-f6e3e3b182be","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:12.296Z","_audit_hmac":"77281eb49bdb4cdd095ebd048a2cdc0d02006d02b90ea2b2d285fe1a2704918d"} {"type":"system","subtype":"status","status":"requesting","uuid":"05275155-6843-4b5c-8582-313bd4bfc54d","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:12.317Z","_audit_hmac":"09af6355e34584898283cba4f86c0a70f09cb8ecfeb65e7f92c2aea2a9606eda"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01DF1UoEyztrAY7B3Ufw1yEj","type":"message","role":"assistant","content":[{"type":"text","text":"Tamam, durumu anladım. Scriptiniz WanGP API üzerinden çalışıyor (localhost:7860). Şimdi CPU RAM'e taşıma konusuna gelelim.\n\nFikri, sorunun büyük ihtimalle şunlardan biri olduğunu düşünüyorum:\n\n**WanGP veya LTX güncellenmiş olabilir** — yeni versiyonda VRAM yönetimi değişmiş olabilir, daha önce çalışan ayarlar artık OOM (Out of Memory) veriyor olabilir.\n\n**CPU offload (RAM'e taşıma) konusu:**\n\nWanGP zaten modeli parçalar halinde GPU'ya yükleyip CPU RAM'e offload etme desteğine sahip. Bu genelde \"cpu_offload\" veya \"model_offload\" seçeneği olarak WanGP arayüzünde bulunur. Senin scriptinde doğrudan bu ayar yok, çünkü WanGP'nin kendi backend'i bu işi yapıyor.\n\nYapabileceğin şeyler:\n\n1. **WanGP arayüzünde (localhost:7860) \"CPU Offload\" veya \"Model Offload\" seçeneğini aç** — bu modelin kullanılmayan kısımlarını RAM'e taşır, VRAM'i rahatlatır.\n\n2. **Çözünürlüğü düşür** — 720p yerine 480p dene, veya frame sayısını azalt.\n\n3. **MPEG/MP4 format farkı sorunu değil** — sorunun formatla ilgisi yok, her ikisi de aynı VRAM'i kullanır.\n\nŞimdi senin scriptini `--cpu-offload` parametresi ekleyecek şekilde güncelleyebilirim. Bu parametre WanGP API'ye gönderildiğinde modeli RAM'e offload eder. İster misin bunu ekleyeyim?\n\nYoksa WanGP arayüzünü açıp bana ekran görüntüsü gösterebilirsen, hangi ayarın açılması gerektiğini direkt gösterebilirim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4995,"cache_read_input_tokens":40283,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4995},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"8beeaf8c-35a6-42b2-95b8-c76d9d484ea6","_audit_timestamp":"2026-04-19T22:00:31.065Z","_audit_hmac":"0726bec9862c74eeca6c375ce45cb186c9cef91e6f0239f95fd3ad2338552d91"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"allowed","overageResetsAt":1777593600,"isUsingOverage":false},"uuid":"038d1ba7-a1fa-4e40-9418-6e978877efc7","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:00:31.557Z","_audit_hmac":"43eb179b015b6d29e57e1c81ce39f236bc70b8b5efa6733cc013071566e75db2"} {"type":"result","subtype":"success","is_error":false,"api_error_status":null,"duration_ms":49827,"duration_api_ms":50554,"num_turns":8,"result":"Tamam, durumu anladım. Scriptiniz WanGP API üzerinden çalışıyor (localhost:7860). Şimdi CPU RAM'e taşıma konusuna gelelim.\n\nFikri, sorunun büyük ihtimalle şunlardan biri olduğunu düşünüyorum:\n\n**WanGP veya LTX güncellenmiş olabilir** — yeni versiyonda VRAM yönetimi değişmiş olabilir, daha önce çalışan ayarlar artık OOM (Out of Memory) veriyor olabilir.\n\n**CPU offload (RAM'e taşıma) konusu:**\n\nWanGP zaten modeli parçalar halinde GPU'ya yükleyip CPU RAM'e offload etme desteğine sahip. Bu genelde \"cpu_offload\" veya \"model_offload\" seçeneği olarak WanGP arayüzünde bulunur. Senin scriptinde doğrudan bu ayar yok, çünkü WanGP'nin kendi backend'i bu işi yapıyor.\n\nYapabileceğin şeyler:\n\n1. **WanGP arayüzünde (localhost:7860) \"CPU Offload\" veya \"Model Offload\" seçeneğini aç** — bu modelin kullanılmayan kısımlarını RAM'e taşır, VRAM'i rahatlatır.\n\n2. **Çözünürlüğü düşür** — 720p yerine 480p dene, veya frame sayısını azalt.\n\n3. **MPEG/MP4 format farkı sorunu değil** — sorunun formatla ilgisi yok, her ikisi de aynı VRAM'i kullanır.\n\nŞimdi senin scriptini `--cpu-offload` parametresi ekleyecek şekilde güncelleyebilirim. Bu parametre WanGP API'ye gönderildiğinde modeli RAM'e offload eder. İster misin bunu ekleyeyim?\n\nYoksa WanGP arayüzünü açıp bana ekran görüntüsü gösterebilirsen, hangi ayarın açılması gerektiğini direkt gösterebilirim.","stop_reason":"end_turn","session_id":"432fa85e-5db5-4830-9283-9556969b5029","total_cost_usd":0.327114,"usage":{"input_tokens":8,"cache_creation_input_tokens":30612,"cache_read_input_tokens":193354,"output_tokens":1544,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":30612,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[{"input_tokens":1,"output_tokens":581,"cache_read_input_tokens":40283,"cache_creation_input_tokens":4995,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4995},"type":"message"}],"speed":"standard"},"modelUsage":{"claude-haiku-4-5-20251001":{"inputTokens":392,"outputTokens":16,"cacheReadInputTokens":0,"cacheCreationInputTokens":0,"webSearchRequests":0,"costUSD":0.000472,"contextWindow":200000,"maxOutputTokens":32000},"claude-opus-4-6":{"inputTokens":8,"outputTokens":1544,"cacheReadInputTokens":193354,"cacheCreationInputTokens":30612,"webSearchRequests":0,"costUSD":0.3266420000000001,"contextWindow":200000,"maxOutputTokens":64000}},"permission_denials":[],"terminal_reason":"completed","fast_mode_state":"off","uuid":"3911b9ca-a8e2-4373-be05-03e8003f1899","_audit_timestamp":"2026-04-19T22:00:31.568Z","_audit_hmac":"99136dc4ad8912097f44a3c8c49be9d3eaaf4e15d7d34b738551bc4b80376a4b"} 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file \"/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py\" 2>&1","description":"Check if file exists"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":253,"cache_read_input_tokens":46465,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":253},"output_tokens":60,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"2783a9cd-c7e7-4569-be3c-31a2dc274d79","_audit_timestamp":"2026-04-19T22:01:20.434Z","_audit_hmac":"63ba06d509067e01d050d76e47803b8a58b2239c8bb2e7ef2dc705dd34db0644"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"a6309cc1-873c-430d-bd9e-a84381151207","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:01:20.494Z","_audit_hmac":"d848618f5ebc7f3d5076a978a02c3dfd68c05a22a5ec4bf05c7defc65415b60e"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016aWj3P5jrhogxEzq8cPyRH","type":"tool_result","content":"ls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py': No such file or directory\n/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py: cannot open `/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py' (No such file or directory)","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"d18d567e-a344-4b20-90da-f9a41d519f42","timestamp":"2026-04-19T22:01:20.499Z","tool_use_result":{"stdout":"ls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py': No such file or directory\n/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py: cannot open `/sessions/gifted-clever-bardeen/mnt/video-uretim/wangp_video_uret.py' (No such file or directory)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:01:20.500Z","_audit_hmac":"10dd9461a9e28f70806c7289534f96892bd84507afd3a3d52c2a48860216e9fa"} {"type":"system","subtype":"status","status":"requesting","uuid":"7fa27692-f2cf-467f-9909-b56b3e1b6b38","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:01:20.504Z","_audit_hmac":"69c096c494b2db25f8454fa0371dbb71f33835ca2c7d23e0f6a7f4c6e904383b"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013KruPkCKSmtn6UFvPHEjb5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012rKtBeDY9AssTWXDbSx6ur","name":"Bash","input":{"command":"ls -la /sessions/gifted-clever-bardeen/mnt/video-uretim/*.py 2>&1; ls -la /sessions/gifted-clever-bardeen/mnt/video-uretim/*.bat 2>&1","description":"List py and bat files"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":269,"cache_read_input_tokens":46718,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":269},"output_tokens":68,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"edba5706-c104-4fc8-8532-315ae65be77a","_audit_timestamp":"2026-04-19T22:01:24.355Z","_audit_hmac":"83100681710f9f7dc723d5f40fbbb32b7e80f1ac2ea2f66a8a6e6d04e88db8ee"} {"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/*.py': No such file or directory\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/*.bat': No such file or directory","is_error":true,"tool_use_id":"toolu_012rKtBeDY9AssTWXDbSx6ur"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"8e70cc54-a333-42b4-8fa2-3da47b30e519","timestamp":"2026-04-19T22:01:24.388Z","tool_use_result":"Error: Exit code 2\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/*.py': No such file or directory\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/video-uretim/*.bat': No such file or directory","_audit_timestamp":"2026-04-19T22:01:24.392Z","_audit_hmac":"c00718962598ffd7235d4c4ce232dea64949e2dee54f0e7af3a1d7f1ef4b575b"} {"type":"system","subtype":"status","status":"requesting","uuid":"5bf46a49-35d0-4f36-98b4-1e09f3b26c1f","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:01:24.396Z","_audit_hmac":"e7c719408199c6144d6ac7b235a949a7ee09d01424122451c65a520e9cfca6a7"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01JqAtioRUtdcB33XFPsV37d","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CBHpjsCZWY4ptzstu6ASDc","name":"Bash","input":{"command":"ls -la /sessions/gifted-clever-bardeen/mnt/video-uretim/","description":"List video-uretim directory"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":486,"cache_read_input_tokens":46718,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":486},"output_tokens":72,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"db03b095-bd7d-4244-b30f-865b89d54d20","_audit_timestamp":"2026-04-19T22:01:28.996Z","_audit_hmac":"4606eb1b2b2056b53749a5ea94448118a40a039d1c87e23e7c38f3944d378856"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CBHpjsCZWY4ptzstu6ASDc","type":"tool_result","content":"total 0\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:34 .\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 20:46 ..","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"398cad16-4047-42e2-bbcf-e68c07d478fe","timestamp":"2026-04-19T22:01:29.031Z","tool_use_result":{"stdout":"total 0\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:34 .\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 20:46 ..","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:01:29.044Z","_audit_hmac":"676a5b4d5fd0b1ba7ce1f8917368ae256d63febe78c8ab57de6c29e51e6f6e81"} {"type":"system","subtype":"status","status":"requesting","uuid":"8ec1f207-8c27-4fa9-b604-f96cce07b3a8","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:01:29.048Z","_audit_hmac":"f98190871c4868beb0da28615b3326424e88bdec229bb6a4b5f4006291801362"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NQCowfVDoamSyRsjddXaZA","type":"message","role":"assistant","content":[{"type":"text","text":"Klasör boş görünüyor — dosyalar muhtemelen Windows tarafında farklı bir path'te. Daha önce okuduğum içerik cache'ten geldi. Şimdi doğru yolu bulayım."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":184,"cache_read_input_tokens":47204,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":184},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"9b63414f-0151-49f6-a0f0-6ac51c1907ef","_audit_timestamp":"2026-04-19T22:01:36.204Z","_audit_hmac":"a8e3821dada960439ce79e4c1de0767d1b4885511f8c12f0f19dc9a1106bddb4"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NQCowfVDoamSyRsjddXaZA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LvKHiUszoVRddCDRcYaB4E","name":"Bash","input":{"command":"ls -la /sessions/gifted-clever-bardeen/mnt/","description":"List mount root"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":184,"cache_read_input_tokens":47204,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":184},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3974a186-1286-4335-92b1-6068be33aa03","_audit_timestamp":"2026-04-19T22:01:36.482Z","_audit_hmac":"6a4d12b99c780e97a3b17c0cb4779cb3e51e7f471af651d943269688053bf11c"} {"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/Config.Msi': Permission denied\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/System Volume Information': Permission denied\ntotal 4\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 9 2025 $AV_ASW\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jun 22 2024 $RECYCLE.BIN\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 20:46 .\ndrwxr-x--- 6 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 20 00:59 ..\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 20 01:01 .claude\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-lib\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-perm-req\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-perm-resp\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 11 15:58 .pnpm-store\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:12 .remote-plugins\nd????????? ? ? ? ? ? Config.Msi\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 14 06:14 Hatıra\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jun 29 2025 Oyun\nd????????? ? ? ? ? ? System Volume Information\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Oct 26 2024 Windows Kits\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jan 13 2024 __OtoCalis__\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Feb 24 21:30 __sistem__\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Aug 3 2025 msdownld.tmp\n-rwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 62 Dec 10 00:37 nul\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:13 uploads\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:34 video-uretim\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 19 04:38 yedek","is_error":true,"tool_use_id":"toolu_01LvKHiUszoVRddCDRcYaB4E"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"04331484-cba4-4ad7-bc18-857366f91e9f","timestamp":"2026-04-19T22:01:36.531Z","tool_use_result":"Error: Exit code 1\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/Config.Msi': Permission denied\nls: cannot access '/sessions/gifted-clever-bardeen/mnt/System Volume Information': Permission denied\ntotal 4\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 9 2025 $AV_ASW\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jun 22 2024 $RECYCLE.BIN\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 20:46 .\ndrwxr-x--- 6 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 20 00:59 ..\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 20 01:01 .claude\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-lib\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-perm-req\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 16:05 .cowork-perm-resp\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 11 15:58 .pnpm-store\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:12 .remote-plugins\nd????????? ? ? ? ? ? Config.Msi\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 14 06:14 Hatıra\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jun 29 2025 Oyun\nd????????? ? ? ? ? ? System Volume Information\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Oct 26 2024 Windows Kits\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Jan 13 2024 __OtoCalis__\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Feb 24 21:30 __sistem__\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Aug 3 2025 msdownld.tmp\n-rwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 62 Dec 10 00:37 nul\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:13 uploads\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 18 17:34 video-uretim\ndrwx------ 1 gifted-clever-bardeen gifted-clever-bardeen 4096 Apr 19 04:38 yedek","_audit_timestamp":"2026-04-19T22:01:36.532Z","_audit_hmac":"b90db9a7cb796aacb2b8a0d5a2189e939aa5192fdf6590b93e11b113154a8840"} {"type":"system","subtype":"status","status":"requesting","uuid":"dd1c4773-61ab-4e5f-8dc4-fa635dc71ae7","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:01:36.538Z","_audit_hmac":"fd5d622f5a72169688e19e7c16e019e93a552da5cfa0fce22b807f749b0f7322"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01BeiZEB5dQxW3ooDfEGgHgj","type":"message","role":"assistant","content":[{"type":"text","text":"Hmm, `video-uretim` klasörü boş ama önceki oturumda dosyalar oradaydı. Muhtemelen dosyalar Windows'ta `D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\` altında. BAT dosyasındaki path'lerden bunu biliyorum. 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PC.rdp\nFikoYT Reklam.png\nFikoYT-10319593.xml\nFikoYT.Projeler\nFikoYT.Yeni.Cag\nFikoÇizim.Fikowrl\nIFikoCikariciDialog\nfikoyt_center\nfikoyt_yazi.png","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"945c5e96-8c52-4dd3-9c6a-4a151f66f357","timestamp":"2026-04-19T22:03:51.287Z","tool_use_result":{"stdout":"AQKS786ARKA.FikoApt\nFiko Kutuk-Uyar.CATPart\nFikoYT - PC.rdp\nFikoYT Reklam.png\nFikoYT-10319593.xml\nFikoYT.Projeler\nFikoYT.Yeni.Cag\nFikoÇizim.Fikowrl\nIFikoCikariciDialog\nfikoyt_center\nfikoyt_yazi.png","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:03:51.508Z","_audit_hmac":"e903be045eaebb547d8e0de26bbccc485a8179aaa54ca1fd2a4575fbb2491232"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"c846ca84-f4d2-4a1d-8b99-3187d112c482","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:03:51.552Z","_audit_hmac":"0895f4f93df2b6e62116d1d671fcae0171e6e60c5d46fbfde09d347569be80ae"} {"type":"system","subtype":"status","status":"requesting","uuid":"e6791471-f310-42be-94d0-ab5c3f628b52","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:03:51.557Z","_audit_hmac":"a7c9994cb0b27848096011332d5ec35e6e59c105d5c1c0cf2d340ed30a8f42b5"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_014ZyVorgeQ21USvSwUbpZM4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FB72GwurcyLGG8jgN2S2z3","name":"Bash","input":{"command":"ls \"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/\" 2>/dev/null | head -30","description":"List video-uretim in FikoYT folder"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":247,"cache_read_input_tokens":49132,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":247},"output_tokens":66,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"973a5574-a01d-4654-9d86-09814fc02083","_audit_timestamp":"2026-04-19T22:03:56.448Z","_audit_hmac":"22238932aa0d08ee5b53324ab6ea7601da8434a910d4e2bf68312df2b1d71e7c"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FB72GwurcyLGG8jgN2S2z3","type":"tool_result","content":"ACE-Step-Baslat.bat\nACE-Step-Guncelle.bat\nACE-Step-Kur.bat\nACE-Step-README.md\nACE-Step.bat\nBulgarca-Şarkılar\nLTX-Desktop-WanGP-Baslat.bat\nLTX-Desktop-WanGP-Guncelle.bat\nLTX-Desktop-WanGP-Kur.bat\nMuzik-Video-Uret.bat\nRVC-Kur-ve-Baslat.bat\nVideo-Studyo-Baslat.bat\nWanGP-Baslat.bat\nWanGP-Guncelle.bat\nWanGP-Klip-Rehberi.md\nWanGP-Kur.bat\nWanGP-Video-Uret.bat\n__pycache__\nace-step\nfiko-video-studio\nltx-desktop-wangp\nltx_token.txt\nmuzik_video_uret.py\noutput\nwangp_video_uret.py\nİlahiler","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"24b43ad7-fe5d-4548-ba5c-47deb17eaca6","timestamp":"2026-04-19T22:03:56.487Z","tool_use_result":{"stdout":"ACE-Step-Baslat.bat\nACE-Step-Guncelle.bat\nACE-Step-Kur.bat\nACE-Step-README.md\nACE-Step.bat\nBulgarca-Şarkılar\nLTX-Desktop-WanGP-Baslat.bat\nLTX-Desktop-WanGP-Guncelle.bat\nLTX-Desktop-WanGP-Kur.bat\nMuzik-Video-Uret.bat\nRVC-Kur-ve-Baslat.bat\nVideo-Studyo-Baslat.bat\nWanGP-Baslat.bat\nWanGP-Guncelle.bat\nWanGP-Klip-Rehberi.md\nWanGP-Kur.bat\nWanGP-Video-Uret.bat\n__pycache__\nace-step\nfiko-video-studio\nltx-desktop-wangp\nltx_token.txt\nmuzik_video_uret.py\noutput\nwangp_video_uret.py\nİlahiler","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:03:56.488Z","_audit_hmac":"a0209ea584beb5c4e1822d5d8a5ce85d8a04b1e5e7de34900f36f80648052f3b"} {"type":"system","subtype":"status","status":"requesting","uuid":"be482c21-0f9a-48c4-8d6b-9bdc74827745","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:03:56.493Z","_audit_hmac":"425b31332fced07668cedd5269f580614961f08110b411f8d6f3c9799d23b897"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01H9j6Df7pzSbT8FSNvHsSCo","type":"message","role":"assistant","content":[{"type":"text","text":"Buldum! Dosyalar burada. Şimdi scriptin API çağrı kısmını detaylıca okuyup CPU offload ekleyeceğim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":424,"cache_read_input_tokens":49379,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":424},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"9a34be18-9537-476d-9e59-d5bd582f0225","_audit_timestamp":"2026-04-19T22:04:00.456Z","_audit_hmac":"2f2d200750cc3edaeb48f8f4a27355cf7d57cc162414e6b2b6a89ccdcf015170"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01H9j6Df7pzSbT8FSNvHsSCo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017TWQbukQtnR8F2FBKnArzG","name":"Bash","input":{"command":"FILEPATH=\"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py\" && wc -l \"$FILEPATH\"","description":"Count lines in script"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":424,"cache_read_input_tokens":49379,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":424},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3eca561c-ed81-4173-bd2a-d6c7363a0e50","_audit_timestamp":"2026-04-19T22:04:01.285Z","_audit_hmac":"6fb071cea233fe36b3e0ce4839061183c9ec50d747197d78e47db699c31d36e3"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017TWQbukQtnR8F2FBKnArzG","type":"tool_result","content":"1430 /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"cedfd005-d691-4c3d-8fba-d33a530249c6","timestamp":"2026-04-19T22:04:01.316Z","tool_use_result":{"stdout":"1430 /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:04:01.318Z","_audit_hmac":"d0867cb20ab0e0e4f6bbc68836df47ded8f73530504dcb5ccbd32d84a7394935"} {"type":"system","subtype":"status","status":"requesting","uuid":"3e719e88-9d8f-4318-9da1-127c6a594cdd","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:01.320Z","_audit_hmac":"04ae286479eaf780a9370b6f4d33314b62e27555f97b1248012316d9e1e1f08b"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NuHYVcZ4y2FHgg4qXcv6jp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0149WfS7A7rxKnP83MWyn3ER","name":"Grep","input":{"pattern":"offload|payload|manifest|task_data|json\\.dumps|api_url|requests\\.|session\\.|generate|model_load","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","output_mode":"content","-n":true},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":246,"cache_read_input_tokens":49803,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":246},"output_tokens":34,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"cac384a9-43b0-4b55-bb14-b83cd0d60bf0","_audit_timestamp":"2026-04-19T22:04:05.394Z","_audit_hmac":"0e8077e71a4494097a780020efc171a8c9ea07e74dc207a1267cc5c5bc4dd389"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0149WfS7A7rxKnP83MWyn3ER","type":"tool_result","content":"511:def generate_all_segments(manifest_path, wangp_root, tasks_dir, segments_dir, frames_dir,\n519: manifest_path: Tum segment task'larinin listesini iceren JSON\n537: python _wangp_batch_runner.py \n631: print(\"Kullanim: _wangp_batch_runner.py \")\n635: manifest_path = sys.argv[2]\n642: with open(manifest_path, \"r\", encoding=\"utf-8\") as f:\n643: manifest = json.load(f)\n645: tasks = manifest[\"tasks\"]\n646: frames_dir = manifest[\"frames_dir\"]\n647: segments_dir = manifest[\"segments_dir\"]\n710: session._output_dir = Path(seg_output_dir)\n743: result = session.run_task(task_params)\n748: print(f\" [*] Dosyalar: {len(result.generated_files) if result.generated_files else 0}\")\n756: # Yontem 1: API sonucu (generated_files)\n757: if result.success and result.generated_files:\n758: for f in result.generated_files:\n838: session.close()\n849: result_path = manifest_path.replace(\".json\", \"_results.json\")\n878: [python_exe, str(runner_script), str(wangp_root), str(manifest_path)],\n890: results_path = str(manifest_path).replace(\".json\", \"_results.json\")\n1226: manifest_tasks = []\n1356: manifest_tasks.append({\n1364: manifest_path = tasks_dir / \"manifest.json\"\n1365: manifest_data = {\n1366: \"tasks\": manifest_tasks,\n1370: with open(manifest_path, \"w\", encoding=\"utf-8\") as f:\n1371: json.dump(manifest_data, f, indent=2, ensure_ascii=False)\n1373: print(f\"\\n[*] Manifest yazildi: {manifest_path}\")\n1378: video_segments = generate_all_segments(\n1379: manifest_path=manifest_path,"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"77408a6d-9605-49f3-9bad-ad9feee1112f","timestamp":"2026-04-19T22:04:05.404Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"511:def generate_all_segments(manifest_path, wangp_root, tasks_dir, segments_dir, frames_dir,\n519: manifest_path: Tum segment task'larinin listesini iceren JSON\n537: python _wangp_batch_runner.py \n631: print(\"Kullanim: _wangp_batch_runner.py \")\n635: manifest_path = sys.argv[2]\n642: with open(manifest_path, \"r\", encoding=\"utf-8\") as f:\n643: manifest = json.load(f)\n645: tasks = manifest[\"tasks\"]\n646: frames_dir = manifest[\"frames_dir\"]\n647: segments_dir = manifest[\"segments_dir\"]\n710: session._output_dir = Path(seg_output_dir)\n743: result = session.run_task(task_params)\n748: print(f\" [*] Dosyalar: {len(result.generated_files) if result.generated_files else 0}\")\n756: # Yontem 1: API sonucu (generated_files)\n757: if result.success and result.generated_files:\n758: for f in result.generated_files:\n838: session.close()\n849: result_path = manifest_path.replace(\".json\", \"_results.json\")\n878: [python_exe, str(runner_script), str(wangp_root), str(manifest_path)],\n890: results_path = str(manifest_path).replace(\".json\", \"_results.json\")\n1226: manifest_tasks = []\n1356: manifest_tasks.append({\n1364: manifest_path = tasks_dir / \"manifest.json\"\n1365: manifest_data = {\n1366: \"tasks\": manifest_tasks,\n1370: with open(manifest_path, \"w\", encoding=\"utf-8\") as f:\n1371: json.dump(manifest_data, f, indent=2, ensure_ascii=False)\n1373: print(f\"\\n[*] Manifest yazildi: {manifest_path}\")\n1378: video_segments = generate_all_segments(\n1379: manifest_path=manifest_path,","numLines":30},"_audit_timestamp":"2026-04-19T22:04:05.409Z","_audit_hmac":"f8bcf8b7f13402e25ff1eac8d472b1232be42a7d45cc21635c3a7d2888ad5cff"} {"type":"system","subtype":"status","status":"requesting","uuid":"603bab23-4cbe-4420-bc91-bae5cc23e49a","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:05.413Z","_audit_hmac":"b98bbc0258aeeebf366ebaaee369aef81eb6c04b07f181834bca9e7897025b4b"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01XKHYLFvTsFK3ciNrYna4s6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VjEaui6FhmmBfNs3AWHHFu","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","offset":630,"limit":150},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":762,"cache_read_input_tokens":50049,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":762},"output_tokens":72,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"08caebd3-7e50-4d70-9ac4-5c7f0bf39513","_audit_timestamp":"2026-04-19T22:04:08.632Z","_audit_hmac":"d49912e70e2c37caaa566f883f33c0889a6c61a0daeef5124cb10d85028f9e21"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VjEaui6FhmmBfNs3AWHHFu","type":"tool_result","content":"630\t if len(sys.argv) < 3:\n631\t print(\"Kullanim: _wangp_batch_runner.py \")\n632\t sys.exit(1)\n633\t\n634\t wangp_root = sys.argv[1]\n635\t manifest_path = sys.argv[2]\n636\t\n637\t # WanGP'yi import edebilmek icin path ekle\n638\t if wangp_root not in sys.path:\n639\t sys.path.insert(0, wangp_root)\n640\t os.chdir(wangp_root)\n641\t\n642\t with open(manifest_path, \"r\", encoding=\"utf-8\") as f:\n643\t manifest = json.load(f)\n644\t\n645\t tasks = manifest[\"tasks\"]\n646\t frames_dir = manifest[\"frames_dir\"]\n647\t segments_dir = manifest[\"segments_dir\"]\n648\t num_tasks = len(tasks)\n649\t\n650\t print(f\"\\n{'='*60}\")\n651\t print(f\"[RUNNER] {num_tasks} segment uretilecek (TEK SESSION)\")\n652\t print(f\"[RUNNER] Model 1 kere yuklenecek, RAM'de kalacak\")\n653\t print(f\"{'='*60}\")\n654\t\n655\t # WanGP API'yi import et\n656\t try:\n657\t from shared.api import WanGPSession\n658\t except ImportError as e:\n659\t print(f\"[RUNNER] HATA: WanGP API import edilemedi: {e}\")\n660\t print(f\"[RUNNER] wangp_root: {wangp_root}\")\n661\t print(f\"[RUNNER] sys.path: {sys.path[:3]}\")\n662\t sys.exit(1)\n663\t\n664\t # TEK SESSION AC - model 1 kere yuklenir\n665\t # output_dir olarak ilk segmentin dizinini ver\n666\t first_output_dir = tasks[0][\"output_dir\"]\n667\t os.makedirs(first_output_dir, exist_ok=True)\n668\t\n669\t # ── CUDA OOM FIX: Video uretiminden once GPU bellegini temizle ──\n670\t # Audio separation modeli GPU'da bellek parcalanmasina neden olabilir\n671\t try:\n672\t import torch\n673\t if torch.cuda.is_available():\n674\t torch.cuda.empty_cache()\n675\t torch.cuda.synchronize()\n676\t gc.collect()\n677\t free_mem = torch.cuda.mem_get_info()[0] / 1024**3\n678\t total_mem = torch.cuda.mem_get_info()[1] / 1024**3\n679\t print(f\"[RUNNER] GPU bellek: {free_mem:.1f}/{total_mem:.1f} GiB bos (cache temizlendi)\")\n680\t except Exception as e:\n681\t print(f\"[RUNNER] GPU bellek temizleme uyarisi: {e}\")\n682\t\n683\t print(f\"[RUNNER] WanGPSession aciliyor...\")\n684\t print(f\"[RUNNER] Output: {first_output_dir}\")\n685\t\n686\t try:\n687\t session = WanGPSession(\n688\t root=wangp_root,\n689\t output_dir=first_output_dir,\n690\t console_output=True,\n691\t )\n692\t except Exception as e:\n693\t print(f\"[RUNNER] HATA: Session acilamadi: {e}\")\n694\t traceback.print_exc()\n695\t sys.exit(1)\n696\t\n697\t print(f\"[RUNNER] Session acildi, model yuklendi!\\n\")\n698\t\n699\t results = []\n700\t total_start = time.time()\n701\t\n702\t for idx, task_info in enumerate(tasks):\n703\t seg_output_dir = task_info[\"output_dir\"]\n704\t task_params = dict(task_info[\"params\"]) # kopya al\n705\t task_params[\"repeat_generation\"] = 1\n706\t\n707\t # Output dizinini guncelle\n708\t os.makedirs(seg_output_dir, exist_ok=True)\n709\t try:\n710\t session._output_dir = Path(seg_output_dir)\n711\t except Exception:\n712\t pass # eger _output_dir yoksa sorun degil, WanGP kendi dizinine kaydeder\n713\t\n714\t print(f\"\\n{'='*60}\")\n715\t print(f\"[SEGMENT {idx+1}/{num_tasks}]\")\n716\t print(f\" Output : {seg_output_dir}\")\n717\t print(f\" Image : {task_params.get('image_start', 'N/A')}\")\n718\t print(f\" Audio : {os.path.basename(task_params.get('audio_source', 'N/A'))}\")\n719\t if task_params.get('audio_guide'):\n720\t print(f\" LipSync: ACIK\")\n721\t print(f\"{'='*60}\")\n722\t\n723\t # Mevcut mp4'leri kaydet (sonra yenileri bulmak icin)\n724\t existing_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n725\t # WanGP bazen farkli dizine de kaydedebilir - session output_dir'i de kontrol et\n726\t wangp_save_dir = seg_output_dir\n727\t existing_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n728\t\n729\t seg_start = time.time()\n730\t\n731\t # ── CUDA OOM FIX: Her segment oncesi GPU cache temizle ──\n732\t try:\n733\t import torch\n734\t if torch.cuda.is_available():\n735\t torch.cuda.empty_cache()\n736\t gc.collect()\n737\t except Exception:\n738\t pass\n739\t\n740\t try:\n741\t # run_task = submit_task + result bekle (senkron, basit)\n742\t print(f\" [*] Video uretiliyor (run_task)...\")\n743\t result = session.run_task(task_params)\n744\t seg_elapsed = time.time() - seg_start\n745\t\n746\t print(f\" [*] Task tamamlandi ({seg_elapsed:.1f}s)\")\n747\t print(f\" [*] Basarili: {result.success}\")\n748\t print(f\" [*] Dosyalar: {len(result.generated_files) if result.generated_files else 0}\")\n749\t if result.errors:\n750\t for err in result.errors:\n751\t print(f\" [HATA] {err}\")\n752\t\n753\t # Video bul - 4 kademeli arama\n754\t video_path = None\n755\t\n756\t # Yontem 1: API sonucu (generated_files)\n757\t if result.success and result.generated_files:\n758\t for f in result.generated_files:\n759\t fstr = str(f)\n760\t if fstr.endswith(\".mp4\") and os.path.exists(fstr):\n761\t video_path = fstr\n762\t print(f\" [BULUNDU] API sonucu: {os.path.basename(fstr)}\")\n763\t break\n764\t\n765\t # Yontem 2: Segment output dizininde yeni mp4\n766\t if not video_path:\n767\t current_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n768\t new_mp4 = current_mp4 - existing_mp4\n769\t if new_mp4:\n770\t video_path = sorted(new_mp4, key=os.path.getsize, reverse=True)[0]\n771\t print(f\" [BULUNDU] Yeni mp4: {os.path.basename(video_path)}\")\n772\t\n773\t # Yontem 3: Session'in ilk output dizininde yeni mp4\n774\t if not video_path and str(first_output_dir) != seg_output_dir:\n775\t current_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n776\t new_mp4_save = current_mp4_save - existing_mp4_save\n777\t if new_mp4_save:\n778\t video_path = sorted(new_mp4_save, key=os.path.getsize, reverse=True)[0]\n779\t print(f\" [BULUNDU] Session dir: {os.path.basename(video_path)}\")"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"ef08282e-7966-4357-ba95-067d4eea2b4f","timestamp":"2026-04-19T22:04:08.650Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","content":" if len(sys.argv) < 3:\n print(\"Kullanim: _wangp_batch_runner.py \")\n sys.exit(1)\n\n wangp_root = sys.argv[1]\n manifest_path = sys.argv[2]\n\n # WanGP'yi import edebilmek icin path ekle\n if wangp_root not in sys.path:\n sys.path.insert(0, wangp_root)\n os.chdir(wangp_root)\n\n with open(manifest_path, \"r\", encoding=\"utf-8\") as f:\n manifest = json.load(f)\n\n tasks = manifest[\"tasks\"]\n frames_dir = manifest[\"frames_dir\"]\n segments_dir = manifest[\"segments_dir\"]\n num_tasks = len(tasks)\n\n print(f\"\\n{'='*60}\")\n print(f\"[RUNNER] {num_tasks} segment uretilecek (TEK SESSION)\")\n print(f\"[RUNNER] Model 1 kere yuklenecek, RAM'de kalacak\")\n print(f\"{'='*60}\")\n\n # WanGP API'yi import et\n try:\n from shared.api import WanGPSession\n except ImportError as e:\n print(f\"[RUNNER] HATA: WanGP API import edilemedi: {e}\")\n print(f\"[RUNNER] wangp_root: {wangp_root}\")\n print(f\"[RUNNER] sys.path: {sys.path[:3]}\")\n sys.exit(1)\n\n # TEK SESSION AC - model 1 kere yuklenir\n # output_dir olarak ilk segmentin dizinini ver\n first_output_dir = tasks[0][\"output_dir\"]\n os.makedirs(first_output_dir, exist_ok=True)\n\n # ── CUDA OOM FIX: Video uretiminden once GPU bellegini temizle ──\n # Audio separation modeli GPU'da bellek parcalanmasina neden olabilir\n try:\n import torch\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n torch.cuda.synchronize()\n gc.collect()\n free_mem = torch.cuda.mem_get_info()[0] / 1024**3\n total_mem = torch.cuda.mem_get_info()[1] / 1024**3\n print(f\"[RUNNER] GPU bellek: {free_mem:.1f}/{total_mem:.1f} GiB bos (cache temizlendi)\")\n except Exception as e:\n print(f\"[RUNNER] GPU bellek temizleme uyarisi: {e}\")\n\n print(f\"[RUNNER] WanGPSession aciliyor...\")\n print(f\"[RUNNER] Output: {first_output_dir}\")\n\n try:\n session = WanGPSession(\n root=wangp_root,\n output_dir=first_output_dir,\n console_output=True,\n )\n except Exception as e:\n print(f\"[RUNNER] HATA: Session acilamadi: {e}\")\n traceback.print_exc()\n sys.exit(1)\n\n print(f\"[RUNNER] Session acildi, model yuklendi!\\n\")\n\n results = []\n total_start = time.time()\n\n for idx, task_info in enumerate(tasks):\n seg_output_dir = task_info[\"output_dir\"]\n task_params = dict(task_info[\"params\"]) # kopya al\n task_params[\"repeat_generation\"] = 1\n\n # Output dizinini guncelle\n os.makedirs(seg_output_dir, exist_ok=True)\n try:\n session._output_dir = Path(seg_output_dir)\n except Exception:\n pass # eger _output_dir yoksa sorun degil, WanGP kendi dizinine kaydeder\n\n print(f\"\\n{'='*60}\")\n print(f\"[SEGMENT {idx+1}/{num_tasks}]\")\n print(f\" Output : {seg_output_dir}\")\n print(f\" Image : {task_params.get('image_start', 'N/A')}\")\n print(f\" Audio : {os.path.basename(task_params.get('audio_source', 'N/A'))}\")\n if task_params.get('audio_guide'):\n print(f\" LipSync: ACIK\")\n print(f\"{'='*60}\")\n\n # Mevcut mp4'leri kaydet (sonra yenileri bulmak icin)\n existing_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n # WanGP bazen farkli dizine de kaydedebilir - session output_dir'i de kontrol et\n wangp_save_dir = seg_output_dir\n existing_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n\n seg_start = time.time()\n\n # ── CUDA OOM FIX: Her segment oncesi GPU cache temizle ──\n try:\n import torch\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n gc.collect()\n except Exception:\n pass\n\n try:\n # run_task = submit_task + result bekle (senkron, basit)\n print(f\" [*] Video uretiliyor (run_task)...\")\n result = session.run_task(task_params)\n seg_elapsed = time.time() - seg_start\n\n print(f\" [*] Task tamamlandi ({seg_elapsed:.1f}s)\")\n print(f\" [*] Basarili: {result.success}\")\n print(f\" [*] Dosyalar: {len(result.generated_files) if result.generated_files else 0}\")\n if result.errors:\n for err in result.errors:\n print(f\" [HATA] {err}\")\n\n # Video bul - 4 kademeli arama\n video_path = None\n\n # Yontem 1: API sonucu (generated_files)\n if result.success and result.generated_files:\n for f in result.generated_files:\n fstr = str(f)\n if fstr.endswith(\".mp4\") and os.path.exists(fstr):\n video_path = fstr\n print(f\" [BULUNDU] API sonucu: {os.path.basename(fstr)}\")\n break\n\n # Yontem 2: Segment output dizininde yeni mp4\n if not video_path:\n current_mp4 = set(glob.glob(os.path.join(seg_output_dir, \"*.mp4\")))\n new_mp4 = current_mp4 - existing_mp4\n if new_mp4:\n video_path = sorted(new_mp4, key=os.path.getsize, reverse=True)[0]\n print(f\" [BULUNDU] Yeni mp4: {os.path.basename(video_path)}\")\n\n # Yontem 3: Session'in ilk output dizininde yeni mp4\n if not video_path and str(first_output_dir) != seg_output_dir:\n current_mp4_save = set(glob.glob(os.path.join(str(first_output_dir), \"*.mp4\")))\n new_mp4_save = current_mp4_save - existing_mp4_save\n if new_mp4_save:\n video_path = sorted(new_mp4_save, key=os.path.getsize, reverse=True)[0]\n print(f\" [BULUNDU] Session dir: {os.path.basename(video_path)}\")","numLines":150,"startLine":630,"totalLines":1431}},"_audit_timestamp":"2026-04-19T22:04:08.658Z","_audit_hmac":"ab4ad4d1b080dad1bf873fc86ab94c2821e1e7fa16eca3ca79d1142859b12598"} {"type":"system","subtype":"status","status":"requesting","uuid":"b94d77fd-b696-4a8e-8f48-37ccb462e052","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:08.680Z","_audit_hmac":"05668b80e019310772ef1f1110aa72b95d003ab6b313357538fcc99de60f6919"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01XFL4mrH1hWFhSH8rr6bRhT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ED56UUbb8cWQbyV9fL9aHV","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","offset":1030,"limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3254,"cache_read_input_tokens":50049,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3254},"output_tokens":72,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"ef4bb809-5766-434f-b8e3-100dbbab8553","_audit_timestamp":"2026-04-19T22:04:13.054Z","_audit_hmac":"1a5b21234bd3a450bd6ff13c6f2c82781a80360e571f656daebd486c3468f678"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ED56UUbb8cWQbyV9fL9aHV","type":"tool_result","content":"1030\t adv_group.add_argument(\"--nag-tau\", type=float, default=None,\n1031\t help=\"NAG tau parametresi\")\n1032\t adv_group.add_argument(\"--nag-alpha\", type=float, default=None,\n1033\t help=\"NAG alpha parametresi\")\n1034\t adv_group.add_argument(\"--self-refiner\", type=int, default=0, choices=[0, 1, 2],\n1035\t help=\"Self-refiner (0=kapalı, 1=P1-Norm, 2=P2-Norm)\")\n1036\t adv_group.add_argument(\"--temporal-upsampling\", default=None,\n1037\t help=\"Temporal upsampling metodu\")\n1038\t adv_group.add_argument(\"--spatial-upsampling\", default=None,\n1039\t help=\"Spatial upsampling metodu\")\n1040\t adv_group.add_argument(\"--film-grain\", type=float, default=0.0,\n1041\t help=\"Film grain yoğunluğu (0.0-1.0)\")\n1042\t adv_group.add_argument(\"--control-net-weight\", type=float, default=None,\n1043\t help=\"Control net ağırlığı (0.0-2.0)\")\n1044\t adv_group.add_argument(\"--motion-amplitude\", type=float, default=None,\n1045\t help=\"Hareket genliği (1.0-1.4)\")\n1046\t adv_group.add_argument(\"--image-end\", default=None,\n1047\t help=\"Bitiş görseli yolu (destekleyen modeller için)\")\n1048\t\n1049\t # Segment ve çıkış ayarları\n1050\t out_group = parser.add_argument_group(\"Segment ve Çıkış Ayarları\")\n1051\t out_group.add_argument(\"--segment-duration\", \"-s\", type=float, default=DEFAULT_SEGMENT_DURATION,\n1052\t help=f\"Segment süresi saniye (varsayılan: {DEFAULT_SEGMENT_DURATION})\")\n1053\t out_group.add_argument(\"--output\", \"-o\", default=None,\n1054\t help=\"Çıkış dosyası yolu\")\n1055\t out_group.add_argument(\"--repeat\", type=int, default=1,\n1056\t help=\"Her segment için tekrar sayısı (en iyisini seç)\")\n1057\t out_group.add_argument(\"--timeout\", type=int, default=600,\n1058\t help=\"Segment başına maksimum bekleme süresi (saniye)\")\n1059\t\n1060\t # Face swap\n1061\t face_group = parser.add_argument_group(\"Face Swap Ayarları\")\n1062\t face_group.add_argument(\"--face-swap\", \"-f\", default=None,\n1063\t help=\"Face swap için kaynak yüz resmi\")\n1064\t\n1065\t # Bilgi komutları\n1066\t info_group = parser.add_argument_group(\"Bilgi Komutları\")\n1067\t info_group.add_argument(\"--list-models\", action=\"store_true\",\n1068\t help=\"Kullanılabilir modelleri listele\")\n1069\t info_group.add_argument(\"--list-loras\", action=\"store_true\",\n1070\t help=\"Kullanılabilir LoRA'ları listele\")\n1071\t\n1072\t args = parser.parse_args()\n1073\t\n1074\t # Bilgi komutları\n1075\t if args.list_models:\n1076\t models = get_available_models()\n1077\t print(\"=\" * 70)\n1078\t print(\" KULLANILABILIR MODELLER\")\n1079\t print(\"=\" * 70)\n1080\t for model_id, info in models.items():\n1081\t print(f\" {model_id:<40} {info['name']}\")\n1082\t print(f\"\\n Toplam: {len(models)} model\")\n1083\t print(\"=\" * 70)\n1084\t return\n1085\t\n1086\t if args.list_loras:\n1087\t loras = get_available_loras()\n1088\t print(\"=\" * 70)\n1089\t print(\" KULLANILABILIR LoRA'LAR\")\n1090\t print(\"=\" * 70)\n1091\t for l in loras:\n1092\t print(f\" {l}\")\n1093\t if not loras:\n1094\t print(\" (LoRA bulunamadı)\")\n1095\t print(f\"\\n Toplam: {len(loras)} LoRA\")\n1096\t print(\"=\" * 70)\n1097\t return\n1098\t\n1099\t # Girdi dosyaları kontrolü\n1100\t if not args.audio or not args.image:\n1101\t parser.error(\"Ses dosyası ve görsel gerekli! Kullanım: wangp_video_uret.py \")\n1102\t\n1103\t audio_path = Path(args.audio).resolve()\n1104\t image_path = Path(args.image).resolve()\n1105\t\n1106\t if not audio_path.exists():\n1107\t print(f\"HATA: Ses dosyası bulunamadı: {audio_path}\")\n1108\t sys.exit(1)\n1109\t if not image_path.exists():\n1110\t print(f\"HATA: Görsel dosyası bulunamadı: {image_path}\")\n1111\t sys.exit(1)\n1112\t\n1113\t face_swap_source = None\n1114\t if args.face_swap:\n1115\t face_swap_source = Path(args.face_swap).resolve()\n1116\t if not face_swap_source.exists():\n1117\t print(f\"HATA: Face swap resmi bulunamadı: {face_swap_source}\")\n1118\t sys.exit(1)\n1119\t\n1120\t # End image kontrolü\n1121\t image_end_path = None\n1122\t if args.image_end:\n1123\t image_end_path = Path(args.image_end).resolve()\n1124\t if not image_end_path.exists():\n1125\t print(f\"HATA: Bitiş görseli bulunamadı: {image_end_path}\")\n1126\t sys.exit(1)\n1127\t\n1128\t # Segment süresi ve frame hesaplama\n1129\t # --segment-duration her zaman ana kaynak, frame sayisi bundan hesaplanir"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"880f9c51-dd1c-4793-8308-de05008983fd","timestamp":"2026-04-19T22:04:13.062Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","content":" adv_group.add_argument(\"--nag-tau\", type=float, default=None,\n help=\"NAG tau parametresi\")\n adv_group.add_argument(\"--nag-alpha\", type=float, default=None,\n help=\"NAG alpha parametresi\")\n adv_group.add_argument(\"--self-refiner\", type=int, default=0, choices=[0, 1, 2],\n help=\"Self-refiner (0=kapalı, 1=P1-Norm, 2=P2-Norm)\")\n adv_group.add_argument(\"--temporal-upsampling\", default=None,\n help=\"Temporal upsampling metodu\")\n adv_group.add_argument(\"--spatial-upsampling\", default=None,\n help=\"Spatial upsampling metodu\")\n adv_group.add_argument(\"--film-grain\", type=float, default=0.0,\n help=\"Film grain yoğunluğu (0.0-1.0)\")\n adv_group.add_argument(\"--control-net-weight\", type=float, default=None,\n help=\"Control net ağırlığı (0.0-2.0)\")\n adv_group.add_argument(\"--motion-amplitude\", type=float, default=None,\n help=\"Hareket genliği (1.0-1.4)\")\n adv_group.add_argument(\"--image-end\", default=None,\n help=\"Bitiş görseli yolu (destekleyen modeller için)\")\n\n # Segment ve çıkış ayarları\n out_group = parser.add_argument_group(\"Segment ve Çıkış Ayarları\")\n out_group.add_argument(\"--segment-duration\", \"-s\", type=float, default=DEFAULT_SEGMENT_DURATION,\n help=f\"Segment süresi saniye (varsayılan: {DEFAULT_SEGMENT_DURATION})\")\n out_group.add_argument(\"--output\", \"-o\", default=None,\n help=\"Çıkış dosyası yolu\")\n out_group.add_argument(\"--repeat\", type=int, default=1,\n help=\"Her segment için tekrar sayısı (en iyisini seç)\")\n out_group.add_argument(\"--timeout\", type=int, default=600,\n help=\"Segment başına maksimum bekleme süresi (saniye)\")\n\n # Face swap\n face_group = parser.add_argument_group(\"Face Swap Ayarları\")\n face_group.add_argument(\"--face-swap\", \"-f\", default=None,\n help=\"Face swap için kaynak yüz resmi\")\n\n # Bilgi komutları\n info_group = parser.add_argument_group(\"Bilgi Komutları\")\n info_group.add_argument(\"--list-models\", action=\"store_true\",\n help=\"Kullanılabilir modelleri listele\")\n info_group.add_argument(\"--list-loras\", action=\"store_true\",\n help=\"Kullanılabilir LoRA'ları listele\")\n\n args = parser.parse_args()\n\n # Bilgi komutları\n if args.list_models:\n models = get_available_models()\n print(\"=\" * 70)\n print(\" KULLANILABILIR MODELLER\")\n print(\"=\" * 70)\n for model_id, info in models.items():\n print(f\" {model_id:<40} {info['name']}\")\n print(f\"\\n Toplam: {len(models)} model\")\n print(\"=\" * 70)\n return\n\n if args.list_loras:\n loras = get_available_loras()\n print(\"=\" * 70)\n print(\" KULLANILABILIR LoRA'LAR\")\n print(\"=\" * 70)\n for l in loras:\n print(f\" {l}\")\n if not loras:\n print(\" (LoRA bulunamadı)\")\n print(f\"\\n Toplam: {len(loras)} LoRA\")\n print(\"=\" * 70)\n return\n\n # Girdi dosyaları kontrolü\n if not args.audio or not args.image:\n parser.error(\"Ses dosyası ve görsel gerekli! Kullanım: wangp_video_uret.py \")\n\n audio_path = Path(args.audio).resolve()\n image_path = Path(args.image).resolve()\n\n if not audio_path.exists():\n print(f\"HATA: Ses dosyası bulunamadı: {audio_path}\")\n sys.exit(1)\n if not image_path.exists():\n print(f\"HATA: Görsel dosyası bulunamadı: {image_path}\")\n sys.exit(1)\n\n face_swap_source = None\n if args.face_swap:\n face_swap_source = Path(args.face_swap).resolve()\n if not face_swap_source.exists():\n print(f\"HATA: Face swap resmi bulunamadı: {face_swap_source}\")\n sys.exit(1)\n\n # End image kontrolü\n image_end_path = None\n if args.image_end:\n image_end_path = Path(args.image_end).resolve()\n if not image_end_path.exists():\n print(f\"HATA: Bitiş görseli bulunamadı: {image_end_path}\")\n sys.exit(1)\n\n # Segment süresi ve frame hesaplama\n # --segment-duration her zaman ana kaynak, frame sayisi bundan hesaplanir","numLines":100,"startLine":1030,"totalLines":1431}},"_audit_timestamp":"2026-04-19T22:04:13.111Z","_audit_hmac":"0a55fb61eaf767b95426aa4182ff4dc27a8c98fdecd8925a3a00bef5c6ee8be7"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"allowed","overageResetsAt":1777593600,"isUsingOverage":false},"uuid":"a4800ed9-b19e-45d2-8aea-b3c0b8d6d493","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:13.134Z","_audit_hmac":"b6b3bdbb2991afbe98f85dbbdfb5872e6667b79fd9ac2cc6fe349da6d57decb5"} {"type":"system","subtype":"status","status":"requesting","uuid":"b095c8a5-d6e5-4ae4-a02b-11f1fa2f1a99","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:13.149Z","_audit_hmac":"ca86c354f81ac33676dfe4256d90a9f4fda9db59c0a9160b7c7bd68efd4e36bb"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01G8GpcknENPkEKJmyYW42m6","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi manifest task oluşturma ve WanGP API'ye gönderilen parametreleri görelim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2031,"cache_read_input_tokens":53303,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2031},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"8435eea2-0f1c-45c1-adff-75e3a6481df7","_audit_timestamp":"2026-04-19T22:04:16.469Z","_audit_hmac":"7f4fd966e5ad435859409d79232af4522fadfd4b740406112d60b6e0d1263d57"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01G8GpcknENPkEKJmyYW42m6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018t2j1A32RGm7WjHcKbqJXH","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","offset":1200,"limit":200},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2031,"cache_read_input_tokens":53303,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2031},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"cb360840-d59f-49ee-a4c9-5b83abaeb9f8","_audit_timestamp":"2026-04-19T22:04:17.243Z","_audit_hmac":"116079f5190edcc99dfee41d36f2ca2d4141b2d159a1bc599cc8fe85ad8bd2b7"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018t2j1A32RGm7WjHcKbqJXH","type":"tool_result","content":"1200\t\n1201\t # ── 2. İlk Görseli Hazırla ──\n1202\t input_image = frames_dir / f\"input_000.jpg\"\n1203\t shutil.copy2(image_path, input_image)\n1204\t\n1205\t if face_swap_source:\n1206\t print(\"\\n[*] İlk görsele face swap uygulanıyor...\")\n1207\t swapped_input = frames_dir / f\"input_000_swapped.jpg\"\n1208\t if face_swap_image(face_swap_source, input_image, swapped_input):\n1209\t input_image = swapped_input\n1210\t\n1211\t # ── 3. Tüm Segment Task'larını Hazırla ve Manifest Oluştur ──\n1212\t start_time = time.time()\n1213\t\n1214\t # Lip-sync desteği kontrolü\n1215\t model_supports_lipsync = args.model.startswith(\"ltx2\") or args.model in [\n1216\t \"multitalk\", \"vace_multitalk_14B\", \"infinitetalk\",\n1217\t \"magi_human\", \"longcat_avatar\", \"hunyuan_1_5_i2v_avatar\",\n1218\t ]\n1219\t\n1220\t if not args.no_lip_sync and model_supports_lipsync:\n1221\t print(f\"\\n [LIP-SYNC] ACIK - {args.model} destekliyor\")\n1222\t elif not args.no_lip_sync and not model_supports_lipsync:\n1223\t print(f\"\\n [LIP-SYNC] KAPALI - {args.model} modeli lip-sync desteklemiyor!\")\n1224\t print(f\" Lip-sync icin LTX2 modeli kullanin (profil 1-3)\")\n1225\t\n1226\t manifest_tasks = []\n1227\t\n1228\t for i, (seg_audio, seg_start, seg_end, seg_actual_dur) in enumerate(audio_segments):\n1229\t\n1230\t # Task ayarlarını oluştur\n1231\t task_params = {\n1232\t \"model_type\": args.model,\n1233\t \"prompt\": args.prompt,\n1234\t \"negative_prompt\": args.negative_prompt,\n1235\t \"resolution\": args.resolution,\n1236\t \"video_length\": frames_per_segment,\n1237\t \"num_inference_steps\": args.steps,\n1238\t \"guidance_scale\": args.guidance_scale,\n1239\t \"flow_shift\": args.flow_shift,\n1240\t \"sample_solver\": args.solver,\n1241\t \"seed\": args.seed,\n1242\t \"prompt_enhancer\": args.prompt_enhancer,\n1243\t \"image_start\": str(input_image), # ilk segment icin; sonrakiler runner tarafindan guncellenir\n1244\t \"audio_source\": str(seg_audio),\n1245\t \"image_prompt_type\": \"S\", # Start image\n1246\t \"repeat_generation\": 1, # Tekrar yok, 1 video uret\n1247\t }\n1248\t\n1249\t # Lip-sync ayarlari\n1250\t if not args.no_lip_sync and model_supports_lipsync:\n1251\t task_params[\"audio_guide\"] = str(seg_audio)\n1252\t apt_flags = \"A\"\n1253\t if not args.no_vocal_isolation:\n1254\t apt_flags += \"V\"\n1255\t apt_flags += \"N\"\n1256\t task_params[\"audio_prompt_type\"] = apt_flags\n1257\t\n1258\t # Kullanicinin manuel audio_prompt_type ayari\n1259\t if args.audio_prompt_type is not None:\n1260\t task_params[\"audio_prompt_type\"] = args.audio_prompt_type\n1261\t\n1262\t # Opsiyonel parametreler\n1263\t if args.guidance2_scale is not None:\n1264\t task_params[\"guidance2_scale\"] = args.guidance2_scale\n1265\t if args.guidance3_scale is not None:\n1266\t task_params[\"guidance3_scale\"] = args.guidance3_scale\n1267\t if args.guidance_phases is not None:\n1268\t task_params[\"guidance_phases\"] = args.guidance_phases\n1269\t if args.embedded_guidance is not None:\n1270\t task_params[\"embedded_guidance_scale\"] = args.embedded_guidance\n1271\t if args.alt_guidance is not None:\n1272\t task_params[\"alt_guidance_scale\"] = args.alt_guidance\n1273\t if args.alt_scale is not None:\n1274\t task_params[\"alt_scale\"] = args.alt_scale\n1275\t if args.denoising_strength is not None:\n1276\t task_params[\"denoising_strength\"] = args.denoising_strength\n1277\t if args.audio_guidance_scale is not None:\n1278\t task_params[\"audio_guidance_scale\"] = args.audio_guidance_scale\n1279\t if args.audio_scale is not None:\n1280\t task_params[\"audio_scale\"] = args.audio_scale\n1281\t if args.model.startswith(\"ltx2\") and \"distilled\" not in args.model and args.audio_guidance_scale is None:\n1282\t task_params[\"audio_guidance_scale\"] = 7.0\n1283\t if args.control_net_weight is not None:\n1284\t task_params[\"control_net_weight\"] = args.control_net_weight\n1285\t if args.motion_amplitude is not None:\n1286\t task_params[\"motion_amplitude\"] = args.motion_amplitude\n1287\t if args.nag_scale is not None:\n1288\t task_params[\"NAG_scale\"] = args.nag_scale\n1289\t if args.nag_tau is not None:\n1290\t task_params[\"NAG_tau\"] = args.nag_tau\n1291\t if args.nag_alpha is not None:\n1292\t task_params[\"NAG_alpha\"] = args.nag_alpha\n1293\t\n1294\t # End image (sadece son segment)\n1295\t if image_end_path and i == num_segments - 1:\n1296\t task_params[\"image_end\"] = str(image_end_path)\n1297\t task_params[\"image_prompt_type\"] = \"SE\"\n1298\t\n1299\t # LoRA\n1300\t if args.lora:\n1301\t loras_dir = WANGP_ROOT / \"loras\"\n1302\t lora_paths = []\n1303\t for l in args.lora:\n1304\t lp = loras_dir / l\n1305\t if lp.exists():\n1306\t lora_paths.append(str(lp))\n1307\t elif Path(l).exists():\n1308\t lora_paths.append(str(Path(l).resolve()))\n1309\t else:\n1310\t print(f\" UYARI: LoRA bulunamadı: {l}\")\n1311\t if lora_paths:\n1312\t task_params[\"activated_loras\"] = lora_paths\n1313\t task_params[\"loras_multipliers\"] = args.lora_weight\n1314\t\n1315\t # Sliding window\n1316\t if args.sliding_window_size > 0:\n1317\t task_params[\"sliding_window_size\"] = args.sliding_window_size\n1318\t task_params[\"sliding_window_overlap\"] = args.sliding_window_overlap\n1319\t task_params[\"sliding_window_color_correction_strength\"] = args.sliding_window_color_correction\n1320\t task_params[\"sliding_window_overlap_noise\"] = args.sliding_window_overlap_noise\n1321\t\n1322\t # Perturbation\n1323\t if args.perturbation > 0:\n1324\t task_params[\"perturbation_switch\"] = args.perturbation\n1325\t task_params[\"perturbation_start_perc\"] = args.perturbation_start\n1326\t task_params[\"perturbation_end_perc\"] = args.perturbation_end\n1327\t if args.perturbation_layers:\n1328\t task_params[\"perturbation_layers\"] = args.perturbation_layers\n1329\t\n1330\t # APG / CFG*\n1331\t if args.apg > 0:\n1332\t task_params[\"apg_switch\"] = args.apg\n1333\t if args.cfg_star > 0:\n1334\t task_params[\"cfg_star_switch\"] = args.cfg_star\n1335\t\n1336\t # Self-refiner\n1337\t if args.self_refiner > 0:\n1338\t task_params[\"self_refiner_setting\"] = args.self_refiner\n1339\t\n1340\t # Post-processing\n1341\t if args.temporal_upsampling:\n1342\t task_params[\"temporal_upsampling\"] = args.temporal_upsampling\n1343\t if args.spatial_upsampling:\n1344\t task_params[\"spatial_upsampling\"] = args.spatial_upsampling\n1345\t if args.film_grain > 0:\n1346\t task_params[\"film_grain_intensity\"] = args.film_grain\n1347\t\n1348\t # FPS override\n1349\t if args.fps != DEFAULT_FPS:\n1350\t task_params[\"force_fps\"] = args.fps\n1351\t\n1352\t # Output dizini\n1353\t video_output_dir = tasks_dir / f\"seg_{i:03d}\"\n1354\t video_output_dir.mkdir(exist_ok=True)\n1355\t\n1356\t manifest_tasks.append({\n1357\t \"output_dir\": str(video_output_dir),\n1358\t \"params\": task_params,\n1359\t })\n1360\t\n1361\t print(f\" Task {i+1}/{num_segments}: {seg_start:.1f}s-{seg_end:.1f}s -> {video_output_dir.name}\")\n1362\t\n1363\t # Manifest JSON'u yaz\n1364\t manifest_path = tasks_dir / \"manifest.json\"\n1365\t manifest_data = {\n1366\t \"tasks\": manifest_tasks,\n1367\t \"frames_dir\": str(frames_dir),\n1368\t \"segments_dir\": str(segments_dir),\n1369\t }\n1370\t with open(manifest_path, \"w\", encoding=\"utf-8\") as f:\n1371\t json.dump(manifest_data, f, indent=2, ensure_ascii=False)\n1372\t\n1373\t print(f\"\\n[*] Manifest yazildi: {manifest_path}\")\n1374\t print(f\"[*] {num_segments} segment TEK SESSION'da uretilecek (model 1 kere yuklenir)\\n\")\n1375\t\n1376\t # Tum segmentleri tek session'da uret\n1377\t total_timeout = args.timeout * num_segments + 300 # ekstra 5dk tampon\n1378\t video_segments = generate_all_segments(\n1379\t manifest_path=manifest_path,\n1380\t wangp_root=WANGP_ROOT,\n1381\t tasks_dir=tasks_dir,\n1382\t segments_dir=segments_dir,\n1383\t frames_dir=frames_dir,\n1384\t face_swap_source=face_swap_source,\n1385\t timeout=total_timeout,\n1386\t )\n1387\t\n1388\t # Face swap (runner son kareleri cikarip zincirler, ama face swap burada yapilir)\n1389\t if face_swap_source and video_segments:\n1390\t print(\"\\n[*] Face swap segment videolarina uygulanacak...\")\n1391\t # Face swap sadece son karelere uygulanir (runner'da zaten yapiliyor)\n1392\t # Ama eger istenirse segment videolarina da uygulanabilir (opsiyonel)\n1393\t\n1394\t if not video_segments:\n1395\t print(\"\\nHATA: Hiç video segmenti üretilemedi!\")\n1396\t sys.exit(1)\n1397\t\n1398\t # ── 4. Videoları Birleştir ──\n1399\t print(f\"\\n{'=' * 60}\")"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6f3d23ea-2c00-46ec-b6e1-7cc3a718c41f","timestamp":"2026-04-19T22:04:17.246Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/wangp_video_uret.py","content":"\n # ── 2. İlk Görseli Hazırla ──\n input_image = frames_dir / f\"input_000.jpg\"\n shutil.copy2(image_path, input_image)\n\n if face_swap_source:\n print(\"\\n[*] İlk görsele face swap uygulanıyor...\")\n swapped_input = frames_dir / f\"input_000_swapped.jpg\"\n if face_swap_image(face_swap_source, input_image, swapped_input):\n input_image = swapped_input\n\n # ── 3. Tüm Segment Task'larını Hazırla ve Manifest Oluştur ──\n start_time = time.time()\n\n # Lip-sync desteği kontrolü\n model_supports_lipsync = args.model.startswith(\"ltx2\") or args.model in [\n \"multitalk\", \"vace_multitalk_14B\", \"infinitetalk\",\n \"magi_human\", \"longcat_avatar\", \"hunyuan_1_5_i2v_avatar\",\n ]\n\n if not args.no_lip_sync and model_supports_lipsync:\n print(f\"\\n [LIP-SYNC] ACIK - {args.model} destekliyor\")\n elif not args.no_lip_sync and not model_supports_lipsync:\n print(f\"\\n [LIP-SYNC] KAPALI - {args.model} modeli lip-sync desteklemiyor!\")\n print(f\" Lip-sync icin LTX2 modeli kullanin (profil 1-3)\")\n\n manifest_tasks = []\n\n for i, (seg_audio, seg_start, seg_end, seg_actual_dur) in enumerate(audio_segments):\n\n # Task ayarlarını oluştur\n task_params = {\n \"model_type\": args.model,\n \"prompt\": args.prompt,\n \"negative_prompt\": args.negative_prompt,\n \"resolution\": args.resolution,\n \"video_length\": frames_per_segment,\n \"num_inference_steps\": args.steps,\n \"guidance_scale\": args.guidance_scale,\n \"flow_shift\": args.flow_shift,\n \"sample_solver\": args.solver,\n \"seed\": args.seed,\n \"prompt_enhancer\": args.prompt_enhancer,\n \"image_start\": str(input_image), # ilk segment icin; sonrakiler runner tarafindan guncellenir\n \"audio_source\": str(seg_audio),\n \"image_prompt_type\": \"S\", # Start image\n \"repeat_generation\": 1, # Tekrar yok, 1 video uret\n }\n\n # Lip-sync ayarlari\n if not args.no_lip_sync and model_supports_lipsync:\n task_params[\"audio_guide\"] = str(seg_audio)\n apt_flags = \"A\"\n if not args.no_vocal_isolation:\n apt_flags += \"V\"\n apt_flags += \"N\"\n task_params[\"audio_prompt_type\"] = apt_flags\n\n # Kullanicinin manuel audio_prompt_type ayari\n if args.audio_prompt_type is not None:\n task_params[\"audio_prompt_type\"] = args.audio_prompt_type\n\n # Opsiyonel parametreler\n if args.guidance2_scale is not None:\n task_params[\"guidance2_scale\"] = args.guidance2_scale\n if args.guidance3_scale is not None:\n task_params[\"guidance3_scale\"] = args.guidance3_scale\n if args.guidance_phases is not None:\n task_params[\"guidance_phases\"] = args.guidance_phases\n if args.embedded_guidance is not None:\n task_params[\"embedded_guidance_scale\"] = args.embedded_guidance\n if args.alt_guidance is not None:\n task_params[\"alt_guidance_scale\"] = args.alt_guidance\n if args.alt_scale is not None:\n task_params[\"alt_scale\"] = args.alt_scale\n if args.denoising_strength is not None:\n task_params[\"denoising_strength\"] = args.denoising_strength\n if args.audio_guidance_scale is not None:\n task_params[\"audio_guidance_scale\"] = args.audio_guidance_scale\n if args.audio_scale is not None:\n task_params[\"audio_scale\"] = args.audio_scale\n if args.model.startswith(\"ltx2\") and \"distilled\" not in args.model and args.audio_guidance_scale is None:\n task_params[\"audio_guidance_scale\"] = 7.0\n if args.control_net_weight is not None:\n task_params[\"control_net_weight\"] = args.control_net_weight\n if args.motion_amplitude is not None:\n task_params[\"motion_amplitude\"] = args.motion_amplitude\n if args.nag_scale is not None:\n task_params[\"NAG_scale\"] = args.nag_scale\n if args.nag_tau is not None:\n task_params[\"NAG_tau\"] = args.nag_tau\n if args.nag_alpha is not None:\n task_params[\"NAG_alpha\"] = args.nag_alpha\n\n # End image (sadece son segment)\n if image_end_path and i == num_segments - 1:\n task_params[\"image_end\"] = str(image_end_path)\n task_params[\"image_prompt_type\"] = \"SE\"\n\n # LoRA\n if args.lora:\n loras_dir = WANGP_ROOT / \"loras\"\n lora_paths = []\n for l in args.lora:\n lp = loras_dir / l\n if lp.exists():\n lora_paths.append(str(lp))\n elif Path(l).exists():\n lora_paths.append(str(Path(l).resolve()))\n else:\n print(f\" UYARI: LoRA bulunamadı: {l}\")\n if lora_paths:\n task_params[\"activated_loras\"] = lora_paths\n task_params[\"loras_multipliers\"] = args.lora_weight\n\n # Sliding window\n if args.sliding_window_size > 0:\n task_params[\"sliding_window_size\"] = args.sliding_window_size\n task_params[\"sliding_window_overlap\"] = args.sliding_window_overlap\n task_params[\"sliding_window_color_correction_strength\"] = args.sliding_window_color_correction\n task_params[\"sliding_window_overlap_noise\"] = args.sliding_window_overlap_noise\n\n # Perturbation\n if args.perturbation > 0:\n task_params[\"perturbation_switch\"] = args.perturbation\n task_params[\"perturbation_start_perc\"] = args.perturbation_start\n task_params[\"perturbation_end_perc\"] = args.perturbation_end\n if args.perturbation_layers:\n task_params[\"perturbation_layers\"] = args.perturbation_layers\n\n # APG / CFG*\n if args.apg > 0:\n task_params[\"apg_switch\"] = args.apg\n if args.cfg_star > 0:\n task_params[\"cfg_star_switch\"] = args.cfg_star\n\n # Self-refiner\n if args.self_refiner > 0:\n task_params[\"self_refiner_setting\"] = args.self_refiner\n\n # Post-processing\n if args.temporal_upsampling:\n task_params[\"temporal_upsampling\"] = args.temporal_upsampling\n if args.spatial_upsampling:\n task_params[\"spatial_upsampling\"] = args.spatial_upsampling\n if args.film_grain > 0:\n task_params[\"film_grain_intensity\"] = args.film_grain\n\n # FPS override\n if args.fps != DEFAULT_FPS:\n task_params[\"force_fps\"] = args.fps\n\n # Output dizini\n video_output_dir = tasks_dir / f\"seg_{i:03d}\"\n video_output_dir.mkdir(exist_ok=True)\n\n manifest_tasks.append({\n \"output_dir\": str(video_output_dir),\n \"params\": task_params,\n })\n\n print(f\" Task {i+1}/{num_segments}: {seg_start:.1f}s-{seg_end:.1f}s -> {video_output_dir.name}\")\n\n # Manifest JSON'u yaz\n manifest_path = tasks_dir / \"manifest.json\"\n manifest_data = {\n \"tasks\": manifest_tasks,\n \"frames_dir\": str(frames_dir),\n \"segments_dir\": str(segments_dir),\n }\n with open(manifest_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(manifest_data, f, indent=2, ensure_ascii=False)\n\n print(f\"\\n[*] Manifest yazildi: {manifest_path}\")\n print(f\"[*] {num_segments} segment TEK SESSION'da uretilecek (model 1 kere yuklenir)\\n\")\n\n # Tum segmentleri tek session'da uret\n total_timeout = args.timeout * num_segments + 300 # ekstra 5dk tampon\n video_segments = generate_all_segments(\n manifest_path=manifest_path,\n wangp_root=WANGP_ROOT,\n tasks_dir=tasks_dir,\n segments_dir=segments_dir,\n frames_dir=frames_dir,\n face_swap_source=face_swap_source,\n timeout=total_timeout,\n )\n\n # Face swap (runner son kareleri cikarip zincirler, ama face swap burada yapilir)\n if face_swap_source and video_segments:\n print(\"\\n[*] Face swap segment videolarina uygulanacak...\")\n # Face swap sadece son karelere uygulanir (runner'da zaten yapiliyor)\n # Ama eger istenirse segment videolarina da uygulanabilir (opsiyonel)\n\n if not video_segments:\n print(\"\\nHATA: Hiç video segmenti üretilemedi!\")\n sys.exit(1)\n\n # ── 4. Videoları Birleştir ──\n print(f\"\\n{'=' * 60}\")","numLines":200,"startLine":1200,"totalLines":1431}},"_audit_timestamp":"2026-04-19T22:04:17.271Z","_audit_hmac":"5f645fb77dbd50e35b1a552ac5c5135fe2897ec96334612d363e6f03c2899576"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"05b301f3-53cd-424d-9b92-2da599939087","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:17.291Z","_audit_hmac":"9d9998e3a798527e0072dbf8fce4cbed41b8c740502e882d0951def1f61d686a"} {"type":"system","subtype":"status","status":"requesting","uuid":"413be5d8-09a8-46a6-b547-deef6a496c35","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:04:17.310Z","_audit_hmac":"6230fc9130b75e25c413fc252d16d720f372b2040aceb6af87b9566ec91fbd05"} {"type":"user","uuid":"d46277dc-c9b0-49c5-9d25-29693238cf27","session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"message":{"role":"user","content":"LTX-Desktop-WanGP-Baslat.bat bunada yap"},"_audit_timestamp":"2026-04-19T22:04:22.224Z","_audit_hmac":"5afa7660a2d9bbb0c6a5f9c3fbd513cbd49368b3c244e91ece8039fdac425d84"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01MxokqoJ4nsA2P8sWzfNHvc","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi tam resmi görüyorum. WanGP `WanGPSession` API'sini kullanıyorsun. Session oluşturulurken ve task params'da CPU offload ayarı eklemem gerekiyor. Ayrıca BAT dosyasına da profil seçenekleri ekleyeceğim.\n\nÖnce WanGP API'nin `cpu_offload` veya benzeri parametrelerini kontrol edeyim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":3466,"cache_read_input_tokens":55334,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3466},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"35ec8af3-1d76-4444-b665-d329264883eb","_audit_timestamp":"2026-04-19T22:04:22.987Z","_audit_hmac":"6e8999fd446c590734638d00a5e6c2898182d891cb38ae9e1177942ce98ae5a1"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01MxokqoJ4nsA2P8sWzfNHvc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SaJZfLDzYg6Ng2EZV5jcyT","name":"Bash","input":{"command":"find 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yap"},"session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"uuid":"d46277dc-c9b0-49c5-9d25-29693238cf27","timestamp":"2026-04-19T22:05:21.012Z","isReplay":true,"_audit_timestamp":"2026-04-19T22:05:21.013Z","_audit_hmac":"7d8ba8c7d2f74f579c83a4b5bfb8773247e523c00b98eaa738b9600dc97db28b"} {"type":"system","subtype":"status","status":"requesting","uuid":"6bcd4d36-b272-4da0-b069-80e3d9bee0b2","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:05:21.018Z","_audit_hmac":"bdf93aac99353908b026ac679cb9d532eef2e53c20ce60e303e57f3da08a832a"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01EQNTDsQt45o1hs16DFvoGC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015DxsF1YL19gBx6uTepEytV","name":"Bash","input":{"command":"wc -l 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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014yxmag5KU75sE2NQww9kQQ","type":"tool_result","content":"155:def _coerce_api_video_tensor_uint8(output_video_frames: Any) -> Any:\n160: if torch is not None and torch.is_tensor(output_video_frames):\n161: return output_video_frames\n162: if isinstance(output_video_frames, list) and len(output_video_frames) == 1 and torch is not None and torch.is_tensor(output_video_frames[0]):\n163: return output_video_frames[0]\n171:def build_api_output_artifact_payload(client_id: str, video_path: Any, media_type: str, output_video_frames: Any, output_audio_data: Any, output_audio_sampling_rate: Any, output_fps: Any) -> dict[str, Any] | None:\n180: \"video_tensor_uint8\": _coerce_api_video_tensor_uint8(output_video_frames),\n187:def store_api_output_artifact(gen: dict[str, Any], client_id: str, video_path: Any, media_type: str, output_video_frames: Any, output_audio_data: Any, output_audio_sampling_rate: Any, output_fps: Any) -> bool:\n188: payload = build_api_output_artifact_payload(client_id, video_path, media_type, output_video_frames, output_audio_data, output_audio_sampling_rate, output_fps)\n526: params = copy.deepcopy(WanGPSession._get_task_settings(task))\n527: client_id = str(params.get(\"client_id\", \"\") or \"\").strip()\n530: params[\"client_id\"] = client_id\n532: params[\"priority\"] = True\n533: elif \"priority\" in params and not params[\"priority\"]:\n534: params.pop(\"priority\", None)\n535: task[\"params\"] = params\n542: params = copy.deepcopy(self._get_task_settings(task))\n543: params[\"priority\"] = True\n544: task[\"params\"] = params\n547: \"params\": copy.deepcopy(params),\n770: if \"settings\" in normalized and \"params\" not in normalized:\n771: normalized[\"params\"] = normalized.pop(\"settings\")\n772: if \"params\" not in normalized:\n773: normalized = {\"id\": task_index, \"params\": normalized, \"plugin_data\": {}}\n776: normalized.setdefault(\"params\", {})\n779: settings = normalized[\"params\"]\n805: settings = task.get(\"params\")\n853: settings = normalized.get(\"params\")"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6abf7931-7e58-4d6d-b00d-a58acb007d0a","timestamp":"2026-04-19T22:05:30.050Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"155:def _coerce_api_video_tensor_uint8(output_video_frames: Any) -> Any:\n160: if torch is not None and torch.is_tensor(output_video_frames):\n161: return output_video_frames\n162: if isinstance(output_video_frames, list) and len(output_video_frames) == 1 and torch is not None and torch.is_tensor(output_video_frames[0]):\n163: return output_video_frames[0]\n171:def build_api_output_artifact_payload(client_id: str, video_path: Any, media_type: str, output_video_frames: Any, output_audio_data: Any, output_audio_sampling_rate: Any, output_fps: Any) -> dict[str, Any] | None:\n180: \"video_tensor_uint8\": _coerce_api_video_tensor_uint8(output_video_frames),\n187:def store_api_output_artifact(gen: dict[str, Any], client_id: str, video_path: Any, media_type: str, output_video_frames: Any, output_audio_data: Any, output_audio_sampling_rate: Any, output_fps: Any) -> bool:\n188: payload = build_api_output_artifact_payload(client_id, video_path, media_type, output_video_frames, output_audio_data, output_audio_sampling_rate, output_fps)\n526: params = copy.deepcopy(WanGPSession._get_task_settings(task))\n527: client_id = str(params.get(\"client_id\", \"\") or \"\").strip()\n530: params[\"client_id\"] = client_id\n532: params[\"priority\"] = True\n533: elif \"priority\" in params and not params[\"priority\"]:\n534: params.pop(\"priority\", None)\n535: task[\"params\"] = params\n542: params = copy.deepcopy(self._get_task_settings(task))\n543: params[\"priority\"] = True\n544: task[\"params\"] = params\n547: \"params\": copy.deepcopy(params),\n770: if \"settings\" in normalized and \"params\" not in normalized:\n771: normalized[\"params\"] = normalized.pop(\"settings\")\n772: if \"params\" not in normalized:\n773: normalized = {\"id\": task_index, \"params\": normalized, \"plugin_data\": {}}\n776: normalized.setdefault(\"params\", {})\n779: settings = normalized[\"params\"]\n805: settings = task.get(\"params\")\n853: settings = normalized.get(\"params\")","numLines":28},"_audit_timestamp":"2026-04-19T22:05:30.066Z","_audit_hmac":"b4949eac3f04dc7ac61c8ee5d6b05c09337ad10decaf99b409d139dd78501229"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"eb7dbff9-aba9-40b7-9e77-fd68cec8801b","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:05:30.087Z","_audit_hmac":"06bee53ae720a591b420dbba446670e7dc4ead128c15296dad5be03a07f9b0d6"} {"type":"system","subtype":"status","status":"requesting","uuid":"351bcebc-d766-47a7-a9b0-3f25803e04dd","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:05:30.091Z","_audit_hmac":"6f096db52c9f300dc737f630ec8047f2371fe0e7b5507b33c77e9baf3617cfa6"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01E7gf9YbYyMpsBvm29YZtny","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KHRMMTXwR4UBfcAxocCYDG","name":"Grep","input":{"pattern":"offload|cpu_offload|model_offload|aggressive","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP","output_mode":"content","-n":true,"head_limit":50},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":853,"cache_read_input_tokens":59469,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":853},"output_tokens":68,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"bc0d1ad8-7e4f-453c-8512-48d1b5714690","_audit_timestamp":"2026-04-19T22:05:33.211Z","_audit_hmac":"7eb3fde0c2ece42a11dbe5194aa1cbcee2556521c22f4ffcf29d74774ba7bce4"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KHRMMTXwR4UBfcAxocCYDG","type":"tool_result","content":"mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/LICENSE.txt:63: (b) speed, throughput, caching, offloading, batching, or performance\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:26:from mmgp import offload, safetensors2, profile_type , quant_router\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:150:offloadobj = enhancer_offloadobj = wan_model = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:194: if \"_cache\" in offload.shared_state:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:195: del offload.shared_state[\"_cache\"]\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:200: global wan_model, offloadobj, reload_needed\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:203: if \"_cache\" in offload.shared_state:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:204: del offload.shared_state[\"_cache\"]\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:205: if offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:206: offloadobj.release()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:207: offloadobj = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:208: offload.flush_torch_caches()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2836: from mmgp import offload \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2867: offload.save_model(model, model_filename_path, config_file_path=config_file, filter_sd=filter)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2880: offload.save_model(model, quanto_filename_path, config_file_path=config_file, do_quantize= True, filter_sd=filter)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2903: from mmgp import offload\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2922: offload.save_model(model, model_filename_path, do_quantize= True, config_file_path=config_file)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3120:offload.default_verboseLevel = verbose_level\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3209: loras = offload.load_loras_into_model(transformer, loras, activate_all_loras=False, check_only= True, preprocess_sd=get_loras_preprocessor(transformer, base_model_type), split_linear_modules_map = split_linear_modules_map) #lora_multiplier,\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3310: global reset_prompt_enhancer_requested, prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer, enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3319: if enhancer_offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3320: enhancer_offloadobj.release()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3321: enhancer_offloadobj = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3347: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3350: if enhancer_offloadobj is None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3359: enhancer_offloadobj = offload.profile(pipe, profile_no=mmgp_profile, **kwargs)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3485: offloadobj = offload.profile(pipe, profile_no= mmgp_profile, compile = compile_modules, quantizeTransformer = False, loras = loras_transformer, perc_reserved_mem_max = perc_reserved_mem_max , vram_safety_coefficient = vram_safety_coefficient , convertWeightsFloatTo = transformer_dtype, **kwargs) \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3490: return wan_model, offloadobj \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3493: wan_model, offloadobj, transformer = None, None, None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3496: wan_model, offloadobj = load_models(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3577: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5265: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5298: if len(prompt_images) > 0 and enhancer_offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5300: post_image_caption_hook = enhancer_offloadobj.unload_all\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5348: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5374: enhancer_offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5381: enhancer_offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5728: global wan_model, offloadobj, reload_needed\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5787: wan_model, offloadobj = load_models(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5816: offload.shared_state[\"_attention\"] = attn\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5877: offload.load_loras_into_model(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5892: offload.sync_models_loras(trans_lora, trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6596: offloadobj = offloadobj,\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6625: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6629: offload.unload_loras_from_model(trans_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6631: offload.unload_loras_from_model(trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6689: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6928: offload.unload_loras_from_model(trans_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6930: offload.unload_loras_from_model(trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:8871: global reload_needed, wan_model, offloadobj\n\n[Showing results with pagination = limit: 50]"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"38714759-d5ba-4742-8a90-7a4d716798f3","timestamp":"2026-04-19T22:05:53.248Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/LICENSE.txt:63: (b) speed, throughput, caching, offloading, batching, or performance\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:26:from mmgp import offload, safetensors2, profile_type , quant_router\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:150:offloadobj = enhancer_offloadobj = wan_model = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:194: if \"_cache\" in offload.shared_state:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:195: del offload.shared_state[\"_cache\"]\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:200: global wan_model, offloadobj, reload_needed\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:203: if \"_cache\" in offload.shared_state:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:204: del offload.shared_state[\"_cache\"]\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:205: if offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:206: offloadobj.release()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:207: offloadobj = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:208: offload.flush_torch_caches()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2836: from mmgp import offload \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2867: offload.save_model(model, model_filename_path, config_file_path=config_file, filter_sd=filter)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2880: offload.save_model(model, quanto_filename_path, config_file_path=config_file, do_quantize= True, filter_sd=filter)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2903: from mmgp import offload\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:2922: offload.save_model(model, model_filename_path, do_quantize= True, config_file_path=config_file)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3120:offload.default_verboseLevel = verbose_level\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3209: loras = offload.load_loras_into_model(transformer, loras, activate_all_loras=False, check_only= True, preprocess_sd=get_loras_preprocessor(transformer, base_model_type), split_linear_modules_map = split_linear_modules_map) #lora_multiplier,\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3310: global reset_prompt_enhancer_requested, prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer, enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3319: if enhancer_offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3320: enhancer_offloadobj.release()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3321: enhancer_offloadobj = None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3347: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3350: if enhancer_offloadobj is None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3359: enhancer_offloadobj = offload.profile(pipe, profile_no=mmgp_profile, **kwargs)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3485: offloadobj = offload.profile(pipe, profile_no= mmgp_profile, compile = compile_modules, quantizeTransformer = False, loras = loras_transformer, perc_reserved_mem_max = perc_reserved_mem_max , vram_safety_coefficient = vram_safety_coefficient , convertWeightsFloatTo = transformer_dtype, **kwargs) \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3490: return wan_model, offloadobj \nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3493: wan_model, offloadobj, transformer = None, None, None\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3496: wan_model, offloadobj = load_models(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:3577: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5265: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5298: if len(prompt_images) > 0 and enhancer_offloadobj is not None:\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5300: post_image_caption_hook = enhancer_offloadobj.unload_all\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5348: global enhancer_offloadobj\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5374: enhancer_offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5381: enhancer_offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5728: global wan_model, offloadobj, reload_needed\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5787: wan_model, offloadobj = load_models(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5816: offload.shared_state[\"_attention\"] = attn\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5877: offload.load_loras_into_model(\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:5892: offload.sync_models_loras(trans_lora, trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6596: offloadobj = offloadobj,\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6625: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6629: offload.unload_loras_from_model(trans_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6631: offload.unload_loras_from_model(trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6689: offloadobj.unload_all()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6928: offload.unload_loras_from_model(trans_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:6930: offload.unload_loras_from_model(trans2_lora)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py:8871: global reload_needed, wan_model, offloadobj","numLines":50,"appliedLimit":50},"_audit_timestamp":"2026-04-19T22:05:53.251Z","_audit_hmac":"57a95c23fd421a20a31845f4b75fc75c0d5b7300a8d32f44500b09be00bf00d6"} {"type":"system","subtype":"status","status":"requesting","uuid":"c80c58d2-b15b-4f08-a363-9fa99e2c1122","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:05:53.253Z","_audit_hmac":"b0eabb3861e06bb32abcffa3b064564ae5465de72c14dedee0490055bb404af3"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_011nE98tFV73G6dUqAwMoKo7","type":"message","role":"assistant","content":[{"type":"text","text":"Mükemmel! WanGP `mmgp` profil sistemini kullanıyor (`offload.profile`). Profil numarası (`mmgp_profile`) modelin ne kadarının CPU RAM'e taşınacağını kontrol ediyor. Şimdi profil numaralarını ve nasıl ayarlandığını görelim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4546,"cache_read_input_tokens":60322,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4546},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3123c52d-05d4-4177-bff0-bf057fe12986","_audit_timestamp":"2026-04-19T22:06:01.069Z","_audit_hmac":"03686c878d2bd5451a44a662b85198086c3aa67b3c423fba40c980dbf33d51cf"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_011nE98tFV73G6dUqAwMoKo7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P3XkXqUA4HWhVBKcmqNNQ5","name":"Grep","input":{"pattern":"mmgp_profile|profile_no|profile_type|MMGP_PROFILE|--profile","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","output_mode":"content","-n":true,"head_limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4546,"cache_read_input_tokens":60322,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4546},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"582d5c1c-b909-4ebc-a593-146a2fd8db41","_audit_timestamp":"2026-04-19T22:06:01.680Z","_audit_hmac":"c9d15cd236cd826c7aac8a000de6f39ac46f980bda99e9b379a9d07d017ddf7d"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P3XkXqUA4HWhVBKcmqNNQ5","type":"tool_result","content":"26:from mmgp import offload, safetensors2, profile_type , quant_router\n2043:force_profile_no = float(args.profile)\n2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n2754:default_profile_video = force_profile_no if force_profile_no >= 0 else server_config[\"video_profile\"]\n2755:default_profile_image = force_profile_no if force_profile_no >= 0 else server_config[\"image_profile\"]\n2756:default_profile_audio = force_profile_no if force_profile_no >= 0 else server_config[\"audio_profile\"]\n2758:loaded_profile = force_profile_no = -1\n3247: if force_profile_no >= 0:\n3248: return force_profile_no\n3275: mmgp_profile = int(profile)\n3276: if mmgp_profile in (2, 4, 5):\n3286: elif mmgp_profile == 3:\n3298: return mmgp_profile\n3358: mmgp_profile = init_pipe(pipe, kwargs, profile)\n3359: enhancer_offloadobj = offload.profile(pipe, profile_no=mmgp_profile, **kwargs)\n3471: mmgp_profile = init_pipe(pipe, kwargs, profile)\n3485: offloadobj = offload.profile(pipe, profile_no= mmgp_profile, compile = compile_modules, quantizeTransformer = False, loras = loras_transformer, perc_reserved_mem_max = perc_reserved_mem_max , vram_safety_coefficient = vram_safety_coefficient , convertWeightsFloatTo = transformer_dtype, **kwargs)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"275fa776-bf53-41b9-9278-ca549953b05f","timestamp":"2026-04-19T22:06:01.699Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"26:from mmgp import offload, safetensors2, profile_type , quant_router\n2043:force_profile_no = float(args.profile)\n2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n2754:default_profile_video = force_profile_no if force_profile_no >= 0 else server_config[\"video_profile\"]\n2755:default_profile_image = force_profile_no if force_profile_no >= 0 else server_config[\"image_profile\"]\n2756:default_profile_audio = force_profile_no if force_profile_no >= 0 else server_config[\"audio_profile\"]\n2758:loaded_profile = force_profile_no = -1\n3247: if force_profile_no >= 0:\n3248: return force_profile_no\n3275: mmgp_profile = int(profile)\n3276: if mmgp_profile in (2, 4, 5):\n3286: elif mmgp_profile == 3:\n3298: return mmgp_profile\n3358: mmgp_profile = init_pipe(pipe, kwargs, profile)\n3359: enhancer_offloadobj = offload.profile(pipe, profile_no=mmgp_profile, **kwargs)\n3471: mmgp_profile = init_pipe(pipe, kwargs, profile)\n3485: offloadobj = offload.profile(pipe, profile_no= mmgp_profile, compile = compile_modules, quantizeTransformer = False, loras = loras_transformer, perc_reserved_mem_max = perc_reserved_mem_max , vram_safety_coefficient = vram_safety_coefficient , convertWeightsFloatTo = transformer_dtype, **kwargs)","numLines":21},"_audit_timestamp":"2026-04-19T22:06:01.952Z","_audit_hmac":"474c3096e0543ef5ef10483e89c9f6f8938efca963a859f3ac9d09471cd7d4ee"} {"type":"system","subtype":"status","status":"requesting","uuid":"41327fb2-0137-47f8-ae46-b759e944601a","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:01.960Z","_audit_hmac":"6eeaddf8bf839b6695baab9f602027d373a2de78a5811132767e010a1cb30a0e"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01GNGddHJSSKjEupSCYFJ86C","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019pW62kYgTtPkk7XRqDonmG","name":"Grep","input":{"pattern":"profile_type\\.","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","output_mode":"content","-n":true,"head_limit":20},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":864,"cache_read_input_tokens":64868,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":864},"output_tokens":50,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"e7d10ce3-cbfd-48ef-962b-bbb0dc3fb96d","_audit_timestamp":"2026-04-19T22:06:05.433Z","_audit_hmac":"14a8c9a5658360f94a2b46b8b4ce32ae04e81b4a53fe6547ee56a2621802d82d"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019pW62kYgTtPkk7XRqDonmG","type":"tool_result","content":"2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"da1185b7-ccb5-48d7-879b-682fc1e19ece","timestamp":"2026-04-19T22:06:05.445Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)","numLines":5},"_audit_timestamp":"2026-04-19T22:06:05.461Z","_audit_hmac":"043808414cc87b7b0f88aef260f93d6b5eda5b72e52d0193b14de5dc2dcb5662"} {"type":"system","subtype":"status","status":"requesting","uuid":"18647d18-e2f5-496d-aef3-599a94df2e8a","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:05.470Z","_audit_hmac":"f022880c14c94b0f8c7f3876272d42fa868cad540ce3c28c58be2c702f52190e"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01QdPZnGAjr3Gstvxkjqi1xA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UJGffpFxkDVMA8kuhidVSR","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","offset":2030,"limit":60},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":450,"cache_read_input_tokens":65732,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":450},"output_tokens":61,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"296c022c-91e0-4fb5-8116-368ed6328469","_audit_timestamp":"2026-04-19T22:06:10.218Z","_audit_hmac":"d4459479ddd7192a9ebc00b51b6e291e6cf88ba037dd306f9188ba3c03d41406"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UJGffpFxkDVMA8kuhidVSR","type":"tool_result","content":"2030\t bfloat16_supported = False\n2031\telse:\n2032\t bfloat16_supported = True\n2033\t\n2034\targs.flow_reverse = True\n2035\tprocessing_device = args.gpu\n2036\tif len(processing_device) == 0:\n2037\t processing_device =\"cuda\"\n2038\t# torch.backends.cuda.matmul.allow_fp16_accumulation = True\n2039\tlock_ui_attention = False\n2040\tlock_ui_transformer = False\n2041\tlock_ui_compile = False\n2042\t\n2043\tforce_profile_no = float(args.profile)\n2044\tverbose_level = int(args.verbose)\n2045\tcheck_loras = args.check_loras ==1\n2046\t\n2047\twith open(\"models/_settings.json\", \"r\", encoding=\"utf-8\") as f:\n2048\t primary_settings = json.load(f)\n2049\t\n2050\twgp_root = os.path.abspath(os.getcwd())\n2051\tconfig_dir = args.config.strip()\n2052\tserver_config_filename = CONFIG_FILENAME\n2053\tserver_config_fallback = server_config_filename\n2054\tif config_dir:\n2055\t config_dir = os.path.abspath(config_dir)\n2056\t os.makedirs(config_dir, exist_ok=True)\n2057\t server_config_filename = os.path.join(config_dir, CONFIG_FILENAME)\n2058\t server_config_fallback = os.path.join(wgp_root, CONFIG_FILENAME)\n2059\t AUTOSAVE_PATH = os.path.join(config_dir, AUTOSAVE_FILENAME)\n2060\t AUTOSAVE_TEMPLATE_PATH = os.path.join(wgp_root, AUTOSAVE_FILENAME)\n2061\telse:\n2062\t AUTOSAVE_PATH = AUTOSAVE_FILENAME\n2063\t AUTOSAVE_TEMPLATE_PATH = AUTOSAVE_FILENAME\n2064\t\n2065\tif not os.path.isdir(\"settings\"):\n2066\t os.mkdir(\"settings\")\n2067\tif os.path.isfile(\"t2v_settings.json\"):\n2068\t for f in glob.glob(os.path.join(\".\", \"*_settings.json*\")):\n2069\t target_file = os.path.join(\"settings\", Path(f).parts[-1])\n2070\t shutil.move(f, target_file)\n2071\t\n2072\tconfig_load_filename = server_config_filename\n2073\tif config_dir and not Path(server_config_filename).is_file():\n2074\t if Path(server_config_fallback).is_file():\n2075\t config_load_filename = server_config_fallback\n2076\t\n2077\tsrc_move = [ \"ltx-2-19b-dev-fp4_diffusion_model.safetensors\" ]\n2078\ttgt_move = [ \"ltx-2-19b-dev-nvfp4_diffusion_model.safetensors\" ]\n2079\tfor src_name, tgt_name in zip(src_move, tgt_move):\n2080\t src = fl.locate_file(src_name, error_if_none=False)\n2081\t if src is not None:\n2082\t tgt = os.path.join(os.path.dirname(src), tgt_name)\n2083\t try:\n2084\t if os.path.isfile(tgt):\n2085\t os.remove(src)\n2086\t else:\n2087\t os.replace(src, tgt)\n2088\t except:\n2089\t pass"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"4880925c-0013-4c15-a16c-9e5b9951213e","timestamp":"2026-04-19T22:06:10.229Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","content":" bfloat16_supported = False\nelse:\n bfloat16_supported = True\n\nargs.flow_reverse = True\nprocessing_device = args.gpu\nif len(processing_device) == 0:\n processing_device =\"cuda\"\n# torch.backends.cuda.matmul.allow_fp16_accumulation = True\nlock_ui_attention = False\nlock_ui_transformer = False\nlock_ui_compile = False\n\nforce_profile_no = float(args.profile)\nverbose_level = int(args.verbose)\ncheck_loras = args.check_loras ==1\n\nwith open(\"models/_settings.json\", \"r\", encoding=\"utf-8\") as f:\n primary_settings = json.load(f)\n\nwgp_root = os.path.abspath(os.getcwd())\nconfig_dir = args.config.strip()\nserver_config_filename = CONFIG_FILENAME\nserver_config_fallback = server_config_filename\nif config_dir:\n config_dir = os.path.abspath(config_dir)\n os.makedirs(config_dir, exist_ok=True)\n server_config_filename = os.path.join(config_dir, CONFIG_FILENAME)\n server_config_fallback = os.path.join(wgp_root, CONFIG_FILENAME)\n AUTOSAVE_PATH = os.path.join(config_dir, AUTOSAVE_FILENAME)\n AUTOSAVE_TEMPLATE_PATH = os.path.join(wgp_root, AUTOSAVE_FILENAME)\nelse:\n AUTOSAVE_PATH = AUTOSAVE_FILENAME\n AUTOSAVE_TEMPLATE_PATH = AUTOSAVE_FILENAME\n\nif not os.path.isdir(\"settings\"):\n os.mkdir(\"settings\")\nif os.path.isfile(\"t2v_settings.json\"):\n for f in glob.glob(os.path.join(\".\", \"*_settings.json*\")):\n target_file = os.path.join(\"settings\", Path(f).parts[-1])\n shutil.move(f, target_file)\n\nconfig_load_filename = server_config_filename\nif config_dir and not Path(server_config_filename).is_file():\n if Path(server_config_fallback).is_file():\n config_load_filename = server_config_fallback\n\nsrc_move = [ \"ltx-2-19b-dev-fp4_diffusion_model.safetensors\" ]\ntgt_move = [ \"ltx-2-19b-dev-nvfp4_diffusion_model.safetensors\" ]\nfor src_name, tgt_name in zip(src_move, tgt_move):\n src = fl.locate_file(src_name, error_if_none=False)\n if src is not None:\n tgt = os.path.join(os.path.dirname(src), tgt_name)\n try:\n if os.path.isfile(tgt):\n os.remove(src)\n else:\n os.replace(src, tgt)\n except:\n pass","numLines":60,"startLine":2030,"totalLines":11997}},"_audit_timestamp":"2026-04-19T22:06:10.242Z","_audit_hmac":"c277ed5e6f1168e8108562cc555e31e637dc01ed6a2edc12922d6dbe9582295a"} {"type":"system","subtype":"status","status":"requesting","uuid":"5e5023f9-5863-473e-bf7f-d2987aff0118","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:10.263Z","_audit_hmac":"3da903e6abb68dcdd2becdc51d32d55f781f7ce81c7ef507added27c68df8355"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_012pDYZ9UsguFG8HuhZsG77T","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Dx9XpTvLzQQLs6nfyAWc4U","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","offset":3240,"limit":60},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1123,"cache_read_input_tokens":66182,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1123},"output_tokens":65,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"a634d57f-8545-4bf4-ac64-e4d05cb2f329","_audit_timestamp":"2026-04-19T22:06:14.087Z","_audit_hmac":"a8c6b37e08fab6d5644392d807a7176b97565e9e43f38dcd49342e8e5b2e3a8d"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Dx9XpTvLzQQLs6nfyAWc4U","type":"tool_result","content":"3240\t return \"video\"\n3241\t output_type = str(output_type).lower()\n3242\t if output_type not in (\"video\", \"image\", \"audio\"):\n3243\t return \"video\"\n3244\t return output_type\n3245\t\n3246\tdef get_default_profile(output_type):\n3247\t if force_profile_no >= 0:\n3248\t return force_profile_no\n3249\t output_type = _normalize_output_type(output_type)\n3250\t if output_type == \"image\":\n3251\t return default_profile_image\n3252\t if output_type == \"audio\":\n3253\t return default_profile_audio\n3254\t return default_profile_video\n3255\t\n3256\tdef compute_profile(override_profile, output_type=\"video\"):\n3257\t return override_profile if override_profile != -1 else get_default_profile(output_type)\n3258\t\n3259\tdef get_output_type_for_model(model_type, image_mode=0):\n3260\t model_def = get_model_def(model_type)\n3261\t if model_def is not None and model_def.get(\"audio_only\", False):\n3262\t return \"audio\"\n3263\t if image_mode and image_mode > 0:\n3264\t return \"image\"\n3265\t return \"video\"\n3266\t\n3267\tdef init_pipe(pipe, kwargs, profile):\n3268\t preload =int(args.preload)\n3269\t if preload == 0:\n3270\t preload = server_config.get(\"preload_in_VRAM\", 0)\n3271\t\n3272\t kwargs[\"extraModelsToQuantize\"]= None\n3273\t source_budgets = kwargs.get(\"budgets\", None)\n3274\t if source_budgets is None: kwargs[\"budgets\"] = source_budgets = {}\n3275\t mmgp_profile = int(profile)\n3276\t if mmgp_profile in (2, 4, 5):\n3277\t default_transformer_budget = default_transformer2_budget= kwargs.get(\"budgets\", 100) \n3278\t if isinstance(default_transformer_budget, dict):\n3279\t default_transformer_budget = default_transformer_budget.get(\"transformer\", 100) \n3280\t default_transformer2_budget = default_transformer2_budget.get(\"transformer2\", 100) \n3281\t\n3282\t budgets = { \"transformer\" : default_transformer_budget if preload == 0 else preload, \"text_encoder\" : 100 if preload == 0 else preload, \"*\" : max(1000 if profile==5 else 3000 , preload) }\n3283\t if \"transformer2\" in pipe:\n3284\t budgets[\"transformer2\"] = default_transformer2_budget if preload == 0 else preload\n3285\t source_budgets.update(budgets)\n3286\t elif mmgp_profile == 3:\n3287\t source_budgets.update({ \"*\" : \"70%\" })\n3288\t\n3289\t if \"transformer2\" in pipe:\n3290\t if profile in [3,4]:\n3291\t kwargs[\"pinnedMemory\"] = [\"transformer\", \"transformer2\"]\n3292\t \n3293\t if profile == 4.5:\n3294\t kwargs[\"asyncTransfers\"] = False\n3295\t elif profile == 3.5:\n3296\t kwargs[\"pinnedMemory\"] = False\n3297\t\n3298\t return mmgp_profile\n3299\t"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"5f639f11-6547-4090-8c02-e9144a7f3270","timestamp":"2026-04-19T22:06:14.095Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","content":" return \"video\"\n output_type = str(output_type).lower()\n if output_type not in (\"video\", \"image\", \"audio\"):\n return \"video\"\n return output_type\n\ndef get_default_profile(output_type):\n if force_profile_no >= 0:\n return force_profile_no\n output_type = _normalize_output_type(output_type)\n if output_type == \"image\":\n return default_profile_image\n if output_type == \"audio\":\n return default_profile_audio\n return default_profile_video\n\ndef compute_profile(override_profile, output_type=\"video\"):\n return override_profile if override_profile != -1 else get_default_profile(output_type)\n\ndef get_output_type_for_model(model_type, image_mode=0):\n model_def = get_model_def(model_type)\n if model_def is not None and model_def.get(\"audio_only\", False):\n return \"audio\"\n if image_mode and image_mode > 0:\n return \"image\"\n return \"video\"\n\ndef init_pipe(pipe, kwargs, profile):\n preload =int(args.preload)\n if preload == 0:\n preload = server_config.get(\"preload_in_VRAM\", 0)\n\n kwargs[\"extraModelsToQuantize\"]= None\n source_budgets = kwargs.get(\"budgets\", None)\n if source_budgets is None: kwargs[\"budgets\"] = source_budgets = {}\n mmgp_profile = int(profile)\n if mmgp_profile in (2, 4, 5):\n default_transformer_budget = default_transformer2_budget= kwargs.get(\"budgets\", 100) \n if isinstance(default_transformer_budget, dict):\n default_transformer_budget = default_transformer_budget.get(\"transformer\", 100) \n default_transformer2_budget = default_transformer2_budget.get(\"transformer2\", 100) \n\n budgets = { \"transformer\" : default_transformer_budget if preload == 0 else preload, \"text_encoder\" : 100 if preload == 0 else preload, \"*\" : max(1000 if profile==5 else 3000 , preload) }\n if \"transformer2\" in pipe:\n budgets[\"transformer2\"] = default_transformer2_budget if preload == 0 else preload\n source_budgets.update(budgets)\n elif mmgp_profile == 3:\n source_budgets.update({ \"*\" : \"70%\" })\n\n if \"transformer2\" in pipe:\n if profile in [3,4]:\n kwargs[\"pinnedMemory\"] = [\"transformer\", \"transformer2\"]\n \n if profile == 4.5:\n kwargs[\"asyncTransfers\"] = False\n elif profile == 3.5:\n kwargs[\"pinnedMemory\"] = False\n\n return mmgp_profile\n","numLines":60,"startLine":3240,"totalLines":11997}},"_audit_timestamp":"2026-04-19T22:06:14.149Z","_audit_hmac":"826b0c920cfb60ecbf70a533948c2e377f138757b7ebc3dcc07cf835727974c6"} {"type":"system","subtype":"status","status":"requesting","uuid":"cca3738d-f51a-4f4b-bbe0-2a9cc0209ac4","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:14.175Z","_audit_hmac":"3a75a8546a3a6b6ae50a9b393220da69e5e4ac83aebaa0e7ed300beba9437066"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013kfJeFG7nFx4FptX3uD8Nt","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi anladım! WanGP'nin MMGP profil sistemi şöyle çalışıyor:\n\n- **Profil 2, 4, 5**: Model parçalarını CPU RAM'e taşıyıp gerektiğinde GPU'ya yüklüyor (offload)\n- **Profil 3**: %70 RAM'de tutar\n- **Profil 5**: En agresif offload (en az VRAM kullanır)\n\nWanGP başlatılırken `--profile` argümanı ile bu ayarlanıyor. Şimdi LTX-Desktop-WanGP-Baslat.bat'ı da görelim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2219,"cache_read_input_tokens":66182,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2219},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"d206bf5f-352f-4697-a8ff-9494a96da574","_audit_timestamp":"2026-04-19T22:06:21.941Z","_audit_hmac":"e6817810114bff11fc6fc5125d1dd0faf477ad00bbb72550eb6f228f83f8272c"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013kfJeFG7nFx4FptX3uD8Nt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KmVKcCLGJ3Yi7PruyjASvh","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2219,"cache_read_input_tokens":66182,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2219},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"e973ac76-77e4-493b-ae7b-3fef5b145a91","_audit_timestamp":"2026-04-19T22:06:23.800Z","_audit_hmac":"1f3f4c4edaf746f51907c0023844181b5f71138848c421bfd6924647aadcbf87"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KmVKcCLGJ3Yi7PruyjASvh","type":"tool_result","content":"1\t@echo off\n2\tchcp 65001 >nul\n3\ttitle LTX-Desktop-WanGP - Video AI Studio\n4\tcolor 0B\n5\t\n6\tREM ============================================================\n7\tREM LTX-Desktop-WanGP Baslatma Scripti\n8\tREM Electron frontend + WanGP backend birlikte calisir\n9\tREM ============================================================\n10\t\n11\tset \"KOK=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\"\n12\tset \"LTX=%KOK%\\ltx-desktop-wangp\"\n13\tset \"REPO=%LTX%\\LTX-Desktop-WanGP\"\n14\t\n15\tREM Tum cache ve modeller ltx-desktop-wangp klasoru icinde kalsin\n16\tset \"HF_HOME=%LTX%\\hf-cache\"\n17\tset \"HUGGINGFACE_HUB_CACHE=%LTX%\\hf-cache\"\n18\tset \"TRANSFORMERS_CACHE=%LTX%\\hf-cache\"\n19\tset \"TORCH_HOME=%LTX%\\torch-cache\"\n20\tset \"XDG_CACHE_HOME=%LTX%\\xdg-cache\"\n21\t\n22\techo =========================================================\n23\techo LTX-Desktop-WanGP Baslatiliyor...\n24\techo Video uretim arayuzu yuklenecek\n25\techo =========================================================\n26\techo.\n27\techo Klasor : %REPO%\n28\techo Cache : %HF_HOME%\n29\techo.\n30\t\n31\tREM --- Repo kontrolu ---\n32\tif not exist \"%REPO%\" (\n33\t color 0C\n34\t echo [HATA] LTX-Desktop-WanGP kaynak kodu bulunamadi!\n35\t echo Beklenen: %REPO%\n36\t echo.\n37\t echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n38\t pause\n39\t exit /b 1\n40\t)\n41\t\n42\tREM --- package.json kontrolu ---\n43\tif not exist \"%REPO%\\package.json\" (\n44\t color 0C\n45\t echo [HATA] package.json bulunamadi!\n46\t echo Kurulum eksik olabilir.\n47\t echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n48\t pause\n49\t exit /b 1\n50\t)\n51\t\n52\tREM --- pnpm kontrolu ---\n53\twhere pnpm >nul 2>&1\n54\tif errorlevel 1 (\n55\t color 0C\n56\t echo [HATA] pnpm bulunamadi!\n57\t echo Lutfen once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin\n58\t echo veya \"npm install -g pnpm\" komutu ile pnpm kurun.\n59\t pause\n60\t exit /b 1\n61\t)\n62\t\n63\tcd /d \"%REPO%\"\n64\t\n65\techo [+] pnpm dev baslatiliyor...\n66\techo Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\n67\techo.\n68\t\n69\tcall pnpm dev\n70\t\n71\tpause\n72\t"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"4d9b2db3-f90e-4d40-b1fa-e483dd59c5df","timestamp":"2026-04-19T22:06:23.808Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat","content":"@echo off\nchcp 65001 >nul\ntitle LTX-Desktop-WanGP - Video AI Studio\ncolor 0B\n\nREM ============================================================\nREM LTX-Desktop-WanGP Baslatma Scripti\nREM Electron frontend + WanGP backend birlikte calisir\nREM ============================================================\n\nset \"KOK=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\"\nset \"LTX=%KOK%\\ltx-desktop-wangp\"\nset \"REPO=%LTX%\\LTX-Desktop-WanGP\"\n\nREM Tum cache ve modeller ltx-desktop-wangp klasoru icinde kalsin\nset \"HF_HOME=%LTX%\\hf-cache\"\nset \"HUGGINGFACE_HUB_CACHE=%LTX%\\hf-cache\"\nset \"TRANSFORMERS_CACHE=%LTX%\\hf-cache\"\nset \"TORCH_HOME=%LTX%\\torch-cache\"\nset \"XDG_CACHE_HOME=%LTX%\\xdg-cache\"\n\necho =========================================================\necho LTX-Desktop-WanGP Baslatiliyor...\necho Video uretim arayuzu yuklenecek\necho =========================================================\necho.\necho Klasor : %REPO%\necho Cache : %HF_HOME%\necho.\n\nREM --- Repo kontrolu ---\nif not exist \"%REPO%\" (\n color 0C\n echo [HATA] LTX-Desktop-WanGP kaynak kodu bulunamadi!\n echo Beklenen: %REPO%\n echo.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- package.json kontrolu ---\nif not exist \"%REPO%\\package.json\" (\n color 0C\n echo [HATA] package.json bulunamadi!\n echo Kurulum eksik olabilir.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- pnpm kontrolu ---\nwhere pnpm >nul 2>&1\nif errorlevel 1 (\n color 0C\n echo [HATA] pnpm bulunamadi!\n echo Lutfen once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin\n echo veya \"npm install -g pnpm\" komutu ile pnpm kurun.\n pause\n exit /b 1\n)\n\ncd /d \"%REPO%\"\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev\n\npause\n","numLines":72,"startLine":1,"totalLines":72}},"_audit_timestamp":"2026-04-19T22:06:23.830Z","_audit_hmac":"8d9de1fd8342c725d18e26503afd8fae95094f1d4e35c99165c7b191c7210bfe"} {"type":"system","subtype":"status","status":"requesting","uuid":"8ab7f226-0abf-438a-9780-8510dc1a2821","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:23.850Z","_audit_hmac":"97c6621d19835df79597d50d1af43d06c31f45138837a3ecdbd9a8aab85b07a7"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_0171ES1dirKMTcPHoy5fziqs","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi WanGP backend'in nasıl başlatıldığını görelim — `pnpm dev` ne yapıyor."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1213,"cache_read_input_tokens":68401,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1213},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"289868cf-ab81-41d0-bb6c-dbeaff67bfb6","_audit_timestamp":"2026-04-19T22:06:27.861Z","_audit_hmac":"3111ea2cb6559d1f56536e34bfba6783801c3d4febf5c594d5e7480fec39041c"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_0171ES1dirKMTcPHoy5fziqs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MkQ8RHnpcUE4fKTi9qiuSL","name":"Grep","input":{"pattern":"\"dev\"|wgp\\.py|--profile|python.*wgp","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json","output_mode":"content","-n":true},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1213,"cache_read_input_tokens":68401,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1213},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"b45acd1d-c3da-48c1-bb42-e708e6a504c6","_audit_timestamp":"2026-04-19T22:06:29.690Z","_audit_hmac":"80a9db77658fba8d5f44c36364f59f49236d132f016787b98019d717be52759b"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MkQ8RHnpcUE4fKTi9qiuSL","type":"tool_result","content":"15: \"dev\": \"vite\","}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6c4856bc-7ca3-4e44-8029-55be2e026dfb","timestamp":"2026-04-19T22:06:29.702Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"15: \"dev\": \"vite\",","numLines":1},"_audit_timestamp":"2026-04-19T22:06:29.763Z","_audit_hmac":"744465fb6268a326630dd79ab9997dd1cbd6dbe9e25f3c8a29c920f19ca0c809"} {"type":"system","subtype":"status","status":"requesting","uuid":"60433868-d389-45ab-b9a8-dfed7800e1a9","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:06:29.768Z","_audit_hmac":"e1c144bc70541091ba215ba57392c486fd81c88a3324e3b1516878afc73c68ad"} 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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015j8UzDyh2f4f6jCMHbccTa","type":"tool_result","content":"mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:19: \"typecheck:py\": \"cd backend && uv run pyright\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:20: \"backend:test\": \"cd backend && uv sync --frozen --extra test --extra dev && uv run pytest -v --tb=short\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:23: \"build:mac:skip-python\": \"bash scripts/local-build.sh --platform mac --skip-python\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:24: \"build:win:skip-python\": \"powershell -ExecutionPolicy Bypass -File scripts/local-build.ps1 -SkipPython\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:25: \"build:fast:mac\": \"bash scripts/local-build.sh --platform mac --unpack --skip-python\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:27: \"prepare:python:mac\": \"bash scripts/prepare-python.sh\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:28: \"prepare:python:win\": \"powershell -ExecutionPolicy Bypass -File scripts/prepare-python.ps1\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:37:echo \"Setting up Python backend venv...\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:38:cd \"$PROJECT_DIR/backend\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:47: .venv/bin/python -c \"import torch; mps=hasattr(torch.backends,'mps') and torch.backends.mps.is_available(); print(f'MPS available: {mps}')\" || true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:50: .venv/bin/python -c \"import torch; cuda=torch.cuda.is_available(); print(f'CUDA available: {cuda}')\" || true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:2:# prepare-python.sh\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:8:# Uses python-build-standalone (https://github.com/astral-sh/python-build-standalone)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:21:PYTHON_VERSION=\"${PYTHON_VERSION:-$(cat \"$(dirname \"$0\")/../backend/.python-version\" | tr -d '[:space:]')}\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:23:OUTPUT_DIR=\"python-embed\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:28:BACKEND_DIR=\"$PROJECT_DIR/backend\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:32:# Map architecture names for python-build-standalone\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:39:PBS_URL=\"https://github.com/astral-sh/python-build-standalone/releases/download/${PBS_TAG}/cpython-${PYTHON_VERSION}+${PBS_TAG}-${PBS_ARCH}-apple-darwin-install_only_stripped.tar.gz\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:114:PYTHON_TAR=\"$TEMP_DIR/python-standalone.tar.gz\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:118:# python-build-standalone extracts to a `python/` directory\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:122:# Move contents from python/ into our output path\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:123:mv \"$TEMP_DIR/python/\"* 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true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:178:rm -rf \"$OUTPUT_PATH/lib/python\"*/site-packages/setuptools-*.dist-info 2>/dev/null || true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:217: mps = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/local-build.sh:12:# --skip-python Use existing python-embed/ directory\n\n[Showing results with pagination = limit: 30]"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"bb237bb3-bfc8-4133-bf94-a73d44e60874","timestamp":"2026-04-19T22:06:54.936Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:19: \"typecheck:py\": \"cd backend && uv run pyright\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:20: \"backend:test\": \"cd backend && uv sync --frozen --extra test --extra dev && uv run pytest -v --tb=short\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:23: \"build:mac:skip-python\": \"bash scripts/local-build.sh --platform mac --skip-python\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:24: \"build:win:skip-python\": \"powershell -ExecutionPolicy Bypass -File scripts/local-build.ps1 -SkipPython\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:25: \"build:fast:mac\": \"bash scripts/local-build.sh --platform mac --unpack --skip-python\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:27: \"prepare:python:mac\": \"bash scripts/prepare-python.sh\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/package.json:28: \"prepare:python:win\": \"powershell -ExecutionPolicy Bypass -File scripts/prepare-python.ps1\",\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:37:echo \"Setting up Python backend venv...\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:38:cd \"$PROJECT_DIR/backend\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:47: .venv/bin/python -c \"import torch; mps=hasattr(torch.backends,'mps') and torch.backends.mps.is_available(); print(f'MPS available: {mps}')\" || true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/setup-dev.sh:50: .venv/bin/python -c \"import torch; cuda=torch.cuda.is_available(); print(f'CUDA available: {cuda}')\" || true\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:2:# prepare-python.sh\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:8:# Uses python-build-standalone (https://github.com/astral-sh/python-build-standalone)\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:21:PYTHON_VERSION=\"${PYTHON_VERSION:-$(cat \"$(dirname \"$0\")/../backend/.python-version\" | tr -d '[:space:]')}\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:23:OUTPUT_DIR=\"python-embed\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:28:BACKEND_DIR=\"$PROJECT_DIR/backend\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:32:# Map architecture names for python-build-standalone\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:39:PBS_URL=\"https://github.com/astral-sh/python-build-standalone/releases/download/${PBS_TAG}/cpython-${PYTHON_VERSION}+${PBS_TAG}-${PBS_ARCH}-apple-darwin-install_only_stripped.tar.gz\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:114:PYTHON_TAR=\"$TEMP_DIR/python-standalone.tar.gz\"\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:118:# python-build-standalone extracts to a `python/` directory\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:122:# Move contents from python/ into our output path\nmnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/scripts/prepare-python.sh:123:mv \"$TEMP_DIR/python/\"* 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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Kda1PcgN6dVffAsNXAoyHo","type":"tool_result","content":"No matches found"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"57d65087-9784-48aa-a2a6-cb2f76f40d8c","timestamp":"2026-04-19T22:07:11.739Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"","numLines":0},"_audit_timestamp":"2026-04-19T22:07:11.787Z","_audit_hmac":"02dc61109938beff2dd6dd6caeee41c0553f375061c9770c390126dcc9faa0ce"} {"type":"system","subtype":"status","status":"requesting","uuid":"680d3776-9837-4b23-9506-cd02d1b6f9bd","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:11.799Z","_audit_hmac":"5e409a788b7312ea73d88975ea9d31d1e18ae5fe362248457ab8ff5e43b29a03"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01LJt3SnrgRwcuMC9GdxTftC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RLxHnysA5R9Va4BzVsFNQt","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/backend/services/wangp_bridge.py","offset":80,"limit":60},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":200,"cache_read_input_tokens":73039,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":200},"output_tokens":61,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"f62cb46e-f49a-4c4d-859d-dd34d0415e5d","_audit_timestamp":"2026-04-19T22:07:16.387Z","_audit_hmac":"821cd654c2f464c7895fcad6e68f354a9ba73fc606588f45959222b77d3188eb"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RLxHnysA5R9Va4BzVsFNQt","type":"tool_result","content":"80\t return root_config\n81\t return self._config_dir / \"wgp_config.json\"\n82\t\n83\t def get_status(self) -> WanGPBridgeStatus:\n84\t if not self._enabled:\n85\t return WanGPBridgeStatus(\n86\t available=False,\n87\t root=self._root,\n88\t python_executable=self._python,\n89\t reason=\"WanGP bridge is disabled\",\n90\t )\n91\t\n92\t if self._root is None:\n93\t return WanGPBridgeStatus(\n94\t available=False,\n95\t root=None,\n96\t python_executable=self._python,\n97\t reason=\"WanGP root was not resolved\",\n98\t )\n99\t\n100\t wgp_path = self._root / \"wgp.py\"\n101\t if not wgp_path.exists():\n102\t return WanGPBridgeStatus(\n103\t available=False,\n104\t root=self._root,\n105\t python_executable=self._python,\n106\t reason=f\"Missing {wgp_path}\",\n107\t )\n108\t\n109\t api_path = self._root / \"shared\" / \"api.py\"\n110\t if not api_path.exists():\n111\t return WanGPBridgeStatus(\n112\t available=False,\n113\t root=self._root,\n114\t python_executable=self._python,\n115\t reason=f\"Missing {api_path}\",\n116\t )\n117\t\n118\t try:\n119\t self._load_api_module()\n120\t except Exception as exc:\n121\t return WanGPBridgeStatus(\n122\t available=False,\n123\t root=self._root,\n124\t python_executable=self._python,\n125\t reason=f\"Unable to import WanGP API: {exc}\",\n126\t )\n127\t\n128\t return WanGPBridgeStatus(\n129\t available=True,\n130\t root=self._root,\n131\t python_executable=self._python,\n132\t )\n133\t\n134\t def generate_video(\n135\t self,\n136\t *,\n137\t prompt: str,\n138\t resolution_label: str,\n139\t aspect_ratio: str,"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"9bb8bf3c-e3e9-4e44-8632-f62b2d98fc58","timestamp":"2026-04-19T22:07:16.394Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/ltx-desktop-wangp/LTX-Desktop-WanGP/backend/services/wangp_bridge.py","content":" return root_config\n return self._config_dir / \"wgp_config.json\"\n\n def get_status(self) -> WanGPBridgeStatus:\n if not self._enabled:\n return WanGPBridgeStatus(\n available=False,\n root=self._root,\n python_executable=self._python,\n reason=\"WanGP bridge is disabled\",\n )\n\n if self._root is None:\n return WanGPBridgeStatus(\n available=False,\n root=None,\n python_executable=self._python,\n reason=\"WanGP root was not resolved\",\n )\n\n wgp_path = self._root / \"wgp.py\"\n if not wgp_path.exists():\n return WanGPBridgeStatus(\n available=False,\n root=self._root,\n python_executable=self._python,\n reason=f\"Missing {wgp_path}\",\n )\n\n api_path = self._root / \"shared\" / \"api.py\"\n if not api_path.exists():\n return WanGPBridgeStatus(\n available=False,\n root=self._root,\n python_executable=self._python,\n reason=f\"Missing {api_path}\",\n )\n\n try:\n self._load_api_module()\n except Exception as exc:\n return WanGPBridgeStatus(\n available=False,\n root=self._root,\n python_executable=self._python,\n reason=f\"Unable to import WanGP API: {exc}\",\n )\n\n return WanGPBridgeStatus(\n available=True,\n root=self._root,\n python_executable=self._python,\n )\n\n def generate_video(\n self,\n *,\n prompt: str,\n resolution_label: str,\n aspect_ratio: str,","numLines":60,"startLine":80,"totalLines":590}},"_audit_timestamp":"2026-04-19T22:07:16.424Z","_audit_hmac":"4ef61a21be423e233cd4ef049b6a483c4248b8af1926a321249b4761b2647d72"} {"type":"system","subtype":"status","status":"requesting","uuid":"03478d1a-4e8c-4547-a18f-cd979c8edfb7","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:16.459Z","_audit_hmac":"a1ba5fae2559e993d31c2ff497f970cd0a9b4a3ec942f84ef01e42de994affb2"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AWytUSoUbsgTiWwLWJJh4V","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi WanGP'nin `_settings.json` dosyasını kontrol edeyim — burada profil ayarı var."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":889,"cache_read_input_tokens":73239,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":889},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"7bad1731-e66e-4aab-bbb7-3a2c3b67a5e0","_audit_timestamp":"2026-04-19T22:07:20.634Z","_audit_hmac":"5fe32d7ce384cd7658128ec0a2203d9ca55a04e58e79552f2ee421c178f17127"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AWytUSoUbsgTiWwLWJJh4V","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017WFBFoWdwRcYGaAX3J3NLm","name":"Bash","input":{"command":"cat \"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/models/_settings.json\" 2>/dev/null | head -50","description":"Read WanGP settings"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":889,"cache_read_input_tokens":73239,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":889},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"17e9182d-2506-4da7-a0e6-e8df38779442","_audit_timestamp":"2026-04-19T22:07:21.977Z","_audit_hmac":"619eba7ecf2fa4f442d8798de78e9d661ae9dedf9eceeb3ed73713baf5beeb52"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017WFBFoWdwRcYGaAX3J3NLm","type":"tool_result","content":"{\r\n\t\"client_id\": \"\",\r\n\t\"prompt\": \"\",\r\n\t\"alt_prompt\": \"\",\r\n\t\"image_mode\":0,\r\n\t\"image_prompt_type\": \"\",\r\n\t\"model_mode\": null,\r\n\t\"keep_frames_video_source\": \"\",\r\n\t\"video_prompt_type\": \"\",\r\n\t\"denoising_strength\": 0.5,\r\n\t\"input_video_strength\": 1.0,\r\n\t\"keep_frames_video_guide\": \"\",\r\n\t\"video_guide_outpainting\": \"#\",\r\n\t\"video_guide_outpainting_ratio\": \"\",\r\n\t\"masking_strength\": 1,\r\n\t\"mask_expand\": 0,\r\n\t\"frames_positions\": \"\",\r\n\t\"image_refs_relative_size\": 50,\r\n\t\"remove_background_images_ref\": 1,\r\n\t\"audio_prompt_type\": \"\",\r\n\t\"speakers_locations\": \"0:45 55:100\",\r\n\t\"prompt_enhancer\": \"\",\r\n\t\"custom_settings\": null,\r\n\t\"resolution\": \"832x480\",\r\n\t\"batch_size\": 1,\r\n\t\"video_length\": 81,\r\n\t\"duration_seconds\": 0,\r\n\t\"pause_seconds\": 0,\r\n\t\"force_fps\": \"\",\r\n\t\"num_inference_steps\": 30,\r\n\t\"seed\": -1,\r\n\t\"guidance_phases\": 1,\r\n\t\"model_switch_phase\": 1,\r\n\t\"switch_threshold\": 0,\r\n\t\"switch_threshold2\": 0,\r\n\t\"guidance_scale\":5,\r\n\t\"guidance2_scale\":5,\r\n\t\"guidance3_scale\":5,\r\n\t\"audio_guidance_scale\": 4,\r\n\t\"audio_scale\": 1,\r\n\t\"embedded_guidance_scale\": 6.0,\r\n\t\"alt_guidance_scale\": 1.0,\r\n\t\"alt_scale\": 0.0,\r\n\t\"temperature\": 0.8,\r\n\t\"top_p\": 0.9,\r\n\t\"top_k\": 50,\r\n\t\"sample_solver\": \"\",\r\n\t\"flow_shift\":3,\r\n\t\"control_net_weight\": 1,\r\n\t\"control_net_weight2\": 1,","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"5d6d9823-29c5-4a14-b952-43b3a18dd146","timestamp":"2026-04-19T22:07:22.025Z","tool_use_result":{"stdout":"{\r\n\t\"client_id\": \"\",\r\n\t\"prompt\": \"\",\r\n\t\"alt_prompt\": \"\",\r\n\t\"image_mode\":0,\r\n\t\"image_prompt_type\": \"\",\r\n\t\"model_mode\": null,\r\n\t\"keep_frames_video_source\": \"\",\r\n\t\"video_prompt_type\": \"\",\r\n\t\"denoising_strength\": 0.5,\r\n\t\"input_video_strength\": 1.0,\r\n\t\"keep_frames_video_guide\": \"\",\r\n\t\"video_guide_outpainting\": \"#\",\r\n\t\"video_guide_outpainting_ratio\": \"\",\r\n\t\"masking_strength\": 1,\r\n\t\"mask_expand\": 0,\r\n\t\"frames_positions\": \"\",\r\n\t\"image_refs_relative_size\": 50,\r\n\t\"remove_background_images_ref\": 1,\r\n\t\"audio_prompt_type\": \"\",\r\n\t\"speakers_locations\": \"0:45 55:100\",\r\n\t\"prompt_enhancer\": \"\",\r\n\t\"custom_settings\": null,\r\n\t\"resolution\": \"832x480\",\r\n\t\"batch_size\": 1,\r\n\t\"video_length\": 81,\r\n\t\"duration_seconds\": 0,\r\n\t\"pause_seconds\": 0,\r\n\t\"force_fps\": \"\",\r\n\t\"num_inference_steps\": 30,\r\n\t\"seed\": -1,\r\n\t\"guidance_phases\": 1,\r\n\t\"model_switch_phase\": 1,\r\n\t\"switch_threshold\": 0,\r\n\t\"switch_threshold2\": 0,\r\n\t\"guidance_scale\":5,\r\n\t\"guidance2_scale\":5,\r\n\t\"guidance3_scale\":5,\r\n\t\"audio_guidance_scale\": 4,\r\n\t\"audio_scale\": 1,\r\n\t\"embedded_guidance_scale\": 6.0,\r\n\t\"alt_guidance_scale\": 1.0,\r\n\t\"alt_scale\": 0.0,\r\n\t\"temperature\": 0.8,\r\n\t\"top_p\": 0.9,\r\n\t\"top_k\": 50,\r\n\t\"sample_solver\": \"\",\r\n\t\"flow_shift\":3,\r\n\t\"control_net_weight\": 1,\r\n\t\"control_net_weight2\": 1,","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:07:22.027Z","_audit_hmac":"7f9aed76015351d30d71dff6fb7d2507e9c3bf8a515dd9b976a723a28f7f15ee"} {"type":"system","subtype":"status","status":"requesting","uuid":"5eb3a77b-39c2-47d9-b1d8-f37111d28369","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:22.033Z","_audit_hmac":"aeb7a2d2ef790ad42a0ad42e9bd8c88e95b46e261348b1bd71a79566f91913ad"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01TJPJ27BvKkPSGvjUvR8DT5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KPATzNFxzA9wzUySpRJFZx","name":"Bash","input":{"command":"find \"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP\" -name \"wgp_config.json\" -o -name \"server_config.json\" -o -name \"config.json\" 2>/dev/null | head -5","description":"Find config files"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":733,"cache_read_input_tokens":74128,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":733},"output_tokens":70,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"642516b5-5133-4379-b0f5-0d38313655ea","_audit_timestamp":"2026-04-19T22:07:29.564Z","_audit_hmac":"874f70cc99ddfd7b2c0d7691e47b99c948118517e177676e9ceb72d38c71c37d"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KPATzNFxzA9wzUySpRJFZx","type":"tool_result","content":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/chinese-wav2vec2-base/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/mask/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/wav2vec/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/models/TTS/ace_step/models/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/models/TTS/index_tts2/s2mel/modules/bigvgan/config.json","is_error":false}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"43fabe1c-324d-44cf-806a-862e65a6541b","timestamp":"2026-04-19T22:07:50.613Z","tool_use_result":{"stdout":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/chinese-wav2vec2-base/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/mask/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/ckpts/wav2vec/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/models/TTS/ace_step/models/config.json\n/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/models/TTS/index_tts2/s2mel/modules/bigvgan/config.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"_audit_timestamp":"2026-04-19T22:07:50.615Z","_audit_hmac":"c2a59965023b8d91a7c2ece0c8998c294ab1477e90e2a65e2abb9cff2873af62"} {"type":"system","subtype":"status","status":"requesting","uuid":"e35f997a-430c-4d52-85d7-848c7e99e4c2","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:50.619Z","_audit_hmac":"32c140f2808c671696348104ac223273d17eeabffd5294d02e1d70074312a5ca"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_016oGDhwjPb1m6rabCaxcCGp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FvtF98zLG6PvkDFLmEaXzS","name":"Grep","input":{"pattern":"CONFIG_FILENAME|wgp_config","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","output_mode":"content","-n":true,"head_limit":10},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":615,"cache_read_input_tokens":74861,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":615},"output_tokens":39,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"1cc15bcf-c767-47a9-b8be-dfa1eaf547b1","_audit_timestamp":"2026-04-19T22:07:55.740Z","_audit_hmac":"39b8551016ae6fe154417c814126beca2f03d211f1b0940e3957484144e0a250"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FvtF98zLG6PvkDFLmEaXzS","type":"tool_result","content":"119:CONFIG_FILENAME = \"wgp_config.json\"\n2024:args = parse_wgp_args(family_handlers, CONFIG_FILENAME, DEFAULT_LORA_ROOT)\n2052:server_config_filename = CONFIG_FILENAME\n2057: server_config_filename = os.path.join(config_dir, CONFIG_FILENAME)\n2058: server_config_fallback = os.path.join(wgp_root, CONFIG_FILENAME)\n2725: print(f\"Model '{model_type}' is missing. Either install it in the finetune folder or remove this model from ley 'transformer_types' in {CONFIG_FILENAME}\")"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"b0ca0836-72f5-4a12-8469-03d40b6d6f50","timestamp":"2026-04-19T22:07:55.754Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"119:CONFIG_FILENAME = \"wgp_config.json\"\n2024:args = parse_wgp_args(family_handlers, CONFIG_FILENAME, DEFAULT_LORA_ROOT)\n2052:server_config_filename = CONFIG_FILENAME\n2057: server_config_filename = os.path.join(config_dir, CONFIG_FILENAME)\n2058: server_config_fallback = os.path.join(wgp_root, CONFIG_FILENAME)\n2725: print(f\"Model '{model_type}' is missing. Either install it in the finetune folder or remove this model from ley 'transformer_types' in {CONFIG_FILENAME}\")","numLines":6},"_audit_timestamp":"2026-04-19T22:07:55.769Z","_audit_hmac":"cd077f2b5f984079cb78008bd39ae0132b95732c5cd5f3e75c0b7ec45066531e"} {"type":"system","subtype":"status","status":"requesting","uuid":"b296581d-8946-469b-8dd2-f1d60d3ba7a4","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:55.774Z","_audit_hmac":"2507c7f196449d3576ed6be4fe5571211bda117f3095d3505bb4e39fe2ff2b7d"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01MCcTXhgmvqWqSLbL8YGz7X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011ySgvEqWqZcEy7o1yxCyDA","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","offset":2090,"limit":120},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":383,"cache_read_input_tokens":75476,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":383},"output_tokens":61,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"4dce500d-8e35-46e7-ab5b-f8e5628936e2","_audit_timestamp":"2026-04-19T22:07:59.405Z","_audit_hmac":"9fddb9eb0352caa392e3aae6e09e2285d6387dd9d399c76f261252d032737d10"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011ySgvEqWqZcEy7o1yxCyDA","type":"tool_result","content":"2090\t \n2091\t\n2092\tif not Path(config_load_filename).is_file():\n2093\t server_config = {\n2094\t \"attention_mode\" : \"auto\", \n2095\t \"transformer_types\": [], \n2096\t \"transformer_quantization\": \"int8\",\n2097\t \"text_encoder_quantization\" : \"int8\",\n2098\t \"lm_decoder_engine\": \"\",\n2099\t \"save_path\": \"outputs\", \n2100\t \"image_save_path\": \"outputs\", \n2101\t \"compile\" : \"\",\n2102\t \"metadata_type\": \"metadata\",\n2103\t \"boost\" : 1,\n2104\t \"enable_int8_kernels\": 1,\n2105\t \"clear_file_list\" : 5,\n2106\t \"keep_intermediate_sliding_windows\": 1,\n2107\t \"enable_4k_resolutions\": 0,\n2108\t \"max_reserved_loras\": -1,\n2109\t \"vae_config\": 0,\n2110\t \"profile\" : profile_type.LowRAM_LowVRAM,\n2111\t \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112\t \"image_profile\": profile_type.LowRAM_LowVRAM,\n2113\t \"audio_profile\": 3.5,\n2114\t \"preload_model_policy\": [],\n2115\t \"UI_theme\": \"default\",\n2116\t \"checkpoints_paths\": fl.default_checkpoints_paths,\n2117\t \"loras_root\": DEFAULT_LORA_ROOT,\n2118\t \"save_queue_if_crash\": 1,\n2119\t \"queue_color_scheme\": \"pastel\",\n2120\t \"process_queues_when_browser_unfocused\": 1,\n2121\t \"model_hierarchy_type\": 1,\n2122\t \"mmaudio_mode\": 0,\n2123\t \"mmaudio_persistence\": 1,\n2124\t \"rife_version\": \"v4\",\n2125\t **get_deepy_default_runtime_config(),\n2126\t \"prompt_enhancer_quantization\": \"quanto_int8\",\n2127\t \"prompt_enhancer_temperature\": 0.6,\n2128\t \"prompt_enhancer_top_p\": 0.9,\n2129\t \"prompt_enhancer_randomize_seed\": True,\n2130\t \"audio_save_path\": \"outputs\",\n2131\t }\n2132\t\n2133\t with open(server_config_filename, \"w\", encoding=\"utf-8\") as writer:\n2134\t writer.write(json.dumps(server_config))\n2135\telse:\n2136\t with open(config_load_filename, \"r\", encoding=\"utf-8\") as reader:\n2137\t text = reader.read()\n2138\t server_config = json.loads(text)\n2139\t\n2140\tserver_config.setdefault(\"prompt_enhancer_quantization\", \"quanto_int8\")\n2141\tserver_config.setdefault(gradio_queue_focus_patch.FOCUS_QUEUE_SERVER_CONFIG_KEY, 1)\n2142\tgradio_queue_focus_patch.BACKGROUND_SCHEDULER_DEFAULT_ENABLED = bool(server_config.get(gradio_queue_focus_patch.FOCUS_QUEUE_SERVER_CONFIG_KEY, 1))\n2143\tgradio_queue_focus_patch.install()\n2144\t\n2145\tcheckpoints_paths = server_config.get(\"checkpoints_paths\", None)\n2146\tif checkpoints_paths is None: checkpoints_paths = server_config[\"checkpoints_paths\"] = fl.default_checkpoints_paths\n2147\tfl.set_checkpoints_paths(checkpoints_paths)\n2148\tthree_levels_hierarchy = server_config.get(\"model_hierarchy_type\", 1) == 1\n2149\t\n2150\tMMAUDIO_MODE_OFF = 0\n2151\tMMAUDIO_MODE_V2 = 1\n2152\tMMAUDIO_MODE_NEW = 2\n2153\tMMAUDIO_PERSIST_UNLOAD = 1\n2154\tMMAUDIO_PERSIST_RAM = 2\n2155\tMMAUDIO_STANDARD = \"mmaudio_large_44k_v2.pth\"\n2156\tMMAUDIO_ALTERNATE = \"mmaudio_large_44k_gold_8.5k_final_fp16.safetensors\"\n2157\t\n2158\tdef _normalize_mmaudio_config(config):\n2159\t mode = config.get(\"mmaudio_mode\", None)\n2160\t persistence = config.get(\"mmaudio_persistence\", None)\n2161\t if mode is None:\n2162\t old = config.get(\"mmaudio_enabled\", 0)\n2163\t mode = MMAUDIO_MODE_OFF if old == 0 else MMAUDIO_MODE_V2\n2164\t if persistence is None:\n2165\t old = config.get(\"mmaudio_enabled\", 0)\n2166\t persistence = MMAUDIO_PERSIST_RAM if old == MMAUDIO_PERSIST_RAM else MMAUDIO_PERSIST_UNLOAD\n2167\t if mode not in (MMAUDIO_MODE_OFF, MMAUDIO_MODE_V2, MMAUDIO_MODE_NEW):\n2168\t mode = MMAUDIO_MODE_OFF\n2169\t if persistence not in (MMAUDIO_PERSIST_UNLOAD, MMAUDIO_PERSIST_RAM):\n2170\t persistence = MMAUDIO_PERSIST_UNLOAD\n2171\t config[\"mmaudio_mode\"] = mode\n2172\t config[\"mmaudio_persistence\"] = persistence\n2173\t return mode, persistence\n2174\t\n2175\tdef get_mmaudio_settings(config):\n2176\t mode, persistence = _normalize_mmaudio_config(config)\n2177\t enabled = mode != MMAUDIO_MODE_OFF\n2178\t if mode == MMAUDIO_MODE_V2:\n2179\t model_name = \"large_44k_v2\"\n2180\t model_path = MMAUDIO_STANDARD\n2181\t elif mode == MMAUDIO_MODE_NEW:\n2182\t model_name = \"large_44k\"\n2183\t model_path = MMAUDIO_ALTERNATE\n2184\t else:\n2185\t model_name = None\n2186\t model_path = None\n2187\t return enabled, mode, persistence, model_name, model_path\n2188\t\n2189\t_normalize_mmaudio_config(server_config)\n2190\t\n2191\tdef _normalize_profile_defaults(config):\n2192\t if \"profile\" not in config:\n2193\t config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194\t base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n2195\t config.setdefault(\"video_profile\", base_profile)\n2196\t config.setdefault(\"image_profile\", base_profile)\n2197\t config.setdefault(\"audio_profile\", 3.5)\n2198\t return config[\"video_profile\"], config[\"image_profile\"], config[\"audio_profile\"]\n2199\t\n2200\tdef _normalize_output_paths(config):\n2201\t if \"save_path\" not in config:\n2202\t config[\"save_path\"] = \"outputs\"\n2203\t if \"image_save_path\" not in config:\n2204\t config[\"image_save_path\"] = config[\"save_path\"]\n2205\t if \"audio_save_path\" not in config:\n2206\t config[\"audio_save_path\"] = config[\"save_path\"]\n2207\t\n2208\t_normalize_profile_defaults(server_config)\n2209\t_normalize_output_paths(server_config)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"ef60bc4f-4cc2-4aa6-a5f4-b5e0f5526c0b","timestamp":"2026-04-19T22:07:59.415Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","content":" \n\nif not Path(config_load_filename).is_file():\n server_config = {\n \"attention_mode\" : \"auto\", \n \"transformer_types\": [], \n \"transformer_quantization\": \"int8\",\n \"text_encoder_quantization\" : \"int8\",\n \"lm_decoder_engine\": \"\",\n \"save_path\": \"outputs\", \n \"image_save_path\": \"outputs\", \n \"compile\" : \"\",\n \"metadata_type\": \"metadata\",\n \"boost\" : 1,\n \"enable_int8_kernels\": 1,\n \"clear_file_list\" : 5,\n \"keep_intermediate_sliding_windows\": 1,\n \"enable_4k_resolutions\": 0,\n \"max_reserved_loras\": -1,\n \"vae_config\": 0,\n \"profile\" : profile_type.LowRAM_LowVRAM,\n \"video_profile\": profile_type.LowRAM_LowVRAM,\n \"image_profile\": profile_type.LowRAM_LowVRAM,\n \"audio_profile\": 3.5,\n \"preload_model_policy\": [],\n \"UI_theme\": \"default\",\n \"checkpoints_paths\": fl.default_checkpoints_paths,\n \"loras_root\": DEFAULT_LORA_ROOT,\n \"save_queue_if_crash\": 1,\n \"queue_color_scheme\": \"pastel\",\n \"process_queues_when_browser_unfocused\": 1,\n \"model_hierarchy_type\": 1,\n \"mmaudio_mode\": 0,\n \"mmaudio_persistence\": 1,\n \"rife_version\": \"v4\",\n **get_deepy_default_runtime_config(),\n \"prompt_enhancer_quantization\": \"quanto_int8\",\n \"prompt_enhancer_temperature\": 0.6,\n \"prompt_enhancer_top_p\": 0.9,\n \"prompt_enhancer_randomize_seed\": True,\n \"audio_save_path\": \"outputs\",\n }\n\n with open(server_config_filename, \"w\", encoding=\"utf-8\") as writer:\n writer.write(json.dumps(server_config))\nelse:\n with open(config_load_filename, \"r\", encoding=\"utf-8\") as reader:\n text = reader.read()\n server_config = json.loads(text)\n\nserver_config.setdefault(\"prompt_enhancer_quantization\", \"quanto_int8\")\nserver_config.setdefault(gradio_queue_focus_patch.FOCUS_QUEUE_SERVER_CONFIG_KEY, 1)\ngradio_queue_focus_patch.BACKGROUND_SCHEDULER_DEFAULT_ENABLED = bool(server_config.get(gradio_queue_focus_patch.FOCUS_QUEUE_SERVER_CONFIG_KEY, 1))\ngradio_queue_focus_patch.install()\n\ncheckpoints_paths = server_config.get(\"checkpoints_paths\", None)\nif checkpoints_paths is None: checkpoints_paths = server_config[\"checkpoints_paths\"] = fl.default_checkpoints_paths\nfl.set_checkpoints_paths(checkpoints_paths)\nthree_levels_hierarchy = server_config.get(\"model_hierarchy_type\", 1) == 1\n\nMMAUDIO_MODE_OFF = 0\nMMAUDIO_MODE_V2 = 1\nMMAUDIO_MODE_NEW = 2\nMMAUDIO_PERSIST_UNLOAD = 1\nMMAUDIO_PERSIST_RAM = 2\nMMAUDIO_STANDARD = \"mmaudio_large_44k_v2.pth\"\nMMAUDIO_ALTERNATE = \"mmaudio_large_44k_gold_8.5k_final_fp16.safetensors\"\n\ndef _normalize_mmaudio_config(config):\n mode = config.get(\"mmaudio_mode\", None)\n persistence = config.get(\"mmaudio_persistence\", None)\n if mode is None:\n old = config.get(\"mmaudio_enabled\", 0)\n mode = MMAUDIO_MODE_OFF if old == 0 else MMAUDIO_MODE_V2\n if persistence is None:\n old = config.get(\"mmaudio_enabled\", 0)\n persistence = MMAUDIO_PERSIST_RAM if old == MMAUDIO_PERSIST_RAM else MMAUDIO_PERSIST_UNLOAD\n if mode not in (MMAUDIO_MODE_OFF, MMAUDIO_MODE_V2, MMAUDIO_MODE_NEW):\n mode = MMAUDIO_MODE_OFF\n if persistence not in (MMAUDIO_PERSIST_UNLOAD, MMAUDIO_PERSIST_RAM):\n persistence = MMAUDIO_PERSIST_UNLOAD\n config[\"mmaudio_mode\"] = mode\n config[\"mmaudio_persistence\"] = persistence\n return mode, persistence\n\ndef get_mmaudio_settings(config):\n mode, persistence = _normalize_mmaudio_config(config)\n enabled = mode != MMAUDIO_MODE_OFF\n if mode == MMAUDIO_MODE_V2:\n model_name = \"large_44k_v2\"\n model_path = MMAUDIO_STANDARD\n elif mode == MMAUDIO_MODE_NEW:\n model_name = \"large_44k\"\n model_path = MMAUDIO_ALTERNATE\n else:\n model_name = None\n model_path = None\n return enabled, mode, persistence, model_name, model_path\n\n_normalize_mmaudio_config(server_config)\n\ndef _normalize_profile_defaults(config):\n if \"profile\" not in config:\n config[\"profile\"] = profile_type.LowRAM_LowVRAM\n base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n config.setdefault(\"video_profile\", base_profile)\n config.setdefault(\"image_profile\", base_profile)\n config.setdefault(\"audio_profile\", 3.5)\n return config[\"video_profile\"], config[\"image_profile\"], config[\"audio_profile\"]\n\ndef _normalize_output_paths(config):\n if \"save_path\" not in config:\n config[\"save_path\"] = \"outputs\"\n if \"image_save_path\" not in config:\n config[\"image_save_path\"] = config[\"save_path\"]\n if \"audio_save_path\" not in config:\n config[\"audio_save_path\"] = config[\"save_path\"]\n\n_normalize_profile_defaults(server_config)\n_normalize_output_paths(server_config)","numLines":120,"startLine":2090,"totalLines":11997}},"_audit_timestamp":"2026-04-19T22:07:59.701Z","_audit_hmac":"fae2899f8926b9915f85de92b2102bc866c4accec30ee742b6f00402a74e7861"} {"type":"system","subtype":"status","status":"requesting","uuid":"7af96551-61f9-4771-9186-c7fadee3f3c2","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:07:59.729Z","_audit_hmac":"9a465e4ae687c92a3bb7c0fe3fa3bac72987cfc53547da332df535b18b20aef6"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01KCZzRGgyUcPSEaMjQWx8C9","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi `profile_type` değerlerini görelim:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2206,"cache_read_input_tokens":75859,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2206},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"1001478e-74d9-43d1-9c66-899e0088ed31","_audit_timestamp":"2026-04-19T22:08:02.470Z","_audit_hmac":"934ff186805303379d92e3e31c91f27f63cfaa4071ee929c26d683ee51866f7f"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01KCZzRGgyUcPSEaMjQWx8C9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MqAqmRrqtPAPHSHhgb78wD","name":"Grep","input":{"pattern":"LowRAM_LowVRAM|HighRAM|profile_type","path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","output_mode":"content","-n":true,"head_limit":10},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":2206,"cache_read_input_tokens":75859,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2206},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"4ccefe1f-a85b-410a-a4d4-a240b22a7039","_audit_timestamp":"2026-04-19T22:08:04.003Z","_audit_hmac":"17c690421d8deed81979eba89d96e00afb708bf97fea617340d227fe54f201c2"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MqAqmRrqtPAPHSHhgb78wD","type":"tool_result","content":"26:from mmgp import offload, safetensors2, profile_type , quant_router\n2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n9462:memory_profile_choices= [ (\"Profile 1, HighRAM_HighVRAM: at least 64 GB of RAM and 24 GB of VRAM, the fastest for short videos with a RTX 3090 / RTX 4090\", 1),\n9463: (\"Profile 2, HighRAM_LowVRAM: at least 64 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos\", 2),\n9466: (\"Profile 4, LowRAM_LowVRAM (Recommended): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos\",4),\n9467: (\"Profile 4+, LowRAM_LowVRAM+: at least 32 GB of RAM and 12 GB of VRAM, variant of Profile 4, slightly slower but needs less VRAM\",4.5),"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3ce6e44f-1f63-4a2c-8914-2176fab7c7f1","timestamp":"2026-04-19T22:08:04.032Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"26:from mmgp import offload, safetensors2, profile_type , quant_router\n2110: \"profile\" : profile_type.LowRAM_LowVRAM,\n2111: \"video_profile\": profile_type.LowRAM_LowVRAM,\n2112: \"image_profile\": profile_type.LowRAM_LowVRAM,\n2193: config[\"profile\"] = profile_type.LowRAM_LowVRAM\n2194: base_profile = config.get(\"profile\", profile_type.LowRAM_LowVRAM)\n9462:memory_profile_choices= [ (\"Profile 1, HighRAM_HighVRAM: at least 64 GB of RAM and 24 GB of VRAM, the fastest for short videos with a RTX 3090 / RTX 4090\", 1),\n9463: (\"Profile 2, HighRAM_LowVRAM: at least 64 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos\", 2),\n9466: (\"Profile 4, LowRAM_LowVRAM (Recommended): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos\",4),\n9467: (\"Profile 4+, LowRAM_LowVRAM+: at least 32 GB of RAM and 12 GB of VRAM, variant of Profile 4, slightly slower but needs less VRAM\",4.5),","numLines":10},"_audit_timestamp":"2026-04-19T22:08:04.034Z","_audit_hmac":"29cbba9e79e5f7318c9f9577ce30b1a7efca46ef6eb8f60b45117627f9da009c"} {"type":"system","subtype":"status","status":"requesting","uuid":"4257fabf-6302-47cd-a318-bcbfbebbd061","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:08:04.043Z","_audit_hmac":"f6bbc54eac8883ed0624808a44fb8f8326e5132e36b6b433619f953dcf417b03"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01VWeVMwxbagtK3CHoZkqnAV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HjmwRkBNYjxhRorVwbWZqg","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","offset":9462,"limit":15},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":681,"cache_read_input_tokens":78065,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":681},"output_tokens":72,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"d4289b6d-f2a9-4513-880d-67d4a960a825","_audit_timestamp":"2026-04-19T22:08:09.717Z","_audit_hmac":"7a0de1e2090b4491c20b41063f55706056ed0e3df80f2473888c20bffc3c25eb"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HjmwRkBNYjxhRorVwbWZqg","type":"tool_result","content":"9462\tmemory_profile_choices= [ (\"Profile 1, HighRAM_HighVRAM: at least 64 GB of RAM and 24 GB of VRAM, the fastest for short videos with a RTX 3090 / RTX 4090\", 1),\n9463\t (\"Profile 2, HighRAM_LowVRAM: at least 64 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos\", 2),\n9464\t (\"Profile 3, LowRAM_HighVRAM: at least 32 GB of RAM and 24 GB of VRAM, adapted for RTX 3090 / RTX 4090 with limited RAM for good speed short video\",3),\n9465\t (\"Profile 3+, VeryLowRAM_HighVRAM: at least 32 GB of RAM and 24 GB of VRAM, variant of Profile 3 that won't used Reserved Memory to reduce RAM usage\",3.5),\n9466\t (\"Profile 4, LowRAM_LowVRAM (Recommended): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos\",4),\n9467\t (\"Profile 4+, LowRAM_LowVRAM+: at least 32 GB of RAM and 12 GB of VRAM, variant of Profile 4, slightly slower but needs less VRAM\",4.5),\n9468\t (\"Profile 5, VerylowRAM_LowVRAM (Fail safe): at least 24 GB of RAM and 10 GB of VRAM, if you don't have much it won't be fast but maybe it will work\",5)]\n9469\t\n9470\tdef check_attn(mode):\n9471\t if mode not in attention_modes_installed: return \" (NOT INSTALLED)\"\n9472\t if mode not in attention_modes_supported: return \" (NOT SUPPORTED)\"\n9473\t return \"\"\n9474\t\n9475\tattention_modes_choices= [\n9476\t (\"Auto: Best available (sage2 > sage > sdpa)\", \"auto\"),"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"e136cd3f-0b1b-4c5e-a6d9-a1a5c8b1f6f5","timestamp":"2026-04-19T22:08:09.728Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp.py","content":"memory_profile_choices= [ (\"Profile 1, HighRAM_HighVRAM: at least 64 GB of RAM and 24 GB of VRAM, the fastest for short videos with a RTX 3090 / RTX 4090\", 1),\n (\"Profile 2, HighRAM_LowVRAM: at least 64 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos\", 2),\n (\"Profile 3, LowRAM_HighVRAM: at least 32 GB of RAM and 24 GB of VRAM, adapted for RTX 3090 / RTX 4090 with limited RAM for good speed short video\",3),\n (\"Profile 3+, VeryLowRAM_HighVRAM: at least 32 GB of RAM and 24 GB of VRAM, variant of Profile 3 that won't used Reserved Memory to reduce RAM usage\",3.5),\n (\"Profile 4, LowRAM_LowVRAM (Recommended): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos\",4),\n (\"Profile 4+, LowRAM_LowVRAM+: at least 32 GB of RAM and 12 GB of VRAM, variant of Profile 4, slightly slower but needs less VRAM\",4.5),\n (\"Profile 5, VerylowRAM_LowVRAM (Fail safe): at least 24 GB of RAM and 10 GB of VRAM, if you don't have much it won't be fast but maybe it will work\",5)]\n\ndef check_attn(mode):\n if mode not in attention_modes_installed: return \" (NOT INSTALLED)\"\n if mode not in attention_modes_supported: return \" (NOT SUPPORTED)\"\n return \"\"\n\nattention_modes_choices= [\n (\"Auto: Best available (sage2 > sage > sdpa)\", \"auto\"),","numLines":15,"startLine":9462,"totalLines":11997}},"_audit_timestamp":"2026-04-19T22:08:09.745Z","_audit_hmac":"441c9ef32c3fe77e37960c3a61b2726b655e42e5567295206949b05e55b94076"} {"type":"system","subtype":"status","status":"requesting","uuid":"e42809ec-0633-457c-b5e8-50dbd343ce4b","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:08:09.767Z","_audit_hmac":"e323aebd8953bff4b089fe12d162188012cce7cf73fd0070bf278433bb1df73d"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01QD6VDmKHY2ER1d2b7hRCqG","type":"message","role":"assistant","content":[{"type":"text","text":"Mükemmel! Şimdi tam resmi gördüm. WanGP'nin profil sistemi:\n\n- **Profil 1**: HighRAM_HighVRAM (64GB RAM + 24GB VRAM) — en hızlı\n- **Profil 2**: HighRAM_LowVRAM (64GB RAM + 12GB VRAM) — RAM'e offload, en esnek\n- **Profil 3**: LowRAM_HighVRAM (32GB RAM + 24GB VRAM)\n- **Profil 4**: LowRAM_LowVRAM (32GB RAM + 12GB VRAM) — önerilen\n- **Profil 5**: VeryLowRAM_LowVRAM (24GB RAM + 10GB VRAM) — en yavaş ama çalışır\n\nSenin RTX 4090 (24GB VRAM) + 64GB RAM'in var. Daha önce Profil 1'de çalışıyordu, şimdi sorun yaşıyorsun. **Profil 2'ye** geçmek en mantıklı çözüm — 64GB RAM'in olduğu için modeli RAM'e taşıyıp VRAM'i rahatlatır.\n\nŞimdi hem `wgp_config.json`'u bulup profili kontrol edeyim, hem de scriptleri güncelleyeyim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":755,"cache_read_input_tokens":78746,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":755},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"156bbc8b-b489-47f7-b24e-59010ef3f6fc","_audit_timestamp":"2026-04-19T22:08:20.642Z","_audit_hmac":"fe103fa471740a16a4673cf29003e65fb2c68fec86f570f9c8d5b8a5e2282184"} 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wgp_config.json"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":755,"cache_read_input_tokens":78746,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":755},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"d42bafbc-54e3-4d4c-a85b-8a81d8f94b4f","_audit_timestamp":"2026-04-19T22:08:21.581Z","_audit_hmac":"a2665a0550c478410232ae912e9024b107086aa299525989d9d1f3dfd7464b2d"} 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{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016pf6YkUrohcE9RHL47Nsko","type":"tool_result","content":"1\t{\n2\t \"attention_mode\": \"sage2\",\n3\t \"compile\": \"transformer\",\n4\t \"video_profile\": 4,\n5\t \"image_profile\": 4,\n6\t \"audio_profile\": 4\n7\t}\n8\t"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"e1a1836a-2df2-4341-b3d8-2c81433c4841","timestamp":"2026-04-19T22:08:28.153Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json","content":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 4,\n \"image_profile\": 4,\n \"audio_profile\": 4\n}\n","numLines":8,"startLine":1,"totalLines":8}},"_audit_timestamp":"2026-04-19T22:08:28.207Z","_audit_hmac":"ca69a4b5491d6acfd24859202ce8c3b5b7fc2a704cacc14027e8dc4acb7f3362"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"4b51ecd3-89ab-4bc5-b6d0-dc548ce33ebc","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:08:28.240Z","_audit_hmac":"35b9906bcb28ab4d3e4aec4597f814cfa8015cff919aa97891305c3a9bbe342b"} {"type":"system","subtype":"status","status":"requesting","uuid":"3a68b3f9-2f4e-4024-84cb-b50725545888","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:08:28.283Z","_audit_hmac":"125eec06ee17ba4c117a6ea85cb4c0d3fc82c94df5ad60714a8e68397d700125"} {"type":"user","uuid":"ce99c7b2-1237-4065-acc4-2e78d1d7c1f3","session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"message":{"role":"user","content":"profil 3 yap"},"_audit_timestamp":"2026-04-19T22:08:44.527Z","_audit_hmac":"23436e8a39efdc22447d185ef06072372656842f0ee2c6b4ee6a132135713161"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WmYo9gcbFsNjLqKcUx7Lin","type":"message","role":"assistant","content":[{"type":"text","text":"İşte sorunun kaynağı! Profil **4** olarak ayarlanmış — bu `LowRAM_LowVRAM` profili (32GB RAM + 12GB VRAM). Ama senin 64GB RAM + 24GB VRAM (RTX 4090) var. **Profil 2'ye** geçmek modelin büyük kısmını RAM'e taşıyıp VRAM'i rahatlatacak, 64GB RAM'in bu iş için ideal.\n\nŞimdi tüm değişiklikleri yapıyorum:\n\n1. `wgp_config.json` → profili 2'ye çevirelim\n2. `wangp_video_uret.py` → `--mmgp-profile` parametresi ekleyelim\n3. `WanGP-Video-Uret.bat` → profil seçeneği ekleyelim\n4. `LTX-Desktop-WanGP-Baslat.bat` → `--profile` parametresi ekleyelim"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":208,"cache_read_input_tokens":80237,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":208},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6b921e35-df7e-4ef2-a641-bcb7242f0962","_audit_timestamp":"2026-04-19T22:09:02.692Z","_audit_hmac":"2d00c3ae96d1fa339ca278122fc6b70d1b87f0519adb0df11e8bbdebdef6bd3b"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WmYo9gcbFsNjLqKcUx7Lin","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QfAmDYHRxNDHCY9iUGTGUp","name":"Edit","input":{"replace_all":false,"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json","old_string":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 4,\n \"image_profile\": 4,\n \"audio_profile\": 4\n}","new_string":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 2,\n \"image_profile\": 2,\n \"audio_profile\": 2\n}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":208,"cache_read_input_tokens":80237,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":208},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"bd7f89b2-7dca-465f-a361-3fe650ca4d30","_audit_timestamp":"2026-04-19T22:09:04.092Z","_audit_hmac":"1ee180d8f98b17c5c7c9292e0581ce7b298be62811a18d51568af5cd710856fe"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QfAmDYHRxNDHCY9iUGTGUp","type":"tool_result","content":"The file /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6232c0fd-76da-401e-ac35-0a2442ae831f","timestamp":"2026-04-19T22:09:04.120Z","tool_use_result":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json","oldString":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 4,\n \"image_profile\": 4,\n \"audio_profile\": 4\n}","newString":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 2,\n \"image_profile\": 2,\n \"audio_profile\": 2\n}","originalFile":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 4,\n \"image_profile\": 4,\n \"audio_profile\": 4\n}\n","structuredPatch":[{"oldStart":1,"oldLines":7,"newStart":1,"newLines":7,"lines":[" {"," \"attention_mode\": \"sage2\","," \"compile\": \"transformer\",","- \"video_profile\": 4,","- \"image_profile\": 4,","- \"audio_profile\": 4","+ \"video_profile\": 2,","+ \"image_profile\": 2,","+ \"audio_profile\": 2"," }"]}],"userModified":false,"replaceAll":false},"_audit_timestamp":"2026-04-19T22:09:04.125Z","_audit_hmac":"1cd37e49ea0221a5cebecd46b78eeb13770049e196699ab73319cd5d05ac37af"} {"type":"user","message":{"role":"user","content":"profil 3 yap"},"session_id":"432fa85e-5db5-4830-9283-9556969b5029","parent_tool_use_id":null,"uuid":"ce99c7b2-1237-4065-acc4-2e78d1d7c1f3","timestamp":"2026-04-19T22:09:04.131Z","isReplay":true,"_audit_timestamp":"2026-04-19T22:09:04.132Z","_audit_hmac":"b6e59c4ecca9aeea2346670e785cef3db371f2762e201f256ba8a66a90d6f1ef"} {"type":"system","subtype":"status","status":"requesting","uuid":"4843b17d-d74f-40af-805f-14a1bbeb1071","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:04.136Z","_audit_hmac":"5e1990e6f1b135d3888e45dd717e032acd215516206fa150d2d72b9c646cba26"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01STud6DGYtdrmvvGq3ENE3X","type":"message","role":"assistant","content":[{"type":"text","text":"Kullanıcı profil 3 istiyor. Hemen değiştiriyorum."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":717,"cache_read_input_tokens":80445,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":717},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"3e7d791f-71c0-42c6-b80e-7616cbd70002","_audit_timestamp":"2026-04-19T22:09:07.490Z","_audit_hmac":"79b1de4bb538217f1ab93bb3e7d04b37f906b28612cdf57448157656a24d1bf5"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01STud6DGYtdrmvvGq3ENE3X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MSJahXPCHrmwzwvdw85GVP","name":"Edit","input":{"replace_all":false,"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json","old_string":" \"video_profile\": 2,\n \"image_profile\": 2,\n \"audio_profile\": 2","new_string":" \"video_profile\": 3,\n \"image_profile\": 3,\n \"audio_profile\": 3"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":717,"cache_read_input_tokens":80445,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":717},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"46f85eb3-e52f-4789-b02b-37d3388b8105","_audit_timestamp":"2026-04-19T22:09:08.721Z","_audit_hmac":"09ac9fadbaf53aec3bad589e21362615ee854f1566e508ae62693e11b29fd5ce"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MSJahXPCHrmwzwvdw85GVP","type":"tool_result","content":"The file /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"2486efe3-d6fe-420c-b86f-393b1a6b6a94","timestamp":"2026-04-19T22:09:08.743Z","tool_use_result":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP/LTX-Desktop-WanGP/Wan2GP/wgp_config.json","oldString":" \"video_profile\": 2,\n \"image_profile\": 2,\n \"audio_profile\": 2","newString":" \"video_profile\": 3,\n \"image_profile\": 3,\n \"audio_profile\": 3","originalFile":"{\n \"attention_mode\": \"sage2\",\n \"compile\": \"transformer\",\n \"video_profile\": 2,\n \"image_profile\": 2,\n \"audio_profile\": 2\n}\n","structuredPatch":[{"oldStart":1,"oldLines":7,"newStart":1,"newLines":7,"lines":[" {"," \"attention_mode\": \"sage2\","," \"compile\": \"transformer\",","- \"video_profile\": 2,","- \"image_profile\": 2,","- \"audio_profile\": 2","+ \"video_profile\": 3,","+ \"image_profile\": 3,","+ \"audio_profile\": 3"," }"]}],"userModified":false,"replaceAll":false},"_audit_timestamp":"2026-04-19T22:09:08.753Z","_audit_hmac":"81da2484e35ea02c86b636b735ae91fb8ea827504641a62e4dade9e3ca84ef20"} {"type":"system","subtype":"status","status":"requesting","uuid":"efc26ca1-b8b3-47aa-ad21-9019087ea75b","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:08.760Z","_audit_hmac":"a5007a280e5fb2e0266079f2a381055706702872db5cacf898b93b1d94640932"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01RPUhJUQCdXqHUe7hXMEokv","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi `LTX-Desktop-WanGP-Baslat.bat`'a profil seçimi ekleyeyim ve `WanGP-Video-Uret.bat`'a da MMGP profil seçeneği ekleyeyim."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":375,"cache_read_input_tokens":81162,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":375},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"b9c0b3a2-a407-488e-a901-9e181a01233e","_audit_timestamp":"2026-04-19T22:09:14.110Z","_audit_hmac":"2972b60ae763e98ccd810644df37aaff99bd347c71bd8a74fc3bb320dbb12996"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01RPUhJUQCdXqHUe7hXMEokv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T3f1WJJ9nuREk4kNJ47Mdj","name":"Edit","input":{"replace_all":false,"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat","old_string":"cd /d \"%REPO%\"\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev","new_string":"cd /d \"%REPO%\"\n\nREM ============================================================\nREM MMGP PROFIL SECIMI - CPU RAM Offload Ayari\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho MMGP BELLEK PROFILI - Modeli RAM'e tasima ayari\necho ------------------------------------------------------------\necho.\necho 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)\necho En hizli, kisa videolar icin ideal\necho.\necho 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)\necho RAM'e offload, uzun video ve buyuk batch icin\necho.\necho 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)\necho VRAM agirlikli, iyi hiz [MEVCUT AYAR]\necho.\necho 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)\necho Dusuk bellek, onerilen genel profil\necho.\necho 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)\necho En az bellek, yavas ama calisir\necho.\necho Enter = Mevcut ayari kullan (config'den)\necho ------------------------------------------------------------\necho.\nset /p MMGP_PROFIL=\" Profil secin [1-5, Enter=mevcut]: \"\n\nset PROFILE_ARG=\nif \"%MMGP_PROFIL%\"==\"1\" set PROFILE_ARG=--profile 1\nif \"%MMGP_PROFIL%\"==\"2\" set PROFILE_ARG=--profile 2\nif \"%MMGP_PROFIL%\"==\"3\" set PROFILE_ARG=--profile 3\nif \"%MMGP_PROFIL%\"==\"4\" set PROFILE_ARG=--profile 4\nif \"%MMGP_PROFIL%\"==\"5\" set PROFILE_ARG=--profile 5\n\nif not \"%PROFILE_ARG%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...\n) else (\n echo [*] Config dosyasindaki profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\nREM MMGP profil argumani WanGP backend'e iletilir\nif not \"%PROFILE_ARG%\"==\"\" (\n set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"\n)\n\ncall pnpm dev"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":375,"cache_read_input_tokens":81162,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":375},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"9414196c-cc80-4d4b-8af4-cc4f63777de6","_audit_timestamp":"2026-04-19T22:09:28.673Z","_audit_hmac":"77ef24b043f93f8d6ea1ab0f41905a7102557db6b4e3682f58461c3a5f9d448a"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T3f1WJJ9nuREk4kNJ47Mdj","type":"tool_result","content":"The file /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"8e0b0fa6-dccc-4d5b-a212-65ff8bbabc97","timestamp":"2026-04-19T22:09:28.697Z","tool_use_result":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat","oldString":"cd /d \"%REPO%\"\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev","newString":"cd /d \"%REPO%\"\n\nREM ============================================================\nREM MMGP PROFIL SECIMI - CPU RAM Offload Ayari\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho MMGP BELLEK PROFILI - Modeli RAM'e tasima ayari\necho ------------------------------------------------------------\necho.\necho 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)\necho En hizli, kisa videolar icin ideal\necho.\necho 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)\necho RAM'e offload, uzun video ve buyuk batch icin\necho.\necho 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)\necho VRAM agirlikli, iyi hiz [MEVCUT AYAR]\necho.\necho 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)\necho Dusuk bellek, onerilen genel profil\necho.\necho 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)\necho En az bellek, yavas ama calisir\necho.\necho Enter = Mevcut ayari kullan (config'den)\necho ------------------------------------------------------------\necho.\nset /p MMGP_PROFIL=\" Profil secin [1-5, Enter=mevcut]: \"\n\nset PROFILE_ARG=\nif \"%MMGP_PROFIL%\"==\"1\" set PROFILE_ARG=--profile 1\nif \"%MMGP_PROFIL%\"==\"2\" set PROFILE_ARG=--profile 2\nif \"%MMGP_PROFIL%\"==\"3\" set PROFILE_ARG=--profile 3\nif \"%MMGP_PROFIL%\"==\"4\" set PROFILE_ARG=--profile 4\nif \"%MMGP_PROFIL%\"==\"5\" set PROFILE_ARG=--profile 5\n\nif not \"%PROFILE_ARG%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...\n) else (\n echo [*] Config dosyasindaki profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\nREM MMGP profil argumani WanGP backend'e iletilir\nif not \"%PROFILE_ARG%\"==\"\" (\n set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"\n)\n\ncall pnpm dev","originalFile":"@echo off\nchcp 65001 >nul\ntitle LTX-Desktop-WanGP - Video AI Studio\ncolor 0B\n\nREM ============================================================\nREM LTX-Desktop-WanGP Baslatma Scripti\nREM Electron frontend + WanGP backend birlikte calisir\nREM ============================================================\n\nset \"KOK=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\"\nset \"LTX=%KOK%\\ltx-desktop-wangp\"\nset \"REPO=%LTX%\\LTX-Desktop-WanGP\"\n\nREM Tum cache ve modeller ltx-desktop-wangp klasoru icinde kalsin\nset \"HF_HOME=%LTX%\\hf-cache\"\nset \"HUGGINGFACE_HUB_CACHE=%LTX%\\hf-cache\"\nset \"TRANSFORMERS_CACHE=%LTX%\\hf-cache\"\nset \"TORCH_HOME=%LTX%\\torch-cache\"\nset \"XDG_CACHE_HOME=%LTX%\\xdg-cache\"\n\necho =========================================================\necho LTX-Desktop-WanGP Baslatiliyor...\necho Video uretim arayuzu yuklenecek\necho =========================================================\necho.\necho Klasor : %REPO%\necho Cache : %HF_HOME%\necho.\n\nREM --- Repo kontrolu ---\nif not exist \"%REPO%\" (\n color 0C\n echo [HATA] LTX-Desktop-WanGP kaynak kodu bulunamadi!\n echo Beklenen: %REPO%\n echo.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- package.json kontrolu ---\nif not exist \"%REPO%\\package.json\" (\n color 0C\n echo [HATA] package.json bulunamadi!\n echo Kurulum eksik olabilir.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- pnpm kontrolu ---\nwhere pnpm >nul 2>&1\nif errorlevel 1 (\n color 0C\n echo [HATA] pnpm bulunamadi!\n echo Lutfen once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin\n echo veya \"npm install -g pnpm\" komutu ile pnpm kurun.\n pause\n exit /b 1\n)\n\ncd /d \"%REPO%\"\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev\n\npause\n","structuredPatch":[{"oldStart":62,"oldLines":10,"newStart":62,"newLines":57,"lines":[" "," cd /d \"%REPO%\""," ","+REM ============================================================","+REM MMGP PROFIL SECIMI - CPU RAM Offload Ayari","+REM ============================================================","+echo.","+echo ------------------------------------------------------------","+echo MMGP BELLEK PROFILI - Modeli RAM'e tasima ayari","+echo ------------------------------------------------------------","+echo.","+echo 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)","+echo En hizli, kisa videolar icin ideal","+echo.","+echo 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)","+echo RAM'e offload, uzun video ve buyuk batch icin","+echo.","+echo 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)","+echo VRAM agirlikli, iyi hiz [MEVCUT AYAR]","+echo.","+echo 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)","+echo Dusuk bellek, onerilen genel profil","+echo.","+echo 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)","+echo En az bellek, yavas ama calisir","+echo.","+echo Enter = Mevcut ayari kullan (config'den)","+echo ------------------------------------------------------------","+echo.","+set /p MMGP_PROFIL=\" Profil secin [1-5, Enter=mevcut]: \"","+","+set PROFILE_ARG=","+if \"%MMGP_PROFIL%\"==\"1\" set PROFILE_ARG=--profile 1","+if \"%MMGP_PROFIL%\"==\"2\" set PROFILE_ARG=--profile 2","+if \"%MMGP_PROFIL%\"==\"3\" set PROFILE_ARG=--profile 3","+if \"%MMGP_PROFIL%\"==\"4\" set PROFILE_ARG=--profile 4","+if \"%MMGP_PROFIL%\"==\"5\" set PROFILE_ARG=--profile 5","+","+if not \"%PROFILE_ARG%\"==\"\" (","+ echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...","+) else (","+ echo [*] Config dosyasindaki profil kullanilacak...","+)","+echo.","+"," echo [+] pnpm dev baslatiliyor..."," echo Kapamak icin bu pencereyi kapatin veya Ctrl+C basin."," echo."," ","+REM MMGP profil argumani WanGP backend'e iletilir","+if not \"%PROFILE_ARG%\"==\"\" (","+ set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"","+)","+"," call pnpm dev"," "," pause"]}],"userModified":false,"replaceAll":false},"_audit_timestamp":"2026-04-19T22:09:28.700Z","_audit_hmac":"d0ee1a6686b8305c6e332480ec652175484565c9860238f1f3151e98be4b8fa5"} {"type":"system","subtype":"status","status":"requesting","uuid":"407d52d5-8a6a-4306-947f-626d983db7a0","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:28.714Z","_audit_hmac":"adbddafa62296522a4334518ddae2013e4996fa0d8392ac5afe11d31b2e71b75"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NLevESjvCFyFqC6joWMnDf","type":"message","role":"assistant","content":[{"type":"text","text":"Bir dakika — `pnpm dev` Electron frontend'i başlatıyor, WanGP backend'i ayrıca başlıyor. `--profile` argümanının `wgp.py`'ye nasıl gittiğini görmem lazım. Config dosyasını zaten değiştirdim (profil 3), bu WanGP yeniden başlatıldığında otomatik okunacak. BAT'taki profil seçimi de config'i güncelleyebilir.\n\nAslında en etkili yol zaten config dosyasını değiştirmek — bunu yaptık. Şimdi BAT'ı daha pratik yapalım: profil seçildiğinde doğrudan config dosyasını güncellesin."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1114,"cache_read_input_tokens":81537,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1114},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"12a870d8-fba4-4351-b2be-4d89dd3b4080","_audit_timestamp":"2026-04-19T22:09:36.630Z","_audit_hmac":"d27d2faa96e4267c582e449ad2f04f027bacd21bccf08b9352679d4574b434bf"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NLevESjvCFyFqC6joWMnDf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01B9qqjfMWRZxgXqW4CFytC9","name":"Edit","input":{"replace_all":false,"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat","old_string":"if not \"%PROFILE_ARG%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...\n) else (\n echo [*] Config dosyasindaki profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\nREM MMGP profil argumani WanGP backend'e iletilir\nif not \"%PROFILE_ARG%\"==\"\" (\n set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"\n)\n\ncall pnpm dev","new_string":"set \"WGP_CONFIG=%REPO%\\Wan2GP\\wgp_config.json\"\n\nif not \"%MMGP_PROFIL%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ayarlaniyor...\n REM Config dosyasini guncelle - Python ile JSON yaziyoruz\n python -c \"import json; f=open(r'%WGP_CONFIG%','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=%MMGP_PROFIL%; c['image_profile']=%MMGP_PROFIL%; c['audio_profile']=%MMGP_PROFIL%; f=open(r'%WGP_CONFIG%','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\"\n) else (\n echo [*] Config dosyasindaki mevcut profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1114,"cache_read_input_tokens":81537,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1114},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"ad732a50-f887-4fa8-b0f5-7a3394296a66","_audit_timestamp":"2026-04-19T22:09:42.602Z","_audit_hmac":"3a457169ad5e3a9b1e3b33d93ffcd698604f39311678ec19dddcd0daaf040758"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B9qqjfMWRZxgXqW4CFytC9","type":"tool_result","content":"The file /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"068c4628-afcc-4540-956c-b76ff5e94674","timestamp":"2026-04-19T22:09:42.617Z","tool_use_result":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/LTX-Desktop-WanGP-Baslat.bat","oldString":"if not \"%PROFILE_ARG%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...\n) else (\n echo [*] Config dosyasindaki profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\nREM MMGP profil argumani WanGP backend'e iletilir\nif not \"%PROFILE_ARG%\"==\"\" (\n set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"\n)\n\ncall pnpm dev","newString":"set \"WGP_CONFIG=%REPO%\\Wan2GP\\wgp_config.json\"\n\nif not \"%MMGP_PROFIL%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ayarlaniyor...\n REM Config dosyasini guncelle - Python ile JSON yaziyoruz\n python -c \"import json; f=open(r'%WGP_CONFIG%','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=%MMGP_PROFIL%; c['image_profile']=%MMGP_PROFIL%; c['audio_profile']=%MMGP_PROFIL%; f=open(r'%WGP_CONFIG%','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\"\n) else (\n echo [*] Config dosyasindaki mevcut profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\ncall pnpm dev","originalFile":"@echo off\nchcp 65001 >nul\ntitle LTX-Desktop-WanGP - Video AI Studio\ncolor 0B\n\nREM ============================================================\nREM LTX-Desktop-WanGP Baslatma Scripti\nREM Electron frontend + WanGP backend birlikte calisir\nREM ============================================================\n\nset \"KOK=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\"\nset \"LTX=%KOK%\\ltx-desktop-wangp\"\nset \"REPO=%LTX%\\LTX-Desktop-WanGP\"\n\nREM Tum cache ve modeller ltx-desktop-wangp klasoru icinde kalsin\nset \"HF_HOME=%LTX%\\hf-cache\"\nset \"HUGGINGFACE_HUB_CACHE=%LTX%\\hf-cache\"\nset \"TRANSFORMERS_CACHE=%LTX%\\hf-cache\"\nset \"TORCH_HOME=%LTX%\\torch-cache\"\nset \"XDG_CACHE_HOME=%LTX%\\xdg-cache\"\n\necho =========================================================\necho LTX-Desktop-WanGP Baslatiliyor...\necho Video uretim arayuzu yuklenecek\necho =========================================================\necho.\necho Klasor : %REPO%\necho Cache : %HF_HOME%\necho.\n\nREM --- Repo kontrolu ---\nif not exist \"%REPO%\" (\n color 0C\n echo [HATA] LTX-Desktop-WanGP kaynak kodu bulunamadi!\n echo Beklenen: %REPO%\n echo.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- package.json kontrolu ---\nif not exist \"%REPO%\\package.json\" (\n color 0C\n echo [HATA] package.json bulunamadi!\n echo Kurulum eksik olabilir.\n echo Once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin.\n pause\n exit /b 1\n)\n\nREM --- pnpm kontrolu ---\nwhere pnpm >nul 2>&1\nif errorlevel 1 (\n color 0C\n echo [HATA] pnpm bulunamadi!\n echo Lutfen once \"LTX-Desktop-WanGP-Kur.bat\" dosyasini calistirin\n echo veya \"npm install -g pnpm\" komutu ile pnpm kurun.\n pause\n exit /b 1\n)\n\ncd /d \"%REPO%\"\n\nREM ============================================================\nREM MMGP PROFIL SECIMI - CPU RAM Offload Ayari\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho MMGP BELLEK PROFILI - Modeli RAM'e tasima ayari\necho ------------------------------------------------------------\necho.\necho 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)\necho En hizli, kisa videolar icin ideal\necho.\necho 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)\necho RAM'e offload, uzun video ve buyuk batch icin\necho.\necho 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)\necho VRAM agirlikli, iyi hiz [MEVCUT AYAR]\necho.\necho 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)\necho Dusuk bellek, onerilen genel profil\necho.\necho 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)\necho En az bellek, yavas ama calisir\necho.\necho Enter = Mevcut ayari kullan (config'den)\necho ------------------------------------------------------------\necho.\nset /p MMGP_PROFIL=\" Profil secin [1-5, Enter=mevcut]: \"\n\nset PROFILE_ARG=\nif \"%MMGP_PROFIL%\"==\"1\" set PROFILE_ARG=--profile 1\nif \"%MMGP_PROFIL%\"==\"2\" set PROFILE_ARG=--profile 2\nif \"%MMGP_PROFIL%\"==\"3\" set PROFILE_ARG=--profile 3\nif \"%MMGP_PROFIL%\"==\"4\" set PROFILE_ARG=--profile 4\nif \"%MMGP_PROFIL%\"==\"5\" set PROFILE_ARG=--profile 5\n\nif not \"%PROFILE_ARG%\"==\"\" (\n echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...\n) else (\n echo [*] Config dosyasindaki profil kullanilacak...\n)\necho.\n\necho [+] pnpm dev baslatiliyor...\necho Kapamak icin bu pencereyi kapatin veya Ctrl+C basin.\necho.\n\nREM MMGP profil argumani WanGP backend'e iletilir\nif not \"%PROFILE_ARG%\"==\"\" (\n set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"\n)\n\ncall pnpm dev\n\npause\n","structuredPatch":[{"oldStart":97,"oldLines":10,"newStart":97,"newLines":14,"lines":[" if \"%MMGP_PROFIL%\"==\"4\" set PROFILE_ARG=--profile 4"," if \"%MMGP_PROFIL%\"==\"5\" set PROFILE_ARG=--profile 5"," ","-if not \"%PROFILE_ARG%\"==\"\" (","- echo [*] MMGP Profil %MMGP_PROFIL% ile baslatiliyor...","+set \"WGP_CONFIG=%REPO%\\Wan2GP\\wgp_config.json\"","+","+if not \"%MMGP_PROFIL%\"==\"\" (","+ echo [*] MMGP Profil %MMGP_PROFIL% ayarlaniyor...","+ REM Config dosyasini guncelle - Python ile JSON yaziyoruz","+ python -c \"import json; f=open(r'%WGP_CONFIG%','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=%MMGP_PROFIL%; c['image_profile']=%MMGP_PROFIL%; c['audio_profile']=%MMGP_PROFIL%; f=open(r'%WGP_CONFIG%','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\""," ) else (","- echo [*] Config dosyasindaki profil kullanilacak...","+ echo [*] Config dosyasindaki mevcut profil kullanilacak..."," )"," echo."," "]},{"oldStart":108,"oldLines":11,"newStart":112,"newLines":6,"lines":[" echo Kapamak icin bu pencereyi kapatin veya Ctrl+C basin."," echo."," ","-REM MMGP profil argumani WanGP backend'e iletilir","-if not \"%PROFILE_ARG%\"==\"\" (","- set \"WANGP_EXTRA_ARGS=%PROFILE_ARG%\"","-)","-"," call pnpm dev"," "," pause"]}],"userModified":false,"replaceAll":false},"_audit_timestamp":"2026-04-19T22:09:42.662Z","_audit_hmac":"ed42cb9df5100ec5124ccb29a103ced3dd9cb1020777049dc2d3879c339c6a07"} {"type":"system","subtype":"status","status":"requesting","uuid":"eacfb6bf-992c-4156-b6c9-b342b9a653fd","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:42.667Z","_audit_hmac":"b1ae04195b5a1e1bcf710998e737864e365d108a86eb44837b52444313de6b04"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01162FzGuWgEUTRY59a9uvXy","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi `WanGP-Video-Uret.bat`'a da MMGP profil seçeneği ekleyeyim. Video üretim pipeline'ı da config'i güncellesin."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":955,"cache_read_input_tokens":82651,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":955},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"6034fc51-8a78-42ff-b6b3-81ca3e08f3cc","_audit_timestamp":"2026-04-19T22:09:47.535Z","_audit_hmac":"d5cb40abca9d426b25a335606910399e42739d02d7ca2b3ec14d36ee2bcc3f88"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01162FzGuWgEUTRY59a9uvXy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BQFfHRARW9GmqKsrXfuYvm","name":"Read","input":{"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/WanGP-Video-Uret.bat","offset":1,"limit":30},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":955,"cache_read_input_tokens":82651,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":955},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"22402d08-a57c-42b8-b2dc-2c922d43b928","_audit_timestamp":"2026-04-19T22:09:48.065Z","_audit_hmac":"be5f0936ecd22e23adb8b03959c4080b9c170d807f184e8d867b38bd76f49f4f"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BQFfHRARW9GmqKsrXfuYvm","type":"tool_result","content":"1\t@echo off\n2\ttitle WanGP Muzik Video Uretici\n3\tsetlocal enabledelayedexpansion\n4\t\n5\tREM ── CUDA OOM FIX: Bellek parcalanmasini onle ──\n6\tset PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\n7\t\n8\tset PYTHON=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\backend\\.venv\\Scripts\\python.exe\"\n9\tset SCRIPT=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\wangp_video_uret.py\"\n10\t\n11\tset IMAGE=\n12\tset AUDIO=\n13\tset FACEIMG=\n14\t\n15\techo.\n16\techo ============================================================\n17\techo WANGP MUZIK VIDEO URETICI\n18\techo RTX 4090 Optimize - Hizli ve Kaliteli\n19\techo ============================================================\n20\techo.\n21\t\n22\tREM --- Surukle-birak ile gelen dosyalari ayikla ---\n23\tfor %%A in (%*) do (\n24\t set \"FILE=%%~A\"\n25\t set \"EXT=%%~xA\"\n26\t\n27\t if /i \"!EXT!\"==\".png\" set \"IMAGE=%%~A\"\n28\t if /i \"!EXT!\"==\".jpg\" set \"IMAGE=%%~A\"\n29\t if /i \"!EXT!\"==\".jpeg\" set \"IMAGE=%%~A\"\n30\t if /i \"!EXT!\"==\".webp\" set \"IMAGE=%%~A\""}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"c14074f4-21c8-42ac-a328-73d247897225","timestamp":"2026-04-19T22:09:48.069Z","tool_use_result":{"type":"text","file":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/WanGP-Video-Uret.bat","content":"@echo off\ntitle WanGP Muzik Video Uretici\nsetlocal enabledelayedexpansion\n\nREM ── CUDA OOM FIX: Bellek parcalanmasini onle ──\nset PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\n\nset PYTHON=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\backend\\.venv\\Scripts\\python.exe\"\nset SCRIPT=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\wangp_video_uret.py\"\n\nset IMAGE=\nset AUDIO=\nset FACEIMG=\n\necho.\necho ============================================================\necho WANGP MUZIK VIDEO URETICI\necho RTX 4090 Optimize - Hizli ve Kaliteli\necho ============================================================\necho.\n\nREM --- Surukle-birak ile gelen dosyalari ayikla ---\nfor %%A in (%*) do (\n set \"FILE=%%~A\"\n set \"EXT=%%~xA\"\n\n if /i \"!EXT!\"==\".png\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".jpg\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".jpeg\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".webp\" set \"IMAGE=%%~A\"","numLines":30,"startLine":1,"totalLines":595}},"_audit_timestamp":"2026-04-19T22:09:48.129Z","_audit_hmac":"78f595698dad2d80950360db1ce3eadd15459734175e6d0c5914349d736f3a89"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"allowed","overageResetsAt":1777593600,"isUsingOverage":false},"uuid":"0f0fecca-1d3e-486e-839e-0c5348a4614c","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:48.162Z","_audit_hmac":"f72d65b1e9cdfdb148053d145ebb06bcff2142f9a3455028a73bf00086d9ae17"} {"type":"system","subtype":"status","status":"requesting","uuid":"227a8cec-7cfb-4cac-b667-97b159ddd5aa","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:09:48.177Z","_audit_hmac":"053513e0d98bb062c4b02cdaecbb86d50807e62c35ab2a0367d61db257aa6fa5"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01TYQRt9QhYvWqQYt7TJpGC9","type":"message","role":"assistant","content":[{"type":"text","text":"Şimdi BAT dosyasında \"OZET VE BASLATMA\" kısmından hemen önce, MMGP profil seçeneği ekleyeyim:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":776,"cache_read_input_tokens":83606,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":776},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"77a7c986-ff32-467e-884a-95a83c0bd7de","_audit_timestamp":"2026-04-19T22:09:55.357Z","_audit_hmac":"a01c00f91928b413091f97604bceb1dfd2e98fcc8a4a98c744524426a4810bba"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01TYQRt9QhYvWqQYt7TJpGC9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JXPg7quZQAwSpbvJhZ6DsW","name":"Edit","input":{"replace_all":false,"file_path":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/WanGP-Video-Uret.bat","old_string":"REM ============================================================\nREM OZET VE BASLATMA\nREM ============================================================","new_string":"REM ============================================================\nREM MMGP BELLEK PROFILI - CPU RAM'e Offload\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho MMGP BELLEK PROFILI - Video uretim oncesi RAM ayari\necho ------------------------------------------------------------\necho.\necho 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)\necho En hizli, kisa videolar icin\necho.\necho 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)\necho RAM'e offload, uzun video icin\necho.\necho 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)\necho VRAM agirlikli, orta bellek [MEVCUT AYAR]\necho.\necho 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)\necho Dusuk bellek genel profil\necho.\necho 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)\necho En az bellek, yavas ama calisir\necho.\necho Enter = Mevcut ayari koru (Profil 3)\necho ------------------------------------------------------------\nset /p MMGP_PROFIL=\" MMGP Profil [1-5, Enter=mevcut]: \"\n\nset \"WGP_CONFIG=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\LTX-Desktop-WanGP\\Wan2GP\\wgp_config.json\"\nif not \"%MMGP_PROFIL%\"==\"\" (\n echo [*] MMGP Profil !MMGP_PROFIL! ayarlaniyor...\n python -c \"import json; f=open(r'!WGP_CONFIG!','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=!MMGP_PROFIL!; c['image_profile']=!MMGP_PROFIL!; c['audio_profile']=!MMGP_PROFIL!; f=open(r'!WGP_CONFIG!','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\"\n)\n\nREM ============================================================\nREM OZET VE BASLATMA\nREM ============================================================"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":776,"cache_read_input_tokens":83606,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":776},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"f83ed492-5e3b-46e3-bab8-b2ca3e1ebc1a","_audit_timestamp":"2026-04-19T22:10:02.693Z","_audit_hmac":"a0e67d2a2554bd3a24a674dd52ad6f790b50c1288c092136aa8de0f6a7a4a066"} {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JXPg7quZQAwSpbvJhZ6DsW","type":"tool_result","content":"The file /sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/WanGP-Video-Uret.bat has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"4e470d02-b309-4fe9-bf4d-6175c99cabd7","timestamp":"2026-04-19T22:10:02.716Z","tool_use_result":{"filePath":"/sessions/gifted-clever-bardeen/mnt/__sistem__/Masaustu/FikoYT.Yeni.Cag/video-uretim/WanGP-Video-Uret.bat","oldString":"REM ============================================================\nREM OZET VE BASLATMA\nREM ============================================================","newString":"REM ============================================================\nREM MMGP BELLEK PROFILI - CPU RAM'e Offload\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho MMGP BELLEK PROFILI - Video uretim oncesi RAM ayari\necho ------------------------------------------------------------\necho.\necho 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)\necho En hizli, kisa videolar icin\necho.\necho 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)\necho RAM'e offload, uzun video icin\necho.\necho 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)\necho VRAM agirlikli, orta bellek [MEVCUT AYAR]\necho.\necho 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)\necho Dusuk bellek genel profil\necho.\necho 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)\necho En az bellek, yavas ama calisir\necho.\necho Enter = Mevcut ayari koru (Profil 3)\necho ------------------------------------------------------------\nset /p MMGP_PROFIL=\" MMGP Profil [1-5, Enter=mevcut]: \"\n\nset \"WGP_CONFIG=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\LTX-Desktop-WanGP\\Wan2GP\\wgp_config.json\"\nif not \"%MMGP_PROFIL%\"==\"\" (\n echo [*] MMGP Profil !MMGP_PROFIL! ayarlaniyor...\n python -c \"import json; f=open(r'!WGP_CONFIG!','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=!MMGP_PROFIL!; c['image_profile']=!MMGP_PROFIL!; c['audio_profile']=!MMGP_PROFIL!; f=open(r'!WGP_CONFIG!','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\"\n)\n\nREM ============================================================\nREM OZET VE BASLATMA\nREM ============================================================","originalFile":"@echo off\ntitle WanGP Muzik Video Uretici\nsetlocal enabledelayedexpansion\n\nREM ── CUDA OOM FIX: Bellek parcalanmasini onle ──\nset PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\n\nset PYTHON=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\backend\\.venv\\Scripts\\python.exe\"\nset SCRIPT=\"D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\wangp_video_uret.py\"\n\nset IMAGE=\nset AUDIO=\nset FACEIMG=\n\necho.\necho ============================================================\necho WANGP MUZIK VIDEO URETICI\necho RTX 4090 Optimize - Hizli ve Kaliteli\necho ============================================================\necho.\n\nREM --- Surukle-birak ile gelen dosyalari ayikla ---\nfor %%A in (%*) do (\n set \"FILE=%%~A\"\n set \"EXT=%%~xA\"\n\n if /i \"!EXT!\"==\".png\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".jpg\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".jpeg\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".webp\" set \"IMAGE=%%~A\"\n if /i \"!EXT!\"==\".bmp\" set \"IMAGE=%%~A\"\n\n if /i \"!EXT!\"==\".mp3\" set \"AUDIO=%%~A\"\n if /i \"!EXT!\"==\".wav\" set \"AUDIO=%%~A\"\n if /i \"!EXT!\"==\".flac\" set \"AUDIO=%%~A\"\n if /i \"!EXT!\"==\".m4a\" set \"AUDIO=%%~A\"\n if /i \"!EXT!\"==\".ogg\" set \"AUDIO=%%~A\"\n)\n\nREM --- Dosya bulunamadiysa sor ---\nif \"%IMAGE%\"==\"\" (\n echo [!] Resim dosyasi bulunamadi.\n echo Resim ve sarki dosyalarini bu BAT'in ustune surukleyin.\n echo.\n echo VEYA asagiya elle yazin:\n echo.\n set /p IMAGE=\" Resim yolu (png/jpg): \"\n)\nif \"%AUDIO%\"==\"\" (\n echo.\n echo [!] Sarki dosyasi bulunamadi.\n set /p AUDIO=\" Sarki yolu (mp3/wav): \"\n)\nif \"%IMAGE%\"==\"\" (\n echo HATA: Resim dosyasi gerekli!\n pause\n exit /b 1\n)\nif \"%AUDIO%\"==\"\" (\n echo HATA: Sarki dosyasi gerekli!\n pause\n exit /b 1\n)\n\necho.\necho Resim : %IMAGE%\necho Sarki : %AUDIO%\necho.\n\nREM ============================================================\nREM PROFIL SECIMI\nREM ============================================================\necho ------------------------------------------------------------\necho PROFIL SEC - RTX 4090 24GB + 64GB RAM Icin Optimize\necho ------------------------------------------------------------\necho.\necho *** LTX-2.3 - ONERILEN - Hizli, Gercekci, Lip-Sync ***\necho.\necho 1) LTX2 HIZLI - LTX2 22B Distilled 480p, 10s\necho ~35sn/segment ONERILEN - en hizli, iyi kalite\necho.\necho 2) LTX2 HD - LTX2 22B 720p, 10s\necho ~90sn/segment 720p HD, en kaliteli\necho.\necho 3) LTX2 KALITE++ - LTX2 22B 720p, perturbation+phases\necho ~120sn/segment Tum LTX2 ozellikler acik, profesyonel\necho.\necho *** WAN2.1 IMAGE2VIDEO - Farkli Stil [LIP-SYNC YOK!] ***\necho.\necho 4) WAN2 HIZLI - Wan2.1 I2V 480p, 10s\necho ~80sn/segment Wan2 stili, LIP-SYNC DESTEKLEMIYOR\necho.\necho 5) WAN2 KALITE - Wan2.1 I2V 480p, 15s\necho ~150sn/segment Uzun sahneler, LIP-SYNC DESTEKLEMIYOR\necho.\necho 6) OZEL AYARLAR - Tum parametreleri elle gir\necho.\necho ------------------------------------------------------------\necho.\nset /p PROFIL=\" Profil secin [1-6, Enter=1]: \"\nif \"%PROFIL%\"==\"\" set PROFIL=1\n\nREM --- Varsayilan degerler (LTX2 Distilled) ---\nset MODEL=ltx2_22B_distilled\nset RESOLUTION=832x480\nset VIDEOLENGTH=241\nset STEPS=20\nset GUIDANCE=3.0\nset FLOWSHIFT=5.0\nset SOLVER=euler\nset SEGDUR=10\nset FPS=24\nset \"NEG_PROMPT=blurry, low quality, distorted face, deformed, extra fingers, extra limbs, multiple people, duplicate, cartoon, animated, watermark, text, logo, static, frozen, jerky motion\"\nset SLIDINGWIN=0\nset SLIDINGOVERLAP=1\nset PERTURBATION=0\nset SELFREFINER=0\nset REPEAT=1\nset SEED=-1\nset LORA=\nset LORAWEIGHT=1.0\nset ENDIMAGE=\nset AUDIOGUIDE=\nset AUDIOSCALE=\nset ALTGUIDANCE=\nset ALTSCALE=\nset EMBEDDEDGUIDE=\nset GUIDANCEPHASES=\nset FILMGRAIN=0\nset TEMPORALUP=\nset SPATIALUP=\nset NOLIPSYNC=0\nset NOVOCALISOL=0\n\nREM --- Profil ayarlari (RTX 4090 24GB optimize) ---\nREM Profil 1: LTX2 HIZLI (varsayilan degerler zaten LTX2 Distilled)\nif \"%PROFIL%\"==\"1\" (\n set MODEL=ltx2_22B_distilled\n set RESOLUTION=832x480\n set VIDEOLENGTH=241\n set STEPS=20\n set GUIDANCE=3.0\n set FLOWSHIFT=5.0\n set SEGDUR=10\n set FPS=24\n set SOLVER=euler\n)\nREM Profil 2: LTX2 HD\nif \"%PROFIL%\"==\"2\" (\n set MODEL=ltx2_22B\n set RESOLUTION=1280x720\n set VIDEOLENGTH=241\n set STEPS=30\n set GUIDANCE=3.0\n set FLOWSHIFT=5.0\n set SEGDUR=10\n set FPS=24\n set SOLVER=euler\n set GUIDANCEPHASES=2\n set ALTGUIDANCE=3.0\n set ALTSCALE=0.7\n)\nREM Profil 3: LTX2 KALITE++\nif \"%PROFIL%\"==\"3\" (\n set MODEL=ltx2_22B\n set RESOLUTION=1280x720\n set VIDEOLENGTH=241\n set STEPS=40\n set GUIDANCE=3.0\n set FLOWSHIFT=5.0\n set SEGDUR=10\n set FPS=24\n set SOLVER=euler\n set GUIDANCEPHASES=2\n set ALTGUIDANCE=3.0\n set ALTSCALE=0.7\n set PERTURBATION=2\n set AUDIOSCALE=1.0\n)\nREM Profil 4: WAN2 HIZLI\nif \"%PROFIL%\"==\"4\" (\n set MODEL=i2v\n set RESOLUTION=832x480\n set VIDEOLENGTH=161\n set STEPS=30\n set GUIDANCE=5.0\n set FLOWSHIFT=7.0\n set SEGDUR=10\n set FPS=16\n set SOLVER=unipc\n)\nREM Profil 5: WAN2 KALITE\nif \"%PROFIL%\"==\"5\" (\n set MODEL=i2v\n set RESOLUTION=832x480\n set VIDEOLENGTH=241\n set STEPS=30\n set GUIDANCE=5.0\n set FLOWSHIFT=7.0\n set SEGDUR=15\n set FPS=16\n set SOLVER=unipc\n)\n\nif not \"%PROFIL%\"==\"6\" goto :PROMPT_AYAR\n\nREM ============================================================\nREM OZEL AYARLAR (Profil 6)\nREM ============================================================\n\necho.\necho --- MODEL SECIMI ---\necho *** YUKLU MODELLER ***\necho 1) i2v - Wan2.1 I2V 480p (Varsayilan)\necho 2) ltx2_22B - LTX-2 22B (En kaliteli)\necho 3) ltx2_22B_distilled - LTX-2 22B Distilled (Hizli)\necho.\necho *** INDIRILMESI GEREKEN (otomatik indirir) ***\necho 4) i2v_720p - Wan2.1 I2V 720p\necho 5) i2v_2_2 - Wan2.1 2.2 I2V (Yeni versiyon)\necho 6) t2v - Wan2.1 Text2Video\necho 7) hunyuan_1_5_i2v - HunyuanVideo 1.5 I2V\necho 8) Elle gir (model ID)\necho.\nset /p MODELCHOICE=\" Model (1-8, Enter=1): \"\nif \"%MODELCHOICE%\"==\"\" set MODELCHOICE=1\nif \"%MODELCHOICE%\"==\"1\" set MODEL=i2v\nif \"%MODELCHOICE%\"==\"2\" (\n set MODEL=ltx2_22B\n set FLOWSHIFT=5.0\n set GUIDANCE=3.0\n set FPS=24\n set SOLVER=euler\n)\nif \"%MODELCHOICE%\"==\"3\" (\n set MODEL=ltx2_22B_distilled\n set FLOWSHIFT=5.0\n set GUIDANCE=3.0\n set FPS=24\n set SOLVER=euler\n)\nif \"%MODELCHOICE%\"==\"4\" (\n set MODEL=i2v_720p\n set RESOLUTION=1280x720\n)\nif \"%MODELCHOICE%\"==\"5\" set MODEL=i2v_2_2\nif \"%MODELCHOICE%\"==\"6\" set MODEL=t2v\nif \"%MODELCHOICE%\"==\"7\" set MODEL=hunyuan_1_5_i2v\nif \"%MODELCHOICE%\"==\"8\" (\n set /p MODEL=\" Model ID (orn: i2v_2_2): \"\n)\n\necho.\necho --- COZUNURLUK ---\necho 1) 832x480 - 480p 16:9 (Varsayilan - Hizli)\necho 2) 480x832 - 480p 9:16 (Dikey/Shorts)\necho 3) 1280x720 - 720p 16:9 (HD)\necho 4) 720x1280 - 720p 9:16 (HD Dikey)\necho 5) 960x544 - 540p 16:9\necho 6) Elle gir (WIDTHxHEIGHT)\necho.\nset /p RESCHOICE=\" Cozunurluk (1-6, Enter=1): \"\nif \"%RESCHOICE%\"==\"\" set RESCHOICE=1\nif \"%RESCHOICE%\"==\"1\" set RESOLUTION=832x480\nif \"%RESCHOICE%\"==\"2\" set RESOLUTION=480x832\nif \"%RESCHOICE%\"==\"3\" set RESOLUTION=1280x720\nif \"%RESCHOICE%\"==\"4\" set RESOLUTION=720x1280\nif \"%RESCHOICE%\"==\"5\" set RESOLUTION=960x544\nif \"%RESCHOICE%\"==\"6\" (\n set /p RESOLUTION=\" Cozunurluk (WxH): \"\n)\n\necho.\necho --- SEGMENT SURESI / FRAME SAYISI ---\necho 16fps: 81 frame=5s, 121=7.5s, 161=10s, 241=15s\necho 24fps: 81 frame=3.4s, 121=5s, 161=6.7s, 241=10s\necho.\necho 1) 81 frame (~5s @16fps) - Varsayilan, hizli\necho 2) 121 frame (~7.5s) - Orta sure\necho 3) 161 frame (~10s) - Uzun\necho 4) 241 frame (~15s) - Cok uzun\necho 5) Elle gir (4n+1 olmali: 81,121,161,201,241...)\necho.\nset /p FRAMECHOICE=\" Frame sayisi (1-5, Enter=1): \"\nif \"%FRAMECHOICE%\"==\"\" set FRAMECHOICE=1\nif \"%FRAMECHOICE%\"==\"1\" set VIDEOLENGTH=81\nif \"%FRAMECHOICE%\"==\"2\" set VIDEOLENGTH=121\nif \"%FRAMECHOICE%\"==\"3\" set VIDEOLENGTH=161\nif \"%FRAMECHOICE%\"==\"4\" set VIDEOLENGTH=241\nif \"%FRAMECHOICE%\"==\"5\" (\n set /p VIDEOLENGTH=\" Frame sayisi (4n+1): \"\n)\n\nREM Segment suresini frame sayisindan otomatik hesapla\nset /a SEGDUR=!VIDEOLENGTH! / !FPS!\n\necho.\necho --- INFERENCE STEPS ---\necho 1) 20 - Hizli (test/deneme)\necho 2) 30 - Dengeli (Varsayilan)\necho 3) 40 - Yuksek kalite\necho 4) 50 - Maksimum kalite (yavas)\necho 5) Elle gir\necho.\nset /p STEPCHOICE=\" Steps (1-5, Enter=2): \"\nif \"%STEPCHOICE%\"==\"\" set STEPCHOICE=2\nif \"%STEPCHOICE%\"==\"1\" set STEPS=20\nif \"%STEPCHOICE%\"==\"2\" set STEPS=30\nif \"%STEPCHOICE%\"==\"3\" set STEPS=40\nif \"%STEPCHOICE%\"==\"4\" set STEPS=50\nif \"%STEPCHOICE%\"==\"5\" (\n set /p STEPS=\" Steps (1-100): \"\n)\n\necho.\necho --- GUIDANCE SCALE ---\necho Dusuk = serbest/yaratici, Yuksek = prompt'a bagli\necho.\necho 1) 3.0 - Serbest (LTX2 icin ideal)\necho 2) 5.0 - Dengeli (Wan2 icin ideal, Varsayilan)\necho 3) 7.0 - Prompt'a yakin\necho 4) Elle gir\necho.\nset /p GCHOICE=\" Guidance (1-4, Enter=2): \"\nif \"%GCHOICE%\"==\"\" set GCHOICE=2\nif \"%GCHOICE%\"==\"1\" set GUIDANCE=3.0\nif \"%GCHOICE%\"==\"2\" set GUIDANCE=5.0\nif \"%GCHOICE%\"==\"3\" set GUIDANCE=7.0\nif \"%GCHOICE%\"==\"4\" (\n set /p GUIDANCE=\" Guidance Scale: \"\n)\n\necho.\necho --- FLOW SHIFT ---\necho 1) 5.0 - LTX2 varsayilan\necho 2) 7.0 - Wan2 varsayilan\necho 3) 10.0 - Daha dinamik\necho 4) Elle gir\necho.\nset /p FSCHOICE=\" Flow Shift (1-4, Enter=2): \"\nif \"%FSCHOICE%\"==\"\" set FSCHOICE=2\nif \"%FSCHOICE%\"==\"1\" set FLOWSHIFT=5.0\nif \"%FSCHOICE%\"==\"2\" set FLOWSHIFT=7.0\nif \"%FSCHOICE%\"==\"3\" set FLOWSHIFT=10.0\nif \"%FSCHOICE%\"==\"4\" (\n set /p FLOWSHIFT=\" Flow Shift (1-25): \"\n)\n\necho.\necho --- SOLVER ---\necho 1) unipc - Varsayilan (Wan2 icin iyi)\necho 2) euler - LTX2 icin ideal\necho.\nset /p SOLCHOICE=\" Solver (1/2, Enter=1): \"\nif \"%SOLCHOICE%\"==\"\" set SOLCHOICE=1\nif \"%SOLCHOICE%\"==\"1\" set SOLVER=unipc\nif \"%SOLCHOICE%\"==\"2\" set SOLVER=euler\n\necho.\necho --- SEED ---\necho -1 = her seferinde rastgele (Varsayilan)\necho Sabit sayi = tekrarlanabilir sonuc\necho.\nset /p SEED=\" Seed (-1=rastgele, Enter=-1): \"\nif \"%SEED%\"==\"\" set SEED=-1\n\necho.\necho --- GELISMIS AYARLAR (istege bagli, Enter=atla) ---\n\necho.\necho Perturbation (Detay Cesitliligi)\necho 0=Kapali 1=Hafif 2=Normal 3=Guclu 4=Max\nset /p PERTURBATION=\" Perturbation (0-4, Enter=0): \"\nif \"%PERTURBATION%\"==\"\" set PERTURBATION=0\n\necho.\necho Self-Refiner (Coklu Gecis Kalite Artirma)\necho 0=Kapali 1=P1-Norm 2=P2-Norm\nset /p SELFREFINER=\" Self-Refiner (0-2, Enter=0): \"\nif \"%SELFREFINER%\"==\"\" set SELFREFINER=0\n\necho.\necho Guidance Phases (Coklu Faz Rehberlik)\necho Bos=varsayilan 1=Tek faz 2=Iki faz 3=Uc faz\nset /p GUIDANCEPHASES=\" Guidance Phases (Enter=varsayilan): \"\n\necho.\necho Film Grain (Film Tane Efekti)\necho 0=Kapali, 0.1-1.0=Yogunluk\nset /p FILMGRAIN=\" Film Grain (0-1, Enter=0): \"\nif \"%FILMGRAIN%\"==\"\" set FILMGRAIN=0\n\necho.\necho LoRA Dosyasi (bos=kullanma)\nset /p LORA=\" LoRA dosya adi (Enter=yok): \"\nif not \"%LORA%\"==\"\" (\n set /p LORAWEIGHT=\" LoRA agirligi (Enter=1.0): \"\n if \"!LORAWEIGHT!\"==\"\" set LORAWEIGHT=1.0\n)\n\necho.\necho Bitis Gorseli (son sahne icin, bos=kullanma)\nset /p ENDIMAGE=\" Bitis gorseli yolu (Enter=yok): \"\n\n:PROMPT_AYAR\n\nREM ============================================================\nREM SEGMENT SURESI AYARI\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho SEGMENT SURESI - Her parca kac saniye olsun?\necho ------------------------------------------------------------\necho Simdi ayarli: %SEGDUR% saniye / segment\necho Model: %MODEL% FPS: %FPS% Frames: %VIDEOLENGTH%\necho.\necho Uzun segment = daha az gecis, daha akici klip\necho Kisa segment = daha fazla sahne degisimi\necho.\necho 1) 5 saniye - 81 frame Wan / 121 frame LTX - Hizli\necho 2) 10 saniye - 161 frame Wan / 241 frame LTX - Dengeli\necho 3) 15 saniye - 241 frame Wan / 361 frame LTX - Uzun\necho 4) 20 saniye - 321 frame Wan / 481 frame LTX - Cok uzun\necho 5) Elle gir\necho Enter = Mevcut ayari koru [%SEGDUR%s]\necho.\nset /p SEGCHOICE=\" Segment suresi [1-5, Enter=mevcut]: \"\nif \"%SEGCHOICE%\"==\"\" goto :SEG_DONE\nif \"%SEGCHOICE%\"==\"1\" set SEGDUR=5\nif \"%SEGCHOICE%\"==\"2\" set SEGDUR=10\nif \"%SEGCHOICE%\"==\"3\" set SEGDUR=15\nif \"%SEGCHOICE%\"==\"4\" set SEGDUR=20\nif \"%SEGCHOICE%\"==\"5\" set /p SEGDUR=\" Segment suresi [saniye]: \"\nREM Frame sayisini FPS'e gore hesapla (4n+1 formatina yuvarla)\nset /a VIDEOLENGTH=!SEGDUR! * !FPS! + 1\nREM 4n+1 formatina yuvarla\nset /a _REMAINDER=(!VIDEOLENGTH! - 1) %% 4\nif not \"!_REMAINDER!\"==\"0\" set /a VIDEOLENGTH=!VIDEOLENGTH! + 4 - !_REMAINDER!\necho Ayarlandi: %SEGDUR%s = !VIDEOLENGTH! frame\n:SEG_DONE\n\nREM ============================================================\nREM PROMPT GIRISI (Tum profiller icin ortak)\nREM ============================================================\necho.\necho --- PROMPT ---\necho Video icin aciklama girin.\necho Enter = Varsayilan muzik klibi prompt'u\necho.\nset /p VIDPROMPT=\" Prompt: \"\nif \"%VIDPROMPT%\"==\"\" set \"VIDPROMPT=cinematic music video, single person performing with deep emotion, dramatic cinematic lighting with volumetric rays, professional film cinematography, anamorphic lens, shallow depth of field, film grain, color graded, 4K ultra high quality, smooth natural motion, dynamic slow camera push, person singing with expressive lip movements matching the music, background music playing\"\n\necho.\necho --- NEGATIF PROMPT ---\necho Enter = Varsayilan negatif prompt\necho.\nset /p NEG=\" Negatif Prompt (Enter=varsayilan): \"\nif not \"%NEG%\"==\"\" set \"NEG_PROMPT=%NEG%\"\n\nREM ============================================================\nREM FACE SWAP\nREM ============================================================\necho.\necho --- FACE SWAP ---\necho 1) Kapali (Varsayilan)\necho 2) Acik - Yuz resmi sec\necho.\nset /p FSWCHOICE=\" Face Swap (1/2, Enter=1): \"\nif \"%FSWCHOICE%\"==\"\" set FSWCHOICE=1\nset FACE_SWAP_ARG=\nif \"%FSWCHOICE%\"==\"2\" (\n echo.\n set /p FACEIMG=\" Yuz resmi yolu (png/jpg): \"\n if not \"!FACEIMG!\"==\"\" set \"FACE_SWAP_ARG=--face-swap \"!FACEIMG!\"\"\n)\n\nREM ============================================================\nREM LIP-SYNC AYARLARI\nREM ============================================================\necho.\necho ------------------------------------------------------------\necho LIP-SYNC - Dudak Senkronizasyonu\necho ------------------------------------------------------------\necho.\necho 1) LIP-SYNC ACIK - Dudaklar muzige uyar - ONERILEN\necho 2) LIP-SYNC KAPALI - Ses sadece son videoya eklenir\necho.\nset /p LIPSYNC_CHOICE=\" Lip-sync secin [1-2, Enter=1]: \"\nif \"%LIPSYNC_CHOICE%\"==\"\" set LIPSYNC_CHOICE=1\nif \"%LIPSYNC_CHOICE%\"==\"2\" set NOLIPSYNC=1\nif \"%NOLIPSYNC%\"==\"1\" goto :SKIP_VOCAL\necho.\necho Vokal izolasyonu arka plan muzigini ayirir,\necho lip-sync icin daha temiz ses saglar.\necho.\necho 1) VOKAL IZOLASYONU ACIK - ONERILEN\necho 2) VOKAL IZOLASYONU KAPALI\necho.\nset /p VOCAL_CHOICE=\" Secin [1-2, Enter=1]: \"\nif \"%VOCAL_CHOICE%\"==\"\" set VOCAL_CHOICE=1\nif \"%VOCAL_CHOICE%\"==\"2\" set NOVOCALISOL=1\n:SKIP_VOCAL\n\nREM ============================================================\nREM OZET VE BASLATMA\nREM ============================================================\necho.\necho ============================================================\necho AYARLAR OZETI\necho ============================================================\necho Resim : %IMAGE%\necho Sarki : %AUDIO%\necho Model : %MODEL%\necho Cozunurluk: %RESOLUTION%\necho Frames : %VIDEOLENGTH% (%SEGDUR%s segment)\necho Steps : %STEPS%\necho Guidance : %GUIDANCE%\necho Flow Shift: %FLOWSHIFT%\necho Solver : %SOLVER%\necho Seed : %SEED%\necho FPS : %FPS%\necho Prompt : %VIDPROMPT:~0,50%...\nif not \"%LORA%\"==\"\" echo LoRA : %LORA% (x%LORAWEIGHT%)\nif not \"%PERTURBATION%\"==\"0\" echo Perturbat.: %PERTURBATION%\nif not \"%SELFREFINER%\"==\"0\" echo Refiner : %SELFREFINER%\nif not \"%GUIDANCEPHASES%\"==\"\" echo G.Phases : %GUIDANCEPHASES%\nif not \"%FILMGRAIN%\"==\"0\" echo Film Grain: %FILMGRAIN%\nif not \"%FACEIMG%\"==\"\" echo Face Swap : %FACEIMG%\nif not \"%ENDIMAGE%\"==\"\" echo End Image : %ENDIMAGE%\nif \"%NOLIPSYNC%\"==\"1\" echo Lip-Sync : KAPALI\nif \"%NOLIPSYNC%\"==\"0\" echo Lip-Sync : ACIK\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"0\" echo Vokal Izol: ACIK\nif \"%NOLIPSYNC%\"==\"0\" if \"%NOVOCALISOL%\"==\"1\" echo Vokal Izol: KAPALI\necho ============================================================\necho NOT: WanGP'nin localhost:7860'da acik olmasi gerekir!\necho ============================================================\necho.\necho Baslatmak icin Enter'a basin, iptal icin Ctrl+C...\npause >nul\n\nREM --- Komut olustur ---\nset CMD=%PYTHON% %SCRIPT% \"%AUDIO%\" \"%IMAGE%\"\nset CMD=%CMD% --model %MODEL%\nset CMD=%CMD% --resolution %RESOLUTION%\nset CMD=%CMD% --video-length %VIDEOLENGTH%\nset CMD=%CMD% --steps %STEPS%\nset CMD=%CMD% --guidance-scale %GUIDANCE%\nset CMD=%CMD% --flow-shift %FLOWSHIFT%\nset CMD=%CMD% --solver %SOLVER%\nset CMD=%CMD% --seed %SEED%\nset CMD=%CMD% --fps %FPS%\nset CMD=%CMD% --segment-duration %SEGDUR%\nset CMD=%CMD% --prompt \"%VIDPROMPT%\"\nset CMD=%CMD% --negative-prompt \"%NEG_PROMPT%\"\n\nif not \"%LORA%\"==\"\" (\n set CMD=!CMD! --lora \"%LORA%\" --lora-weight %LORAWEIGHT%\n)\nif not \"%PERTURBATION%\"==\"0\" (\n set CMD=!CMD! --perturbation %PERTURBATION%\n)\nif not \"%SELFREFINER%\"==\"0\" (\n set CMD=!CMD! --self-refiner %SELFREFINER%\n)\nif not \"%GUIDANCEPHASES%\"==\"\" (\n set CMD=!CMD! --guidance-phases %GUIDANCEPHASES%\n)\nif not \"%FILMGRAIN%\"==\"0\" (\n set CMD=!CMD! --film-grain %FILMGRAIN%\n)\nif not \"%ENDIMAGE%\"==\"\" (\n set CMD=!CMD! --image-end \"%ENDIMAGE%\"\n)\nif not \"%FACEIMG%\"==\"\" (\n set CMD=!CMD! --face-swap \"%FACEIMG%\"\n)\nif \"%NOLIPSYNC%\"==\"1\" (\n set CMD=!CMD! --no-lip-sync\n)\nif \"%NOVOCALISOL%\"==\"1\" (\n set CMD=!CMD! --no-vocal-isolation\n)\n\necho.\necho [*] Calistiriliyor...\necho.\n%CMD%\n\necho.\necho ============================================================\necho Islem tamamlandi!\necho ============================================================\necho.\npause\n","structuredPatch":[{"oldStart":502,"oldLines":6,"newStart":502,"newLines":39,"lines":[" :SKIP_VOCAL"," "," REM ============================================================","+REM MMGP BELLEK PROFILI - CPU RAM'e Offload","+REM ============================================================","+echo.","+echo ------------------------------------------------------------","+echo MMGP BELLEK PROFILI - Video uretim oncesi RAM ayari","+echo ------------------------------------------------------------","+echo.","+echo 1) Profil 1 - HighRAM+HighVRAM (64GB RAM + 24GB VRAM)","+echo En hizli, kisa videolar icin","+echo.","+echo 2) Profil 2 - HighRAM+LowVRAM (64GB RAM + 12GB VRAM)","+echo RAM'e offload, uzun video icin","+echo.","+echo 3) Profil 3 - LowRAM+HighVRAM (32GB RAM + 24GB VRAM)","+echo VRAM agirlikli, orta bellek [MEVCUT AYAR]","+echo.","+echo 4) Profil 4 - LowRAM+LowVRAM (32GB RAM + 12GB VRAM)","+echo Dusuk bellek genel profil","+echo.","+echo 5) Profil 5 - VeryLow (24GB RAM + 10GB VRAM)","+echo En az bellek, yavas ama calisir","+echo.","+echo Enter = Mevcut ayari koru (Profil 3)","+echo ------------------------------------------------------------","+set /p MMGP_PROFIL=\" MMGP Profil [1-5, Enter=mevcut]: \"","+","+set \"WGP_CONFIG=D:\\__sistem__\\Masaustu\\FikoYT.Yeni.Cag\\video-uretim\\LTX-Desktop-WanGP\\LTX-Desktop-WanGP\\Wan2GP\\wgp_config.json\"","+if not \"%MMGP_PROFIL%\"==\"\" (","+ echo [*] MMGP Profil !MMGP_PROFIL! ayarlaniyor...","+ python -c \"import json; f=open(r'!WGP_CONFIG!','r',encoding='utf-8'); c=json.load(f); f.close(); c['video_profile']=!MMGP_PROFIL!; c['image_profile']=!MMGP_PROFIL!; c['audio_profile']=!MMGP_PROFIL!; f=open(r'!WGP_CONFIG!','w',encoding='utf-8'); json.dump(c,f,indent=4); f.close(); print(' Config guncellendi: profil',c['video_profile'])\"","+)","+","+REM ============================================================"," REM OZET VE BASLATMA"," REM ============================================================"," echo."]}],"userModified":false,"replaceAll":false},"_audit_timestamp":"2026-04-19T22:10:02.751Z","_audit_hmac":"52062b17f3492bd64bcc5d4721c8ab9f88c37fc8954060047b69e56c2215dea2"} {"type":"system","subtype":"status","status":"requesting","uuid":"35e8184d-1c33-478d-9022-4c781ec8ac93","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:10:02.759Z","_audit_hmac":"58e8acfd8354611ba84d084f574500b3c37480ea0fda485708aea610b90176fe"} {"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01G1Hy3m1uTWfHaoxpEHvY1b","type":"message","role":"assistant","content":[{"type":"text","text":"Güzel, tüm değişiklikleri yaptım. Özetleyelim:\n\n**Yapılan değişiklikler:**\n\n1. **`wgp_config.json`** — Profil 4'ten **Profil 3'e** değiştirildi (LowRAM_HighVRAM: 32GB RAM + 24GB VRAM). Bu senin RTX 4090'ına uygun, modelin çoğunu VRAM'de tutup ihtiyaç halinde RAM'e taşıyacak.\n\n2. **`LTX-Desktop-WanGP-Baslat.bat`** — Başlatırken profil seçimi menüsü eklendi. Seçtiğin profil otomatik olarak `wgp_config.json`'a yazılıyor.\n\n3. **`WanGP-Video-Uret.bat`** — Video üretim öncesi profil seçimi eklendi. Orada da profil değiştirme imkanı var.\n\n**Profil 3 neden iyi:**\nSenin sistemin RTX 4090 (24GB VRAM) + 64GB RAM. Profil 3, VRAM'i en verimli kullanan profil — modelin transformer kısmını VRAM'de tutar, diğer parçaları RAM'e taşır. Daha önce profil 4'teydin ki bu \"LowRAM_LowVRAM\" yani hem RAM hem VRAM'i kısıtlı kullanıyordu, optimal değildi.\n\n**Eğer hala OOM alırsan:** Profil 2'yi dene — bu 64GB RAM'i daha agresif kullanarak VRAM'i tamamen rahatlatır. BAT menüsünden kolayca değiştirebilirsin.\n\nWanGP'yi yeniden başlatman gerekiyor — yeni profil ancak restart'ta etkili olur."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1011,"cache_read_input_tokens":84382,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1011},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"432fa85e-5db5-4830-9283-9556969b5029","uuid":"7611e6be-1232-48dc-9a8d-4321c7fd909a","_audit_timestamp":"2026-04-19T22:10:15.767Z","_audit_hmac":"f8a9ca971382a3dae13f4caa1284847e2cfbd9a131ffeb84550213d202636f33"} {"type":"rate_limit_event","rate_limit_info":{"status":"allowed","resetsAt":1776643200,"rateLimitType":"five_hour","overageStatus":"rejected","overageDisabledReason":"org_level_disabled","isUsingOverage":false},"uuid":"250d1862-14c7-4d97-8c6f-6f317397d18d","session_id":"432fa85e-5db5-4830-9283-9556969b5029","_audit_timestamp":"2026-04-19T22:10:15.851Z","_audit_hmac":"fb3d4280e597e4888767f1106ba932579830a0140463e9ff54e97de07ab0aca6"} {"type":"result","subtype":"success","is_error":false,"api_error_status":null,"duration_ms":578388,"duration_api_ms":339172,"num_turns":46,"result":"Güzel, tüm değişiklikleri yaptım. Özetleyelim:\n\n**Yapılan değişiklikler:**\n\n1. **`wgp_config.json`** — Profil 4'ten **Profil 3'e** değiştirildi (LowRAM_HighVRAM: 32GB RAM + 24GB VRAM). Bu senin RTX 4090'ına uygun, modelin çoğunu VRAM'de tutup ihtiyaç halinde RAM'e taşıyacak.\n\n2. **`LTX-Desktop-WanGP-Baslat.bat`** — Başlatırken profil seçimi menüsü eklendi. Seçtiğin profil otomatik olarak `wgp_config.json`'a yazılıyor.\n\n3. **`WanGP-Video-Uret.bat`** — Video üretim öncesi profil seçimi eklendi. Orada da profil değiştirme imkanı var.\n\n**Profil 3 neden iyi:**\nSenin sistemin RTX 4090 (24GB VRAM) + 64GB RAM. Profil 3, VRAM'i en verimli kullanan profil — modelin transformer kısmını VRAM'de tutar, diğer parçaları RAM'e taşır. Daha önce profil 4'teydin ki bu \"LowRAM_LowVRAM\" yani hem RAM hem VRAM'i kısıtlı kullanıyordu, optimal değildi.\n\n**Eğer hala OOM alırsan:** Profil 2'yi dene — bu 64GB RAM'i daha agresif kullanarak VRAM'i tamamen rahatlatır. BAT menüsünden kolayca değiştirebilirsin.\n\nWanGP'yi yeniden başlatman gerekiyor — yeni profil ancak restart'ta etkili olur.","stop_reason":"end_turn","session_id":"432fa85e-5db5-4830-9283-9556969b5029","total_cost_usd":2.0115495,"usage":{"input_tokens":48,"cache_creation_input_tokens":43064,"cache_read_input_tokens":2910369,"output_tokens":11479,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":43064,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[{"input_tokens":1,"output_tokens":511,"cache_read_input_tokens":84382,"cache_creation_input_tokens":1011,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1011},"type":"message"}],"speed":"standard"},"modelUsage":{"claude-opus-4-6":{"inputTokens":48,"outputTokens":11479,"cacheReadInputTokens":2910369,"cacheCreationInputTokens":43064,"webSearchRequests":0,"costUSD":2.0115495,"contextWindow":200000,"maxOutputTokens":64000}},"permission_denials":[],"terminal_reason":"completed","fast_mode_state":"off","uuid":"b9fbf578-6e16-4ded-8dda-73c6eee227f4","_audit_timestamp":"2026-04-19T22:10:15.862Z","_audit_hmac":"328aa1b0ca887b141300281e9ccc2504b81a0b97145316a6c982bafb024d8932"}