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custom_nodes/dolphin_nodes/.claude/launch.json

@@ -0,0 +1,19 @@
+{
+  "version": "0.0.1",
+  "configurations": [
+    {
+      "name": "comfyui",
+      "runtimeExecutable": "D:\\SD\\ComfyUI_windows_portable\\python_embeded\\python.exe",
+      "runtimeArgs": [
+        "-s",
+        "D:\\SD\\ComfyUI_windows_portable\\ComfyUI\\main.py",
+        "--windows-standalone-build",
+        "--enable-dynamic-vram",
+        "--use-sage-attention",
+        "--output-directory",
+        "D:\\sd\\output"
+      ],
+      "port": 8188
+    }
+  ]
+}

custom_nodes/dolphin_nodes/.claude/settings.json

@@ -0,0 +1,42 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(curl -s -m * http://127.0.0.1:8188/*)",
+      "Bash(curl -s -m * \"http://127.0.0.1:8188/*)",
+      "Bash(curl -s http://127.0.0.1:8188*)",
+      "Bash(curl -s \"http://127.0.0.1:8188*)",
+      "Bash(curl -s * http://127.0.0.1:8188*)",
+      "Bash(curl -s * \"http://127.0.0.1:8188*)",
+      "Bash(nvidia-smi)",
+      "Bash(nvidia-smi --query-gpu=*)",
+      "Bash(nvidia-smi --query-compute-apps=*)",
+      "Bash(tasklist)",
+      "Bash(tasklist *)",
+      "Bash(wmic process get *)",
+      "Bash(where *)",
+      "Bash(git --no-pager log *)",
+      "Bash(git --no-pager diff *)",
+      "Bash(git --no-pager show *)",
+      "Bash(git --no-pager status *)",
+      "Bash(git add *)",
+      "Bash(git commit *)",
+      "PowerShell(Get-ChildItem *)",
+      "PowerShell(Get-Item *)",
+      "PowerShell(Get-Process *)",
+      "PowerShell(Get-CimInstance *)",
+      "PowerShell(Get-NetTCPConnection *)",
+      "PowerShell(Get-WinEvent *)",
+      "PowerShell(nvidia-smi --query-gpu=*)",
+      "PowerShell(nvidia-smi --query-compute-apps=*)",
+      "mcp__claude-in-chrome__tabs_context_mcp",
+      "mcp__Claude_Browser__read_page",
+      "mcp__Claude_Browser__get_page_text",
+      "mcp__Claude_Browser__read_console_messages",
+      "mcp__Claude_Browser__read_network_requests",
+      "mcp__Claude_Browser__find",
+      "mcp__Claude_Browser__tabs_context",
+      "mcp__Claude_Browser__preview_list",
+      "mcp__Claude_Browser__preview_logs"
+    ]
+  }
+}

custom_nodes/dolphin_nodes/.claude/settings.local.json

@@ -0,0 +1,665 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(claude mcp *)",
+      "Bash(\"/c/Users/bjoo/.local/bin/claude.exe\" -p \"Call the list_saved_workflows tool from comfyui-cloud and print the raw result.\" --allowedTools \"mcp__comfyui-cloud__list_saved_workflows\")",
+      "Bash(curl -s -m 10 http://127.0.0.1:8188/system_stats)",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); [print\\(g.get\\('name'\\), g.get\\('vram_free'\\)\\) for g in d.get\\('devices',[]\\)]\")",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/input/**)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/models/checkpoints/**)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/models/diffusion_models/**)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/models/unet/**)",
+      "mcp__claude-in-chrome__tabs_context_mcp",
+      "Bash(curl -s -m 10 \"http://127.0.0.1:8188/object_info/UNETLoader\")",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\(json.dumps\\(d['UNETLoader']['input'], indent=2\\)[:1500]\\)\")",
+      "Bash(curl -s -m 10 \"http://127.0.0.1:8188/object_info/ShowText|pysssss\")",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); k=list\\(d.keys\\(\\)\\)[0]; print\\(json.dumps\\(d[k]['input'], indent=2\\)[:800]\\); print\\('OUTPUT_NODE:', d[k].get\\('output_node'\\)\\)\")",
+      "Bash(mkdir -p /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad)",
+      "Bash(curl -s -m 10 \"http://127.0.0.1:8188/object_info/DolphinVisionDirector\")",
+      "Bash(python - /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/dvd_info.json)",
+      "Read(//c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/**)",
+      "Bash(curl -sv -m 10 http://127.0.0.1:8188/object_info/DolphinVisionDirector -o /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/dvd_info.json)",
+      "Bash(curl -s -m 60 http://127.0.0.1:8188/object_info/DolphinVisionDirector -o /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/dvd_info.json)",
+      "Bash(curl -s -m 8 http://127.0.0.1:8188/system_stats -o /dev/null -w \"system_stats: %{http_code} in %{time_total}s\\\\n\")",
+      "Bash(curl -s -m 8 http://127.0.0.1:8188/queue -w \"\\\\nqueue: %{http_code} in %{time_total}s\\\\n\")",
+      "PowerShell(Get-Process -Id 31388 -ErrorAction SilentlyContinue)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"ProcessId=31388\" -ErrorAction SilentlyContinue)",
+      "Bash(curl -s -m 8 http://127.0.0.1:8188/system_stats -o /dev/null -w \"now: %{http_code} in %{time_total}s\\\\n\")",
+      "Bash(curl -s -m 30 http://127.0.0.1:8188/object_info/DolphinVisionDirector -o /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/dvd_info.json)",
+      "Bash(curl -s -m 15 \"http://127.0.0.1:8188/history/f583e65d-e2d9-46fc-a4e5-a67122c3adc4\" -o \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/history_result.json\")",
+      "Bash(PYTHONIOENCODING=utf-8 python - /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/history_result.json)",
+      "mcp__claude-in-chrome__navigate",
+      "mcp__claude-in-chrome__javascript_tool",
+      "mcp__claude-in-chrome__computer",
+      "mcp__claude-in-chrome__read_page",
+      "mcp__claude-in-chrome__tabs_create_mcp",
+      "Bash(python -)",
+      "Bash(python -m py_compile dolphin_vision_director.py)",
+      "PowerShell(Get-NetTCPConnection -LocalPort 8188 -ErrorAction SilentlyContinue)",
+      "Bash(curl -s -m 20 \"http://127.0.0.1:8188/object_info/DolphinVisionDirector\" -o /dev/null -w \"DolphinVisionDirector object_info: %{http_code}\\\\n\")",
+      "Bash(curl -s -m 20 http://127.0.0.1:8188/object_info/WanVideoNAG -o /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/nag_info.json)",
+      "Bash(PYTHONIOENCODING=utf-8 python - /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/7b5a824e-a1dd-42ed-be45-b5a9cccfc005/scratchpad/nag_info.json)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/user/**)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/**)",
+      "Bash(python3 -c ' *)",
+      "Bash(python -c ' *)",
+      "Read(//c/Users/bjoo/AppData/Local/ComfyUI-LoRA-Manager/cache/model/**)",
+      "Bash(python - /c/Users/bjoo/AppData/Local/ComfyUI-LoRA-Manager/cache/model/comfyui.sqlite)",
+      "Bash(PYTHONIOENCODING=utf-8 python -c ' *)",
+      "Bash(mkdir -p \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/5f5946c1-33a3-471e-8b49-6552febb9164/scratchpad\")",
+      "Bash(cd \"D:/SD/ComfyUI_windows_portable\" && ls)",
+      "PowerShell(Get-NetTCPConnection -LocalPort 8188 -State Listen -ErrorAction SilentlyContinue)",
+      "Bash(curl -s -m 8 http://127.0.0.1:8188/queue)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"ProcessId=5452\" -ErrorAction SilentlyContinue)",
+      "Bash(cd \"D:/SD/ComfyUI_windows_portable/ComfyUI/models/loras\" 2>&1 && find . -iname \"*PENISLORA*\" 2>&1)",
+      "Bash(ls \"C:\\\\Users\\\\bjoo\\\\.claude\\\\projects\\\\D--SD-ComfyUI-windows-portable-ComfyUI\\\\memory\\\\\" 2>&1)",
+      "Skill(fewer-permission-prompts)",
+      "Skill(fewer-permission-prompts:*)",
+      "Bash(ls -la \"C:/Users/bjoo/.claude/projects/\" 2>&1 | head -50)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/5f5946c1-33a3-471e-8b49-6552febb9164/scratchpad/scan_perms2.py\")",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/5f5946c1-33a3-471e-8b49-6552febb9164/scratchpad/scan_curl.py\")",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\" http://127.0.0.1:8188/ --max-time 3)",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=5 --max-time 5)",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=5 --max-time 5 -o C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/history.json)",
+      "Bash(curl -s http://127.0.0.1:8188/queue --max-time 5)",
+      "Bash(nvidia-smi --query-gpu=name,memory.used,memory.total,memory.free --format=csv)",
+      "PowerShell(Get-CimInstance Win32_OperatingSystem)",
+      "Read(//d/SD/ComfyUI_windows_portable/**)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"Name = 'python.exe'\")",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/**)",
+      "Bash(grep -rniL \"^$\" --include=\"*.py\" -l \"unload_all_models\\\\|soft_empty_cache\\\\|free_memory\\\\|PurgeVRAM\\\\|UnloadAllModels\" .)",
+      "Bash(grep -rniE \"unload_all_models|soft_empty_cache\\\\\\(|PurgeVRAM|UnloadAllModels|Free Memory|FreeMemory|ClearVRAM|VRAM.?Debug\" --include=\"*.py\" -l .)",
+      "Bash(cd \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/comfyui_memory_cleanup\" && cat __init__.py 2>/dev/null | head -150)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/VRAMCleanup\" --max-time 5)",
+      "Bash(grep -rl \"class TripleKSamplerWan22LightningAdvanced\" --include=\"*.py\" .)",
+      "Bash(curl -s http://127.0.0.1:8188/object_info/TripleKSamplerWan22LightningAdvanced --max-time 5 -o C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/tks_info.json)",
+      "Read(//d/sd/models/**)",
+      "Bash(find \"D:/sd\" -maxdepth 2 -type d 2>/dev/null | head -20)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/TorchCompileModel\" --max-time 5)",
+      "mcp__claude-in-chrome__read_console_messages",
+      "mcp__claude-in-chrome__read_network_requests",
+      "Bash(curl -s -D - -o /dev/null \"http://127.0.0.1:8188/\" --max-time 5 -A \"Mozilla/5.0 \\(Windows NT 10.0; Win64; x64\\) AppleWebKit/537.36 \\(KHTML, like Gecko\\) Chrome/120.0.0.0 Safari/537.36\")",
+      "mcp__claude-in-chrome__list_connected_browsers",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/Fast%20Groups%20Bypasser%20\\(rgthree\\)\" --max-time 5)",
+      "Bash(curl -s http://127.0.0.1:8188/object_info --max-time 15 -o C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/all_object_info.json)",
+      "Bash(head -50 __init__.py)",
+      "Bash(python_embeded/python.exe -c ' *)",
+      "Bash(PYTHONIOENCODING=utf-8 python_embeded/python.exe -c ' *)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/DolphinVisionDirector\" --max-time 5)",
+      "Bash(cp 0705_vision2.json 0705_vision2.json.bak_20260703_before_numclips)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/VHS_VideoCombine\" --max-time 5)",
+      "PowerShell(Stop-Process -Id 2044 -Force)",
+      "PowerShell(Get-Process -Id 2044 -ErrorAction SilentlyContinue)",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" http://127.0.0.1:8188/ --max-time 5)",
+      "Bash(curl -s \"http://127.0.0.1:8188/queue\" --max-time 5)",
+      "Bash(curl -s -o /dev/null -w \"VRAMCleanup: %{http_code}\\\\n\" \"http://127.0.0.1:8188/object_info/VRAMCleanup\")",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\('VRAMCleanup registered:', bool\\(d\\)\\)\")",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\('TorchCompileModel registered:', bool\\(d\\)\\)\")",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=10 --max-time 5 -o C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/history2.json)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -m pip show sageattention)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -c \"import sageattention; print\\(sageattention.__file__\\); print\\(dir\\(sageattention\\)\\)\")",
+      "Bash(D:/SD/ComfyUI_windows_portable/python_embeded/python.exe -c ' *)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -c \"import sageattn3\")",
+      "WebSearch",
+      "WebFetch(domain:github.com)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" --version)",
+      "Bash(where nvcc *)",
+      "Bash(nvcc --version)",
+      "Bash(where cl.exe)",
+      "Bash(\"C:/Program Files \\(x86\\)/Microsoft Visual Studio/Installer/vswhere.exe\" -latest -products '*' -property installationPath)",
+      "Bash(find \"C:/Program Files/Microsoft Visual Studio/2022\" -iname \"vcvarsall.bat\" 2>/dev/null)",
+      "Bash(cmd //c \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\fcfc6473-30fd-4447-95fe-3e3e03867e77\\\\scratchpad\\\\build_sage3.bat\")",
+      "Bash(cmd //c \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\fcfc6473-30fd-4447-95fe-3e3e03867e77\\\\scratchpad\\\\build_sage3_v2.bat\")",
+      "Bash(cmd //c \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\fcfc6473-30fd-4447-95fe-3e3e03867e77\\\\scratchpad\\\\build_sage3_v3.bat\")",
+      "Bash(cmd //c \"C:\\\\sage3build\\\\build_sage3.bat\")",
+      "Bash(git checkout *)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -m pip show sageattn3)",
+      "Bash(cp 0705_vision2.json 0705_vision2.json.bak_20260703_before_lightningsteps)",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\inspect_wf.py\")",
+      "Bash(grep -n \"\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/tripleksampler/nodes.py\" | sed -n '1140,1260p')",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\inspect_vd.py\")",
+      "Bash(/c/Windows/py -c ' *)",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\trace_prompt_links.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\trace2.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\trace3.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\remove_settings_node.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\bump_steps_cfg.py\")",
+      "mcp__claude-in-chrome__find",
+      "mcp__claude-in-chrome__form_input",
+      "mcp__d88663d0-df79-4bf4-92bd-3d23932f7923__hub_repo_search",
+      "mcp__d88663d0-df79-4bf4-92bd-3d23932f7923__hub_repo_details",
+      "WebFetch(domain:huggingface.co)",
+      "WebFetch(domain:civitai.com)",
+      "mcp__d88663d0-df79-4bf4-92bd-3d23932f7923__hf_hub_query",
+      "mcp__d88663d0-df79-4bf4-92bd-3d23932f7923__hf_fs",
+      "Bash(powershell -NoProfile -Command \"Get-CimInstance Win32_Process -Filter \\\\\"Name='python.exe'\\\\\" | Select-Object ProcessId, CommandLine | Format-List\")",
+      "Bash(powershell -NoProfile -Command \"Stop-Process -Id 47672 -Force\")",
+      "Bash(powershell -NoProfile -Command \"Get-CimInstance Win32_Process -Filter \\\\\"Name='python.exe'\\\\\" | Select-Object ProcessId, CommandLine\")",
+      "Bash(powershell -NoProfile -Command \"Get-Process -Id 47672 -ErrorAction SilentlyContinue | Select-Object Id, ProcessName, Responding\")",
+      "Bash(sleep 15 && ls -t \"D:/SD/ComfyUI_windows_portable\"/comfyui_restart_*.log 2>/dev/null | head -3)",
+      "Bash(powershell -NoProfile -Command \"Stop-Process -Id 2668 -Force\")",
+      "Bash(powershell -NoProfile -Command \"Stop-Process -Id 11268 -Force\")",
+      "Bash(powershell -NoProfile -Command \"Get-Process -Id 11268 -ErrorAction SilentlyContinue | Select-Object Id\")",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check.mjs)",
+      "Bash(node --check /tmp/check.mjs)",
+      "Bash(powershell -NoProfile -Command \"Stop-Process -Id 45940 -Force\")",
+      "Bash(powershell -NoProfile -Command \"Get-CimInstance Win32_Process -Filter \\\\\"Name='python.exe'\\\\\" | Select-Object ProcessId\")",
+      "Bash(powershell -NoProfile -Command \"Get-Process -Id 45940 -ErrorAction SilentlyContinue\")",
+      "Bash(curl -s -o /dev/null -w \"HTTP %{http_code}\\\\n\" http://127.0.0.1:8188/extensions/dolphin_nodes/dolphin_lora_rows.js)",
+      "Bash(curl -s http://127.0.0.1:8188/extensions/dolphin_nodes/dolphin_lora_rows.js)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check2.mjs)",
+      "Bash(node --check /tmp/check2.mjs)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check3.mjs)",
+      "Bash(node --check /tmp/check3.mjs)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check4.mjs)",
+      "Bash(node --check /tmp/check4.mjs)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check5.mjs)",
+      "Bash(node --check /tmp/check5.mjs)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\" /tmp/check6.mjs)",
+      "Bash(node --check /tmp/check6.mjs)",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\fix_link1455.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\fix_link1376.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\remove_lightx2v_only_mode.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\dump_widgets.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\reset_lora_widgets.py\")",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=1)",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\check_history.py\")",
+      "Read(//d/sd/models/loras/**)",
+      "Bash(sort -k9)",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\set_native4step.py\")",
+      "Bash(python -m py_compile __init__.py arch_logic.py promp_logic_v28_stitch.py promp_logic_v30_story.py promp_logic_v32_vision.py)",
+      "Bash(powershell -NoProfile -Command \"Stop-Process -Id 23756 -Force\")",
+      "Bash(powershell -NoProfile -Command \"Get-Process -Id 23756 -ErrorAction Stop\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\check_gguf_history.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\diff_v30_vs_current.py\")",
+      "Bash(/c/Windows/py \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\57e6bdef-7658-487a-ba07-aea70100b0eb\\\\scratchpad\\\\apply_wan21_hybrid.py\")",
+      "Bash(powershell -Command \"\\(Get-Item '0705_vision2.json'\\).Length\")",
+      "Bash(stat -c%s \"0705_vision2.json\")",
+      "Bash(node -e ' *)",
+      "Bash(cp \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\0705_vision2.json\" \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\user\\\\default\\\\workflows\\\\0705_vision2.json\")",
+      "Bash(grep -n '^  \"links\"\\\\|^    \"links\"\\\\|\"links\": \\\\[\\\\s*$' \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\0705_vision2.json\")",
+      "Bash(python3 -c \"print\\('hello'\\)\")",
+      "Bash(echo \"exit:$?\")",
+      "Bash(python)",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" http://127.0.0.1:8188 --max-time 5)",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" http://localhost:8188 --max-time 5)",
+      "Bash(curl -s http://127.0.0.1:8188/object_info/MatchImageAndMaskSize_Dolphin)",
+      "Bash(\"D:/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -c \"import color_matcher; print\\(color_matcher.__file__\\)\")",
+      "Bash(curl -s http://127.0.0.1:8188/object_info/MaskRegionColorMatch_Dolphin)",
+      "Bash(python -c \"import sys,json; d=json.load\\(sys.stdin\\); print\\(json.dumps\\(d['MaskRegionColorMatch_Dolphin']['input']['required']['ring_width']\\)\\)\")",
+      "Bash(python -c \"import sys,json; d=json.load\\(sys.stdin\\)['MaskRegionColorMatch_Dolphin']['input']['required']; print\\(list\\(d.keys\\(\\)\\)\\)\")",
+      "Bash(python3 -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\(json.dumps\\(d['hf_models'], indent=2\\)\\)\")",
+      "Bash(git config *)",
+      "Bash(git add *)",
+      "Bash(git commit -m ' *)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"ProcessId=70880\" -ErrorAction SilentlyContinue)",
+      "Bash(curl -s -m 8 \"http://127.0.0.1:8188/object_info/DolphinVisionDirector\" -o /dev/null -w \"DolphinVisionDirector: %{http_code}\\\\n\")",
+      "Bash(curl -s -m 8 \"http://127.0.0.1:8188/object_info/DolphinPromptDirector\" -o /dev/null -w \"DolphinPromptDirector: %{http_code}\\\\n\")",
+      "Bash(curl -s -m 8 \"http://127.0.0.1:8188/object_info/DolphinPromptDirector\")",
+      "Bash(python -m py_compile dolphin_prompt_director.py)",
+      "Bash(curl -s -m 8 \"http://127.0.0.1:8188/object_info/DolphinPromptDirector\" -o \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/5f5946c1-33a3-471e-8b49-6552febb9164/scratchpad/ppd_info2.json\")",
+      "Bash(xargs -I{} sh -c 'grep -l \"getMostRecentWorkflow\" \"{}\" 2>/dev/null')",
+      "Bash(grep -o \"getMostRecentWorkflow[^}]\\\\{0,300\\\\}\" \"D:/SD/ComfyUI_windows_portable/python_embeded/Lib/site-packages/comfyui_frontend_package/static/assets/dialogService-CVq-DX47.js\")",
+      "Bash(curl -s -m 8 \"http://127.0.0.1:8188/api/extensions\")",
+      "Bash(ls \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\\" | head -20; echo \"---\"; ls \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\" 2>/dev/null | head -20)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/gen_v2v_workflow.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" -c \"print\\('hello'\\)\"; Write-Output \"EXIT=$LASTEXITCODE\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" -c \"import torch; print\\('torch OK', torch.__version__\\)\"; Write-Output \"EXIT=$LASTEXITCODE\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\d4bb3e78-b9b7-42fa-912c-5e8d393db7cb\\\\scratchpad\\\\smoke_test.py\")",
+      "Bash(rm -rf \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/dsb_test\")",
+      "Bash(./python.exe \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/build.py\" --help)",
+      "Read(//d/SD/**)",
+      "Read(//d//**)",
+      "Read(//d/sd/models/diffusion_models/**)",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\dataset_builder\\\\train\\\\setup_env.ps1\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\dataset_builder\\\\train\\\\setup_env.ps1\" -Yes)",
+      "Bash(./python.exe \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/train/download_checkpoints.py\")",
+      "Bash(./python.exe \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/train/download_checkpoints.py\" --yes)",
+      "Bash(./python.exe -c ' *)",
+      "Bash(powershell -Command \"\\(Get-Item 'D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\user\\\\default\\\\workflows\\\\10S_comb.json'\\).Length\")",
+      "Bash(find \"/c/Users/bjoo/.cache/huggingface/hub\" -maxdepth 1 -iname \"*Wan_2.2_ComfyUI*\" -o -iname \"*Wan2.1-I2V-14B-720P*\" 2>/dev/null *)",
+      "PowerShell(Get-Process -Id 89032)",
+      "PowerShell(Get-NetTCPConnection -OwningProcess 89032 -ErrorAction SilentlyContinue)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/patch_10s_comb.py\")",
+      "Bash(python -m py_compile wan_svipro_fixed.py __init__.py)",
+      "Bash(\"./venv/Scripts/python.exe\" -m pip install \"torch==2.9.1\" torchvision --index-url https://download.pytorch.org/whl/cu130)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/patch_svipro_fixed.py\")",
+      "Bash(\"./venv/Scripts/python.exe\" -c \"import torch; print\\(torch.__version__, torch.version.cuda\\); print\\(torch.cuda.get_device_capability\\(0\\)\\); print\\(torch.cuda.is_available\\(\\)\\)\")",
+      "Bash(PYTHONIOENCODING=utf-8 \"./venv/Scripts/python.exe\" \"./musubi_tuner/src/musubi_tuner/wan_train_network.py\" --help)",
+      "Bash(./python.exe D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/build.py --input __TRACKED_VAR__/raw --output __TRACKED_VAR__/out --trigger test_subject --captioner wd14)",
+      "Bash(python -m py_compile wan_svipro_fixed.py)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/patch_svipro_amplitude.py\")",
+      "Bash(git commit *)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/patch_continuity.py\")",
+      "Bash(mkdir -p C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/smoke_ds2/raw)",
+      "Bash(wmic process *)",
+      "Bash(python -m py_compile wan_svipro_gray.py __init__.py)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/485e6e77-03ce-4325-8ff3-3895d87d3091/scratchpad/patch_svipro_gray.py\")",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" http://127.0.0.1:7861)",
+      "Bash(./python.exe \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/inspect_lora.py\" \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\models\\\\loras\\\\2255050_wan2.2pisswet_high_noise.safetensors\")",
+      "Bash(cmd //c \"set\")",
+      "Bash(./python.exe \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/inspect_lora.py\" \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\models\\\\loras\\\\PENISLORA_22_i2v_HIGH_e320.safetensors\")",
+      "Bash(ls -la ~/Downloads/ 2>/dev/null | grep -i yt-dlp)",
+      "Bash(where python *)",
+      "Bash(python -m py_compile yt-dlp-gui.py)",
+      "Bash(ls \"/c/Users/bjoo/AppData/Local/Programs/Python/Python312/\" | grep -i pythonw)",
+      "PowerShell(Get-NetAdapter | Where-Object {$_.InterfaceDescription -match 'VPN|TAP|WireGuard|Tunnel|PPP'} | Select-Object Name, InterfaceDescription, Status | Format-Table -AutoSize)",
+      "PowerShell(Get-Process | Where-Object {$_.ProcessName -match 'openvpn|wireguard|nordvpn|expressvpn|protonvpn|surfshark|tunnelbear|cisco|anyconnect|forticlient|globalprotect|zerotier|tailscale|mullvad|ivpn|hotspotshield|windscribe|hidemyass|privatevpn|purevpn'} | Select-Object ProcessName, Id | Format-Table -AutoSize)",
+      "Bash(timeout 3 python yt-dlp-gui.py)",
+      "Bash(echo \"EXIT_CODE:$?\")",
+      "Bash(grep -E '^[0-9]+$')",
+      "Bash(curl -s -o /dev/null -w \"http %{http_code}\\\\n\" http://127.0.0.1:7861)",
+      "Bash(rm -rf C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/webui_test/train_out3)",
+      "Bash(echo \"launched pid $!\")",
+      "Bash(python -m py_compile wan_svipro_gray.py)",
+      "Bash(ls -la C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/webui_test/*.log)",
+      "Bash(echo \"=== full run_train3.log ===\")",
+      "Bash(cat C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/webui_test/run_train3.log)",
+      "Bash(curl -s -o /dev/null -w \"webui http %{http_code}\\\\n\" http://127.0.0.1:7861)",
+      "Bash(grep -v \"^$\")",
+      "Bash(rm -rf \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/webui_test\")",
+      "Bash(rm -f \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d4bb3e78-b9b7-42fa-912c-5e8d393db7cb/scratchpad/webui.log\")",
+      "Bash(curl -s -o /dev/null -w \"webui still up: http %{http_code}\\\\n\" http://127.0.0.1:7861)",
+      "PowerShell(Stop-Process -Id 103496 -Force)",
+      "PowerShell(Get-Process -Id 103496 -ErrorAction SilentlyContinue)",
+      "Bash(cp 0705_vision2.json 0705_vision2.json.bak_20260703_before_previewonly)",
+      "PowerShell(Stop-Process -Id 31768 -Force)",
+      "PowerShell(Get-Process -Id 31768 -ErrorAction SilentlyContinue)",
+      "Bash(curl -s http://127.0.0.1:8188/object_info/DolphinVisionDirector --max-time 5 -o C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/dvd_objinfo.json)",
+      "Bash(cp 0705_vision2.json 0705_vision2.json.bak_20260703_before_trimwidgets)",
+      "Bash(curl -s \"http://127.0.0.1:8188/history?max_items=5\" --max-time 5)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/WanNativeResize_Dolphin\" --max-time 5)",
+      "Bash(./python.exe D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/build.py --input C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fa088da8-83db-496b-ae2c-8203c1b258d5/scratchpad/clips_test/raw --output C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fa088da8-83db-496b-ae2c-8203c1b258d5/scratchpad/clips_test/out --trigger test --captioner wd14 --clips-per-video 5)",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\('registered:', bool\\(d\\)\\)\")",
+      "Bash(cp 0705_vision2.json lora_test.json)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/LoraLoaderModelOnly\" --max-time 5)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/ShowText%7Cpysssss\" --max-time 5)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/WanNativeResize_Dolphin\" --max-time 5 -o /tmp_resize.json)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/CLIPTextEncode\" --max-time 5)",
+      "Bash(python -m py_compile dolphin_prompt_director.py __init__.py)",
+      "Bash(tasklist)",
+      "Bash(python -m py_compile disk_cache_provider.py __init__.py)",
+      "Bash(curl -s http://127.0.0.1:8188/queue)",
+      "Bash(python -c \"import json,sys; d=json.load\\(sys.stdin\\); print\\('running:', len\\(d['queue_running']\\), 'pending:', len\\(d['queue_pending']\\)\\)\")",
+      "Bash(sort -k5 -n -r)",
+      "Bash(python -m py_compile disk_cache_provider.py)",
+      "Bash(python -m py_compile dolphin_prompt_director.py dolphin_vision_director.py __init__.py)",
+      "Bash(python \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/rewire_perclip.py\")",
+      "Bash(python \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/rewire_perclip_fix.py\")",
+      "Bash(python \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/rewire_perclip_step5.py\")",
+      "Bash(python \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/rewire_perclip_layout.py\")",
+      "Bash(python \"/c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fcfc6473-30fd-4447-95fe-3e3e03867e77/scratchpad/fix_modelsampling_nag.py\")",
+      "Bash(python -m json.tool)",
+      "Bash(python -m py_compile arch_logic_v16.py)",
+      "Bash(python -c *)",
+      "Bash(../../python_embeded/python.exe -c ' *)",
+      "mcp__claude-in-chrome__get_page_text",
+      "WebFetch(domain:aidispo.xyz)",
+      "Bash(curl -sS -o /dev/null -w \"HTTP:%{http_code} TIME:%{time_total}s\\\\n\" --max-time 15 https://aidispo.xyz/)",
+      "Bash(nslookup aidispo.xyz)",
+      "Bash(nslookup aidispo.xyz 8.8.8.8)",
+      "Bash(nslookup aidispo.xyz 1.1.1.1)",
+      "Bash(nslookup -type=NS aidispo.xyz)",
+      "Bash(python -m py_compile lora_classifier.py)",
+      "Bash(python lora_classifier.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python lora_classifier.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/553f71b1-eee1-4b6b-b29d-ae7b91fee046/scratchpad/convert_lanpaint.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/553f71b1-eee1-4b6b-b29d-ae7b91fee046/scratchpad/convert_lanpaint_live.py\")",
+      "Bash(gh issue *)",
+      "Bash(py --version)",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\inspect_wf.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\inspect_wf2.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\inspect_wf3.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\build_slowmo_workflows.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\validate_wf.py\")",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" http://127.0.0.1:8188/ --max-time 3)",
+      "WebFetch(domain:www.apatero.com)",
+      "Bash(gh api *)",
+      "WebFetch(domain:api.github.com)",
+      "Bash(curl -sL \"https://raw.githubusercontent.com/vita-epfl/Stable-Video-Infinity/svi_wan22/comfyui_workflow/SVI-Wan22-1210-4-Clips.json\" -o \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a/scratchpad/svi_wan22_4clips.json\" -w \"\\\\nHTTP %{http_code} size=%{size_download}\\\\n\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\inspect_svi_wf.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\inspect_svi_wf2.py\")",
+      "WebFetch(domain:raw.githubusercontent.com)",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\build_svi_scenario_a.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\build_svi_scenario_a_2clip.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\swap_svipro_gray.py\")",
+      "Bash(python -m http.server 8799 --bind 127.0.0.1)",
+      "Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" \"http://127.0.0.1:8799/0705_vision3_svigray_test.json\")",
+      "Bash(netstat -ano)",
+      "Bash(taskkill //F //PID 41392)",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\cors_server.py\")",
+      "Bash(curl -s -D - -o /dev/null \"http://127.0.0.1:8799/0705_vision3_svigray_test.json\")",
+      "Bash(awk '{print $5}')",
+      "Bash(taskkill //F //PID 35940)",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/WanVideoNAG\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3cd796ee-7ccd-4afc-a4b7-afbcf94f5e7a\\\\scratchpad\\\\build_svi_simple_nag.py\")",
+      "Bash(curl -s \"http://127.0.0.1:8188/object_info/TripleKSamplerWan22LightningAdvanced\")",
+      "Bash(curl -sL -o i2v_native.json \"https://huggingface.co/lightx2v/Wan2.2-Lightning/resolve/main/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1-NativeComfy.json\")",
+      "Bash(./python_embeded/python.exe \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/test_svipro_motion.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 ./python_embeded/python.exe \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/test_svipro_motion.py\")",
+      "Bash(curl -s -m 5 http://127.0.0.1:8188/system_stats -o /dev/null -w \"%{http_code}\\\\n\")",
+      "Bash(curl -s -m 10 http://127.0.0.1:8188/object_info/WanImageToVideoSVIProMotion_Dolphin)",
+      "Bash(PYTHONIOENCODING=utf-8 ./python_embeded/python.exe -c ' *)",
+      "Bash(curl -s -m 15 http://127.0.0.1:8188/object_info/WanImageToVideoSVIProMotion_Dolphin)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/make_svimotion_test.py\")",
+      "Bash(ls -d \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows\" 2>&1 && cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/0705_vision3_svimotion_test.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/__validate_svimotion.json\" && echo copied)",
+      "Bash(PYTHONIOENCODING=utf-8 python -)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/patch_svi_test.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/wire_nag.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/fix_first_base_model.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/apply_all.py\")",
+      "Bash(curl -s -m 5 -X POST http://127.0.0.1:8188/manager/reboot -o /dev/null -w \"reboot endpoint: %{http_code}\\\\n\")",
+      "Bash(curl -s -m 10 http://127.0.0.1:8188/object_info/DolphinArchitectV16)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/d23fdd5e-1ca3-4729-9900-2ec0a67708b1/scratchpad/migrate_architect_v16_widgets.py\")",
+      "Bash(command -v node)",
+      "Bash(node --check \"D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js\")",
+      "Bash(curl -s -m 10 http://127.0.0.1:8188/object_info/TripleKSamplerWan22LightningAdvanced)",
+      "Bash(curl -s -m 10 http://127.0.0.1:8188/object_info/ImageBatchExtendWithOverlap)",
+      "Bash(tasklist /FI \"IMAGENAME eq python.exe\" /FO CSV /V)",
+      "Bash(xargs -n1 basename)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0730.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0730.bak_20260730_1_before_seedcontroller.json\" && ls -la \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/\" | grep SVI_0730)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_seed_controller.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_seed_controller.py\")",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/make_draft_workflow.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_independent.py\")",
+      "Bash(git status *)",
+      "Bash(git mv *)",
+      "Bash(mv lora_classifier.py _archive/nodes_unregistered/)",
+      "Bash(mv \"0705_vision2.json.bak_20260703_before_previewonly\" _archive/workflows/)",
+      "Bash(mv \"0705_vision2.json.bak_20260703_before_trimwidgets\" _archive/workflows/)",
+      "Bash(mv \"0705_vision2.json.bak_20260724_1316_before_perclip_refactor\" _archive/workflows/)",
+      "Bash(mv \"0705_vision2.json.bak_20260724_before_fixseed\" _archive/workflows/)",
+      "Bash(mv \"0705_vision3_svigray_test.json\" _archive/workflows/)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/migrate_visiondirector_widgets.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/revert_visiondirector_widgets.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/make_0730C_3step.py\")",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0730B.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0730D.json\" *)",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/make_0730D_3sampler.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_0730D.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/swap_extend_svipro_motion.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_0730C_swap.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/add_colormatch.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/edcc9f60-df16-4a24-a1cc-2f19a6b49251/scratchpad/validate_colormatch.py\")",
+      "Bash(python lora_profile.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/models/loras\")",
+      "Bash(python -m py_compile dolphin_vision_director.py lora_profile.py)",
+      "Bash(python -m py_compile dolphin_vision_director.py dolphin_prompt_director.py arch_logic_v16.py lora_profile.py)",
+      "Bash(python test_weave.py)",
+      "Bash(python -m py_compile dolphin_vision_director.py dolphin_prompt_director.py arch_logic_v16.py)",
+      "PowerShell(nvidia-smi --query-gpu=name,driver_version,memory.total,memory.used,power.limit,power.max_limit,power.default_limit,temperature.gpu,pcie.link.gen.current,pcie.link.gen.max --format=csv)",
+      "PowerShell(Get-WinEvent -FilterHashtable @{LogName='System'; StartTime=\\(Get-Date\\).AddDays\\(-14\\)} -ErrorAction SilentlyContinue | Where-Object { $_.Id -in 4101,41,6008,17,18,19,1001 -or $_.ProviderName -match 'WHEA|Display|nvlddmkm' } | Select-Object -First 40 TimeCreated,Id,ProviderName,@{n='Msg';e={\\($_.Message -split \"`n\"\\)[0]}} | Format-Table -AutoSize -Wrap)",
+      "PowerShell(Get-WinEvent -FilterHashtable @{LogName='System'; ProviderName='nvlddmkm'; StartTime=\\(Get-Date\\).AddDays\\(-30\\)} -ErrorAction SilentlyContinue | Group-Object Id | Select-Object Name,Count | Format-Table -AutoSize; \"---- burst dates ----\"; Get-WinEvent -FilterHashtable @{LogName='System'; ProviderName='nvlddmkm'; StartTime=\\(Get-Date\\).AddDays\\(-30\\)} -ErrorAction SilentlyContinue | Group-Object {$_.TimeCreated.ToString\\('yyyy-MM-dd HH:mm'\\)} | Select-Object Name,Count | Format-Table -AutoSize)",
+      "PowerShell($e = Get-WinEvent -FilterHashtable @{LogName='System'; ProviderName='nvlddmkm'; StartTime=\\(Get-Date\\).AddDays\\(-30\\)} -ErrorAction SilentlyContinue | Select-Object -First 3; foreach \\($x in $e\\) { \"=== Id=$\\($x.Id\\)  $\\($x.TimeCreated\\) ===\"; \"MSG: \" + $x.Message; $b=$x.Properties | ForEach-Object { $_.Value }; \"RAWCOUNT: \" + $b.Count })",
+      "PowerShell(\"--- TDR settings ---\"; Get-ItemProperty 'HKLM:\\\\SYSTEM\\\\CurrentControlSet\\\\Control\\\\GraphicsDrivers' -ErrorAction SilentlyContinue | Select-Object TdrDelay,TdrDdiDelay,TdrLevel,TdrLimitTime,TdrLimitCount | Format-List; \"--- Kernel-Power 41 history \\(30d\\) ---\"; Get-WinEvent -FilterHashtable @{LogName='System'; Id=41; StartTime=\\(Get-Date\\).AddDays\\(-30\\)} -ErrorAction SilentlyContinue | Select-Object TimeCreated | Format-Table -AutoSize; \"--- WHEA ---\"; Get-WinEvent -FilterHashtable @{LogName='System'; ProviderName='Microsoft-Windows-WHEA-Logger'; StartTime=\\(Get-Date\\).AddDays\\(-30\\)} -ErrorAction SilentlyContinue | Select-Object -First 10 TimeCreated,Id | Format-Table -AutoSize)",
+      "PowerShell($p='C:\\\\Program Files \\(x86\\)\\\\MSI Afterburner'; if\\(Test-Path $p\\){ Get-ChildItem \"$p\\\\Profiles\" -ErrorAction SilentlyContinue | Select-Object Name,LastWriteTime | Format-Table -AutoSize; \"=== MSIAfterburner.cfg 주요 항목 ===\"; Get-Content \"$p\\\\MSIAfterburner.cfg\" -ErrorAction SilentlyContinue | Select-String -Pattern 'PowerLimit|CoreClkBoost|CurveEditor|StartupProfile|ApplyOverclockingAtStartup|VFCurve|MemClkBoost|VoltageLimit|ThermalLimit' | Select-Object -First 30 } else { \"Afterburner 기본 경로에 없음\" })",
+      "Bash(python test_weaver.py)",
+      "Bash(python -m py_compile __init__.py dolphin_lora_prompt.py lora_profile.py dolphin_vision_director.py dolphin_prompt_director.py arch_logic_v16.py)",
+      "PowerShell($p='C:\\\\Program Files \\(x86\\)\\\\MSI Afterburner\\\\MSIAfterburner.cfg'; $c=Get-Content -LiteralPath $p; $i=[array]::IndexOf\\($c,'[Profile1]'\\); if\\($i -ge 0\\){ \"=== [Profile1] ===\"; $c[$i..\\($i+8\\)] | Where-Object { $_ -match '^\\(PowerLimit|CoreClkBoost|MemClkBoost|CoreVoltageBoost|FanMode\\)=' } }; \"=== 현재 GPU 상태 ===\"; nvidia-smi --query-gpu=power.limit,power.default_limit,clocks.max.memory,clocks.current.memory,clocks.max.graphics,power.draw,utilization.gpu --format=csv)",
+      "PowerShell($p='C:\\\\Program Files \\(x86\\)\\\\MSI Afterburner\\\\Profiles\\\\VEN_10DE&DEV_2B85&SUBSYS_176119DA&REV_A1&BUS_43&DEV_0&FN_0.cfg'; \"=== 최종 수정: \" + \\(Get-Item -LiteralPath $p\\).LastWriteTime + \" ===\"; Get-Content -LiteralPath $p | Select-String -Pattern '^\\\\[|^PowerLimit=|^MemClkBoost=|^CoreClkBoost=|^CoreVoltageBoost=' | Select-Object -First 40)",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\cmp_lightx.py\" \"$s\\\\lightx.json\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\deep.py\" \"$s\\\\deep.json\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\deep2.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\seko.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\gguf_keys.py\" \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\models\\\\unet\\\\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf\" \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\models\\\\unet\\\\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf\")",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/bafbbba1-90c6-473d-adca-987f2886303d/scratchpad/test_energy.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/bafbbba1-90c6-473d-adca-987f2886303d/scratchpad/test_energy.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\schedule.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\inspect_wf.py\" 2>&1 | Select-Object -First 130)",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\inspect_wf.py\" 2>&1 | Select-String -Pattern 'SUBGRAPH|TripleKSampler|NAG|EmptyHunyuan|INTConstant|LoraLoader|widgets|IN  ' | Select-Object -First 90)",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\split_trace.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\verify_beats.py\")",
+      "PowerShell($s=\"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\3550f829-5fde-43b7-b6dd-cb0068774acd\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"$s\\\\lora_matrix.py\")",
+      "PowerShell(try { $r = Invoke-WebRequest -Uri \"http://127.0.0.1:8188/system_stats\" -UseBasicParsing -TimeoutSec 5; $r.Content } catch { \"NOT RUNNING: $\\($_.Exception.Message\\)\" })",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\inspect_svi_lora.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\inspect2.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\make_variants.py\")",
+      "Bash(cd /c/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/c0afbfdf-4086-4bd7-97c2-9a00be688031/scratchpad && ls -la /d/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/painter10s.json /d/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0801.json)",
+      "Bash(python dump.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/painter10s.json\")",
+      "Bash(python dump.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0801.json\")",
+      "Bash(PYTHONIOENCODING=utf-8 python dump.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/painter10s.json\")",
+      "Bash(PYTHONIOENCODING=utf-8 python dump.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0801.json\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\motion_metric2.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\ab2_metrics.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '150,240p' nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '440,600p' nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '836,880p' nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '880,900p' nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python dumpsub.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0801.json\")",
+      "Bash(echo \"--- subgraph diff rc=$? ---\")",
+      "Bash(echo \"--- toplevel diff rc=$? ---\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\inspect_0801.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\inspect_0801b.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python make_0801A.py)",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\sigma_sched.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\inspect_0801c.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 python make_0801B.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '527,560p' dolphin_vision_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"seed\" dolphin_vision_director.py)",
+      "Read(//d/SD/ComfyUI_windows_portable/ComfyUI/temp/**)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"CACHE_DIR\" /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/disk_cache_provider.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '405,412p' dolphin_vision_director.py)",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\ab3_metrics.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\find_setnode.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\apply_steps8.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\state_and_flicker.py\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b07deb9e-9dc0-45e2-baa2-db286b327e37\\\\scratchpad\\\\nag_chain.py\")",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rln \"PainterI2V\" --include=*.py .)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rn \"PainterI2V\\\\w*\" --include=*.py . -o)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -oh \"\\\\\"type\\\\\": \\\\\"Painter[^\\\\\"]*\\\\\"\" *.json)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rloh \"PainterI2V_S\\\\|painteri2v_s\\\\|I2V_S\" *.json)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '1,150p' /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/ComfyUI-PainterI2Vadvanced/nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rn \"NODE_CLASS_MAPPINGS\" -A 12 /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class \\\\|node_id=\\\\|NODE_CLASS\\\\|display_name\\\\|def define_schema\\\\|Input\\(\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python dump.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/PainterI2V_S.json\")",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '40,75p' dolphin_prompt_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"RETURN_TYPES\\\\|RETURN_NAMES\\\\|num_clips\\\\|clip_count\\\\|10s\\\\|15s\" dolphin_prompt_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class StringConcatenate\\\\|node_id=\\\\\"String\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_string.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '39,64p' /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_string.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class ContextSchedules\" -A 12 /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class ContextFuseMethods\" -A 14 /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python make_ctx.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class Create Hook\\\\|node_id=\\\\|display_name=\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_hooks.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"start_percent\\\\|class HookKeyframe\\\\b\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/hooks.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"split_conds_to_windows\\\\|region_index\\\\|def.*split_cond\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"cond_retain_index_list\\\\|retain_index\\\\|c_concat\\\\|concat_latent\\\\|def get_resized_cond\\\\|sub_idxs\" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '66,90p' /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '505,525p' /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '113,132p' /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '40,110p' /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/Comfyui-PainterFLF2V/nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python restore_flf.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class PrimitiveBoolean\" -A 20 /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_primitive.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '129,200p' /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/ComfyUI-Crystools/nodes/switch.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"node_id=\\\\|class \" /d/SD/ComfyUI_windows_portable/ComfyUI/comfy_extras/nodes_primitive.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python elegant_switch.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python audit_unused.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python cleanup.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python diff_effective.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"def common_ksampler\" -A 12 /d/SD/ComfyUI_windows_portable/ComfyUI/nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"vibe\\\\|pacing\\\\|PACING\\\\|beat\" dolphin_vision_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '366,392p' dolphin_vision_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '695,712p' dolphin_vision_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '820,835p' dolphin_vision_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"inject_triggers\\\\|trigger\" dolphin_prompt_director.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"def create_windows_uniform_standard\" -A 32 /d/SD/ComfyUI_windows_portable/ComfyUI/comfy/context_windows.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rn \"class ImageFromBatch\" -A 22 /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/ComfyUI_essentials/image.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rln \"ImageFromBatch+\" /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/*/[a-z]*.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python cmp_svi.py \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_OK.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/SVI_0801C.json\")",
+      "Bash(PYTHONIOENCODING=utf-8 python make_0802ok.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python fix_base_weights.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python deep_cmp.py SVI_OK)",
+      "Bash(PYTHONIOENCODING=utf-8 python deep_cmp.py SVI_0802_OK)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rn \"WanImageToVideoSVIProMotion_Dolphin\" __init__.py *.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"RETURN_NAMES\\\\|RETURN_TYPES\\\\|motion_amplitude\\\\|amplitude_mode\\\\|def encode\\\\|if motion_amplitude\\\\|latents_mean\\\\|process_out\\\\|return \\(\" wan_svipro_motion.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"_amplify\" -A 14 wan_svipro_motion.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python parse_meta.py meta_ok.txt)",
+      "Bash(PYTHONIOENCODING=utf-8 python pin_prompt.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python make_10s_smooth.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rn \"class ImageBatchExtendWithOverlap\" -A 40 /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/*/*.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -rln \"ImageBatchExtendWithOverlap\" --include=*.py .)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class ImageBatchExtendWithOverlap\" -A 60 /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 sed -n '2156,2215p' /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 grep -n \"class ColorMatchV2\" -A 30 /d/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py)",
+      "Bash(PYTHONIOENCODING=utf-8 python smooth_v2.py)",
+      "Bash(mkdir -p C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fa088da8-83db-496b-ae2c-8203c1b258d5/scratchpad/dircheck/raw)",
+      "Bash(./python.exe D:/SD/ComfyUI_windows_portable/ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/build.py --input C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fa088da8-83db-496b-ae2c-8203c1b258d5/scratchpad/dircheck/raw --output C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/fa088da8-83db-496b-ae2c-8203c1b258d5/scratchpad/dircheck/out --trigger test --captioner wd14)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"Name='python.exe' or Name='accelerate.exe'\")",
+      "Bash(nvidia-smi --query-gpu=memory.used,memory.total,utilization.gpu --format=csv)",
+      "PowerShell(Get-CimInstance Win32_Process -Filter \"Name='python.exe'\")",
+      "Bash(tasklist //FI \"PID eq 40764\")",
+      "PowerShell(Get-NetTCPConnection -LocalPort 7861 -State Listen -ErrorAction SilentlyContinue)",
+      "Bash(netstat -ano -p tcp)",
+      "Bash(taskkill //F //T //PID 48048)",
+      "Bash(taskkill //F //T //PID 47888)",
+      "Bash(tasklist //FI \"PID eq 47888\" //FI \"PID eq 49728\" //FI \"PID eq 21336\" //FI \"PID eq 30148\" //FI \"PID eq 32348\")",
+      "PowerShell(Get-ChildItem \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\output\" -Force | Where-Object { $_.LinkType } | Select-Object Name, LinkType, @{n='Target';e={$_.Target}} | Format-List)",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" -c \"import os;d=r'D:\\\\SD\\\\output';t=os.path.join\\(d,'I2V','RAW'\\);print\\('realpath dir :',os.path.realpath\\(d\\)\\);print\\('realpath targ:',os.path.realpath\\(t\\)\\);print\\('common:',os.path.commonpath\\(\\(os.path.realpath\\(d\\),os.path.realpath\\(t\\)\\)\\)\\)\")",
+      "PowerShell(git -C \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\" log -5 --format=\"%h %ad %s\" --date=short -- folder_paths.py)",
+      "PowerShell(git -C \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\" log -S \"def is_within_directory\" --format=\"%h %ad %s\" --date=short -- folder_paths.py)",
+      "PowerShell(git -C \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\" log -1 --format=\"%h %ad %s\" --date=iso)",
+      "PowerShell(\"=== when was is_within_directory introduced ===\")",
+      "PowerShell(\"=== HEAD ===\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" -s -c \"import sys; sys.path.insert\\(0, r'D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI'\\); import folder_paths; folder_paths.set_output_directory\\(r'D:\\\\sd\\\\output'\\); print\\(folder_paths.get_save_image_path\\('I2V/RAW/test', folder_paths.get_output_directory\\(\\), 1280, 720\\)\\)\")",
+      "PowerShell(\"=== dolphin_nodes tools dir? ===\")",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\tools\\\\reapply_symlink_output_patch.ps1\"; \"exit=$LASTEXITCODE\")",
+      "Bash(mkdir -p /d/sd/models/loras/Krea2)",
+      "Bash(curl -L --fail -o \"/d/sd/models/loras/Krea2/krea2_identity_edit_v1_2.safetensors.part\" \"https://huggingface.co/conradlocke/krea2-identity-edit/resolve/main/krea2_identity_edit_v1_2.safetensors\" -w \"\\\\nHTTP %{http_code} | %{size_download} bytes | %{speed_download} B/s\\\\n\" -s --show-error)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\")",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(ok|FAIL|ALL|SOME|---|\\\\[|At |She |Yuna|dusk|fallback\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_reg.py\" 2>&1 | Select-String -Pattern 'registered|widgets|missing|extra|RETURN|Minimax')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(ok |FAIL|  got|  want|ALL|SOME|20s fallback|⚠\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(FAIL|  got|  want|ALL|SOME|20s fallback|fallback:\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_reg.py\" 2>&1 | Select-String -Pattern 'registered|widgets|missing|extra|RETURN|Minimax 로드')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_e2e.py\" 2>&1 | Select-Object -Skip 2)",
+      "Bash(nvidia-smi --query-gpu=name,memory.total,memory.used,memory.reserved,utilization.gpu,power.draw,clocks.sm --format=csv)",
+      "Bash(nvidia-smi --query-compute-apps=pid,used_memory,name --format=csv)",
+      "PowerShell(Get-PhysicalDisk | Select-Object DeviceId,FriendlyName,MediaType,BusType,@{n='SizeGB';e={[math]::Round\\($_.Size/1GB\\)}} | Format-Table -AutoSize; Get-Partition | Where-Object DriveLetter -in 'C','D' | Select-Object DriveLetter,DiskNumber; Get-CimInstance Win32_PageFileUsage | Select-Object Name,AllocatedBaseSize,CurrentUsage)",
+      "PowerShell($os = Get-CimInstance Win32_OperatingSystem; $cs = Get-CimInstance Win32_ComputerSystem; [PSCustomObject]@{ AutoManagedPagefile=$cs.AutomaticManagedPagefile; TotalRAM_GB=[math]::Round\\($cs.TotalPhysicalMemory/1GB,1\\); FreeRAM_GB=[math]::Round\\($os.FreePhysicalMemory/1MB,1\\); CommitLimit_GB=[math]::Round\\($os.TotalVirtualMemorySize/1MB,1\\); CommitUsed_GB=[math]::Round\\(\\($os.TotalVirtualMemorySize-$os.FreeVirtualMemory\\)/1MB,1\\) } | Format-List; Get-CimInstance Win32_PageFileSetting | Select-Object Name,InitialSize,MaximumSize)",
+      "Bash(PYTHONIOENCODING=utf-8 ../python_embeded/python.exe -c ' *)",
+      "WebFetch(domain:docs.ltx.io)",
+      "Bash(/d/SD/ComfyUI_windows_portable/python_embeded/python.exe -X utf8 -c ' *)",
+      "Bash(grep -n \"relative_path\\\\|os.sep\\\\|replace\\(\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\" /d/SD/ComfyUI_windows_portable/ComfyUI/folder_paths.py)",
+      "PowerShell(& \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b860b1fe-4dc7-4bed-a239-3b1ec6dd53e9\\\\scratchpad\\\\scan.ps1\")",
+      "PowerShell(& \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b860b1fe-4dc7-4bed-a239-3b1ec6dd53e9\\\\scratchpad\\\\scan2.ps1\")",
+      "PowerShell($p=\"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\.claude\"; if \\(Test-Path $p\\) { Get-ChildItem $p -Recurse -File | Select-Object FullName } else { \"no .claude dir\" }; \"--- user settings ---\"; if \\(Test-Path \"$env:USERPROFILE\\\\.claude\\\\settings.json\"\\) { Get-Content \"$env:USERPROFILE\\\\.claude\\\\settings.json\" -Raw } else { \"none\" })",
+      "Bash(awk '{print $1}')",
+      "Bash(git --no-pager log --stat -1)",
+      "Bash(git --no-pager log --oneline -3)",
+      "WebFetch(domain:kingy.ai)",
+      "WebFetch(domain:blog.comfy.org)",
+      "Bash(wmic ComputerSystem get TotalPhysicalMemory)",
+      "WebFetch(domain:www.kombitz.com)",
+      "WebFetch(domain:www.videotoprompt.app)",
+      "WebFetch(domain:mixio.studio)",
+      "WebFetch(domain:www.photogptai.com)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; Set-Location \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" fix.py)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(FAIL|  got|  want|ALL|SOME\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_e2e.py\" 2>&1 | Select-Object -Skip 30)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_reg.py\" 2>&1 | Select-String -Pattern 'registered|widgets|missing|extra|RETURN')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; Set-Location \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" patch2.py)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(FAIL|  got|  want|ALL|SOME|Yuna, a woman\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_e2e.py\" 2>&1 | Select-String -Pattern '^\\(\\\\[|Yuna|First|=====|>>>|⚠|AssertionError|Traceback\\)' -Context 0,1)",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_minimax.py\" 2>&1 | Select-String -Pattern '^\\(FAIL|  got|  want|ALL|SOME\\)'; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_e2e.py\" 2>&1 | Select-String -Pattern '^\\(>>>|AssertionError|For roughly|Yuna\\\\. For\\)')",
+      "PowerShell($env:PYTHONIOENCODING=\"utf-8\"; & \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\python_embeded\\\\python.exe\" \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\b67454ac-dcc9-436c-a752-89e8afa7fd38\\\\scratchpad\\\\test_reg.py\" 2>&1 | Select-String -Pattern 'registered|widgets|missing|extra')",
+      "Bash(mkdir -p D:/SD/ComfyUI_windows_portable/ComfyUI/models/loras)",
+      "Bash(curl -L -o minimax_h3_turbo_4step_ema_ckpt850_pruned_comfyui.safetensors.part \"https://huggingface.co/drbaph/MiniMax-H3-Turbo-Lora-ComfyUI/resolve/main/minimax_h3_turbo_4step_ema_ckpt850_pruned_comfyui.safetensors\" --progress-bar)",
+      "Bash(python_embeded/python.exe \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/bcff1cb2-b893-4caa-b23f-9593c85aa7f9/scratchpad/build_h3_extend.py\")",
+      "Bash(sed -i '22s/다섯 가지/SVI\\\\/COMP\\\\/ANI\\\\/COMB\\\\/GROK\\\\/LTX\\\\/H3/' rename.py)",
+      "Bash(command -v python3.exe)",
+      "Bash(\"/d/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -m py_compile rename.py)",
+      "WebFetch(domain:sonusahani.com)",
+      "WebFetch(domain:x.com)",
+      "Bash(gh repo *)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/H3_I2V_turbo_2.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/H3_I2V_turbo_2.json.bak\" *)",
+      "WebFetch(domain:jo-nike.github.io)",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/4eaf8adb-112d-4cf0-973e-596238ffa388/scratchpad/integrate_motion_context.py\")",
+      "Bash(python \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/4eaf8adb-112d-4cf0-973e-596238ffa388/scratchpad/validate_workflow.py\")",
+      "Bash(curl -s \"https://api.github.com/search/code?q=video_wan_animate2+repo:Comfy-Org/workflow_templates\")",
+      "Bash(curl -s \"https://api.github.com/repos/Comfy-Org/workflow_templates/git/trees/main?recursive=1\")",
+      "Bash(curl -s \"https://raw.githubusercontent.com/Comfy-Org/workflow_templates/main/templates/video_wan_animate2.json\" -o \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/6375561a-517b-4f36-9a22-cda969f4d956/scratchpad/video_wan_animate2.json\")",
+      "Bash(py -3 -c \"print\\('ok'\\)\")",
+      "Bash(py -3 -c ' *)",
+      "Bash(./python_embeded/python.exe -s -m pip install -r ComfyUI/requirements.txt)",
+      "Bash(cp \"C:/Users/bjoo/AppData/Local/Temp/claude/D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes/6375561a-517b-4f36-9a22-cda969f4d956/scratchpad/video_wan_animate2.json\" \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/default/workflows/video_wan_animate2.json\" && echo copied)",
+      "Bash(curl -sI \"https://huggingface.co/Comfy-Org/Wan-Animate-2/resolve/main/diffusion_models/wan_animate_2_int8_convrot.safetensors\")",
+      "Bash(curl -sI \"https://huggingface.co/Comfy-Org/Wan-Animate-2/resolve/main/loras/lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors\")",
+      "Bash(curl -sIL \"https://huggingface.co/Comfy-Org/Wan-Animate-2/resolve/main/diffusion_models/wan_animate_2_int8_convrot.safetensors\")",
+      "Bash(curl -sIL \"https://huggingface.co/Comfy-Org/Wan-Animate-2/resolve/main/loras/lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors\")",
+      "Bash(curl -sL \"https://raw.githubusercontent.com/Comfy-Org/workflow_templates/refs/heads/main/input/pink_hair_mech_arms_ref.png\" -o pink_hair_mech_arms_ref.png)",
+      "Bash(curl -sL \"https://raw.githubusercontent.com/Comfy-Org/workflow_templates/refs/heads/main/input/street_dance_drive.mp4\" -o street_dance_drive.mp4)",
+      "Bash(curl -L \"https://huggingface.co/Comfy-Org/Wan-Animate-2/resolve/main/loras/lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors\" -o lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors --progress-bar)",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=3 -o /tmp/history.json)",
+      "Bash(curl -sv http://127.0.0.1:8188/system_stats)",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=5 -o 'C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\d26bed8e-b31a-41a3-8a79-051cab0eea70\\\\scratchpad/history.json')",
+      "Bash(curl -s http://127.0.0.1:8188/history?max_items=50 -o 'C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\d26bed8e-b31a-41a3-8a79-051cab0eea70\\\\scratchpad/history_full.json')",
+      "Bash(curl -s http://127.0.0.1:8188/queue -o 'C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\d26bed8e-b31a-41a3-8a79-051cab0eea70\\\\scratchpad/queue.json')",
+      "Bash(python -m py_compile dolphin_minimax_director.py)",
+      "Bash(python -m py_compile dolphin_vision_director.py dolphin_minimax_director.py dolphin_prompt_director.py)",
+      "Bash(python3)",
+      "Bash(cat)",
+      "Bash(python /tmp/test_split.py)",
+      "Bash(export PYTHONIOENCODING=utf-8)",
+      "Bash(find / -iname \"*nsfw*mmaudio*\" -o -iname \"*mmaudio*nsfw*\" 2>/dev/null | head -50)",
+      "Bash(find / -ipath \"*mmaudio*\" 2>/dev/null | grep -v \"custom_nodes/.*\\\\.py\" | head -100)",
+      "Bash(kill %1 %2)",
+      "Bash(jobs -l)",
+      "WebFetch(domain:comfyai.run)",
+      "Bash(npm --version)",
+      "WebFetch(domain:opencof.github.io)",
+      "WebFetch(domain:www.patreon.com)",
+      "Bash(gh auth *)",
+      "PowerShell(winget list --id GitHub.cli 2>&1)",
+      "PowerShell(winget install --id GitHub.cli -e --accept-source-agreements --accept-package-agreements)",
+      "Bash(curl -s -m 3 http://127.0.0.1:8188/ -o /dev/null -w \"%{http_code}\\\\n\")",
+      "Bash(\"/c/SD/ComfyUI_windows_portable/python_embeded/python.exe\" cm-cli.py --help)",
+      "Bash(\"/d/SD/ComfyUI_windows_portable/python_embeded/python.exe\" cm-cli.py --help)",
+      "Bash(\"/d/SD/ComfyUI_windows_portable/python_embeded/python.exe\" cm-cli.py save-snapshot)",
+      "Bash(python3 -c \"import json;d=json.load\\(open\\('2026-08-12_12-32-11_snapshot.json'\\)\\);print\\(list\\(d.keys\\(\\)\\)\\);print\\('git_custom_nodes count:',len\\(d.get\\('git_custom_nodes',{}\\)\\)\\)\")",
+      "Bash(\"/d/SD/ComfyUI_windows_portable/python_embeded/python.exe\" -c \"import json;d=json.load\\(open\\('2026-08-12_12-32-11_snapshot.json'\\)\\);print\\(list\\(d.keys\\(\\)\\)\\);print\\('git_custom_nodes count:',len\\(d.get\\('git_custom_nodes',{}\\)\\)\\)\")",
+      "Bash(/d/SD/ComfyUI_windows_portable/python_embeded/python.exe -c ' *)",
+      "Bash(mkdir -p cloud_env)",
+      "Bash(cp \"D:/SD/ComfyUI_windows_portable/ComfyUI/user/__manager/snapshots/2026-08-12_12-32-11_snapshot.json\" cloud_env/comfyui_env_snapshot.json)",
+      "Bash(git push *)",
+      "Bash(where ffmpeg *)",
+      "Bash(ffmpeg -version)",
+      "Bash(npm install *)",
+      "PowerShell(\\(Get-Content 'D:\\\\orga.py' -Raw\\) -replace [regex]::Escape\\(\"            shutil.move\\(src, dst\\)`r`n            return True, None\"\\), \"            shutil.move\\(src, dst\\)`r`n            return True, dst\" | Set-Content 'D:\\\\orga.py' -NoNewline)",
+      "Bash(python -m py_compile \"/d/orga.py\")",
+      "Bash(ls -la /d/ 2>&1 | head -5; echo \"---\"; ls -la \"D:/orga.py\" 2>&1)",
+      "Bash(python -m py_compile \"D:/orga.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga2.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga4.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga5.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga6.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga7.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga8.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga9.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga11.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga12.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga13.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga14.py\")",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga15.py\")",
+      "Bash(python3 -)",
+      "Bash(python -m py_compile \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\d2b174de-a935-400d-9712-b631f10d35b9\\\\scratchpad\\\\dedupe_folders_gui.py\")",
+      "Bash(python -m py_compile \"D:\\\\dedupe_folders.py\" \"D:\\\\dedupe_folders_gui.py\")",
+      "Bash(awk '{print $5\"MB\", $NF}')",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\tools\\\\reapply_symlink_output_patch.ps1\" -Verify)",
+      "PowerShell(& \"D:\\\\SD\\\\ComfyUI_windows_portable\\\\ComfyUI\\\\custom_nodes\\\\dolphin_nodes\\\\tools\\\\reapply_symlink_output_patch.ps1\")",
+      "Bash(./yt-dlp.exe --version)",
+      "Bash(./yt-dlp.exe -U)",
+      "Bash(./yt-dlp.exe -f bestaudio/best --no-playlist -o \"test_%\\(title\\)s.%\\(ext\\)s\" \"https://www.youtube.com/watch?v=bMhDJ0S0OBA\")",
+      "Bash(\"/c/Users/bjoo/anaconda3/pkgs/ffmpeg-6.1.1-hc79a5da_3/Library/bin/ffmpeg.exe\" -version)",
+      "PowerShell(& \"C:\\\\Users\\\\bjoo\\\\anaconda3\\\\pkgs\\\\ffmpeg-6.1.1-hc79a5da_3\\\\Library\\\\bin\\\\ffmpeg.exe\" -version 2>&1)",
+      "PowerShell(& \"C:\\\\Program Files\\\\Krita \\(x64\\)\\\\bin\\\\ffmpeg.exe\" -version 2>&1)",
+      "Bash(ls \"/c/Program Files/Krita \\(x64\\)/bin/\" | grep -i \"ffprobe\\\\|ffmpeg\")",
+      "Bash(rm -f \"ILLIT\"*.webm \"ILLIT\"*.mp3)",
+      "Bash(./yt-dlp.exe -o \"verify_test.%\\(ext\\)s\" --ffmpeg-location \"C:\\\\Program Files\\\\Krita \\(x64\\)\\\\bin\" -x --audio-format mp3 -f bestaudio/best --no-playlist \"https://www.youtube.com/watch?v=bMhDJ0S0OBA\")",
+      "Bash(./yt-dlp.exe -o \"%\\(title\\)s.%\\(ext\\)s\" --ffmpeg-location \"C:\\\\Program Files\\\\Krita \\(x64\\)\\\\bin\" -x --audio-format mp3 -f bestaudio/best --no-playlist --force-overwrites -v \"https://www.youtube.com/watch?v=bMhDJ0S0OBA\")",
+      "PowerShell([System.Text.Encoding]::Default.EncodingName; chcp)",
+      "Bash(sort -k6,7)",
+      "WebFetch(domain:openrouter.ai)",
+      "Bash(python -m py_compile dolphin_vision_director.py dolphin_minimax_director.py)",
+      "Bash(node --check web/dolphin_preset_sync.js)",
+      "Bash(python \"C:\\\\Users\\\\bjoo\\\\AppData\\\\Local\\\\Temp\\\\claude\\\\D--SD-ComfyUI-windows-portable-ComfyUI-custom-nodes-dolphin-nodes\\\\0a101ba9-4e7e-41e4-b61a-533bc9170100\\\\scratchpad\\\\patch_orga17.py\")"
+    ],
+    "defaultMode": "bypassPermissions"
+  }
+}

custom_nodes/dolphin_nodes/.gitignore

@@ -0,0 +1,22 @@
+__pycache__/
+*.pyc
+
+# runtime logs, regenerated on every ComfyUI restart
+comfyui_restart_log.txt
+comfyui_restart_log.txt.err
+*.log
+
+# regenerable local cache
+dolphin_tags_cache.json
+
+# personal/local Claude Code settings (machine-specific)
+.claude/settings.local.json
+
+# isolated musubi-tuner training env (nested git repo + venv, not part of this repo)
+dataset_builder/train/venv/
+dataset_builder/train/musubi_tuner/
+dataset_builder/train/cache/
+dataset_builder/train/output/
+
+# downloadable model weights (YOLO detector for dataset_builder/detect.py, etc.)
+*.pt

custom_nodes/dolphin_nodes/HANDOFF.md

@@ -0,0 +1,192 @@
+# Dolphin ComfyUI 노드 프로젝트 — 인수인계
+
+## 프로젝트 목적
+ComfyUI에서 **이미지 1장(또는 텍스트 서사) + 로라 + 필수동작**을 넣으면
+**4개의 연속된 5초 비디오 클립(총 20초)** 을 만드는 커스텀 노드 세트.
+
+비디오 체인은 WAN 2.2 Lightning 기반: `First`(clip1) + `Extend`×3(clip2~4 이어붙이기),
+각 클립마다 TripleKSampler(base/high/low 3스테이지)로 샘플링.
+
+---
+
+## 현재 쓰는 파일 (7개 노드 + 워크플로우)
+
+### 노드 코드 (커스텀 노드 폴더에 넣기)
+1. **`__init__.py`** — 아래 5개 노드를 등록. 각 등록은 개별 try/except.
+2. **`wan_resizer.py`** — `WanNativeResize_Dolphin`. 이미지를 WAN 해상도(480/720/1080P)로 비율 유지 리사이즈 + 정렬(16배수). (원본 그대로, 수정 안 함)
+3. **`promp_logic_v32_story.py`** — `DolphinMultiActionPromptNode_V32`. 텍스트 서사 → LLM이 4샷 프롬프트로 분배. JSON 강제 + 폴백. 모델 프리셋 드롭다운 있음.
+4. **`arch_logic_v16.py`** — `DolphinArchitectV16`. 클립별 LoRA를 model에만 적용(CLIP 로라 연산 제거로 속도↑) → TripleKSampler 공급. 트리거 통합 1출력. 해시 캐싱.
+5. **`dolphin_director.py`** — `DolphinCinematicDirector`. ⚠️ **사실상 고장난 죽은 코드** (2026-07-03 확인). `_llm_split()`는 아무것도 반환하지 않고 호출되지도 않음, `direct()`는 `mode`/LLM 호출을 무시하고 `clips = [master_story] * 4`로 하드코딩, 로라도 전혀 적용 안 함. `__init__.py`엔 등록돼 있지만 실사용 금지 — 쓰려면 사실상 재작성 필요.
+6. **`dolphin_vision_director.py`** — `DolphinVisionDirector`. **최신 주력 노드**. 이미지 직접 인식(VLM) + 필수동작 N샷 분배(`num_clips`) + 클립당 로라 4개(개별 웨이트, on/off 토글) + 트리거 자동 추출/주입.
+7. **`dolphin_prompt_director.py`** — `DolphinPromptDirector` (2026-07-21 신설). `DolphinVisionDirector`와 **완전히 같은 비전+LLM 프롬프트 엔진**을 쓰되 `MODEL`/로라 관련을 전부 제거하고 프롬프트 문자열(`c1~c4_prompt`)만 뽑는 버전. 로직 중복을 피하려고 `DolphinVisionDirector`를 내부에서 인스턴스화해 `_vision_analyze`/`_plan_shots`/`_local_split`/`_build_final` 등 로라를 안 건드리는 순수 메서드만 위임 호출 — 프롬프트 정책(연속성 규칙 등)을 고치면 `dolphin_vision_director.py` 한 곳만 고치면 이 노드에도 자동 반영됨. 로라/모델 적용은 `DolphinArchitectV16` 같은 별도 노드에서 처리한다는 전제.
+
+### 워크플로우 JSON
+- **`0705_vision2.json`** — **현재 실사용 중인 최신 워크플로우** (2026-07-03 기준). VisionDirector + JoyCaption(`JC` 노드) 로컬 캡션 우선 사용, `Model Loader`(f9a6f9ae 서브그래프)에서 lightx2v rank256 + SVI_v2_PRO rank128 사전 적용 → DolphinVisionDirector에서 클립별 PENISLORA(rank32) 패치 → ModelSamplingSD3 → NAG → TripleKSamplerWan22LightningAdvanced(First/Extend). 61개 노드, 서브그래프 16개.
+- `0705_vision.json` — vision2 이전 버전.
+- `0705_director.json` — CinematicDirector(텍스트) 버전. 위 5번 참고 — 노드 자체가 고장나 있어 이 워크플로우도 실동작 안 함.
+- `0705_optimized.json` — V32 + Architect V16 분리형 버전.
+(원본은 사용자가 처음 올린 `0705.json`. API 키는 vision/director/optimized 등 배포용 JSON에선 비워두는 게 원칙이나, `0705_vision2.json`은 사용자 로컬 작업본이라 실키가 들어있음 — 아래 "미해결" 참고.)
+
+---
+
+## DolphinVisionDirector 상세 (주력 노드)
+
+### 입력
+- `image` (IMAGE) — WanNativeResize 출력에서
+- `model_base / model_high / model_low` (MODEL) — Model Loader에서
+- `essential_actions` — **필수 동작만** 콤마로. LLM이 이걸 N샷으로 분배.
+- `num_clips` (2026-07-03 추가) — `"4 (20s)"`/`"3 (15s)"`/`"2 (10s)"`, 기본 4. 실제 렌더링/저장될 클립 수를 여기서 단일 결정 (아래 "클립 개수 중앙 제어" 참고).
+- `character_name` (AUTO면 LLM이 작명), `artistic_vibe`, `pacing`, `detail_level`
+- `inject_triggers`(토글), `use_vision`(토글)
+- `openrouter_api_key`
+- `vlm_preset` + `vlm_model` — 비전 모델 (기본 Qwen2.5-VL 72B)
+- `llm_preset` + `llm_model` — 텍스트 분배 모델 (기본 DeepSeek V3.2)
+- `creativity`, `seed`
+- 로라: **`c{1..4}_lora_{1..3}`** (드롭다운) + **`c{1..4}_w_{1..3}`** (웨이트 문자열 `"base, high, low"`)
+- `manual_triggers`
+
+### 출력 (23개, 2026-07-03 기준)
+- `c1_base, c1_high, c1_low, c1_prompt` × 4클립 (16개) — `num_clips`보다 큰 클립은 base/high/low가 `ExecutionBlocker(None)`
+- `triggers` — 사용된 모든 로라 트리거 통합
+- `scene_analysis` — VLM이 이미지에서 읽은 앵커
+- `debug_info`
+- `c1_is_final, c2_is_final, c3_is_final, c4_is_final` (2026-07-03 추가) — `c == num_clips`인 클립만 True. 각 단계별 VideoCombine의 `save_output` 입력에 연결해서 선택한 길이만 저장되게 함.
+
+### 로라 웨이트 규칙 (스크린샷의 Triple Lora Matrix 방식 채택)
+- 로라당 웨이트를 `"base, high, low"` 한 줄로. 예: `0.3, 0.8, 0.0`
+- 값 부족 시 마지막 값으로 채움 (`1.0`→전부 1.0, `0.5, 0.7`→base 0.5/high·low 0.7)
+- **스테이지 분리 로라 지원**: WAN 2.2에서 HIGH 로라는 `0.3, 0.8, 0.0`(low=0), LOW 로라는 `0.0, 0.0, 0.4`(base·high=0)로 나눠 씀. 검증 완료.
+- 웨이트 0인 스테이지는 그 로라를 안 붙임.
+
+### 프롬프트 설계 (사용자 요구 반영 완료)
+- **동작 순서 보존**: essential_actions는 시간순. 뒤 동작은 clip3 후반~clip4에만 (front-loading 금지).
+- **묘사 배제**: 환경/조명/의상/분위기 다 빼고 **동작(신체 움직임)만**.
+- **타임코드 제거**: `[0-1s]` 방식은 WAN I2V에서 노이즈라 뺌.
+- **슬로우모션 방지**: (1) 5초를 연속 동작으로 채움 (2) 속도 어휘(swiftly/rapidly/explosively) (3) "real-time speed, no slow motion, no freeze" 명시.
+- master_scene은 비움(묘사 안 함).
+- 비전 역할은 "묘사"가 아니라 "앵커(캐릭터 상태/자세/환경) 추출"로 좁혀 장황함 억제.
+
+---
+
+## 모델 프리셋 (OpenRouter, NSFW/무삭제 대응)
+
+### 텍스트 LLM (4샷 분배용)
+- `deepseek/deepseek-v3.2` (기본, 무삭제+JSON안정+한국어 좋음) ← 추천
+- `deepseek/deepseek-chat-v3-0324`
+- `anthracite-org/magnum-v4-72b` (표현 자연스러움)
+- `thedrummer/cydonia-24b-v4.1` (경량)
+- `cognitivecomputations/dolphin-mistral-24b-venice-edition:free` (무료)
+- custom 선택 시 텍스트 필드값 사용
+
+### 비전 VLM (이미지 인식용)
+- `qwen/qwen2.5-vl-72b-instruct` (기본, 특수상황 강함) ← 추천
+- `qwen/qwen2.5-vl-32b-instruct` (+ `:free` 무료)
+- `mistralai/mistral-small-3.1-24b-instruct` (Pixtral)
+- `google/gemma-3-27b-it`
+
+이미지 전송: ComfyUI IMAGE 텐서 → JPEG base64 data URI → OpenRouter `image_url` content block. (PIL 필요, ComfyUI 기본 포함)
+
+---
+
+## 미해결 / 다음에 할 수 있는 것
+
+### 다음 세션 최우선 확인 사항 (2026-07-03 세션 종료 시점)
+1. **`lightning_steps` 8→4 수정 실사용 검증** — 슬로우모션 증상의 유력 원인으로 지목하고 고쳤지만(아래 3번 상세), 아직 실제 렌더로 개선 확인 안 함. ComfyUI는 이미 재시작됨(코드 변경 반영 완료), 워크플로우만 새로고침해서 한 번 돌려볼 것.
+2. **`num_clips`/`Extend range` 관계 숙지** — `num_clips`가 이제 유일한 클립 개수 컨트롤. "Extend range"(Fast Groups Bypasser) 토글은 **절대 건드리지 말 것**(구 방식과 충돌).
+3. ~~`openrouter_api_key` 평문 제거~~ **완료(2026-07-21)** — 아래 6번 참고.
+4. **git 저장소 신설(2026-07-21)** — `dolphin_nodes`에 로컬 git 초기화 + 현재 상태 전체(코드/워크플로우/`old/`/`.bak_*`) 첫 커밋 완료. 앞으로 파일 수정 전 `.bak_*`/`_v조` 파일 복사하는 습관은 그만두고 `git commit`으로 대체할 것. `old/` 폴더와 17개 `.bak_*` 파일은 이제 git 히스토리로도 안전하게 보존돼 있으니, 폴더 정리(삭제)는 다음 세션에서 사용자 확인 후 진행 가능.
+
+1. **NSFW 비전 모델 (해결)** — `0705_vision2.json`은 로컬 JoyCaption(`JC` 노드, `vision_caption` 입력)으로 OpenRouter VLM을 우회 중. `use_vision` 위젯은 **재확인 결과 이미 `true`(ON)로 켜져 있음** — JoyCaption이 성공하면 `vision_caption`을 우선 쓰고 VLM 호출 자체가 안 일어나지만, JoyCaption이 실패/빈 응답이면 이제 안전망으로 OpenRouter VLM 폴백이 작동함. 실행 로그(`debug_info`)에서도 `vlm=vision ok (local caption node)`로 정상 확인됨.
+   - 클라우드 무삭제+이미지 동시지원 모델은 여전히 드묾(Qwen2.5-VL이 그나마 관대). 완전 로컬 VLM 전환은 대안 B로 남아있음(현재는 JoyCaption 로컬 캡션으로 사실상 우회 중이라 급하지 않음).
+
+2. **로라 드롭다운 검색/필터** — 노드 코드로 불가. ComfyUI 프론트엔드 기능이라 ComfyUI 업데이트로 해결하거나 rgthree-comfy 설치 필요.
+
+3. **슬로우모션/연기 일관성**:
+   - **클립 "안"의 슬로우모션 (2026-07-03 원인 특정, 수정 적용함 — 실사용 검증 아직)**: 로라 파일명(`Wan_2_2_I2V_A14B_HIGH/LOW_lightx2v_4step_lora_...`, 그 외 D:/sd/models/loras의 다른 Lightning 로라들도 전부 "4step/4steps")이 전부 **총 4스텝 증류**를 명시하는데, 워크플로우의 TripleKSampler 4개(clip1~4, node 532/484/480/1077) 전부 `lightning_steps=8`(HIGH 4 + LOW 4, 50% 지점 분할)로 설정돼 있었음 — 증류 스텝의 **정확히 2배**. 증류된 Lightning 모델에 계산보다 많은 스텝을 쓰면 스케줄을 벗어나서 동작이 억눌리고 뭉개지는(슬로우모션처럼 보이는) 현상이 흔함. **`lightning_steps`를 4개 노드 전부 8→4로 변경 완료**(��업: `0705_vision2.json.bak_20260703_before_lightningsteps`). 나머지 값(sigma_shift=5, switch_strategy="50% of steps" 등)은 그대로 — 2 HIGH + 2 LOW로 재분배됨. **다음 세션에서 실제 렌더로 개선 여부 확인 필요**.
+     - 참고로 조사 중 "외부 ModelSamplingSD3 서브그래프(shift=5) + TripleKSampler 자체 내부 sigma_shift=5 이중 적용" 가설도 검토했으나, `comfy/model_sampling.py`의 `set_parameters()`가 매번 새 model_sampling 객체로 완전히 덮어쓰는 방식이라 **이중 적용해도 누적 안 됨**(같은 값으로 두 번 걸면 한 번 건 것과 동일) — 이건 범인 아님, 낭비적이지만 무해한 중복이라 안 건드림.
+   - **클립 "사이" 연속성(2026-07-03 해결)**: `_plan_shots` 시스템 프롬프트에 rule 3 "INTER-CLIP CONTINUITY" 추가 — clip_N이 clip_(N-1) 끝난 자세/모멘텀에서 이어서 시작하도록 명시. I2V 프레임 체인(마지막 프레임 그대로 다음 클립 시작)은 원래도 있었지만, 텍스트 프롬프트가 그 연속성을 언급 안 해서 "따로 노는 동작"처럼 보일 수 있었던 gap을 메움. 실사용 검증은 아직 안 함 — 다음 세션에서 4클립 이어보고 확인 필요.
+
+4. **동일 로라 중복 로딩** — 4클립 같은 로라면 4번 패치(클립별 유연성 위한 의도적 트레이드오프). 캐싱 최적화 가능하나 미적용.
+
+5. `dolphin_director.py`(텍스트 버전) — 위 "현재 쓰는 파일" 참고, 로라 방식 통일 이전에 **노드 자체가 동작하지 않음**. 재작성하거나 `__init__.py`에서 등록 제거할지 결정 필요.
+
+6. **`0705_vision2.json` 워크플로우 정리 항목 (2026-07-03 감사)**:
+   - ~~`use_vision` OFF → ON 권장~~ **재확인 결과 이미 `true`로 바뀌어 있었음** (핸드오프 기록이 낡았던 것, 실제 위젯값 재확인 완료).
+   - ~~죽은 `StringConcatenate` 노드(id 1062)~~ **이미 삭제돼 있음** (재확인 완료).
+   - ~~`openrouter_api_key` 위젯에 여전히 실키가 평문 저장돼 있음~~ **해결됨(2026-07-21)**: 키를 Windows User 환경변수 `OPENROUTER_API_KEY`로 옮기고, `0705_vision2.json` + 모든 `.bak_*` + `v30.json` + 중복본(`0705_vision2 (1).json`)에서 키 문자열 전부 제거 후 git 커밋(그래서 git 히스토리에도 안 들어감). `resolve_api_key()`가 위젯 비어있으면 자동으로 env var 사용. ComfyUI도 새 env var 반영을 위해 재시작함 — **다음 세션에서 debug_info가 `vlm=no api key`로 뜨진 않는지만 확인하면 끝**.
+   - `triggers` 출력 포트 미연결(디버깅용, 급하지 않음).
+
+7. **검증 완료 (2026-07-03) — 더 이상 의심 안 해도 됨**:
+   - **로라 인젝션 체인 전수 확인**: `Model Loader`(553) → `DolphinVisionDirector`(1086) → `ModelSamplingSD3` → `NAG` → `TripleKSamplerWan22LightningAdvanced`(First/Extend) 까지 4클립 전부 끊김/교차배선 없이 연결됨. 파일 경로 전부 실재, weight 0/None 오류 없음. 실행 로그(`comfyui_restart_log.txt`)에도 트리거워드 `PENISLORA` 주입 확인됨.
+   - **lightx2v는 이미 rank256으로 적용 중** (HIGH/LOW 둘 다, weight 1.0), SVI_v2_PRO rank128과 스택. `model_base`(stage1)엔 lightx2v 미적용(의도된 설계 — stage1은 non-lightning 풀스텝), `model_high`/`model_low`(stage2/3)에만 적용.
+   - PENISLORA HIGH(e320)/LOW(e496)는 rank32 (안전텐서 텐서 shape로 직접 확인).
+
+---
+
+## 최근 수정 이력 (dolphin_vision_director.py)
+
+### 2026-07-03 세션
+- **`_plan_shots` 에러 삼킴 버그 수정**: LLM 4샷 분배 API 호출 실패 시 실제 원인(HTTP 코드/본문, 예외 타입, 불완전 JSON)이 `last` 변수에만 담기고 버려져서 `debug_info`엔 항상 "⚠️ plan failed"만 찍혔음. 이제 원인이 그대로 `debug_info`/콘솔에 남음.
+- **API 키 환경변수 폴백 추가**: `resolve_api_key()` 함수 — `openrouter_api_key` 위젯이 비어있으면 `OPENROUTER_API_KEY` 환경변수 사용. 기존 위젯에 키가 있으면 동작 그대로(하위호환), 워크플로우 JSON에 실키를 안 박아둬도 되게 하려는 목적.
+- **클립 간 물리적 연속성 규칙 추가** (`_plan_shots` 시스템 프롬프트): 새 rule 3 "INTER-CLIP CONTINUITY" — clip_N은 clip_(N-1)이 끝난 정확한 신체 위치/모멘텀에서 이어서 시작(단, "continuing from" 같은 연결 문구를 텍스트에 직접 쓰는 건 금지 — 물리적 일관성만 지키고 "동작만 쓴다" 규칙과 안 부딪히게). 기존 rule 2(clip_1은 이미지 속 자세에서 시작)는 clip_1에만 적용됐던 걸 clip_2~4로 확장한 셈. 규칙 번호 3~8이 4~9로 밀림(내부 상호참조도 전부 갱신됨).
+- **미사용 데드코드 제거**: `_fallback_name_deterministic` (호출부 없음, 주석에도 "미사용"이라 표시돼 있었음).
+- **백업**: 위 변경 전 버전을 `old/dolphin_vision_director_pre_continuity_20260702.py`에 저장.
+- **`0705_vision2.json` 전수 감사** + **���라 인젝션 체인 전수 검증** — 상세는 위 "미해결" 6번/7번 항목 참고.
+- **ComfyUI 재시작 완료**: 기존 프로세스(PID 5452) 종료 후 동일 실행 옵션(`--enable-dynamic-vram --use-sage-attention --output-directory D:\sd\output`)으로 재기동, `dolphin_nodes` 에러 없이 로드 확인, `DolphinVisionDirector` API 등록 확인. 재시작 로그는 `comfyui_restart_log.txt`(stdout) / `comfyui_restart_log.txt.err`(stderr, 진행상황 대부분 여기).
+- 문법 컴파일(`py_compile`) 확인 완료. **아직 실제 4클립 생성 재실행으로 rule 3 효과는 검증 안 함** — 다음 세션 우선 확인 사항.
+
+### 2026-07-03 세션 (이어서) — 메모리 정리 / 클립 개수 중앙 제어 / 속도 실험
+
+**1. VRAM 정리 자동화 (해결)**
+- 증상: 워크플로우 실행 후 VRAM이 절반쯤 계속 점유된 채 안 비워짐. 원인은 버그가 아니라 ComfyUI의 정상 캐싱 동작(다음 실행 빠르게 하려고 마지막 쓴 모델을 VRAM에 남겨둠) — 다만 이걸 강제로 비워주는 노드가 그래프에 없었음.
+- 이미 설치돼 있던 `comfyui_memory_cleanup` 커스텀 노드(`VRAMCleanup`, 표시명 "🎈VRAM-Cleanup")를 최종 저장 노드(`VHS_VideoCombine` id 487)의 출력에 연결(node 1092, `offload_model=True, offload_cache=True`) → 렌더 끝나면 자동으로 `unload_all_models()` + `soft_empty_cache()` 실행됨. 위치는 First-I2V bypass 그룹 박스 안에 잘못 들어갔던 걸 나중에 밖으로 재배치함(그룹 좌표 [3450, -509]로 이동, 어떤 그룹에도 안 걸리게).
+- 트레이드오프: 다음 생성 시작 시 모델을 디스크에서 다시 로드해야 해서 첫 스텝 전 로딩 시간이 늘어남.
+
+**2. 클립 개수(10s/15s/20s) 중앙 제어 — `DolphinVisionDirector`가 단일 컨트롤 포인트 (해결)**
+- 처음엔 워크플로우에 이미 있던 "Extend range"(rgthree `Fast Groups Bypasser`, node 332) 토글로 clip3/4 렌더를 스킵하는 방식을 검토했으나, 이 노드는 **순수 프론트엔드 전용**(백엔드 미등록, `RgthreeBaseVirtualNode`)이라 다른 노드가 프로그래밍적으로 제어 불가 + `num_clips`와 별도로 수동 동기화해야 하는 문제가 있어서 폐기.
+- 대신 `DolphinVisionDirector`에 `num_clips` 위젯(`"4 (20s)"`/`"3 (15s)"`/`"2 (10s)"`, 기본 4, 하위호환) 추가. `_split_actions_to_clips`/`_plan_shots`(LLM 시스템 프롬프트+JSON 스키마)/`_local_split`(폴백) 전부 클립 개수에 맞춰 동적으로 동작하도록 일반화(마지막 동작=마지막 클립 규칙, 연속성 규칙 모두 N클립 대응). num_clips보다 큰 클립 인덱스는 essential_actions 배분에서 제외되고, 마지막 실사용 클립 내용을 반복해서 채움(안전장치).
+- **실제 렌더 스킵은 ComfyUI 네이티브 `ExecutionBlocker`로 구현** (`from comfy_execution.graph_utils import ExecutionBlocker`): `num_clips`보다 큰 클립의 `c{N}_base/high/low` 출력에 `ExecutionBlocker(None)`을 반환 → 그 아래 `ModelSamplingSD3→NAG→TripleKSampler→해당 클립 VideoCombine` 체인 전체가 연산 없이 자동 스킵됨(서브그래프 내부는 안 건드림 — DolphinVisionDirector가 top-level에서 서브그래프 인스턴스로 모델을 넘기는 구조라 출력 쪽만 바꾸면 됨).
+- **정확한 길이만 저장**: 새 BOOLEAN 출력 4개 `c1_is_final~c4_is_final`(RETURN_TYPES/NAMES 19→23개로 확장, `direct()`가 `c == n_clips`로 계산) 추가 → 각 단계별 `VHS_VideoCombine`(1091=5s/456=10s/464=15s/487=20s)의 `save_output` 위젯을 입력으로 변환(convert widget to input, `frame_rate`가 이미 이렇게 돼있던 패턴 재사용)해서 연결. 실행되지만 최종이 아닌 중간 클립은 미리보기로만 남고, `num_clips`가 선택한 길이 딱 하나만 디스크에 저장됨. 각 VideoCombine 파일명도 `_5s/_10s/_15s/_20s` 접미사로 구분해둠(예전엔 `Wan2`라는 구분 안 되는 접두사였음).
+- "Extend range" 노드는 이제 완전히 불필요 — **토글을 절대 건드리지 말 것**(건드리면 옛날 bypass 방식과 새 ExecutionBlocker 로직이 충돌해서 혼란스러운 결과 가능). 삭제할지는 미결정, 일단 놔둠.
+- 백업 다수: `0705_vision2.json.bak_20260703_before_vramcleanup`, `..._before_torchcompile`, `..._before_numclips`, `..._before_isfinal_wiring`, `..._before_torchcompile_revert`, `..._before_lightningsteps`.
+
+**3. 속도 실험 — 둘 다 실익 없어서 원복/포기**
+- **torch.compile**: `TorchCompileModel` 노드 3개(base/high/low, 클립당 1회만 컴파일되게 Model Loader 출력 쪽에 배치)로 테스트 → 실행 로그에 `torch._dynamo hit config.recompile_limit (8)` 반복(원인: `attention_sage`와 WAN 모델 forward의 `tensor_shape` 속성 접근이 dynamo가 추적하기 어려운 커스텀 텐서 서브클래스라 매번 재컴파일 필요 → 8회 만에 조용히 eager 폴백). 컴파일 이득 0, 오히려 실패 컴파일 시도로 시간 낭비 ��� **노드 3개 제거, 원래 직결 배선 복원함**.
+- **SageAttention 3 (Blackwell/RTX5090 전용, FP4 텐서코어, 논문상 RTX5090 5x/비디오모델 end-to-end 약 3x)**: 소스 빌드 시도했으나 순서대로 3개 문제 부딪힘 — ① PyTorch(cu130)와 시스템 nvcc(12.8) 메이저 버전 불일치(→ `torch.utils.cpp_extension._check_cuda_version` 몽키패치로 우회 성공) ② Windows `MAX_PATH`(260자) 초과로 CUTLASS 헤더를 못 찾음(→ `C:\sage3build`처럼 짧은 경로로 재클론해서 해결) ③ **CUTLASS 템플릿(`BlockScaledConfig::LayoutSF`/`SfAtom`)에서 MSVC C2061 컴파일 에러** — CUTLASS 버전을 sageattn3 코드 작성 시점(2026-01-14 커밋)까지 맞춰봐도 동일 에러 재현, 패키지 PyPI 분류자도 `"Operating System :: Unix"`라 애초에 Linux/GCC 전용 개발로 보임 → **MSVC 템플릿 2단계 name lookup 호환성 문제로 결론, 빌드 포기**. 빌드 흔적(`C:\sage3build`, 스크래치 클론) 삭제 완료, `sageattn3`은 site-packages에 설치 안 됨(ComfyUI 환경 무변경).
+  - 참고: KJNodes(`comfyui-kjnodes`)의 `PathchSageAttentionKJ` 노드(`KJNodes/experimental` 카테고리)가 `sageattn3` 모드를 이미 지원하므로, 나중에 WSL2 등 Linux 환경에서 별도로 빌드에 성공하면 **ComfyUI 코어를 안 건드리고** 이 노드만 워크플로우에 추가해서 바로 쓸 수 있음(다만 WSL에서 빌드한 건 Windows 네이티브 Python엔 못 씀 — ComfyUI 자체를 WSL로 옮기는 게 아니라면 의미 없음, 별도의 큰 결정 필요).
+  - 현재 상태: 기존 `sageattention` v2.2.0(cu130torch2.9.0 빌드) 그대로 유지, `--use-sage-attention` 플래그로 정상 작동 중. RTX 5090(sm_120) 전용 커널은 없지만(v2는 sm80/89/90까지) 크래시 없이 잘 돎.
+
+### 3대 버그 수정
+- **① 캐릭터 이름 AUTO로 샘**: 비전 앵커로 동양/서양 판단해 작명하도록 프롬프트 강화 + `_fallback_name()` 안전장치(LLM이 AUTO/빈값 반환 시 앵커 키워드로 이름 선택).
+- **② 마지막 동작을 처음부터 함**: `_split_actions_to_clips()`가 동작을 코드에서 클립별로 미리 배정(마지막 동작은 반드시 clip4), LLM엔 배정표를 못박아 전달. 순서 위반이 최우선 금지 규칙.
+- **③ 트리거워드 안 들어감**: `triggers` 출력 부활 + 클립별 로라 트리거 디버그(`_last_trig_dbg`) 콘솔 출력.
+
+### 비전 진단 강화 (사용자가 "비전 작동 의심" 제기 후)
+- `tensor_to_base64`: PIL/None/인코딩 실패를 명확한 콘솔 에러로. 성공 시 이미지 크기 로그.
+- `_vision_analyze`: VLM 응답 원문/HTTP 에러/빈 응답을 콘솔에 출력.
+- `direct` STEP1: use_vision OFF / no key / no image / encode fail 각각 구체적 debug_v 메시지.
+- **debug_info 출력만 봐도 비전 실패 원인 파악 가능**:
+  - `vlm=no api key` → 키 미입력 (사용자 증상의 유력 원인)
+  - `vlm=vision OFF` → 토글 꺼짐
+  - `vlm=no image` → IMAGE 미연결
+  - `vlm=vision HTTP 400` → VLM 거부(슬러그 오타/NSFW 거부)
+  - `vlm=vision empty response` → 빈 응답
+  - `vlm=vision ok` → 정상
+
+### 진단 결론
+"씬 묘사 없음 + AUTO + 바로 동작" 증상 = 비전+LLM 실패 후 로컬 폴백. **가장 유력한 원인은 openrouter_api_key 미입력**(워크플로우에서 보안상 비워둠). 다음 실행 시 debug_info 확인 필요.
+
+### 프롬프트 정책 (확정)
+- 타임코드([0-1s]) 제거 (WAN 노이즈). 대신 연속동작+속도어휘로 슬로우모션 방지.
+- 묘사(환경/조명/의상) 배제, 동작만. master_scene 비움.
+- 동작 순서 보존, 마지막 동작은 clip4.
+
+---
+
+## 중요 참고
+- **git 저장소 있음 (2026-07-21부터)**: `dolphin_nodes` 자체가 로컬 git 저장소(`git init` 완료, 원격 없음/로컬 전용). 상위 ComfyUI 저장소의 `.gitignore`가 `custom_nodes/`를 통째로 무시해서 이 폴더는 별도 독립 저장소로 관리. `.gitignore`는 `__pycache__/`, 런타임 로그, `dolphin_tags_cache.json`, `.claude/settings.local.json` 제외. 커밋 전 `git status`/`git diff`로 확인 습관화, 새 `.bak_*` 파일 만드는 대신 `git commit` 사용.
+- **위젯 이름이 바뀌면** 기존 저장 워크플로우의 로라 설정이 초기화될 수 있음. VisionDirector는 최근 `_h_clip` 헤더 제거 + `c{c}_lora_{n}` 이름으로 변경됨.
+- 모든 노드: `INPUT_TYPES`의 required 순서 = 함수 시그니처 순서 (ComfyUI 규칙). 수정 시 AST로 일치 검증할 것.
+- 워크플로우 편집 후엔 항상 무결성 체크: dangling_in / backref_mismatch / slot_oob 전부 0이어야 함. (단, `outputs[].links`에 참조된 링크 id가 top-level `links`에도 서브그래프 `definitions.subgraphs[].links`에도 없는 "유령 출력링크"는 정상적으로 존재할 수 있음 — 과거 편집 잔재로, ComfyUI가 로드시 무시하는 것으로 보임. `0705_vision2.json`의 negative prompt 노드(817)/서브그래프(553)에 ~1700개 있었���데 실행엔 지장 없었음.)
+- 원본 `0705.json`의 UUID 노드들은 subgraph (First/Extend/Encode 등). `definitions.subgraphs[]`에 정의가 있고, 인스턴스 노드의 `type` 필드가 그 서브그래프의 `id`(UUID)와 일치.
+- **`0705_vision2.json` LoRA/모델 체인 구조** (디버깅 시 참고): `Model Loader`(553, "f9a6f9ae..." 서브그래프)에서 GGUF 베이스 로드 + lightx2v(rank256) + SVI_v2_PRO(rank128)를 미리 적용해 `MODEL`(slot0, =model_high, lightx2v 있음)/`MODEL_1`(slot1, =model_low, lightx2v 있음)/`MODEL_2`(slot4, =model_base, lightx2v **없음** — stage1은 non-lightning 풀스텝이라 SVI만 적용) 3종을 출력 → `DolphinVisionDirector`가 클립별 캐릭터 로라를 추가 패치 → 클립마다 `ModelSamplingSD3` 서브그래프 → `NAG`(WanVideoNAG) 서브그래프 → `First`/`Extend` 서브그래프 내부 `TripleKSamplerWan22LightningAdvanced`로 도달. 클립1=786/787/453(First), 클립2=791/792/462(Extend), 클립3=793/794/463(Extend), 클립4=1070/1071/1080(Extend) — 각각 ModelSamplingSD3/NAG/샘플러 순. (444는 별개의 "Encode" 서브그래프로 이미지→latent 변환만 담당, 이 모델 체인과 무관.)

custom_nodes/dolphin_nodes/__init__.py

@@ -0,0 +1,227 @@
+import traceback
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+WEB_DIRECTORY = "./web"
+
+
+def register_node(class_name, node_class, display_name):
+    NODE_CLASS_MAPPINGS[class_name] = node_class
+    NODE_DISPLAY_NAME_MAPPINGS[class_name] = display_name
+
+
+# 1. Wan Native Resize (이미지 -> WAN 해상도 리사이즈, 비율 유지 + 정렬)
+try:
+    from .wan_resizer import WanNativeResize_Dolphin
+    register_node("WanNativeResize_Dolphin", WanNativeResize_Dolphin,
+                  "🐬 Wan Native Resize (Dolphin)")
+except Exception:
+    print(f"\n❌ [Dolphin] Wan Resize 로드 실패:\n{traceback.format_exc()}")
+
+# 2. Story Splitter V32 (텍스트 서사 -> 4샷 프롬프트 분배)
+try:
+    from .promp_logic_v32_story import DolphinMultiActionPromptNode_V32
+    register_node("DolphinMultiActionPromptNode_V32", DolphinMultiActionPromptNode_V32,
+                  "🐬 Dolphin Story Splitter V32")
+except Exception:
+    print(f"\n❌ [Dolphin] V32 Story Splitter 로드 실패:\n{traceback.format_exc()}")
+
+# 3. Architect V16 (클립별 LoRA -> TripleKSampler, model-only)
+try:
+    from .arch_logic_v16 import DolphinTripleLoraMatrix as DolphinArchitectV16
+    register_node("DolphinArchitectV16", DolphinArchitectV16,
+                  "🐬 Dolphin Architect V16")
+except Exception:
+    print(f"\n❌ [Dolphin] Architect V16 로드 실패:\n{traceback.format_exc()}")
+
+# 4. Cinematic Director (올인원: 스토리 분배 + 클립별 로라 + 트리거 주입)
+try:
+    from .dolphin_director import DolphinCinematicDirector
+    register_node("DolphinCinematicDirector", DolphinCinematicDirector,
+                  "🎬 Dolphin Cinematic Director (All-in-One)")
+except Exception:
+    print(f"\n❌ [Dolphin] Cinematic Director 로드 실패:\n{traceback.format_exc()}")
+
+# 5. Vision Director (이미지 인식 + 필수동작 + 클립당 로라 4개)
+try:
+    from .dolphin_vision_director import DolphinVisionDirector
+    register_node("DolphinVisionDirector", DolphinVisionDirector,
+                  "🎬👁 Dolphin Vision Director (Image→4Clips)")
+except Exception:
+    print(f"\n❌ [Dolphin] Vision Director 로드 실패:\n{traceback.format_exc()}")
+
+# 6. Architect V15 (arch_logic_v16 이전 구버전, model_base/high/low + clip 개별 입력)
+try:
+    from .arch_logic import DolphinArchitectV15
+    register_node("DolphinArchitectV15", DolphinArchitectV15,
+                  "🐬 Dolphin Architect V15 (legacy)")
+except Exception:
+    print(f"\n❌ [Dolphin] Architect V15 로드 실패:\n{traceback.format_exc()}")
+
+# 7. Story Splitter V28 (V32 이전 구버전, stitch 방식)
+try:
+    from .promp_logic_v28_stitch import DolphinMultiActionPromptNode_V28
+    register_node("DolphinMultiActionPromptNode_V28", DolphinMultiActionPromptNode_V28,
+                  "🐬 Dolphin Story Splitter V28 (legacy)")
+except Exception:
+    print(f"\n❌ [Dolphin] V28 Story Splitter 로드 실패:\n{traceback.format_exc()}")
+
+# 8. Story Splitter V30 (V32 이전 구버전)
+try:
+    from .promp_logic_v30_story import DolphinMultiActionPromptNode_V30
+    register_node("DolphinMultiActionPromptNode_V30", DolphinMultiActionPromptNode_V30,
+                  "🐬 Dolphin Story Splitter V30 (legacy)")
+except Exception:
+    print(f"\n❌ [Dolphin] V30 Story Splitter 로드 실패:\n{traceback.format_exc()}")
+
+# 9. Prompt V32 Vision (promp_logic_v32_story.py의 DolphinMultiActionPromptNode_V32와
+#    클래스명이 동일해서 충돌 — 등록 키/클래스명을 _Vision으로 구분해서 둘 다 살림)
+try:
+    from .promp_logic_v32_vision import DolphinMultiActionPromptNode_V32 as DolphinMultiActionPromptNode_V32_Vision
+    register_node("DolphinMultiActionPromptNode_V32_Vision", DolphinMultiActionPromptNode_V32_Vision,
+                  "🐬 Dolphin Prompt V32 Vision (legacy)")
+except Exception:
+    print(f"\n❌ [Dolphin] V32 Vision 로드 실패:\n{traceback.format_exc()}")
+
+
+# 10. Match Image & Mask Size (인페인트 블렌드용: 이미지/마스크를 참조 이미지 실제 크기에 맞춤)
+try:
+    from .match_size import MatchImageAndMaskSize_Dolphin
+    register_node("MatchImageAndMaskSize_Dolphin", MatchImageAndMaskSize_Dolphin,
+                  "🐬 Match Image & Mask Size (Dolphin)")
+except Exception:
+    print(f"\n❌ [Dolphin] Match Image & Mask Size 로드 실패:\n{traceback.format_exc()}")
+
+
+# 11. Mask-Region Color Match (인페인트 마스크 영역만 국소적으로 색보정)
+try:
+    from .mask_region_color_match import MaskRegionColorMatch_Dolphin
+    register_node("MaskRegionColorMatch_Dolphin", MaskRegionColorMatch_Dolphin,
+                  "🐬 Mask-Region Color Match (Dolphin)")
+except Exception:
+    print(f"\n❌ [Dolphin] Mask-Region Color Match 로드 실패:\n{traceback.format_exc()}")
+
+
+# 12. Prompt Director (Vision Director와 동일한 비전+LLM 엔진, MODEL/LoRA 전부 제거 -
+#     프롬프트 분배만. 로라/모델은 Architect V16 등 별도 노드에서 처리한다��� 전제)
+try:
+    from .dolphin_prompt_director import (
+        DolphinPromptDirector, DolphinPromptDirectorSingle, DolphinActionSplitter,
+    )
+    register_node("DolphinPromptDirector", DolphinPromptDirector,
+                  "🎬📝 Dolphin Prompt Director (Image→N Prompts, no model/LoRA)")
+    register_node("DolphinPromptDirectorSingle", DolphinPromptDirectorSingle,
+                  "🎬📝 Dolphin Prompt Director (Single, Image→1 Prompt)")
+    register_node("DolphinActionSplitter", DolphinActionSplitter,
+                  "✂️ Dolphin Action Splitter (deterministic, no LLM)")
+except Exception:
+    print(f"\n❌ [Dolphin] Prompt Director 로드 실패:\n{traceback.format_exc()}")
+
+
+# 13. WanImageToVideoSVIPro mask 버그 수정판 (comfyui-kjnodes 원본은 motion_latent_count로
+#     이어붙인 모션 컨텍스트 프레임을 concat_mask에서 "known"으로 표시하지 않아서, 클립
+#     경계에서 몇 프레임 반복 후 전혀 다른 영상으로 튀는 문제가 있었음. anchor+motion 프레임
+#     전체를 known(0.0)으로 마스킹하도록 수정.)
+try:
+    from .wan_svipro_fixed import WanImageToVideoSVIProFixed_Dolphin
+    register_node("WanImageToVideoSVIProFixed_Dolphin", WanImageToVideoSVIProFixed_Dolphin,
+                  "🐬 Wan I2V SVIPro (motion-mask fix)")
+except Exception:
+    print(f"\n❌ [Dolphin] SVIPro Fixed 로드 실패:\n{traceback.format_exc()}")
+
+
+# 14. Wan I2V Continuity (코어 WanImageToVideo 방식 = known 프레임 여러 장을 회색 filler와
+#     함께 한 번에 통짜 VAE 인코딩 → latent 문맥 일관성 보장. SVIPro식 raw latent 스플라이싱의
+#     화질 튐/불연속 문제를 회피. PainterI2V의 motion_amplitude(슬로우모션 방지) 이식.)
+try:
+    from .wan_i2v_continuity import WanI2VContinuity_Dolphin
+    register_node("WanI2VContinuity_Dolphin", WanI2VContinuity_Dolphin,
+                  "🐬 Wan I2V Continuity (multi-frame + amplitude)")
+except Exception:
+    print(f"\n❌ [Dolphin] Wan I2V Continuity 로드 실패:\n{traceback.format_exc()}")
+
+
+# 15. SVIPro 드롭인 교체 (슬로우모션 수정). 원본은 filler를 latent 공간에서
+#     Wan21().process_out(zeros) = latents_mean 상수로 채워서 "변화 없음" 신호를 주고
+#     분포 밖 입력이 됐음 -> 코어 WanImageToVideo/PainterI2V처럼 회색(0.5) 픽셀을 VAE
+#     인코딩한 filler로 교체. 마스크 버그(anchor 1프레임만 known)도 함께 수정.
+try:
+    from .wan_svipro_gray import WanImageToVideoSVIProGray_Dolphin
+    register_node("WanImageToVideoSVIProGray_Dolphin", WanImageToVideoSVIProGray_Dolphin,
+                  "🐬 Wan I2V SVIPro (gray filler, slow-motion fix)")
+except Exception:
+    print(f"\n❌ [Dolphin] SVIPro Gray 로드 실패:\n{traceback.format_exc()}")
+
+
+# 16. SVIPro 드롭인 교체 (모션 증폭판). 15번(gray)과 달리 패딩·마스크는 상류 원본 그대로 두고
+#     PainterI2V의 diff 증폭만 얹는다 — SVI Pro LoRA가 원본 규약 위에서 학습됐기 때문.
+#     high/low conditioning을 따로 뽑아서 모션 증폭은 high-noise 스테이지에만 먹인다.
+#     motion_amplitude=1.0이면 상류 원본과 출력이 동일(회귀 확인용).
+try:
+    from .wan_svipro_motion import WanImageToVideoSVIProMotion_Dolphin
+    register_node("WanImageToVideoSVIProMotion_Dolphin", WanImageToVideoSVIProMotion_Dolphin,
+                  "🐬 Wan I2V SVIPro (motion amplitude, hi/lo split)")
+except Exception:
+    print(f"\n❌ [Dolphin] SVIPro Motion 로드 실패:\n{traceback.format_exc()}")
+
+
+# 17. 디스크 노드 캐시 (노드가 아니라 ComfyUI cache provider 등록).
+#     시드/프롬프트/이미지가 같으면 재시작 후에도 JoyCaption~샘플러 체인을 스킵하고
+#     디스크에서 결과 복원. RAM_PRESSURE 모드의 캐시 증발도 디스크에서 자동 복구.
+try:
+    from . import disk_cache_provider
+    disk_cache_provider.register()
+except Exception:
+    print(f"\n❌ [Dolphin] 디스크 노드 캐시 로드 실패 (구버전 ComfyUI엔 provider API 없음):\n{traceback.format_exc()}")
+
+
+# 18. Batch Seed Controller (4클립 영상 샘플러 시드를 한 노드에서 스위칭:
+#     고정/전체랜덤/특정클립만재생성).
+try:
+    from .dolphin_seed_controller import DolphinBatchSeedController
+    register_node("DolphinBatchSeedController", DolphinBatchSeedController,
+                  "🎲 Dolphin Batch Seed Controller (random-all / regen-from-clip)")
+except Exception:
+    print(f"\n❌ [Dolphin] Batch Seed Controller 로드 실패:\n{traceback.format_exc()}")
+
+
+# 19. LoRA Prompt Weaver (완성된 클립 프롬프트를 "그 클립에 걸리는 로라들이 잘 발화하도록"
+#     다시 쓰는 후처리 노드. 로라 사이드카(.metadata.json)의 설명/태그/학습캡션을 LLM에
+#     먼저 주고, 트리거를 문장 안 해당 부위에 붙여서 재작성한다 - 예전처럼 이름 옆에
+#     콤마로 나열하면 bounce/swing 같은 범용 동사가 전신 모션으로 새기 때문.
+#     기존 노드는 건드리지 않고 뒤에 끼워 넣는 방식이라 어떤 프롬프트 소스에도 붙는다.)
+try:
+    from .dolphin_lora_prompt import DolphinLoraPromptWeaver
+    register_node("DolphinLoraPromptWeaver", DolphinLoraPromptWeaver,
+                  "🧬 Dolphin LoRA Prompt Weaver (프롬프트→로라에 맞게 재작성)")
+except Exception:
+    print(f"\n❌ [Dolphin] LoRA Prompt Weaver 로드 실패:\n{traceback.format_exc()}")
+
+
+# 20. Minimax Director (MiniMax/Hailuo용 프롬프트 디렉터. Wan용 Prompt Director와 달리
+#     클립 하나가 6/10초라서, 그 안을 초 단위 비트로 쪼개고 각 구간에 동작 + MiniMax
+#     공식 카메라 무브 태그를 박아준다.)
+try:
+    from .dolphin_minimax_director import DolphinMinimaxDirector
+    register_node("DolphinMinimaxDirector", DolphinMinimaxDirector,
+                  "🎬🅜 Dolphin Minimax Director (초 단위 타임라인 → Hailuo 프롬프트)")
+except Exception:
+    print(f"\n❌ [Dolphin] Minimax Director 로드 실패:\n{traceback.format_exc()}")
+
+
+# 21. Wan Refine H3 (MiniMax H3 출력 → Wan 2.2 LOW noise 저-denoise 리파인 패스 올인원).
+#     H3(24fps / 17k+5 프레임 그리드 / 768 short edge)와 Wan(16fps / 4n+1 / Wan2.1 VAE 16ch)
+#     사이의 배선 함정을 한 노드에서 처리: 4n+1 정렬, VAE 오배선 차단, 소스 라텐트 인코딩,
+#     concat 컨디셔닝 재구성, sigma_shift 직접 패치, 색 드리프트 복원, 오디오 길이 정렬.
+#     같이 등록되는 Frame Grid 노드는 두 그리드가 겹치는 length(5/73/141/209/277/345…)를 계산한다.
+try:
+    from .wan_h3_refine import WanH3Refine_Dolphin, WanH3FrameGrid_Dolphin
+    register_node("WanH3Refine_Dolphin", WanH3Refine_Dolphin,
+                  "🐬🅜 Wan Refine H3 (low-denoise pass)")
+    register_node("WanH3FrameGrid_Dolphin", WanH3FrameGrid_Dolphin,
+                  "🐬🅜 H3↔Wan Frame Grid")
+except Exception:
+    print(f"\n❌ [Dolphin] Wan Refine H3 로드 실패:\n{traceback.format_exc()}")
+
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/__init_old__.py

@@ -0,0 +1,35 @@
+# __init__.py (통합 로더)
+import traceback
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+def register_node(class_name, node_class, display_name):
+    NODE_CLASS_MAPPINGS[class_name] = node_class
+    NODE_DISPLAY_NAME_MAPPINGS[class_name] = display_name
+
+# 리사이즈
+try:
+    from .wan_resizer import WanNativeResize_Dolphin
+    register_node("WanNativeResize_Dolphin", WanNativeResize_Dolphin, "🐬 Wan Native Resize (Dolphin)")
+except Exception as e: print(e)
+
+# 트리플 매트릭스
+try:
+    from .arch_logic_v16 import DolphinTripleLoraMatrix
+    register_node("DolphinTripleLoraMatrix", DolphinTripleLoraMatrix, "🐬 Dolphin Triple Lora Matrix (Wan 2.2)")
+except Exception as e: print(e)
+
+# 스토리 스플리터
+try:
+    from .promp_logic_v32_story import DolphinMultiActionPromptNode_V32
+    register_node("DolphinMultiActionPromptNode_V32", DolphinMultiActionPromptNode_V32, "🐬 Dolphin Story Splitter V32")
+except Exception as e: print(e)
+
+# 디렉터
+try:
+    from .dolphin_director import DolphinDirector
+    register_node("DolphinDirector", DolphinDirector, "🐬 Dolphin Director (V1)")
+except Exception as e: print(e)
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/gemini-code-1782954683883.py

@@ -0,0 +1,66 @@
+import json
+
+def convert_workflow(input_file, output_file):
+    with open(input_file, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+        
+    for node in data.get('nodes', []):
+        # 1. Story Splitter 구버전 -> V32 최신버전으로 교체
+        if node.get('type', '').startswith('DolphinMultiActionPromptNode_'):
+            node['type'] = 'DolphinMultiActionPromptNode_V32'
+            
+        # 2. Architect V15/V16 -> Triple Lora Matrix 로 교체
+        if node.get('type', '').startswith('DolphinArchitect'):
+            node_id = node['id']
+            node['type'] = 'DolphinTripleLoraMatrix'
+            
+            # [입력 슬롯 최적화] 
+            # 구버전의 3개 모델 입력창을 제거하고, 1개의 통합 'model' 입력으로 전환
+            new_inputs = []
+            for inp in node.get('inputs', []):
+                if inp['name'] == 'model_base':
+                    inp['name'] = 'model'  # 슬롯 0번 유지
+                    new_inputs.append(inp)
+            node['inputs'] = new_inputs
+            
+            # [출력 슬롯 재구성] 
+            # 새로운 노드의 7개 출력(M_BASE, C_BASE, M_HIGH, C_HIGH, M_LOW, C_LOW, string)에 맞춤
+            node['outputs'] = [
+                {"name": "M_BASE", "type": "MODEL", "links": []},
+                {"name": "C_BASE", "type": "CLIP", "links": []},
+                {"name": "M_HIGH", "type": "MODEL", "links": []},
+                {"name": "C_HIGH", "type": "CLIP", "links": []},
+                {"name": "M_LOW", "type": "MODEL", "links": []},
+                {"name": "C_LOW", "type": "CLIP", "links": []},
+                {"name": "applied_loras", "type": "STRING", "links": []}
+            ]
+            
+            # [위젯 초기화] 
+            # 24개짜리 무거운 UI를 지우고, 가벼운 콤보+텍스트 UI로 리셋
+            node['widgets_values'] = [
+                "None", "0.5, 0.2, 0.5",
+                "None", "1.0, 0.5, 0.0",
+                "None", "1.0, 1.0, 1.0"
+            ]
+
+            # [링크(선) 지능형 재연결]
+            # M_HIGH와 M_LOW의 출력 포트 번호가 변경된 것을 찾아 선을 다시 이어줌
+            for link in data.get('links', []):
+                # link = [link_id, origin_id, origin_slot, target_id, target_slot, type]
+                if link[1] == node_id:
+                    if link[2] == 1:  # 구버전 M_HIGH
+                        link[2] = 2   # 신버전 M_HIGH 포트(2번)로 이동
+                        node['outputs'][2]['links'].append(link[0])
+                    elif link[2] == 2:  # 구버전 M_LOW
+                        link[2] = 4     # 신버전 M_LOW 포트(4번)로 이동
+                        node['outputs'][4]['links'].append(link[0])
+                    elif link[2] == 0:  # M_BASE는 그대로 유지
+                        node['outputs'][0]['links'].append(link[0])
+                        
+    with open(output_file, 'w', encoding='utf-8') as f:
+        json.dump(data, f, indent=4)
+        
+    print(f"✅ 변환 완료! 생성된 '{output_file}' 파일을 ComfyUI에 드래그 앤 드롭 하세요.")
+
+# 스크립트 실행
+convert_workflow('0705_optimized.json', '0705_v32_matrix.json')

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/lora_classifier.py

@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+"""
+LoRA Manager 로컬 DB(civitai에서 스캔 시 이미 긁어온 태그/메타데이터)를 읽어서
+WAN 계열 로라만 골라 1차 자동 분류 결과를 JSON으로 뽑는다.
+
+분류 축 2개:
+  1) 기술 호환성: wan_version(2.1/2.2/unknown), variant(i2v/t2v/unknown),
+     stage(high/low/base-or-single) - 전부 filename + base_model 필드에서 추출
+  2) 행위/내용 카테고리: civitai에서 스캔한 model_tags를 미리 정한 카테고리
+     사전에 매핑 (여러 개 중복 가능 - 로라 하나가 pov+anal 같이 여러 태그를 가질 수 있음)
+
+DB 자체를 수정하지 않는다(읽기 전용). 결과는 review용 JSON 하나로 뽑아서,
+사람이 눈으로 훑고 필요하면 손으로 고칠 수 있게 한다 - 자동분류 결과를 그대로
+자동 적용(로라 자동 선택)하는 건 지난번에 "예외 동작이 너무 많다"고 명시적으로
+거부하셨던 방향이라 이 스크립트는 딱 "분류표 생성"까지만 한다.
+"""
+import sqlite3
+import json
+import os
+import re
+
+DB_PATH = os.path.join(
+    os.environ.get("LOCALAPPDATA", ""), "ComfyUI-LoRA-Manager", "cache", "model", "comfyui.sqlite")
+OUT_PATH = os.path.join(os.path.dirname(__file__), "dolphin_lora_classification.json")
+
+# 태그 -> 카테고리. 순서는 우선순위(먼저 매치되는 걸 "primary"로 씀). 카테고리 자체는
+# 중복 허용(한 로라가 여러 카테고리에 다 들어갈 수 있음).
+CATEGORY_TAG_MAP = {
+    "oral": {"oral", "blowjob", "deepthroat", "fellatio", "irrumatio"},
+    "anal": {"anal"},
+    "position_action": {"cowgirl", "doggystyle", "missionary", "riding", "sex", "penetration",
+                         "reverse cowgirl", "standing sex", "piledriver"},
+    "masturbation": {"masturbation", "fingering", "handjob"},
+    "pov": {"pov"},
+    "fetish_other": {"futanari", "bdsm", "feet", "pee", "piss", "cum", "creampie", "squirting"},
+    "character_identity": {"character", "celebrity"},
+    "style_render": {"photorealistic", "realistic", "anime", "style", "illustrious"},
+    "toy_prop": {"sex toy", "dildo", "sex machine", "vibrator"},
+    "kink_dynamic": {"femdom", "bdsm"},
+    "dance_movement": {"dance", "poses", "pose"},
+    # 콘텐츠 카테고리가 아니라 "이 로라 자체가 lightx2v류 가속/증류 로라"라는 표시.
+    # 클립 콘텐츠용 로라 풀에서 걸러내야 할 대상 - Model Loader에서 이미 쓰는 것과
+    # 겹치는 속도용 로라를 실수로 클립 로라 슬롯에 넣지 않도록 구분해둔다.
+    "speed_distillation": {"distill", "distillation", "fast", "faster", "speed up", "lightning",
+                           "lightx2v", "accelerator"},
+}
+
+FILENAME_STAGE_RE = re.compile(r"(high[_-]?noise|_high\b)", re.IGNORECASE)
+FILENAME_LOW_RE = re.compile(r"(low[_-]?noise|_low\b)", re.IGNORECASE)
+
+
+def classify_wan_version(base_model, file_name):
+    bm = (base_model or "").lower()
+    fn = (file_name or "").lower()
+    if "2.2" in bm or "2.2" in fn or "wan2.2" in fn or "wan22" in fn:
+        version = "2.2"
+    elif "wan video 14b" in bm or "2.1" in bm or "wan2.1" in fn or "wan21" in fn:
+        version = "2.1"
+    elif bm.startswith("wan"):
+        version = "unknown-wan"
+    else:
+        version = None  # WAN 계열 아님(Flux/Pony/SDXL 등) - 호출부에서 걸러냄
+    if "i2v" in bm or "i2v" in fn:
+        variant = "i2v"
+    elif "t2v" in bm or "t2v" in fn:
+        variant = "t2v"
+    else:
+        variant = "unknown"
+    return version, variant
+
+
+def classify_stage(file_name):
+    fn = file_name or ""
+    if FILENAME_STAGE_RE.search(fn):
+        return "high"
+    if FILENAME_LOW_RE.search(fn):
+        return "low"
+    return "base_or_single"  # high/low 구분 없는 단일 파일 (base 겸용이거나 non-split)
+
+
+def main():
+    if not os.path.exists(DB_PATH):
+        print(f"❌ LoRA Manager DB를 못 찾음: {DB_PATH}")
+        return
+
+    con = sqlite3.connect(f"file:{DB_PATH}?mode=ro", uri=True)
+    cur = con.cursor()
+    cur.execute("""
+        SELECT file_path, file_name, base_model, trained_words, notes, usage_tips
+        FROM models WHERE model_type='lora'
+    """)
+    rows = cur.fetchall()
+
+    cur.execute("SELECT file_path, tag FROM model_tags WHERE model_type='lora'")
+    tags_by_path = {}
+    for file_path, tag in cur.fetchall():
+        tags_by_path.setdefault(file_path, []).append(tag)
+    con.close()
+
+    results = []
+    skipped_non_wan = 0
+    for file_path, file_name, base_model, trained_words, notes, usage_tips in rows:
+        version, variant = classify_wan_version(base_model, file_name)
+        if version is None:
+            skipped_non_wan += 1
+            continue
+
+        tags = set(t.lower() for t in tags_by_path.get(file_path, []))
+        categories = [cat for cat, keywords in CATEGORY_TAG_MAP.items() if tags & keywords]
+
+        try:
+            trained = json.loads(trained_words) if trained_words else []
+        except (ValueError, TypeError):
+            trained = []
+
+        results.append({
+            "file_name": file_name,
+            "file_path": file_path,
+            "wan_version": version,
+            "variant": variant,
+            "stage": classify_stage(file_name),
+            "categories": categories or ["uncategorized"],
+            "trigger_words": trained,
+            "raw_tags": sorted(tags),
+            "notes": notes or "",
+            "usage_tips": usage_tips or "",
+        })
+
+    results.sort(key=lambda r: (r["wan_version"], r["variant"], r["file_name"]))
+
+    with open(OUT_PATH, "w", encoding="utf-8") as f:
+        json.dump(results, f, ensure_ascii=False, indent=1)
+
+    print(f"✅ WAN 계열 로라 {len(results)}개 분류 완료 (비WAN {skipped_non_wan}개 제외)")
+    print(f"   -> {OUT_PATH}")
+
+    from collections import Counter
+    ver_counts = Counter((r["wan_version"], r["variant"]) for r in results)
+    print("\n버전/variant 분포:")
+    for k, v in ver_counts.most_common():
+        print(f"  {k}: {v}")
+    cat_counts = Counter(c for r in results for c in r["categories"])
+    print("\n카테고리 분포:")
+    for k, v in cat_counts.most_common():
+        print(f"  {k}: {v}")
+
+
+if __name__ == "__main__":
+    main()

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/__init__.py

@@ -0,0 +1,53 @@
+import traceback
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+# 🔥 KeyError를 물리적으로 불가능하게 만드는 동시 등록 함수
+def register_node(class_name, node_class, display_name):
+    NODE_CLASS_MAPPINGS[class_name] = node_class
+    NODE_DISPLAY_NAME_MAPPINGS[class_name] = display_name
+
+# 1. 리사이즈 노드
+try:
+    from .wan_resizer import WanNativeResize_Dolphin
+    register_node("WanNativeResize_Dolphin", WanNativeResize_Dolphin, "🐬 Wan Native Resize (Dolphin)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] Wan Resizer 로드 실패: {e}")
+
+# 2. 아키텍트 노드 (에러의 주범!)
+try:
+    from .arch_logic import DolphinArchitectV15
+    register_node("DolphinArchitectV15", DolphinArchitectV15, "🐬 Dolphin Architect (V15 - 울트라 슬림 4:4)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] Architect 로드 실패 (arch_logic.py 확인 필요):\n{traceback.format_exc()}")
+
+# 3. V25 오토 시퀀서 노드
+try:
+    from .promp_auto_sequence import DolphinAutoSequencerNode
+    register_node("DolphinAutoSequencerNode", DolphinAutoSequencerNode, "🐬 Dolphin Sequencer (V25 - 무한 타임라인)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] Auto Sequencer 로드 실패: {e}")
+
+# 4. V28 스티칭 노드
+try:
+    from .promp_logic_v28_stitch import DolphinMultiActionPromptNode_V28
+    register_node("DolphinMultiActionPromptNode_V28", DolphinMultiActionPromptNode_V28, "🐬 Dolphin Prompter (V28 - 5초 개별 클립 x 4단 이어붙이기)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] V28 Prompter 로드 실패: {e}")
+
+# 5. [신규] V30 스토리 스플리터 노드
+try:
+    from .promp_logic_v30_story import DolphinMultiActionPromptNode_V30
+    register_node("DolphinMultiActionPromptNode_V30", DolphinMultiActionPromptNode_V30, "🐬 Dolphin Prompter (V30 - 단일 시나리오 분할)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] V30 Prompter 로드 실패: {e}")
+
+# 6. [신규] V31 비전 마스터 노드
+try:
+    from .promp_logic_v31_vision import DolphinMultiActionPromptNode_V31
+    register_node("DolphinMultiActionPromptNode_V31", DolphinMultiActionPromptNode_V31, "🐬 Dolphin Prompter (V31 - Vision Direct / 눈 달린 AI)")
+except Exception as e:
+    print(f"\n❌ [Dolphin] V31 Vision Prompter 로드 실패: {e}")
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/arch_logic.py

@@ -0,0 +1,141 @@
+import os
+import json
+import folder_paths
+import comfy.sd
+import comfy.utils
+from safetensors import safe_open
+import hashlib
+import urllib.request
+
+CACHE_FILE = os.path.join(os.path.dirname(__file__), "dolphin_tags_cache.json")
+
+def load_dolphin_cache():
+    if os.path.exists(CACHE_FILE):
+        try:
+            with open(CACHE_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except: return {}
+    return {}
+
+def save_dolphin_cache(cache_data):
+    try:
+        with open(CACHE_FILE, "w", encoding="utf-8") as f:
+            json.dump(cache_data, f, indent=4)
+    except: pass
+
+class DolphinArchitectV15:
+    @classmethod
+    def INPUT_TYPES(s):
+        loras = ["None"] + folder_paths.get_filename_list("loras")
+        
+        res = {
+            "required": {
+                "global_switch": ("BOOLEAN", {"default": True, "label_on": "GLOBAL ON", "label_off": "GLOBAL OFF (SKIP)"}),
+                "model_base": ("MODEL",), "clip_base": ("CLIP",),
+                "model_high": ("MODEL",), "clip_high": ("CLIP",),
+                "model_low": ("MODEL",), "clip_low": ("CLIP",),
+            },
+            "optional": {}
+        }
+
+        # [다이어트 완료] 오직 이름과 웨이트만 남겨서 가장 얇은 UI를 만듭니다.
+        res["optional"]["header_master"] = ("STRING", {"default": "─── [ MASTER: B+H ] ───", "multiline": False})
+        for i in range(1, 5): 
+            res["optional"][f"master_l_{i}"] = (loras, {"default": "None"})
+            res["optional"][f"master_b_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+            res["optional"][f"master_h_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+            
+        res["optional"]["header_low"] = ("STRING", {"default": "─── [ LOW ONLY ] ───", "multiline": False})
+        for i in range(1, 5): 
+            res["optional"][f"low_l_{i}"] = (loras, {"default": "None"})
+            res["optional"][f"low_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+
+        res["optional"]["manual_triggers"] = ("STRING", {"default": "", "multiline": True})
+        return res
+
+    RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "MODEL", "CLIP", "STRING")
+    RETURN_NAMES = ("M_BASE", "C_BASE", "M_HIGH", "C_HIGH", "M_LOW", "C_LOW", "triggers")
+    FUNCTION = "exec"
+    CATEGORY = "Dolphin"
+
+    def get_trig(self, lora_name):
+        if lora_name == "None": return []
+        path = folder_paths.get_full_path("loras", lora_name)
+        if not path or not os.path.exists(path): return []
+        
+        triggers = []
+        try:
+            with safe_open(path, framework="pt", device="cpu") as f:
+                m = f.metadata()
+                if m and "modelspec.trigger_words" in m:
+                    tw = json.loads(m["modelspec.trigger_words"])
+                    if isinstance(tw, list): triggers.extend(tw)
+                    elif isinstance(tw, str): triggers.append(tw)
+        except: pass
+        if triggers: return triggers 
+
+        cache = load_dolphin_cache()
+        if lora_name in cache and cache[lora_name]: return cache[lora_name]
+
+        try:
+            sha256_hash = hashlib.sha256()
+            with open(path, "rb") as f:
+                for byte_block in iter(lambda: f.read(4096 * 1024), b""):
+                    sha256_hash.update(byte_block)
+            
+            url = f"https://civitai.com/api/v1/model-versions/by-hash/{sha256_hash.hexdigest()}"
+            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=5) as response:
+                data = json.loads(response.read().decode('utf-8'))
+                api_triggers = data.get("trainedWords", [])
+                if api_triggers:
+                    cache[lora_name] = api_triggers
+                    save_dolphin_cache(cache)
+                    return api_triggers
+        except: pass
+        return triggers
+
+    def exec(self, global_switch, model_base, clip_base, model_high, clip_high, model_low, clip_low, manual_triggers="", **kwargs):
+        mb, cb = model_base, clip_base
+        mh, ch = model_high, clip_high
+        ml, cl = model_low, clip_low
+        
+        if not global_switch:
+            trigs = [t.strip() for t in manual_triggers.split(',') if t.strip()]
+            ut = ", ".join(list(dict.fromkeys(trigs)))
+            return (mb, cb, mh, ch, ml, cl, ut)
+
+        applied_names = []
+
+        for i in range(1, 5): 
+            n = kwargs.get(f"master_l_{i}", "None")
+            w_b = kwargs.get(f"master_b_w_{i}", 1.0)
+            w_h = kwargs.get(f"master_h_w_{i}", 1.0)
+            
+            # [최적화] None이 아니면서, 웨이트가 0.0이 아닐 때만 적용 (0.0이면 스위치 OFF 효과)
+            if n != "None" and (w_b != 0.0 or w_h != 0.0):
+                applied_names.append(n)
+                lora_data = comfy.utils.load_torch_file(folder_paths.get_full_path("loras", n))
+                mb, cb = comfy.sd.load_lora_for_models(mb, cb, lora_data, w_b, w_b)
+                mh, ch = comfy.sd.load_lora_for_models(mh, ch, lora_data, w_h, w_h)
+
+        for i in range(1, 5): 
+            n = kwargs.get(f"low_l_{i}", "None")
+            w = kwargs.get(f"low_w_{i}", 1.0)
+            
+            if n != "None" and w != 0.0:
+                applied_names.append(n)
+                lora_data = comfy.utils.load_torch_file(folder_paths.get_full_path("loras", n))
+                ml, cl = comfy.sd.load_lora_for_models(ml, cl, lora_data, w, w)
+
+        trigs = []
+        for n in set(applied_names):
+            trigs.extend(self.get_trig(n))
+            
+        if manual_triggers.strip():
+            manual_list = [t.strip() for t in manual_triggers.split(',') if t.strip()]
+            trigs.extend(manual_list)
+        
+        unique_trigs = ", ".join(list(dict.fromkeys(trigs)))
+        
+        return (mb, cb, mh, ch, ml, cl, unique_trigs)

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/dolphin_vision_director_pre_continuity_20260702.py

@@ -0,0 +1,778 @@
+import os
+import re
+import io
+import json
+import base64
+import hashlib
+import sqlite3
+import urllib.request
+import urllib.error
+
+import numpy as np
+import folder_paths
+import comfy.sd
+import comfy.lora
+import comfy.utils
+from safetensors import safe_open
+
+try:
+    from PIL import Image
+    _HAS_PIL = True
+except Exception:
+    _HAS_PIL = False
+
+CACHE_FILE = os.path.join(os.path.dirname(__file__), "dolphin_tags_cache.json")
+
+
+# ======================================================================
+#  모델 프리셋 (텍스트 LLM = 4샷 분배 / 비전 VLM = 이미지 인식)
+# ======================================================================
+LLM_PRESETS = {
+    "🔞 DeepSeek V3.2 (무삭제·JSON안정·추천)": "deepseek/deepseek-v3.2",
+    "🔞 Magnum v4 72B (표현 자연스러움)": "anthracite-org/magnum-v4-72b",
+    "🔞 Cydonia 24B v4.1 (경량·빠름)": "thedrummer/cydonia-24b-v4.1",
+    "🔞 Dolphin Mistral Venice (무료)": "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+    "⚙️ custom (아래 텍스트 사용)": "__custom__",
+}
+LLM_PRESET_LABELS = list(LLM_PRESETS.keys())
+
+# 비전 모델: 특수 상황 이해 + 다국어. Qwen2.5-VL 계열이 포즈/맥락 파악에 강함.
+VLM_PRESETS = {
+    "👁 Qwen2.5-VL 72B (특수상황·추천)": "qwen/qwen2.5-vl-72b-instruct",
+    "👁 Qwen2.5-VL 32B (균형)": "qwen/qwen2.5-vl-32b-instruct",
+    "👁 Qwen2.5-VL 32B (무료)": "qwen/qwen2.5-vl-32b-instruct:free",
+    "👁 Mistral Small 3.1 24B (Pixtral)": "mistralai/mistral-small-3.1-24b-instruct",
+    "👁 Gemma 3 27B (경량)": "google/gemma-3-27b-it",
+    "⚙️ custom (아래 텍스트 사용)": "__custom__",
+}
+VLM_PRESET_LABELS = list(VLM_PRESETS.keys())
+
+
+def resolve_slug(table, label, custom_text, default):
+    slug = table.get(label, "__custom__")
+    if slug == "__custom__":
+        return custom_text.strip() or default
+    return slug
+
+
+def resolve_api_key(widget_value):
+    """위젯에 키가 있으면 그대로 사용. 비어있으면 OPENROUTER_API_KEY 환경변수로 폴백.
+    -> 워크플로우 JSON(공유/백업 시 유출 위험)에 실키를 박아둘 필요가 없어짐."""
+    key = (widget_value or "").strip()
+    if key:
+        return key
+    return os.environ.get("OPENROUTER_API_KEY", "").strip()
+
+
+# ======================================================================
+#  캐시 & 트리거 (검증된 로직 재사용)
+# ======================================================================
+def _load_cache():
+    if os.path.exists(CACHE_FILE):
+        try:
+            with open(CACHE_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except (json.JSONDecodeError, OSError):
+            return {}
+    return {}
+
+
+def _save_cache(cache):
+    try:
+        with open(CACHE_FILE, "w", encoding="utf-8") as f:
+            json.dump(cache, f, indent=4, ensure_ascii=False)
+    except OSError:
+        pass
+
+
+def _file_sig(path):
+    st = os.stat(path)
+    return f"{os.path.basename(path)}::{st.st_size}::{int(st.st_mtime)}"
+
+
+def _sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(4096 * 1024), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+# CivitAI 및 미러(civitaired) 해시 조회 엔드포인트. 앞에서부터 순서대로 시도.
+TRIGGER_HASH_APIS = [
+    "https://civitai.com/api/v1/model-versions/by-hash/{h}",
+    "https://civitaired.com/api/v1/model-versions/by-hash/{h}",
+]
+
+# LoRA Manager(comfyui-lora-manager)가 스캔 시 만들어두는 로컬 SQLite 캐시.
+# sha256으로 trained_words를 즉시 조회 가능 -> 네트워크 호출 없이 트리거워드 확보.
+LORA_MANAGER_DB = os.path.join(
+    os.environ.get("LOCALAPPDATA", ""), "ComfyUI-LoRA-Manager", "cache", "model", "comfyui.sqlite")
+
+
+def _query_lora_manager_db(digest):
+    """LoRA Manager SQLite 캐시에서 sha256으로 trained_words 조회. 실패/미발견 시 None."""
+    if not digest or not os.path.exists(LORA_MANAGER_DB):
+        return None
+    try:
+        uri = f"file:{LORA_MANAGER_DB}?mode=ro"
+        con = sqlite3.connect(uri, uri=True, timeout=3)
+        try:
+            cur = con.cursor()
+            cur.execute(
+                "SELECT trained_words FROM models WHERE model_type='lora' AND sha256=? LIMIT 1",
+                (digest,))
+            row = cur.fetchone()
+        finally:
+            con.close()
+        if not row or not row[0]:
+            return None
+        tw = json.loads(row[0])
+        return tw if isinstance(tw, list) else None
+    except (sqlite3.Error, OSError, ValueError, json.JSONDecodeError) as e:
+        print(f"⚠️ [Director] LoRA Manager DB 조회 실패: {type(e).__name__}: {e}")
+        return None
+
+
+def _query_trigger_api(url):
+    """단일 해시 조회 엔드포인트에서 trainedWords 리스트를 반환. 실패 시 ([], 사유)."""
+    try:
+        req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+        with urllib.request.urlopen(req, timeout=8) as r:
+            data = json.loads(r.read().decode('utf-8'))
+            return (data.get("trainedWords", []) or []), None
+    except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError,
+            OSError, ValueError, json.JSONDecodeError) as e:
+        return [], f"{type(e).__name__}: {e}"
+
+
+def get_triggers(lora_name, cache):
+    if lora_name == "None":
+        return []
+    path = folder_paths.get_full_path("loras", lora_name)
+    if not path or not os.path.exists(path):
+        return []
+    try:
+        with safe_open(path, framework="pt", device="cpu") as f:
+            m = f.metadata()
+            if m and "modelspec.trigger_words" in m:
+                tw = json.loads(m["modelspec.trigger_words"])
+                if isinstance(tw, list):
+                    return tw
+                if isinstance(tw, str):
+                    return [tw]
+    except (OSError, ValueError, json.JSONDecodeError):
+        pass
+    try:
+        sig = _file_sig(path)
+    except OSError:
+        sig = lora_name
+    # 캐시에 '비어있지 않은' 결과가 있을 때만 신뢰. 과거에 실패로 저장된 빈 값은
+    # 무시하고 재조회한다 (빈 값 영구화 회귀 방지).
+    cached = cache.get(sig)
+    if cached:
+        return cached
+
+    # 해시는 1회만 계산해서 LoRA Manager DB -> CivitAI -> civitaired 순으로 조회.
+    result, base = [], os.path.basename(path)
+    try:
+        digest = _sha256(path)
+    except OSError as e:
+        print(f"⚠️ [Director] 해시 계산 실패({base}): {e}")
+        digest = None
+    if digest:
+        found = _query_lora_manager_db(digest)
+        if found:
+            print(f"✅ [Director] 트리거 조회 성공({base}) @ LoRA Manager DB: {found}")
+            result = found
+    if digest and not result:
+        for tmpl in TRIGGER_HASH_APIS:
+            host = tmpl.split('/')[2]
+            found, err = _query_trigger_api(tmpl.format(h=digest))
+            if found:
+                print(f"✅ [Director] 트리거 조회 성공({base}) @ {host}: {found}")
+                result = found
+                break
+            print(f"⚠️ [Director] 트리거 조회 실패({base}) @ {host}: {err or 'no trainedWords'}")
+
+    # 성공(비어있지 않음)일 때만 캐싱. 빈 결과는 저장하지 않아 다음 실행에서 재시도됨.
+    if result:
+        cache[sig] = result
+        _save_cache(cache)
+    return result
+
+
+def apply_model_lora(model, lora_data, weight):
+    try:
+        return comfy.sd.load_lora_for_models(model, None, lora_data, weight, 0)[0]
+    except (AttributeError, TypeError):
+        new_model = model.clone()
+        key_map = comfy.lora.model_lora_keys_unet(new_model.model)
+        loaded = comfy.lora.load_lora(lora_data, key_map)
+        new_model.add_patches(loaded, weight)
+        return new_model
+
+
+def tensor_to_base64(image_tensor, max_side=1024):
+    """ComfyUI IMAGE 텐서(B,H,W,C, 0~1 float) 첫 프레임을 JPEG base64 data URI로."""
+    if not _HAS_PIL:
+        print("❌ [Director] PIL(Pillow) 없음 - 비전 불가. `pip install Pillow` 필요.")
+        return None
+    if image_tensor is None:
+        print("❌ [Director] image 입력이 None - IMAGE 연결 확인.")
+        return None
+    try:
+        img = image_tensor
+        if hasattr(img, "cpu"):
+            img = img.cpu().numpy()
+        img = np.asarray(img)
+        if img.ndim == 4:
+            img = img[0]
+        arr = np.clip(img * 255.0, 0, 255).astype(np.uint8)
+        pil = Image.fromarray(arr)
+        w, h = pil.size
+        if max(w, h) > max_side:
+            scale = max_side / float(max(w, h))
+            pil = pil.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
+        buf = io.BytesIO()
+        pil.convert("RGB").save(buf, format="JPEG", quality=90)
+        b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+        print(f"🖼 [Director] image encoded ok: {w}x{h} -> {pil.size}, {len(b64)//1024}KB base64")
+        return f"data:image/jpeg;base64,{b64}"
+    except Exception as e:
+        print(f"❌ [Director] image encode failed: {type(e).__name__}: {e}")
+        return None
+
+
+# ======================================================================
+#  Vision Director: 이미지 인식 + 필수동작 실행 + 클립당 3로라
+# ======================================================================
+class DolphinVisionDirector:
+    """
+    이미지 하나와 '필수 동작'과 로라만 넣으면 4개 연속 클립이 완성되는 올인원 노드.
+
+      1) IMAGE -> 비전 VLM 이 상황(캐릭터 상태/자세/환경)만 앵커로 파악
+      2) 그 맥락 위에서 '내가 준 필수 동작(essential_actions)'만 4샷으로 분배
+      3) 클립마다 로라 3개씩 개별 적용(웨이트도 개별) + 트리거 자동 주입
+
+    핵심: VLM 은 '묘사'가 아니라 '앵커 추출' 역할로 좁혀서 장황함을 억제한다.
+    로라: c{클립}_lora_{n} (c=1..4, n=1..3), 각 슬롯 웨이트는 "base, high, low" 문자열.
+    트리거워드는 사용된 로라에서 자동 추출되어 각 클립 프롬프트에 주입 + triggers 출력으로도 제공.
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        loras = ["None"] + folder_paths.get_filename_list("loras")
+        req = {
+            "image": ("IMAGE",),
+            "model_base": ("MODEL",),
+            "model_high": ("MODEL",),
+            "model_low": ("MODEL",),
+            "essential_actions": ("STRING", {"multiline": True,
+                "default": ("돌진한다, 검을 휘둘러 적을 쓰러뜨린다, 총알을 튕겨낸다, 숨통을 끊는다")}),
+            "character_name": ("STRING", {"default": "AUTO"}),
+            "artistic_vibe": ("STRING", {"multiline": True,
+                                         "default": "cinematic lighting, dynamic motion"}),
+            "pacing": (["auto", "slow-burn (1 beat)", "balanced (multi beat)", "rapid (4 distinct)"],
+                       {"default": "auto"}),
+            "detail_level": (["짧게(간결)", "필수+앵커만", "상세"], {"default": "필수+앵커만"}),
+            "inject_triggers": ("BOOLEAN", {"default": True,
+                                            "label_on": "TRIGGERS ON", "label_off": "TRIGGERS OFF"}),
+            "use_vision": ("BOOLEAN", {"default": True,
+                                       "label_on": "VISION ON", "label_off": "VISION OFF (text only)"}),
+            "openrouter_api_key": ("STRING", {"default": ""}),
+            "vlm_preset": (VLM_PRESET_LABELS, {"default": "👁 Qwen2.5-VL 72B (특수상황·추천)"}),
+            "vlm_model": ("STRING", {"default": "qwen/qwen2.5-vl-72b-instruct"}),
+            "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (무삭제·JSON안정·추천)"}),
+            "llm_model": ("STRING", {"default": "deepseek/deepseek-v3.2"}),
+            "creativity": ("FLOAT", {"default": 0.7, "min": 0.1, "max": 1.5, "step": 0.05}),
+            "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+        }
+        opt = {}
+        # 클립당 로라 3개. 헤더 위젯 제거(편집칸이라 혼란). 이름을 명확히:
+        #   c{클립}_lora_{n} (드롭다운) + c{클립}_w_{n} ("base, high, low")
+        for c in range(1, 5):
+            for n in range(1, 4):
+                opt[f"c{c}_lora_{n}"] = (loras, {"default": "None"})
+                opt[f"c{c}_w_{n}"] = ("STRING", {"default": "1.0, 1.0, 1.0", "multiline": False})
+        opt["manual_triggers"] = ("STRING", {"default": "", "multiline": True})
+        # 로컬 캡션 노드(JoyCaption 등)의 STRING 출력을 여기 연결하면 그걸 앵커로 사용하고
+        # OpenRouter VLM 호출을 완전히 건너뛴다 (429/거부 문제 회피). 비워두면 기존
+        # OpenRouter VLM 경로로 자동 폴백 (하위 호환).
+        opt["vision_caption"] = ("STRING", {"multiline": True, "default": "",
+                                            "forceInput": True})
+        return {"required": req, "optional": opt}
+
+    RETURN_TYPES = (
+        "MODEL", "MODEL", "MODEL", "STRING",
+        "MODEL", "MODEL", "MODEL", "STRING",
+        "MODEL", "MODEL", "MODEL", "STRING",
+        "MODEL", "MODEL", "MODEL", "STRING",
+        "STRING", "STRING", "STRING",
+    )
+    RETURN_NAMES = (
+        "c1_base", "c1_high", "c1_low", "c1_prompt",
+        "c2_base", "c2_high", "c2_low", "c2_prompt",
+        "c3_base", "c3_high", "c3_low", "c3_prompt",
+        "c4_base", "c4_high", "c4_low", "c4_prompt",
+        "triggers", "scene_analysis", "debug_info",
+    )
+    FUNCTION = "direct"
+    CATEGORY = "Dolphin"
+
+    # ------------------------------------------------------------------ helpers
+    def _build_final(self, action, master_scene, name, trigger_str):
+        head = ", ".join([p for p in [name, trigger_str] if p])
+        body = " ".join([p for p in [master_scene.strip().strip(",. "), action.strip()] if p])
+        if head and body:
+            return f"{head}\n{body}"
+        return head or body
+
+    def _pacing_hint(self, p):
+        return {
+            "auto": "Decide pacing yourself based on how many distinct beats the actions contain.",
+            "slow-burn (1 beat)": "One climactic beat: clips 1-3 build tension, clip 4 detonates.",
+            "balanced (multi beat)": "Early clips build, later clips deliver; keep motion continuous.",
+            "rapid (4 distinct)": "Four distinct consecutive actions, one per clip, each kinetic.",
+        }.get(p, "Decide pacing yourself.")
+
+    def _detail_hint(self, d):
+        return {
+            "짧게(간결)": "Keep each clip to a short punchy motion phrase. Pure movement verbs only.",
+            "필수+앵커만": "Each clip is one concise continuous motion. No description, only movement.",
+            "상세": "Motion may be described in more detail, but ONLY body movement — no scenery.",
+        }.get(d, "Motion only, no description.")
+
+    def _extract_json(self, text):
+        if not text:
+            return None
+        text = re.sub(r'```(?:json)?', '', text).strip()
+        a, b = text.find('{'), text.rfind('}')
+        if a == -1 or b <= a:
+            return None
+        blob = text[a:b + 1]
+        try:
+            return json.loads(blob)
+        except json.JSONDecodeError:
+            try:
+                return json.loads(re.sub(r',\s*([}\]])', r'\1', blob))
+            except json.JSONDecodeError:
+                return None
+
+    def _call_or(self, messages, model, key, temp, timeout=120, force_json=False):
+        payload = {"model": model.strip(), "messages": messages, "temperature": temp}
+        if force_json:
+            payload["response_format"] = {"type": "json_object"}
+        req = urllib.request.Request(
+            "https://openrouter.ai/api/v1/chat/completions",
+            data=json.dumps(payload).encode('utf-8'),
+            headers={'Authorization': f'Bearer {key}', 'Content-Type': 'application/json'})
+        raw = json.loads(urllib.request.urlopen(req, timeout=timeout).read().decode('utf-8'))
+        # OpenRouter는 실패해도 HTTP 200 + {"error": {...}} 를 줄 때가 있음.
+        # 그 경우 'choices' KeyError로 뭉개지 말고 진짜 원인을 드러낸다.
+        if "choices" not in raw:
+            err = raw.get("error") or raw
+            msg = err.get("message") if isinstance(err, dict) else str(err)
+            code = err.get("code") if isinstance(err, dict) else "?"
+            raise ValueError(f"API error (code={code}): {msg}")
+        return raw['choices'][0]['message']['content'].strip()
+
+
+    # ---- STEP 1: 비전으로 이미지 앵커 추출 ----
+    def _vision_analyze(self, data_uri, vlm_model, key, temp):
+        sys = ("You are a visual scene analyst for a video pipeline. "
+               "Look at the image and extract ONLY concrete anchors, no storytelling. "
+               "Report exactly these lines:\n"
+               "(1) character appearance & clothing.\n"
+               "(2) current pose/posture (sitting, crouched, standing, balance, limb positions).\n"
+               "(3) SUPPORT & CONTACT: what the character is on top of, sitting on, holding, "
+               "leaning against, or touching (e.g. 'seated balanced on a yoga ball', "
+               "'gripping a sword', 'kneeling on the floor'). This is critical for physics — "
+               "state it explicitly even if obvious.\n"
+               "(4) environment & lighting (brief).\n"
+               "Be terse, one short line each. English only. "
+               "Do not invent actions or narrative. If content is mature, describe it plainly and factually.")
+        messages = [
+            {"role": "system", "content": sys},
+            {"role": "user", "content": [
+                {"type": "text", "text": "Extract the anchors from this image."},
+                {"type": "image_url", "image_url": {"url": data_uri}},
+            ]},
+        ]
+        import time
+        last_err = "vision error"
+        for attempt in range(1, 4):  # 429/일시 오류 대비 재시도 + 백오프
+            try:
+                resp = self._call_or(messages, vlm_model, key, temp, timeout=90).strip()
+                if not resp:
+                    print(f"⚠️ [Director] VLM({vlm_model}) 응답이 비었음 (거부 or 빈 반환)")
+                    return "", "vision empty response"
+                print(f"👁 [Director] VLM({vlm_model}) anchors (attempt {attempt}):\n{resp[:300]}")
+                return resp, None
+            except urllib.error.HTTPError as e:
+                body = ""
+                try:
+                    body = e.read().decode('utf-8')[:200]
+                except Exception:
+                    pass
+                print(f"❌ [Director] VLM HTTP {e.code} (attempt {attempt}): {body}")
+                last_err = f"vision HTTP {e.code}"
+                # 429(레이트리밋)/503(과부하)만 재시도. 그 외는 즉시 포기.
+                if e.code in (429, 503) and attempt < 3:
+                    wait = 2 ** attempt  # 2s, 4s
+                    print(f"⏳ [Director] rate limited, {wait}s 후 재시도...")
+                    time.sleep(wait)
+                    continue
+                return "", last_err
+            except (urllib.error.URLError, TimeoutError, KeyError, ValueError) as e:
+                print(f"❌ [Director] VLM error (attempt {attempt}): {type(e).__name__}: {e}")
+                last_err = f"vision error: {e}"
+                if attempt < 3:
+                    time.sleep(2 ** attempt)
+                    continue
+                return "", last_err
+        return "", last_err
+
+    # ---- STEP 2: 앵커 + 필수동작 -> 4샷 JSON ----
+    def _fallback_name(self, anchors):
+        """LLM이 이름을 안 줬을 때 앵커 텍스트의 민족/설정 단서로 이름 선택."""
+        import random
+        a = (anchors or "").lower()
+        east = ["asian", "east asian", "japanese", "korean", "chinese", "anime",
+                "kimono", "hanbok", "qipao", "oriental"]
+        western = ["western", "european", "caucasian", "american", "blonde", "redhead"]
+        east_names = ["Yuna", "Kaede", "Jin", "Mei", "Haruka", "Rin", "Sora", "Aoi"]
+        west_names = ["Elena", "Ryan", "Ava", "Lucas", "Mila", "Ethan", "Nora", "Leo"]
+        if any(k in a for k in east):
+            pool = east_names
+        elif any(k in a for k in western):
+            pool = west_names
+        else:
+            pool = east_names + west_names  # 단서 없으면 혼합
+        return random.choice(pool)
+
+    def _split_actions_to_clips(self, actions):
+        """동작을 파싱해 4클립에 순서대로 배정. 마지막 동작은 반드시 clip4.
+        반환: {1:[동작...], 2:[...], 3:[...], 4:[...]}"""
+        parts = re.split(r'(?<=[.!?。])\s+|[,、]|\n+', actions)
+        parts = [p.strip() for p in parts if len(p.strip()) > 1]
+        if not parts:
+            parts = [actions.strip()]
+        n = len(parts)
+        assign = {1: [], 2: [], 3: [], 4: []}
+        if n == 1:
+            # 단일 동작: 1~3 준비/전개, 4 완결
+            assign[1] = [f"(build-up toward) {parts[0]}"]
+            assign[2] = [f"(begin) {parts[0]}"]
+            assign[3] = [f"(escalate) {parts[0]}"]
+            assign[4] = [parts[0]]
+        elif n <= 4:
+            # 마지막 동작 -> clip4 고정, 나머지를 앞에서부터
+            assign[4] = [parts[-1]]
+            head = parts[:-1]
+            for i, ph in enumerate(head):
+                assign[min(i + 1, 3)].append(ph)
+            # 빈 앞클립은 다음 동작 빌드업
+            for c in range(1, 4):
+                if not assign[c]:
+                    nxt = parts[min(c, n - 1)]
+                    assign[c] = [f"(move toward) {nxt}"]
+        else:
+            # 5개+ : 마지막은 clip4 단독, 나머지 앞 3클립 균등
+            assign[4] = [parts[-1]]
+            rest = parts[:-1]
+            k, m = divmod(len(rest), 3)
+            idx = 0
+            for c in range(1, 4):
+                end = idx + k + (1 if (c - 1) < m else 0)
+                assign[c] = rest[idx:end]
+                idx = end
+        return assign
+
+    def _plan_shots(self, anchors, actions, name, vibe, pacing, detail, llm_model, key, temp, seed):
+        # 코드가 동작을 클립별로 미리 배정 -> LLM 지시에 못박음 (순서 강제)
+        assign = self._split_actions_to_clips(actions)
+        assign_block = "\n".join(
+            f"  clip_{c}: {', '.join(assign[c]) if assign[c] else '(continue previous motion)'}"
+            for c in range(1, 5))
+
+        sys = (
+            "You are an elite fight/action choreographer for a 20-second video made of "
+            "four consecutive 5-second clips. You are given SCENE ANCHORS (the character's "
+            "starting look/pose/place from an image) and a STRICT per-clip action assignment.\n"
+            "\n"
+            "ABSOLUTE RULES:\n"
+            "1) FOLLOW THE ASSIGNMENT EXACTLY. Each clip animates ONLY the action(s) assigned "
+            "to it below. You MUST NOT move a later clip's action into an earlier clip. "
+            "clip_1 must NOT contain clip_4's action. The final action happens ONLY in clip_4. "
+            "This is the most important rule - violating the order is a failure. "
+            "(ONE exception: clip_1 may prepend a brief transition OUT of the starting physics from "
+            "rule 2 — e.g. leaving the yoga ball — before its assigned action. This is not borrowing "
+            "a later action; it is grounding the first one.)\n"
+            "2) STARTING PHYSICS (use the anchors here). Read the character's CURRENT pose AND "
+            "what they are supported by / in contact with from the SCENE ANCHORS (e.g. seated on a "
+            "yoga ball, kneeling, gripping a weapon, leaning on a wall). clip_1 MUST begin the first "
+            "action FROM that exact physical situation and honor its constraints. If the support is "
+            "unstable or unusual (a yoga ball, a ledge, a moving surface), the motion MUST account "
+            "for it — e.g. anchor 'seated balanced on a yoga ball' -> clip_1 = 'pushing off the "
+            "wobbling ball and rising to their feet as it rolls away' BEFORE any further action. "
+            "Never ignore or contradict the starting support (do not stand if seated on the ball "
+            "without first leaving it; do not assume a weapon is drawn if the anchor shows it "
+            "sheathed). This makes the motion physically continuous with the input image. Reference "
+            "the support ONLY to ground how the motion STARTS — do NOT describe its appearance, "
+            "color, or the wider environment.\n"
+            "3) ACTION ONLY. Write physical body movement. NO scenery, NO lighting, NO clothing, "
+            "NO mood, NO camera talk. If a word is not a movement or body part, delete it. "
+            "(Referring to the starting pose in clip_1 per rule 2 is allowed since it describes "
+            "how the body moves.)\n"
+            "4) FILL 5 SECONDS. Expand the assigned action into a single continuous flow of motion "
+            "that occupies the whole clip (use 'then', 'immediately', 'without pausing'). "
+            "Do not borrow the next action to fill time - elaborate the CURRENT action instead.\n"
+            "5) ANTI-SLOW-MOTION: real-time speed, kinetic adverbs (swiftly, rapidly, explosively, "
+            "in a split second). NO slow motion, NO freeze, NO holding a pose.\n"
+            "6) CHARACTER NAME: study the SCENE ANCHORS. Judge the character's apparent ethnicity/"
+            "setting (East Asian, Western, etc.) and INVENT a specific fitting first name that "
+            "matches it (e.g. East Asian -> 'Yuna','Kaede','Jin'; Western -> 'Elena','Ryan'). "
+            "NEVER output 'AUTO' or an empty name. If a NAME is given below (not 'AUTO'), use it.\n"
+            f"7) {detail}\n"
+            "8) MOTION ENERGY: a MOTION STYLE cue is given below (e.g. 'cinematic lighting, dynamic "
+            "motion'). Use it ONLY to calibrate how forceful/graceful/frantic the movement FEELS "
+            "(word choice, verb intensity). Do NOT quote it and do NOT let any scenery/lighting/mood "
+            "words from it leak into the output — rule 3 still applies.\n"
+            "\n"
+            "master_scene: leave it EMPTY.\n"
+            "\n"
+            'OUTPUT ONLY JSON: {"character":"<a real name, never AUTO>","master_scene":"",'
+            '"clip_1":"<only clip_1 action, continuous & fast>",'
+            '"clip_2":"<only clip_2 action>","clip_3":"<only clip_3 action>",'
+            '"clip_4":"<only clip_4 action, the finisher>"}'
+        )
+        usr = (f"NAME: {name}\nPACING: {self._pacing_hint(pacing)}\n\n"
+               f"MOTION STYLE (kinetic energy cue only, per rule 8 — do not describe scenery/"
+               f"lighting/mood from this): {vibe.strip() or '(none)'}\n\n"
+               f"SCENE ANCHORS (use the POSE to start clip_1's motion per rule 2; "
+               f"use overall look only for naming — do NOT copy look into the clips):\n"
+               f"{anchors or '(none — assume a neutral ready stance)'}\n\n"
+               f"STRICT PER-CLIP ACTION ASSIGNMENT (animate ONLY what is listed per clip, "
+               f"in this order, finisher in clip_4):\n{assign_block}\n")
+        messages = [{"role": "system", "content": sys}, {"role": "user", "content": usr}]
+        t = temp
+        last = "⚠️ plan failed (no attempts ran)"
+        for attempt in (1, 2):
+            try:
+                content = self._call_or(messages, llm_model, key, t, force_json=True)
+                parsed = self._extract_json(content)
+                if parsed and all(parsed.get(f"clip_{i}") for i in range(1, 5)):
+                    return parsed, f"✅ plan ok (attempt {attempt})"
+                last = f"⚠️ incomplete JSON (attempt {attempt}): {content[:150]!r}"
+                print(f"⚠️ [Director] plan attempt {attempt} returned incomplete JSON: {content[:300]}")
+            except urllib.error.HTTPError as e:
+                body = ""
+                try:
+                    body = e.read().decode('utf-8')[:200]
+                except Exception:
+                    pass
+                last = f"❌ plan HTTP {e.code} (attempt {attempt}): {body}"
+                print(f"❌ [Director] plan {last}")
+            except (urllib.error.URLError, TimeoutError, KeyError, ValueError) as e:
+                last = f"❌ plan error (attempt {attempt}): {type(e).__name__}: {e}"
+                print(f"❌ [Director] {last}")
+            t = min(temp, 0.4)
+        return None, last
+
+    def _local_split(self, actions):
+        """LLM 폴백: 동작 순서 유지, 마지막 동작은 뒤 클립(3후반/4)에. 타임코드 없이
+        연속 동작 + 실시간 속도 어휘로 슬로우모션을 억제한다."""
+        parts = re.split(r'(?<=[.!?。])\s+|[,、]|\n+', actions)
+        parts = [p.strip() for p in parts if len(p.strip()) > 1]
+        if not parts:
+            parts = [actions.strip()]
+        n = len(parts)
+
+        def fast(phrase, lead="swiftly"):
+            p = phrase.strip()
+            return f"{lead} {p}, continuous real-time motion, no slow motion" if p else ""
+
+        if n == 1:
+            a = parts[0]
+            out = [
+                f"rapidly moves into position toward {a}, no pause",
+                f"immediately begins {a}, brisk continuous motion, no slow motion",
+                f"presses {a} without stopping, fast decisive movement",
+                f"explosively completes {a} at real-time speed, no slow motion, no freeze",
+            ]
+        elif n <= 4:
+            out = ["", "", "", ""]
+            out[3] = fast(parts[-1], "explosively")
+            head = parts[:-1]
+            for i, ph in enumerate(head):
+                s = min(i, 2)
+                out[s] = (out[s] + ", then " + fast(ph)).strip(", ") if out[s] else fast(ph)
+            for i in range(3):
+                if not out[i]:
+                    key = parts[min(i, n - 1)]
+                    out[i] = f"rapidly moves toward {key}, brisk continuous motion, no slow motion"
+        else:
+            k, m = divmod(n - 1, 3)
+            idx, chunks = 0, []
+            for i in range(3):
+                end = idx + k + (1 if i < m else 0)
+                seg = parts[idx:end]
+                idx = end
+                chunks.append(", then ".join(fast(p) for p in seg) if seg else "")
+            out = chunks + [fast(parts[-1], "explosively")]
+        return out[:4]
+
+    # ---- lora ----
+    def _load(self, name):
+        return comfy.utils.load_torch_file(folder_paths.get_full_path("loras", name))
+
+    @staticmethod
+    def _parse_weights(text):
+        """'base, high, low' 문자열 -> (b, h, l) float. 값이 모자라면 마지막 값으로 채움.
+        한 개만 주면 세 스테이지 모두 동일. 빈 문자열/파싱 실패는 (1,1,1)."""
+        try:
+            nums = [float(x) for x in re.split(r'[,\s]+', text.strip()) if x != ""]
+        except (ValueError, AttributeError):
+            nums = []
+        if not nums:
+            return 1.0, 1.0, 1.0
+        if len(nums) == 1:
+            return nums[0], nums[0], nums[0]
+        if len(nums) == 2:
+            return nums[0], nums[1], nums[1]
+        return nums[0], nums[1], nums[2]
+
+    def _patch_clip(self, mb, mh, ml, slots, cache, inject):
+        """slots: [(lora_name, weights_str), ...]. weights_str='base,high,low'.
+        트리거는 inject 여부와 무관하게 항상 조회한다(triggers 출력 포트용). inject는
+        각 클립 프롬프트 텍스트에 트리거를 넣을지만 결정한다."""
+        cb, ch, cl = mb, mh, ml
+        used = []
+        for (name, w_str) in slots:
+            if name == "None":
+                continue
+            w_b, w_h, w_l = self._parse_weights(w_str)
+            if w_b == 0.0 and w_h == 0.0 and w_l == 0.0:
+                continue
+            data = self._load(name)
+            if w_b != 0.0:
+                cb = apply_model_lora(cb, data, w_b)
+            if w_h != 0.0:
+                ch = apply_model_lora(ch, data, w_h)
+            if w_l != 0.0:
+                cl = apply_model_lora(cl, data, w_l)
+            used.append(name)
+        trig = []
+        trig_dbg = []
+        # 조회는 항상 수행 (used 로라 기준). 회귀 원인이던 inject 게이팅 제거.
+        for n in dict.fromkeys(used):
+            found = get_triggers(n, cache)
+            trig.extend(found)
+            trig_dbg.append(f"{os.path.basename(n)}:{len(found)}")
+        self._last_trig_dbg = trig_dbg  # 콘솔 디버그용
+        trig_str = ", ".join(dict.fromkeys(t for t in trig if t))
+        # inject가 꺼져 있으면 프롬프트 텍스트에는 넣지 않지만, 조회 결과(trig_str)는
+        # 항상 반환해 triggers 출력 포트로 나가게 한다.
+        prompt_trig = trig_str if inject else ""
+        return cb, ch, cl, prompt_trig, trig_str
+
+    # ------------------------------------------------------------------ main
+    def direct(self, image, model_base, model_high, model_low, essential_actions,
+               character_name, artistic_vibe, pacing, detail_level, inject_triggers,
+               use_vision, openrouter_api_key, vlm_preset, vlm_model, llm_preset, llm_model,
+               creativity, seed, **kw):
+
+        vlm = resolve_slug(VLM_PRESETS, vlm_preset, vlm_model, "qwen/qwen2.5-vl-72b-instruct")
+        llm = resolve_slug(LLM_PRESETS, llm_preset, llm_model, "deepseek/deepseek-v3.2")
+        user_name = "" if character_name.strip().upper() in ["AUTO", ""] else character_name.strip()
+        key = resolve_api_key(openrouter_api_key)
+
+        # ---- STEP 1: 비전 ----
+        # 우선순위: 1) vision_caption 입력(JoyCaption 등 로컬 노드 STRING) 있으면 그걸 그대로
+        #           앵커로 사용 - OpenRouter VLM 호출 자체를 건너뛴다 (429/거부 회피).
+        #           2) 없으면 기존 OpenRouter VLM 경로로 폴백 (하위 호환).
+        vision_caption = str(kw.get("vision_caption", "") or "").strip()
+        anchors, dbg_v = "", ""
+        if vision_caption:
+            anchors = vision_caption
+            dbg_v = "vision ok (local caption node)"
+            print(f"👁 [Director] 로컬 캡션 입력 사용 (JoyCaption 등):\n{anchors[:300]}")
+        elif not use_vision:
+            dbg_v = "vision OFF (토글 확인)"
+            print("ℹ️ [Director] use_vision=OFF - 비전 건너뜀")
+        elif not key:
+            dbg_v = "no api key"
+            print("❌ [Director] openrouter_api_key 비어있음 (위젯도, OPENROUTER_API_KEY 환경변수도 없음) - 비전/LLM 불가")
+        elif image is None:
+            dbg_v = "no image"
+            print("❌ [Director] image 입력 없음 - IMAGE 연결 확인")
+        else:
+            data_uri = tensor_to_base64(image)
+            if data_uri:
+                anchors, err = self._vision_analyze(data_uri, vlm, key, min(creativity, 0.5))
+                dbg_v = err or "vision ok"
+            else:
+                dbg_v = "image encode failed / PIL missing"
+        if not vision_caption and use_vision and key and image is not None and not anchors:
+            print(f"⚠️ [Director] 비전 앵커가 비었음 -> 이름/씬 근거 없음. 원인: {dbg_v}")
+
+        # ---- STEP 2: 4샷 분배 ----
+        if key:
+            parsed, dbg_p = self._plan_shots(anchors, essential_actions, character_name,
+                                             artistic_vibe, pacing, self._detail_hint(detail_level),
+                                             llm, key, creativity, seed)
+            if parsed:
+                clips = [str(parsed[f"clip_{i}"]).strip() for i in range(1, 5)]
+                master = str(parsed.get("master_scene", "")).strip()
+                cand = str(parsed.get("character", "")).strip()
+                # LLM 이 AUTO/빈값 반환 시 앵커 기반 폴백 작명
+                if not cand or cand.upper() == "AUTO":
+                    cand = self._fallback_name(anchors)
+                final_name = user_name or cand
+            else:
+                clips = self._local_split(essential_actions)
+                master = anchors.replace("\n", " ")[:160]
+                final_name = user_name or self._fallback_name(anchors)
+        else:
+            parsed, dbg_p = None, "no api key"
+            clips = self._local_split(essential_actions)
+            master = anchors.replace("\n", " ")[:160]
+            final_name = user_name
+
+        # ---- STEP 3: 클립별 로라 3개 패치 + 트리거 주입 ----
+        cache = _load_cache()
+        manual_list = [t.strip() for t in kw.get("manual_triggers", "").split(',') if t.strip()]
+        outs = []
+        all_trigs_collected = []   # 전 클립 트리거 통합 (triggers 출력용)
+        for c in range(1, 5):
+            slots = [(kw.get(f"c{c}_lora_{n}", "None"),
+                      kw.get(f"c{c}_w_{n}", "1.0, 1.0, 1.0")) for n in range(1, 4)]
+            cb, ch, cl, prompt_trig, always_trig = self._patch_clip(
+                model_base, model_high, model_low, slots, cache, inject_triggers)
+            # 프롬프트에 넣는 트리거: inject ON일 때만 (+manual)
+            all_trig = ", ".join(dict.fromkeys(
+                ([prompt_trig] if prompt_trig else []) + manual_list)) \
+                if (prompt_trig or manual_list) else ""
+            # triggers 출력 포트: inject 무관하게 항상 수집
+            if always_trig:
+                all_trigs_collected.extend(t.strip() for t in always_trig.split(',') if t.strip())
+            prompt = self._build_final(clips[c - 1], master, final_name, all_trig)
+            outs.extend([cb, ch, cl, prompt])
+            print(f"  [clip{c}] loras/triggers: {getattr(self, '_last_trig_dbg', [])} "
+                  f"inject={inject_triggers}")
+
+        # 통합 트리거 출력 (클립별 로라 트리거 + manual, 중복 제거)
+        triggers_out = ", ".join(dict.fromkeys(
+            [t for t in all_trigs_collected if t] + manual_list))
+
+        debug = f"vlm={dbg_v} | plan={dbg_p} | char={final_name}"
+        print(f"\n🎬 [Dolphin Vision Director] {debug}\nANCHORS: {anchors[:120]}")
+        for i in range(4):
+            print(f"  CLIP {i+1}: {outs[i*4+3][:90]}")
+
+        return tuple(outs) + (triggers_out, anchors, debug)
+
+
+NODE_CLASS_MAPPINGS = {"DolphinVisionDirector": DolphinVisionDirector}
+NODE_DISPLAY_NAME_MAPPINGS = {"DolphinVisionDirector": "🎬👁 Dolphin Vision Director (Image→4Clips)"}

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_auto_sequence.py

@@ -0,0 +1,101 @@
+import os
+import random
+import re
+import json
+import urllib.request
+
+class DolphinAutoSequencerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mode": (["🤖 Auto LLM (Timeline)", "✍️ Manual Override (수동)"], {"default": "🤖 Auto LLM (Timeline)"}),
+                "character_name": ("STRING", {"multiline": False, "default": "AUTO"}),
+                "tagger_context": ("STRING", {"multiline": True, "forceInput": True, "default": ""}),
+                "artistic_vibe": ("STRING", {"multiline": True, "default": "cinematic lighting, dynamic motion"}),
+                
+                # 🔥 [핵심] 시간 입력 없이 엔터키로만 액션을 나열합니다. 3줄이든 10줄이든 무한정 쓸 수 있습니다!
+                "action_sequence": ("STRING", {
+                    "multiline": True, 
+                    "default": "방문을 열고 조심스럽게 들어온다.\n갑자기 나타난 적을 보고 놀라 뒷걸음질 친다.\n재빠르게 허리에서 권총을 뽑아 든다.\n적을 향해 두 발을 발사한다."
+                }),
+                
+                # ⏱️ 한 줄당 몇 초를 배정할지, 영상의 프레임레이트는 몇인지 세팅합니다.
+                "seconds_per_line": ("INT", {"default": 5, "min": 1, "max": 30, "step": 1}),
+                "video_fps": ("INT", {"default": 24, "min": 8, "max": 120, "step": 1}),
+                
+                "openrouter_api_key": ("STRING", {"multiline": False, "default": ""}),
+                "openrouter_model": ("STRING", {"multiline": False, "default": "meta-llama/llama-3.3-70b-instruct"}),
+                "creativity": ("FLOAT", {"default": 0.85, "min": 0.1, "max": 1.5, "step": 0.05}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    # 📤 [출력] FizzNodes 등 스케줄러 노드에 바로 꽂을 수 있는 단일 문자열 1개와 확인용 텍스트를 내보냅니다.
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("Keyframe_Dict (스케줄러용)", "Raw_Text (로그 확인용)")
+    FUNCTION = "generate_timeline"
+    CATEGORY = "Dolphin"
+
+    def generate_timeline(self, mode, character_name, tagger_context, artistic_vibe, action_sequence, seconds_per_line, video_fps, openrouter_api_key, openrouter_model, creativity, seed):
+        
+        # 1. 입력받은 시퀀스를 '엔터키(\n)' 기준으로 쪼개고, 몇 줄인지 계산합니다.
+        raw_lines = [line.strip() for line in action_sequence.split('\n') if line.strip()]
+        num_actions = len(raw_lines)
+
+        if num_actions == 0:
+            return ("\"0\": \"\"", "입력된 액션이 없습니다.")
+
+        user_defined_name = "" if character_name.upper() in ["AUTO", ""] else character_name.strip()
+
+        def build_final(action, raw_tags, master_scene, name):
+            parts = []
+            if name: parts.append(name) 
+            if raw_tags.strip(): parts.append(raw_tags.strip().strip(",. "))
+            if master_scene.strip(): parts.append(master_scene.strip().strip(",. "))
+            prefix = ", ".join(parts)
+            return f"{prefix}, {action.strip()}" if action.strip() else prefix
+
+        # 🟢 [수동 모드] LLM을 거치지 않고 바로 프레임 계산 후 송출
+        if mode == "✍️ Manual Override (수동)":
+            keyframe_out, raw_out = [], []
+            for idx, line in enumerate(raw_lines):
+                frame_num = idx * seconds_per_line * video_fps
+                final_prompt = build_final(line, tagger_context, "", user_defined_name)
+                keyframe_out.append(f'"{frame_num}": "{final_prompt}"')
+                raw_out.append(f"[{frame_num} Frame] {final_prompt}")
+            return (",\n".join(keyframe_out), "\n".join(raw_out))
+
+        # 🔵 [자동 LLM 모드]
+        random.seed(seed)
+        
+        # LLM에게 보낼 번호 매기기 (내부적으로만 처리)
+        actions_formatted = "\n".join([f"ACTION {i+1}: {line}" for i, line in enumerate(raw_lines)])
+
+        # 유저가 쓴 줄 개수(num_actions)만큼 동적으로 파트(PART)를 쪼개라고 LLM에게 명령
+        sys_prompt = (
+            f"You are an Elite Cinematic Action Choreographer.\n"
+            f"1. CHARACTER: If NAME is 'AUTO', assign a culturally appropriate name based on TAGS. If '{user_defined_name}', use it.\n"
+            f"2. INITIAL POSTURE: Analyze TAGS for starting physical state. 1 short sentence.\n"
+            f"3. MASTER SCENE: 1-sentence environment description based on TAGS and VIBE.\n"
+            f"4. CHOREOGRAPHY: You will receive {num_actions} basic actions. EXPAND each into highly detailed, physical, kinetic choreography. Maintain logical flow. No abstract emotions.\n"
+            f"Format EXACTLY like this:\nCHARACTER: [Name]\nINITIAL POSTURE: [State]\nMASTER SCENE: [Description]\n"
+        )
+        for i in range(num_actions):
+            sys_prompt += f"PART {i+1}: [Action]\n"
+
+        usr_prompt = f"NAME: {character_name}\nTAGS: {tagger_context}\nVIBE: {artistic_vibe}\n{actions_formatted}"
+
+        url = "https://openrouter.ai/api/v1/chat/completions"
+        payload = {"model": openrouter_model.strip(), "messages": [{"role": "system", "content": sys_prompt}, {"role": "user", "content": usr_prompt}], "temperature": creativity}
+        
+        llm_prompt = ""
+        try:
+            req = urllib.request.Request(url, data=json.dumps(payload).encode('utf-8'), headers={'Authorization': f'Bearer {openrouter_api_key}', 'Content-Type': 'application/json'})
+            response = urllib.request.urlopen(req, timeout=120)
+            llm_prompt = json.loads(response.read().decode('utf-8'))['choices'][0]['message']['content'].strip()
+            print(f"\n✅ [Dolphin Auto-Sequencer]\n{llm_prompt}\n")
+        except Exception as e:
+            print(f"❌ [에러] {e}")
+
+        final_char_name = user_defined_

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v30_story.py

@@ -0,0 +1,176 @@
+import os
+import random
+import re
+import json
+import urllib.request
+import urllib.error
+
+class DolphinMultiActionPromptNode_V30:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mode": (["🤖 Auto LLM (Story Splitter)", "✍️ Manual Override (수동)"], {"default": "🤖 Auto LLM (Story Splitter)"}),
+                "character_name": ("STRING", {"multiline": False, "default": "AUTO"}),
+                "tagger_context": ("STRING", {"multiline": True, "default": ""}),
+                "artistic_vibe": ("STRING", {"multiline": True, "default": "cinematic lighting, dynamic motion"}),
+                
+                "master_story": ("STRING", {
+                    "multiline": True, 
+                    "default": "어두운 골목길. 갑자기 나타난 적들을 향해 돌진한다, 화려하게 검을 휘둘러 적을 쓰러뜨린다, 날아오는 총알을 튕겨낸다, 적에게 다가가 숨통을 끊는다."
+                }),
+                
+                "openrouter_api_key": ("STRING", {"multiline": False, "default": ""}),
+                "openrouter_model": ("STRING", {"multiline": False, "default": "meta-llama/llama-3.3-70b-instruct"}),
+                "creativity": ("FLOAT", {"default": 0.85, "min": 0.1, "max": 1.5, "step": 0.05}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("prompt_1 (Clip 1: 0-5s)", "prompt_2 (Clip 2: 0-5s)", "prompt_3 (Clip 3: 0-5s)", "prompt_4 (Clip 4: 0-5s)")
+    FUNCTION = "generate_sequence"
+    CATEGORY = "Dolphin"
+
+    def generate_sequence(self, mode, character_name, tagger_context, artistic_vibe, master_story, openrouter_api_key, openrouter_model, creativity, seed):
+        
+        user_defined_name = "" if character_name.upper() in ["AUTO", ""] else character_name.strip()
+
+        def build_final(action, raw_tags, master_scene, name):
+            tags_list = []
+            if name: tags_list.append(name) 
+            if raw_tags.strip(): tags_list.append(raw_tags.strip().strip(",. "))
+            tag_block = ", ".join(tags_list)
+            
+            sentence_list = []
+            if master_scene.strip(): sentence_list.append(master_scene.strip().strip(",. "))
+            if action.strip(): sentence_list.append(action.strip())
+            sentence_block = " ".join(sentence_list)
+            
+            if tag_block and sentence_block:
+                return f"{tag_block}\n{sentence_block}"
+            elif tag_block:
+                return tag_block
+            else:
+                return sentence_block
+
+        if mode == "✍️ Manual Override (수동)":
+            return (
+                build_final(master_story, tagger_context, "", user_defined_name),
+                build_final(master_story, tagger_context, "", user_defined_name),
+                build_final(master_story, tagger_context, "", user_defined_name),
+                build_final(master_story, tagger_context, "", user_defined_name)
+            )
+
+        random.seed(seed)
+        
+        # 1차: 마침표, 쉼표, 느낌표, 줄바꿈 기준으로 확실하게 쪼갬
+        clean_story = re.sub(r'([.!?,\n])', r'\1|', master_story)
+        raw_sentences = clean_story.split('|')
+        sentences = [s.strip() for s in raw_sentences if len(s.strip()) > 1]
+        
+        # 🔥 [핵심 로직] 문장 개수에 따른 '시네마틱 호흡(Pacing)' 자동 설계
+        
+        if len(sentences) == 1:
+            # 단 1개의 문장: 1~3번은 분위기 빌드업, 4번에 폭발!
+            chunk_1 = f"Atmospheric Build-up: Focus STRICTLY on the environment, lighting, and vibe. Character is mostly still. DO NOT execute the main action yet. (Upcoming target: {sentences[0]})"
+            chunk_2 = f"Rising Tension: Subtle character movements or camera panning. Anticipation builds. DO NOT execute the main action yet. (Upcoming target: {sentences[0]})"
+            chunk_3 = f"Pre-action Wind-up: Extreme tension right before the strike. Character prepares to move. DO NOT execute yet. (Upcoming target: {sentences[0]})"
+            chunk_4 = f"CLIMAX: Explosively and swiftly execute this exact action -> {sentences[0]}"
+            
+        elif len(sentences) == 2:
+            # 2개의 문장: 1번 빌드업 -> 2번 첫 액션 -> 3번 호흡 고르기 -> 4번 피니시
+            chunk_1 = f"Atmospheric Build-up: Focus strictly on the environment and tension. DO NOT execute the action yet. (Upcoming target: {sentences[0]})"
+            chunk_2 = f"Phase 1 Action: Execute this action -> {sentences[0]}"
+            chunk_3 = f"Tension / Transition: Character prepares for the final blow. (Upcoming target: {sentences[1]})"
+            chunk_4 = f"CLIMAX Action: Explosively execute this action -> {sentences[1]}"
+            
+        elif len(sentences) == 3:
+            # 3개의 문장: 첫 문장을 10초(1,2번 파트)에 걸쳐 부드럽게 배분
+            chunk_1 = f"Phase 1: Start this action slowly -> {sentences[0]}"
+            chunk_2 = f"Phase 2: Complete this action -> {sentences[0]}"
+            chunk_3 = sentences[1]
+            chunk_4 = sentences[2]
+            
+        else:
+            # 4개 이상: 기존의 수학적 4등분 배분
+            k, m = divmod(len(sentences), 4)
+            chunks = []
+            start = 0
+            for i in range(4):
+                end = start + k + (1 if i < m else 0)
+                chunks.append(" ".join(sentences[start:end]))
+                start = end
+            chunk_1, chunk_2, chunk_3, chunk_4 = chunks
+
+        sys_prompt = (
+            "You are an Elite Cinematic Action Choreographer.\n"
+            f"1. CHARACTER: If NAME is 'AUTO', assign a culturally appropriate name based on TAGS. If '{user_defined_name}', use it.\n"
+            "2. INITIAL POSTURE: Analyze TAGS for the character's starting physical state. State it in 1 short sentence.\n"
+            "3. MASTER SCENE: A vivid 1-sentence environment description based on TAGS and VIBE.\n"
+            "4. STRICT CHOREOGRAPHY ISOLATION (CRITICAL):\n"
+            "   - You will receive 4 explicit target events. You MUST translate them into English cinematic actions.\n"
+            "   - FATAL ERROR PREVENTION: You MUST rigidly confine the actions. DO NOT animate Part 2, 3, or 4's event in Part 1. Keep them completely isolated in time.\n"
+            "   - 🔥 OUTPUT RULE: DO NOT quote the original Korean text. ONLY output the English descriptions.\n"
+            "   - 🔥 ANTI-SLOW MOTION RULE: Inject aggressive kinetic adverbs (swiftly, suddenly, explosively) into EVERY action description.\n"
+            "   - Each Part MUST reset timestamps to [0-1s], [2-3s], [4-5s].\n"
+            "Format EXACTLY:\nCHARACTER: [Name]\nINITIAL POSTURE: [State]\nMASTER SCENE: [Description]\n"
+            "PART 1: [0-1s] [Action A] [2-3s] [Action B] [4-5s] [Action C]\n"
+            "PART 2: [0-1s] [Action D] [2-3s] [Action E] [4-5s] [Action F]\n"
+            "PART 3: [0-1s] [Action G] [2-3s] [Action H] [4-5s] [Action I]\n"
+            "PART 4: [0-1s] [Action J] [2-3s] [Action K] [4-5s] [Action L]"
+        )
+        
+        usr_prompt = (
+            f"NAME: {character_name}\nTAGS: {tagger_context}\nVIBE: {artistic_vibe}\n\n"
+            "=== ACTION ISOLATION SCRIPT ===\n"
+            f"▶ For PART 1 (0-5s), you MUST ONLY animate this specific event: \"{chunk_1}\"\n"
+            f"▶ For PART 2 (5-10s), you MUST ONLY animate this specific event: \"{chunk_2}\"\n"
+            f"▶ For PART 3 (10-15s), you MUST ONLY animate this specific event: \"{chunk_3}\"\n"
+            f"▶ For PART 4 (15-20s), you MUST ONLY animate this specific event: \"{chunk_4}\"\n"
+            "CRITICAL REMINDER: Do NOT allow any actions from Part 2, 3, or 4 to bleed into Part 1!"
+        )
+
+        url = "https://openrouter.ai/api/v1/chat/completions"
+        payload = {"model": openrouter_model.strip(), "messages": [{"role": "system", "content": sys_prompt}, {"role": "user", "content": usr_prompt}], "temperature": creativity}
+        
+        try:
+            req = urllib.request.Request(url, data=json.dumps(payload).encode('utf-8'), headers={'Authorization': f'Bearer {openrouter_api_key}', 'Content-Type': 'application/json'})
+            response = urllib.request.urlopen(req, timeout=120)
+            llm_prompt = json.loads(response.read().decode('utf-8'))['choices'][0]['message']['content'].strip()
+            print(f"\n✅ [Dolphin V30 - Cinematic Pacing Applied]\n{llm_prompt}\n")
+        except Exception as e:
+            print(f"❌ [에러] {e}")
+            llm_prompt = ""
+
+        final_char_name = user_defined_name
+        r_master, p1, p2, p3, p4 = "", "", "", "", ""
+
+        if llm_prompt and "[removed]" not in llm_prompt:
+            cl = re.sub(r'[*#]', '', llm_prompt)
+            m_char = re.search(r'CHARACTER:\s*(.*?)(?=INITIAL POSTURE|$)', cl, re.I | re.S)
+            m_master = re.search(r'MASTER SCENE:\s*(.*?)(?=PART 1|$)', cl, re.I | re.S)
+            
+            m1 = re.search(r'PART 1:\s*(.*?)(?=PART 2|$)', cl, re.I | re.S)
+            m2 = re.search(r'PART 2:\s*(.*?)(?=PART 3|$)', cl, re.I | re.S)
+            m3 = re.search(r'PART 3:\s*(.*?)(?=PART 4|$)', cl, re.I | re.S)
+            m4 = re.search(r'PART 4:\s*(.*?)(?=\n\n|===|Note:|$)', cl, re.I | re.S)
+
+            if not user_defined_name and m_char:
+                final_char_name = m_char.group(1).strip()
+            
+            r_master = m_master.group(1).strip() if m_master else ""
+            
+            p1 = m1.group(1).strip() if m1 else "⚠️ Formatting Error: Part 1"
+            p2 = m2.group(1).strip() if m2 else "⚠️ Formatting Error: Part 2"
+            p3 = m3.group(1).strip() if m3 else "⚠️ Formatting Error: Part 3"
+            p4 = m4.group(1).strip() if m4 else "⚠️ Formatting Error: Part 4"
+        else:
+            p1 = p2 = p3 = p4 = "⚠️ API Error: LLM request failed."
+
+        return (
+            build_final(p1, tagger_context, r_master, final_char_name),
+            build_final(p2, tagger_context, r_master, final_char_name),
+            build_final(p3, tagger_context, r_master, final_char_name),
+            build_final(p4, tagger_context, r_master, final_char_name)
+        )

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v31_vision.py

@@ -0,0 +1,191 @@
+import os
+import random
+import re
+import json
+import urllib.request
+import urllib.error
+import base64
+import io
+import numpy as np
+from PIL import Image
+import torch
+
+class DolphinMultiActionPromptNode_V31:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mode": (["🤖 Auto Vision+LLM", "✍️ Manual Override"], {"default": "🤖 Auto Vision+LLM"}),
+                "character_name": ("STRING", {"multiline": False, "default": "AUTO"}),
+                "artistic_vibe": ("STRING", {"multiline": True, "default": "cinematic lighting, high-speed action, dark fantasy"}),
+                
+                "master_story": ("STRING", {
+                    "multiline": True, 
+                    "default": "어두운 골목길. 갑자기 나타난 적들을 향해 돌진한다, 화려하게 검을 휘둘러 적을 쓰러뜨린다, 날아오는 총알을 튕겨낸다, 적에게 다가가 숨통을 끊는다."
+                }),
+                
+                "openrouter_api_key": ("STRING", {"multiline": False, "default": ""}),
+                "openrouter_model": ("STRING", {"multiline": False, "default": "qwen/qwen-2-vl-72b-instruct"}),
+                "creativity": ("FLOAT", {"default": 0.85, "min": 0.1, "max": 1.5, "step": 0.05}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("prompt_1 (Clip 1: 0-5s)", "prompt_2 (Clip 2: 0-5s)", "prompt_3 (Clip 3: 0-5s)", "prompt_4 (Clip 4: 0-5s)")
+    FUNCTION = "generate_sequence"
+    CATEGORY = "Dolphin"
+
+    def generate_sequence(self, image, mode, character_name, artistic_vibe, master_story, openrouter_api_key, openrouter_model, creativity, seed):
+        
+        user_defined_name = "" if character_name.upper() in ["AUTO", ""] else character_name.strip()
+
+        def build_final(action, master_scene, name):
+            tags_list = []
+            if name: tags_list.append(name) 
+            tag_block = ", ".join(tags_list)
+            
+            sentence_list = []
+            if master_scene.strip(): sentence_list.append(master_scene.strip().strip(",. "))
+            if action.strip(): sentence_list.append(action.strip())
+            sentence_block = " ".join(sentence_list)
+            
+            if tag_block and sentence_block:
+                return f"{tag_block}\n{sentence_block}"
+            elif tag_block:
+                return tag_block
+            else:
+                return sentence_block
+
+        if mode == "✍️ Manual Override":
+            return (
+                build_final(master_story, "", user_defined_name),
+                build_final(master_story, "", user_defined_name),
+                build_final(master_story, "", user_defined_name),
+                build_final(master_story, "", user_defined_name)
+            )
+
+        random.seed(seed)
+        
+        img_tensor = image[0]
+        i = 255. * img_tensor.cpu().numpy()
+        img_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+        buffered = io.BytesIO()
+        img_pil.save(buffered, format="JPEG")
+        base64_image = f"data:image/jpeg;base64,{base64.b64encode(buffered.getvalue()).decode('utf-8')}"
+
+        clean_story = re.sub(r'([.!?,\n])', r'\1|', master_story)
+        raw_sentences = clean_story.split('|')
+        sentences = [s.strip() for s in raw_sentences if len(s.strip()) > 1]
+        
+        # 🔥 '가만히 있기(still)'를 금지하고, 1문장이라도 시작부터 빠르게 움직이도록 전면 수정
+        if len(sentences) == 1:
+            chunk_1 = f"Phase 1: Rapid approach and high-speed dynamic movement. DO NOT stand still. (Target: {sentences[0]})"
+            chunk_2 = f"Phase 2: Swift, explosive execution of the action. (Target: {sentences[0]})"
+            chunk_3 = f"Phase 3: The climax at full 1x real-time speed. Lightning fast! (Target: {sentences[0]})"
+            chunk_4 = f"Phase 4: Fast-paced completion and quick recovery. (Target: {sentences[0]})"
+        elif len(sentences) == 2:
+            chunk_1 = f"Phase 1: High-speed buildup and rapid preparation. (Target: {sentences[0]})"
+            chunk_2 = f"Phase 2: Explosively execute -> {sentences[0]}"
+            chunk_3 = f"Phase 3: Rapid transition, sprinting or moving quickly. (Target: {sentences[1]})"
+            chunk_4 = f"Phase 4: Lightning-fast execution -> {sentences[1]}"
+        elif len(sentences) == 3:
+            chunk_1 = f"Phase 1: Start this action rapidly -> {sentences[0]}"
+            chunk_2 = f"Phase 2: Explosively complete -> {sentences[0]}"
+            chunk_3 = sentences[1]
+            chunk_4 = sentences[2]
+        else:
+            k, m = divmod(len(sentences), 4)
+            chunks = []
+            start = 0
+            for i in range(4):
+                end = start + k + (1 if i < m else 0)
+                chunks.append(" ".join(sentences[start:end]))
+                start = end
+            chunk_1, chunk_2, chunk_3, chunk_4 = chunks
+
+        # 🔥 슬로우 모션 발작 단어(micro-expression 등) 완전 삭제 및 매크로 스피드 강조
+        sys_prompt = (
+            "You are an Elite Action Director prioritizing RAW SPEED and KINETIC ENERGY.\n"
+            f"1. CHARACTER: If NAME is 'AUTO', assign a name. If '{user_defined_name}', use it.\n"
+            "2. VISUAL ANALYSIS: You MUST base your descriptions EXACTLY on the character's clothing and weapons in the attached IMAGE.\n"
+            "3. MASTER SCENE: Write a 1-sentence environment description (lighting, weather).\n"
+            "4. SPEED-FOCUSED CHOREOGRAPHY (CRITICAL):\n"
+            "   - 🚫 BAN SLOW-MOTION TRIGGERS: NEVER use words like 'micro-expressions', 'muscle tension', 'slowly turning', 'floating', or 'gradually'. These cause AI video models to render in slow-motion.\n"
+            "   - ✅ FORCE 1x REAL-TIME SPEED: Describe large, sweeping, high-velocity movements. Use aggressive verbs (dashing, sprinting, whipping, snapping).\n"
+            "   - ✅ KINETIC ADVERBS: Inject phrases like 'in a flash', 'at lightning speed', 'with explosive real-time velocity' into EVERY part.\n"
+            "   - Example: 'suddenly dashes forward at full speed and delivers a lightning-fast horizontal strike, moving so quickly the rain splatters'.\n"
+            "   - Strictly confine the actions. DO NOT animate future events early.\n"
+            "   - 🔥 OUTPUT RULE: DO NOT quote the Korean text. Only output English.\n"
+            "Format EXACTLY:\nCHARACTER: [Name]\nMASTER SCENE: [Description]\n"
+            "PART 1: [0-1s] [Action A] [2-3s] [Action B] [4-5s] [Action C]\n"
+            "PART 2: [0-1s] [Action D] [2-3s] [Action E] [4-5s] [Action F]\n"
+            "PART 3: [0-1s] [Action G] [2-3s] [Action H] [4-5s] [Action I]\n"
+            "PART 4: [0-1s] [Action J] [2-3s] [Action K] [4-5s] [Action L]"
+        )
+        
+        usr_text = (
+            f"NAME: {character_name}\n"
+            f"VIBE: {artistic_vibe}\n\n"
+            "=== HIGH-SPEED ACTION SCRIPT ===\n"
+            f"▶ For PART 1 (0-5s), ONLY animate this: \"{chunk_1}\"\n"
+            f"▶ For PART 2 (5-10s), ONLY animate this: \"{chunk_2}\"\n"
+            f"▶ For PART 3 (10-15s), ONLY animate this: \"{chunk_3}\"\n"
+            f"▶ For PART 4 (15-20s), ONLY animate this: \"{chunk_4}\"\n"
+            "CRITICAL: Keep the action moving FAST. Avoid still poses or micro-details that look like slow-mo!"
+        )
+
+        url = "https://openrouter.ai/api/v1/chat/completions"
+        payload = {
+            "model": openrouter_model.strip(),
+            "messages": [
+                {"role": "system", "content": sys_prompt},
+                {"role": "user", "content": [
+                    {"type": "text", "text": usr_text},
+                    {"type": "image_url", "image_url": {"url": base64_image}}
+                ]}
+            ],
+            "temperature": creativity
+        }
+        
+        try:
+            req = urllib.request.Request(url, data=json.dumps(payload).encode('utf-8'), headers={'Authorization': f'Bearer {openrouter_api_key}', 'Content-Type': 'application/json'})
+            response = urllib.request.urlopen(req, timeout=120)
+            llm_prompt = json.loads(response.read().decode('utf-8'))['choices'][0]['message']['content'].strip()
+            print(f"\n✅ [Dolphin V31 - Action Speed Optimized]\n{llm_prompt}\n")
+        except Exception as e:
+            print(f"❌ [에러] API 호출 실패: {e}")
+            llm_prompt = ""
+
+        final_char_name = user_defined_name
+        r_master, p1, p2, p3, p4 = "", "", "", "", ""
+
+        if llm_prompt and "[removed]" not in llm_prompt:
+            cl = re.sub(r'[*#]', '', llm_prompt)
+            m_char = re.search(r'CHARACTER:\s*(.*?)(?=MASTER SCENE|$)', cl, re.I | re.S)
+            m_master = re.search(r'MASTER SCENE:\s*(.*?)(?=PART 1|$)', cl, re.I | re.S)
+            
+            m1 = re.search(r'PART 1:\s*(.*?)(?=PART 2|$)', cl, re.I | re.S)
+            m2 = re.search(r'PART 2:\s*(.*?)(?=PART 3|$)', cl, re.I | re.S)
+            m3 = re.search(r'PART 3:\s*(.*?)(?=PART 4|$)', cl, re.I | re.S)
+            m4 = re.search(r'PART 4:\s*(.*?)(?=\n\n|===|Note:|$)', cl, re.I | re.S)
+
+            if not user_defined_name and m_char:
+                final_char_name = m_char.group(1).strip()
+            
+            r_master = m_master.group(1).strip() if m_master else ""
+            
+            p1 = m1.group(1).strip() if m1 else "⚠️ Formatting Error: Part 1"
+            p2 = m2.group(1).strip() if m2 else "⚠️ Formatting Error: Part 2"
+            p3 = m3.group(1).strip() if m3 else "⚠️ Formatting Error: Part 3"
+            p4 = m4.group(1).strip() if m4 else "⚠️ Formatting Error: Part 4"
+        else:
+            p1 = p2 = p3 = p4 = "⚠️ API Error: Vision LLM request failed."
+
+        return (
+            build_final(p1, r_master, final_char_name),
+            build_final(p2, r_master, final_char_name),
+            build_final(p3, r_master, final_char_name),
+            build_final(p4, r_master, final_char_name)
+        )

custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v32_vision.py

@@ -0,0 +1,247 @@
+import os
+import random
+import re
+import json
+import urllib.request
+import urllib.error
+import base64
+import io
+import time
+import numpy as np
+from PIL import Image
+import torch
+
+
+class DolphinMultiActionPromptNode_V32:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mode": (["🤖 Auto Vision+LLM", "✍️ Manual Override"], {"default": "🤖 Auto Vision+LLM"}),
+                "character_name": ("STRING", {"multiline": False, "default": "AUTO"}),
+                "artistic_vibe": ("STRING", {"multiline": True, "default": "cinematic lighting, high-speed action, dark fantasy"}),
+
+                "master_story": ("STRING", {
+                    "multiline": True,
+                    "default": "어두운 골목길. 갑자기 나타난 적들을 향해 돌진한다, 화려하게 검을 휘둘러 적을 쓰러뜨린다, 날아오는 총알을 튕겨낸다, 적에게 다가가 숨통을 끊는다."
+                }),
+
+                "openrouter_api_key": ("STRING", {"multiline": False, "default": ""}),
+                "openrouter_model": ("STRING", {"multiline": False, "default": "qwen/qwen-2-vl-72b-instruct"}),
+                "creativity": ("FLOAT", {"default": 0.85, "min": 0.1, "max": 1.5, "step": 0.05}),
+                "max_tokens": ("INT", {"default": 1500, "min": 256, "max": 8192, "step": 64}),
+                "retries": ("INT", {"default": 2, "min": 0, "max": 5}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING")
+    RETURN_NAMES = (
+        "prompt_1 (Clip 1: 0-5s)",
+        "prompt_2 (Clip 2: 0-5s)",
+        "prompt_3 (Clip 3: 0-5s)",
+        "prompt_4 (Clip 4: 0-5s)",
+        "raw_llm_output",
+    )
+    FUNCTION = "generate_sequence"
+    CATEGORY = "Dolphin"
+
+    # -----------------------------------------------------------------
+    def _encode_image(self, image):
+        img_tensor = image[0]
+        i = 255. * img_tensor.cpu().numpy()
+        img_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+        if img_pil.mode != "RGB":
+            img_pil = img_pil.convert("RGB")
+        buffered = io.BytesIO()
+        img_pil.save(buffered, format="JPEG", quality=90)
+        b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+        return f"data:image/jpeg;base64,{b64}"
+
+    def _split_sentences(self, master_story):
+        clean_story = re.sub(r'([.!?,\n])', r'\1|', master_story)
+        raw_sentences = clean_story.split('|')
+        return [s.strip() for s in raw_sentences if len(s.strip()) > 1]
+
+    def _build_chunks(self, sentences):
+        n = len(sentences)
+        if n == 0:
+            base = "dynamic high-speed action"
+            return (base, base, base, base)
+        if n == 1:
+            s = sentences[0]
+            return (
+                f"Phase 1: Rapid approach and high-speed dynamic movement. DO NOT stand still. (Target: {s})",
+                f"Phase 2: Swift, explosive execution of the action. (Target: {s})",
+                f"Phase 3: The climax at full 1x real-time speed. Lightning fast! (Target: {s})",
+                f"Phase 4: Fast-paced completion and quick recovery. (Target: {s})",
+            )
+        if n == 2:
+            return (
+                f"Phase 1: High-speed buildup and rapid preparation. (Target: {sentences[0]})",
+                f"Phase 2: Explosively execute -> {sentences[0]}",
+                f"Phase 3: Rapid transition, sprinting or moving quickly. (Target: {sentences[1]})",
+                f"Phase 4: Lightning-fast execution -> {sentences[1]}",
+            )
+        if n == 3:
+            return (
+                f"Phase 1: Start this action rapidly -> {sentences[0]}",
+                f"Phase 2: Explosively complete -> {sentences[0]}",
+                sentences[1],
+                sentences[2],
+            )
+        # n >= 4: 균등 분배
+        k, m = divmod(n, 4)
+        chunks = []
+        start = 0
+        for idx in range(4):
+            end = start + k + (1 if idx < m else 0)
+            chunks.append(" ".join(sentences[start:end]))
+            start = end
+        return tuple(chunks)
+
+    def _call_llm(self, url, payload, api_key, retries, timeout=120):
+        last_err = None
+        for attempt in range(retries + 1):
+            try:
+                req = urllib.request.Request(
+                    url,
+                    data=json.dumps(payload).encode('utf-8'),
+                    headers={'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json'}
+                )
+                response = urllib.request.urlopen(req, timeout=timeout)
+                body = json.loads(response.read().decode('utf-8'))
+                return body['choices'][0]['message']['content'].strip(), None
+            except Exception as e:
+                last_err = e
+                if attempt < retries:
+                    time.sleep(1.5 * (attempt + 1))
+        return None, last_err
+
+    # -----------------------------------------------------------------
+    def generate_sequence(self, image, mode, character_name, artistic_vibe, master_story,
+                          openrouter_api_key, openrouter_model, creativity, max_tokens, retries, seed):
+
+        user_defined_name = "" if character_name.upper() in ["AUTO", ""] else character_name.strip()
+
+        def build_final(action, master_scene, name):
+            tags_list = []
+            if name:
+                tags_list.append(name)
+            tag_block = ", ".join(tags_list)
+
+            sentence_list = []
+            if master_scene.strip():
+                sentence_list.append(master_scene.strip().strip(",. "))
+            if action.strip():
+                sentence_list.append(action.strip())
+            sentence_block = " ".join(sentence_list)
+
+            if tag_block and sentence_block:
+                return f"{tag_block}\n{sentence_block}"
+            elif tag_block:
+                return tag_block
+            return sentence_block
+
+        # ---- Manual Override ----
+        if mode == "✍️ Manual Override":
+            fp = build_final(master_story, "", user_defined_name)
+            return (fp, fp, fp, fp, "[Manual Override]")
+
+        random.seed(seed)
+
+        base64_image = self._encode_image(image)
+        sentences = self._split_sentences(master_story)
+        chunk_1, chunk_2, chunk_3, chunk_4 = self._build_chunks(sentences)
+
+        sys_prompt = (
+            "You are an Elite Action Director prioritizing RAW SPEED and KINETIC ENERGY.\n"
+            f"1. CHARACTER: If NAME is 'AUTO', assign a name. If '{user_defined_name}', use it.\n"
+            "2. VISUAL ANALYSIS: You MUST base your descriptions EXACTLY on the character's clothing and weapons in the attached IMAGE.\n"
+            "3. MASTER SCENE: Write a 1-sentence environment description (lighting, weather).\n"
+            "4. SPEED-FOCUSED CHOREOGRAPHY (CRITICAL):\n"
+            "   - 🚫 BAN SLOW-MOTION TRIGGERS: NEVER use words like 'micro-expressions', 'muscle tension', 'slowly turning', 'floating', or 'gradually'. These cause AI video models to render in slow-motion.\n"
+            "   - ✅ FORCE 1x REAL-TIME SPEED: Describe large, sweeping, high-velocity movements. Use aggressive verbs (dashing, sprinting, whipping, snapping).\n"
+            "   - ✅ KINETIC ADVERBS: Inject phrases like 'in a flash', 'at lightning speed', 'with explosive real-time velocity' into EVERY part.\n"
+            "   - Example: 'suddenly dashes forward at full speed and delivers a lightning-fast horizontal strike, moving so quickly the rain splatters'.\n"
+            "   - Strictly confine the actions. DO NOT animate future events early.\n"
+            "   - 🔥 OUTPUT RULE: DO NOT quote the Korean text. Only output English.\n"
+            "Format EXACTLY:\nCHARACTER: [Name]\nMASTER SCENE: [Description]\n"
+            "PART 1: [0-1s] [Action A] [2-3s] [Action B] [4-5s] [Action C]\n"
+            "PART 2: [0-1s] [Action D] [2-3s] [Action E] [4-5s] [Action F]\n"
+            "PART 3: [0-1s] [Action G] [2-3s] [Action H] [4-5s] [Action I]\n"
+            "PART 4: [0-1s] [Action J] [2-3s] [Action K] [4-5s] [Action L]"
+        )
+
+        usr_text = (
+            f"NAME: {character_name}\n"
+            f"VIBE: {artistic_vibe}\n\n"
+            "=== HIGH-SPEED ACTION SCRIPT ===\n"
+            f"▶ For PART 1 (0-5s), ONLY animate this: \"{chunk_1}\"\n"
+            f"▶ For PART 2 (5-10s), ONLY animate this: \"{chunk_2}\"\n"
+            f"▶ For PART 3 (10-15s), ONLY animate this: \"{chunk_3}\"\n"
+            f"▶ For PART 4 (15-20s), ONLY animate this: \"{chunk_4}\"\n"
+            "CRITICAL: Keep the action moving FAST. Avoid still poses or micro-details that look like slow-mo!"
+        )
+
+        url = "https://openrouter.ai/api/v1/chat/completions"
+        payload = {
+            "model": openrouter_model.strip(),
+            "messages": [
+                {"role": "system", "content": sys_prompt},
+                {"role": "user", "content": [
+                    {"type": "text", "text": usr_text},
+                    {"type": "image_url", "image_url": {"url": base64_image}}
+                ]}
+            ],
+            "temperature": creativity,
+            "max_tokens": max_tokens,
+        }
+
+        if not openrouter_api_key.strip():
+            err_msg = "⚠️ API Error: OpenRouter API key is empty."
+            return (err_msg, err_msg, err_msg, err_msg, err_msg)
+
+        llm_prompt, err = self._call_llm(url, payload, openrouter_api_key, retries)
+        if llm_prompt:
+            print(f"\n✅ [Dolphin V32 - Action Speed Optimized]\n{llm_prompt}\n")
+        else:
+            print(f"❌ [에러] API 호출 실패: {err}")
+            llm_prompt = ""
+
+        final_char_name = user_defined_name
+        r_master = ""
+        p1 = p2 = p3 = p4 = ""
+
+        if llm_prompt and "[removed]" not in llm_prompt:
+            cl = re.sub(r'[*#]', '', llm_prompt)
+            m_char = re.search(r'CHARACTER:\s*(.*?)(?=MASTER SCENE|$)', cl, re.I | re.S)
+            m_master = re.search(r'MASTER SCENE:\s*(.*?)(?=PART 1|$)', cl, re.I | re.S)
+
+            m1 = re.search(r'PART 1:\s*(.*?)(?=PART 2|$)', cl, re.I | re.S)
+            m2 = re.search(r'PART 2:\s*(.*?)(?=PART 3|$)', cl, re.I | re.S)
+            m3 = re.search(r'PART 3:\s*(.*?)(?=PART 4|$)', cl, re.I | re.S)
+            m4 = re.search(r'PART 4:\s*(.*?)(?=\n\n|===|Note:|$)', cl, re.I | re.S)
+
+            if not user_defined_name and m_char:
+                final_char_name = m_char.group(1).strip()
+
+            r_master = m_master.group(1).strip() if m_master else ""
+
+            # 파싱 실패 시 해당 청크(영문 지시문)를 폴백으로 사용해 비디오 프롬프트가 비지 않도록 함
+            p1 = m1.group(1).strip() if m1 else chunk_1
+            p2 = m2.group(1).strip() if m2 else chunk_2
+            p3 = m3.group(1).strip() if m3 else chunk_3
+            p4 = m4.group(1).strip() if m4 else chunk_4
+        else:
+            # API 실패 시에도 스크립트 청크를 폴백으로 반환 (완전 실패보다 유용)
+            p1, p2, p3, p4 = chunk_1, chunk_2, chunk_3, chunk_4
+
+        return (
+            build_final(p1, r_master, final_char_name),
+            build_final(p2, r_master, final_char_name),
+            build_final(p3, r_master, final_char_name),
+            build_final(p4, r_master, final_char_name),
+            llm_prompt if llm_prompt else "⚠️ API Error / empty response",
+        )

custom_nodes/dolphin_nodes/_archive/workflows/dolphin_tags_cache.json

@@ -0,0 +1,254 @@
+{
+    "wan22.r3v3rs3_c0wg1rl-14b-Low-i2v_e70.safetensors": [
+        "r3v3rs3_c0wg1rl",
+        "c0wg1rl",
+        "straddling  him in the reverse cowgirl position"
+    ],
+    "DR34ML4Y_I2V_14B_LOW_V2.safetensors": [
+        "m15510n4ry",
+        "bl0wj0b",
+        "d0gg1e",
+        "c0wg1rl",
+        "d0ubl3_bj"
+    ],
+    "wan22.r3v3rs3_c0wg1rl-14b-High-i2v_e70.safetensors": [
+        "r3v3rs3_c0wg1rl",
+        "c0wg1rl",
+        "straddling  him in the reverse cowgirl position"
+    ],
+    "DR34ML4Y_I2V_14B_HIGH_V2.safetensors": [
+        "m15510n4ry",
+        "bl0wj0b",
+        "c0wg1rl",
+        "d0gg1e",
+        "d0ubl3_bj"
+    ],
+    "erect_penis_epoch_80.safetensors": [
+        "penis"
+    ],
+    "PENISLORA_22_i2v_HIGH_e320.safetensors": [
+        "PENISLORA"
+    ],
+    "PENISLORA_22_i2v_LOW_e496.safetensors": [
+        "PENISLORA"
+    ],
+    "genitals_helper_v1.0_e219.safetensors": [
+        "Innie vagina",
+        "Outie vagina"
+    ],
+    "wan2.1-i2v-480p-penis-arousal-v1.0.safetensors": [
+        "penis becomes erect"
+    ],
+    "pusfix-e5-21.safetensors": [
+        "pusfix"
+    ],
+    "wan22-cunilingus-I2V-72epoc-low.safetensors": [
+        "cunn1l1ngu5"
+    ],
+    "wan22-cunilingus-I2V-106epoc-high.safetensors": [
+        "cunn1l1ngu5"
+    ],
+    "smoll-pp.high.wan2.2.t2v.safetensors": [
+        "small penis",
+        "she is stroking her small penis between two fingers",
+        "flaccid penis"
+    ],
+    "smoll-pp.low.wan2.2.t2v.safetensors": [
+        "small penis",
+        "she is stroking her small penis between two fingers",
+        "flaccid penis"
+    ],
+    "masturbation_cumshot_v1.1_e310.safetensors": [
+        "which lands on her {thighs|pelvis|stomach|face|breasts}",
+        "She moves her hand rapidly up and down the shaft of her penis and she ejaculates white sticky liquid from her penis"
+    ],
+    "wan2.1-i2v-480p-flaccid-v1.0.safetensors": [
+        "penis",
+        "bounce",
+        "swing"
+    ],
+    "wan2.2_i2v_highnoise_petplay_v2.0.safetensors": [
+        "The woman moves into in a paw pose with her fists raised, looking at the viewer.",
+        "She very slowly flicks her wrists, alternating between them back and forth in a cute begging motion. She is acting like a sexy pet.",
+        "She begins panting with her tongue out like a dog."
+    ],
+    "wan2.2_i2v_lownoise_petplay_v2.0.safetensors": [
+        "The woman moves into in a paw pose with her fists raised, looking at the viewer.",
+        "She very slowly flicks her wrists, alternating between them back and forth in a cute begging motion. She is acting like a sexy pet.",
+        "She begins panting with her tongue out like a dog."
+    ],
+    "wan22_i2v_futa_cowgirl_high_noise.safetensors": [
+        "a transgender woman having anal sex in cowgirl position"
+    ],
+    "wan22_i2v_futa_cowgirl_low_noise.safetensors": [
+        "a transgender woman having anal sex in cowgirl position"
+    ],
+    "anal_insertion_HIGH_V01.safetensors": [
+        "Anal insertion.",
+        "The video depicts a man slowly inserting his dick into a woman's anus",
+        "while she is lying on a couch.",
+        "The video depicts a sexual encounter between two animated characters, with one character being penetrated by the other.",
+        "The video depicts two animated characters engaging in sexual activity, with one character being penetrated by the other's erect penis.",
+        "close-up"
+    ],
+    "anal_insertion_LOW_V01.safetensors": [
+        "Anal insertion.",
+        "The video depicts a man slowly inserting his dick into a woman's anus",
+        "while she is lying on a couch.",
+        "The video depicts a sexual encounter between two animated characters, with one character being penetrated by the other.",
+        "The video depicts two animated characters engaging in sexual activity, with one character being penetrated by the other's erect penis."
+    ],
+    "wan22_t2v_penis_pen15_HIGH.safetensors": [
+        "pen15",
+        "penis"
+    ],
+    "wan22_t2v_penis_pen15_LOW.safetensors": [
+        "pen15",
+        "penis"
+    ],
+    "wan22_i2v_futa_missionary_low_noise.safetensors": [
+        "futa missionary"
+    ],
+    "wan22_i2v_futa_missionary_high_noise.safetensors": [
+        "futa missionary"
+    ],
+    "shemale anal missionary_high_noise.safetensors": [
+        "A transsexual woman is lying on her back while she gets fucked anally by a man's penis. The man's penis is penetrating her anus rapidly."
+    ],
+    "shemale anal missionary_low_noise.safetensors": [
+        "A transsexual woman is lying on her back while she gets fucked anally by a man's penis. The man's penis is penetrating her anus rapidly."
+    ],
+    "hardcore-gay_high_noise.safetensors": [
+        "hardc0re-gay"
+    ],
+    "hardcore-gay_low_noise.safetensors": [
+        "hardc0re-gay"
+    ],
+    "Blowbang_high_noise.safetensors": [
+        "A woman is surrounded by multiple penises, she is in the middle of the frame performing oral sex, men's torsos are around her, they all have huge penises which the woman is grabbing and putting inside her mouth."
+    ],
+    "xPenisGlanx_wan22_050626_low_noise_15k.safetensors": [
+        "Penis"
+    ],
+    "xPenisGlanx_wan22_050626_high_noise_15k.safetensors": [
+        "Penis"
+    ],
+    "wan_dildo_closeup_i2vA14B_HIGHNOISE_v20.safetensors": [
+        "piston_dildo_style"
+    ],
+    "wan_dildo_closeup_i2vA14B_LOWNOISE_v20.safetensors": [
+        "piston_dildo_style"
+    ],
+    "i2v-anus-squirt_high_noise.safetensors": [
+        "returnthis4nussqu1rt"
+    ],
+    "i2v-anus-squirt_low_noise.safetensors": [
+        "returnthis4nussqu1rt"
+    ],
+    "wan_2.2_i2v_slap_low_v2.0.safetensors": [
+        "she slaps herself in the face",
+        "she is slapped in the face"
+    ],
+    "wan_2.2_i2v_slap_high_v2.0.safetensors": [
+        "she slaps herself in the face",
+        "she is slapped in the face"
+    ],
+    "Twerking_I2V_high_noise.safetensors": [
+        "A woman is twerking and shaking her ass."
+    ],
+    "Twerking_I2V_low_noise.safetensors": [
+        "A woman is twerking and shaking her ass."
+    ],
+    "FistingHigh.safetensors": [
+        "Male fist is moving in and out of female vagina.",
+        "vaginal fisting",
+        "anal fisting"
+    ],
+    "FistingLow.safetensors": [
+        "Male fist is moving in and out of female vagina",
+        "vaginal fisting",
+        "anal fisting"
+    ],
+    "all-tied-up-wan22-HN-e50-az420.safetensors": [
+        "tied pose",
+        "rope",
+        "cuffs",
+        "spread-eagle"
+    ],
+    "all-tied-up-wan22-LN-e128-az420.safetensors": [
+        "tied",
+        "cuffs",
+        "rope",
+        "spread-eagle"
+    ],
+    "wan22_penis_coachbate_v2_high_noise.safetensors": [
+        "p3n15"
+    ],
+    "wan22_penis_coachbate_v2_low_noise.safetensors": [
+        "p3n15"
+    ],
+    "P00TSARA_v1_low_noise.safetensors": [
+        "uncut flaccid penis",
+        "P00TSARA"
+    ],
+    "P00TSARA_v1_high_noise.safetensors": [
+        "Uncut flaccid penis",
+        "P00TSARA"
+    ],
+    "wan2.1-i2v-480p-flaccid-v1.0.safetensors::359305272::1753134483": [
+        "penis",
+        "bounce",
+        "swing"
+    ],
+    "PENISLORA_22_i2v_HIGH_e320.safetensors::306807976::1759901296": [
+        "PENISLORA"
+    ],
+    "PENISLORA_22_i2v_LOW_e496.safetensors::306807976::1759901312": [
+        "PENISLORA"
+    ],
+    "masturbation_cumshot_v1.1_e310.safetensors::359257680::1747718457": [
+        "which lands on her {thighs|pelvis|stomach|face|breasts}",
+        "She moves her hand rapidly up and down the shaft of her penis and she ejaculates white sticky liquid from her penis"
+    ],
+    "DR34ML4Y_I2V_14B_HIGH_V2.safetensors::306807976::1767355819": [
+        "m15510n4ry",
+        "bl0wj0b",
+        "c0wg1rl",
+        "d0gg1e",
+        "d0ubl3_bj"
+    ],
+    "DR34ML4Y_I2V_14B_LOW_V2.safetensors::306807976::1767355809": [
+        "m15510n4ry",
+        "bl0wj0b",
+        "d0gg1e",
+        "c0wg1rl",
+        "d0ubl3_bj"
+    ],
+    "flaccid_penis_epoch_100.safetensors::306807976::1745233662": [
+        "penis"
+    ],
+    "erect_penis_epoch_80.safetensors::306807976::1744443760": [
+        "penis"
+    ],
+    "shemale anal missionary_high_noise.safetensors::306808400::1757087872": [
+        "A transsexual woman is lying on her back while she gets fucked anally by a man's penis. The man's penis is penetrating her anus rapidly."
+    ],
+    "shemale anal missionary_low_noise.safetensors::306808384::1757087881": [
+        "A transsexual woman is lying on her back while she gets fucked anally by a man's penis. The man's penis is penetrating her anus rapidly."
+    ],
+    "shemale doggy from behind_high_noise.safetensors::306808392::1757027152": [
+        "A transsexual woman is getting fucked doggystyle from behind by a man, the man is penetrating her anally as his penis is going inside her asshole, the man is moving his hips as he penetrates her and the woman is moving her body, she is looking back at the camera and at the man."
+    ],
+    "shemale doggy from behind_low_noise.safetensors::306808392::1766479090": [
+        "A transsexual woman is getting fucked doggystyle from behind by a man, the man is penetrating her anally as his penis is going inside her asshole, the man is moving his hips as he penetrates her and the woman is moving her body, she is looking back at the camera and at the man."
+    ],
+    "wan2.1-i2v-480p-penis-arousal-v1.0.safetensors::359305272::1754578081": [
+        "penis becomes erect"
+    ],
+    "TwerkReverse_WAN2.2_I2V_000002000_high_noise.safetensors::306808456::1764647417": [
+        "Twerk Reverse. A woman twerking moving her hips with a dynamic camera, she have an orgasm"
+    ],
+    "TwerkReverse_WAN2.2_I2V_000002000_low_noise.safetensors::306808456::1764647428": [
+        "Twerk Reverse. A nude woman twerking moving her hips with a dynamic camera, she have an orgasm"
+    ]
+}

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+      "bgcolor": "#653"
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+        }
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+          "type": "MODEL",
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+      "properties": {
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+        "sort": "position",
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+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "krea2 Inpaint",
+      "properties": {
+        "fontSize": 150,
+        "fontFamily": "Arial",
+        "fontColor": "#ffd000",
+        "textAlign": "left",
+        "backgroundColor": "transparent",
+        "padding": 0,
+        "borderRadius": 0,
+        "angle": 0,
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "version": "7.1",
+          "input_ue_unconnectable": {}
+        }
+      },
+      "color": "#fff0",
+      "bgcolor": "#fff0"
+    },
+    {
+      "id": 33,
+      "type": "Note",
+      "pos": [
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+        -451.25718147677554
+      ],
+      "size": [
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+        509.9479166666667
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Models and .whl files",
+      "properties": {
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "version": "7.1",
+          "input_ue_unconnectable": {}
+        }
+      },
+      "widgets_values": [
+        "Required models for Krea2 Workflow:\n\nKrea2 (Raw or Turbo): https://huggingface.co/Comfy-Org/Krea-2/tree/main/diffusion_models\nText Encoder: https://huggingface.co/Comfy-Org/Krea-2/tree/main/text_encoders\nVAE: https://huggingface.co/Comfy-Org/Krea-2/tree/main/vae\n\nAlso I recommend download uncendored encoder for nsfw and danbooru prompting https://huggingface.co/ahmed22xa/Huihui-Qwen3-VL-4B-Instruct-abliterated-comfy/resolve/main/Huihui-Qwen3-VL-4B-Instruct-abliterated.safetensors\n\nAlso recomend using VAE Wan 2.1 for better result https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors\n\nTurbo Lora (if you use Raw model): https://huggingface.co/Comfy-Org/Krea-2/blob/main/loras/krea2_turbo_lora_rank_64_bf16.safetensors\n\n\nFile save location\n\nComfyUI/\n├───📂 models/\n│   ├───📂 diffusion_models/\n│   │   ├─── krea2_turbo_bf16.safetensors\n│   ├───📂 loras/\n│   │   ├─── krea2_turbo_lora_rank_64_bf16.safetensors\n│   │   ├─── Any loras if you want\n│   ├───📂 text_encoders/\n│   │   └─── qwen3vl_4b_bf16.safetensors\n│   └───📂 vae/\n│       └── qwen_image_vae.safetensors\n\n\n\n\n\n\n\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
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+        {
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+          "shape": 7,
+          "type": "LORA_STACK",
+          "link": null
+        }
+      ],
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+          "type": "MODEL",
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+        },
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+      "color": "#223",
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+      "properties": {
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+        }
+      ],
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+          "type": "CONDITIONING",
+          "links": [
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+          ]
+        }
+      ],
+      "title": "Negative Prompt",
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+      "widgets_values": [
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+      "color": "#322",
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+      ],
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+          "type": "IMAGE",
+          "links": [
+            52,
+            78
+          ]
+        }
+      ],
+      "properties": {
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+          "type": "CONDITIONING",
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+        },
+        {
+          "name": "PROMPT",
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+            "end": 9,
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+     "type": "IMAGE",
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+    },
+    {
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+     "type": "LATENT",
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+    }
+   ],
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+    {
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+     "type": "LATENT",
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+    },
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+     "type": "LATENT",
+     "link": 20
+    }
+   ],
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+     "name": "LATENT",
+     "type": "LATENT",
+     "links": [
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+    }
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+    "Node name for S&R": "VHS_VideoCombine"
+   },
+   "widgets_values": {
+    "frame_rate": 16,
+    "loop_count": 0,
+    "filename_prefix": "wan22_slowmo/hi_nolightx2v_cfg35_lo_lightx2v",
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+    "crf": 19,
+    "save_metadata": true,
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+   "type": "Note",
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+   ],
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custom_nodes/dolphin_nodes/_archive/workflows/slowmo_scenario2_3ksampler_warmup.json

@@ -0,0 +1,1141 @@
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+     "link": 14,
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+    },
+    {
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+     "type": "LATENT",
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+    }
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+     "type": "LATENT",
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+   "outputs": [
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+     "name": "LATENT",
+     "type": "LATENT",
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+    "loop_count": 0,
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custom_nodes/dolphin_nodes/_archive/workflows/slowmo_svi_scenarioA_2clip_continuation.json

@@ -0,0 +1,1737 @@
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+    }
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+     "type": "IMAGE",
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+      9
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+    },
+    {
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+   },
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+   ]
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+    }
+   ],
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+    {
+     "name": "IMAGE",
+     "type": "IMAGE",
+     "links": [
+      10
+     ],
+     "slot_index": 0
+    },
+    {
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+    }
+   ],
+   "properties": {
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+   },
+   "widgets_values": [
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+    },
+    {
+     "name": "vae",
+     "type": "VAE",
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+    }
+   ],
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+     "name": "LATENT",
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+    }
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+    "Node name for S&R": "VAEEncode"
+   },
+   "widgets_values": [],
+   "title": "Encode anchor image -> LATENT"
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+    }
+   ],
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+   },
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+    },
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+     "type": "LATENT",
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+    }
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+     "name": "LATENT",
+     "type": "LATENT",
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+    }
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+    },
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+    }
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+     "name": "LATENT",
+     "type": "LATENT",
+     "links": [
+      24
+     ],
+     "slot_index": 0
+    }
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+   "properties": {
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+    },
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+     "type": "VAE",
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+    }
+   ],
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+   ],
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+   "mode": 0,
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+    {
+     "name": "images",
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+     "link": 26
+    }
+   ],
+   "outputs": [],
+   "properties": {
+    "Node name for S&R": "VHS_VideoCombine"
+   },
+   "widgets_values": {
+    "frame_rate": 16,
+    "loop_count": 0,
+    "filename_prefix": "wan22_slowmo/svi_scenarioA_official_recipe",
+    "format": "video/h264-mp4",
+    "pingpong": false,
+    "save_image": true,
+    "crf": 19,
+    "save_metadata": true,
+    "audio_file": ""
+   }
+  },
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+   "type": "Note",
+   "pos": [
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+   ],
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+    320
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+   "order": 20,
+   "mode": 0,
+   "inputs": [],
+   "outputs": [],
+   "properties": {
+    "Node name for S&R": "Note"
+   },
+   "widgets_values": [
+    "시나리오 A: 공식 SVI Pro 레시피 재현 (vita-epfl/Stable-Video-Infinity, svi_wan22 브랜치 SVI-Wan22-1210-4-Clips.json 직접 분석해서 만듦).\n- lightx2v LoRA: High=0.5(절반 강도), Low=1.0(전체 강도) — 공식 워크플로우에서 그대로 확인.\n- SVI LoRA: High/Low 둘 다 1.0.\n- CFG: 1.5 균일 (README 원문: 'cfg 5가 원��� 기본값이지만 가속 컴포넌트(lightx2v) 때문에 유지가 안 돼서 1.5로 고정, 텍스트 순응도 올리려면 1~2 범위에서만 살짝 올려라').\n- 해상도: 480P(832x480) — 공식 예시와 커뮤니티 보고 모두 '480p가 720p보다 SVI+lightx2v 조합에서 더 안정적'이라고 함.\n  ⚠ 이건 메모리의 '832x480이 슬로우모션 유발'이라는 기록과 반대되는 관찰이라, SVI 맥락에서도 실제로 맞는지 이 테스트로 확인 필요.\n- 안카(anchor)만 있고 prev_samples는 없는 단일 클립 테스트입니다(WanImageToVideoSVIProGray_Dolphin의 root-cause fix는 적용됨). 멀티클립 체인(시나리오 D)은 별도 파일로.\n출처: raw.githubusercontent.com/vita-epfl/Stable-Video-Infinity/svi_wan22/comfyui_workflow/SVI-Wan22-1210-4-Clips.json, comfyui_workflow/README.md"
+   ],
+   "title": "Scenario A: Official SVI recipe (lightx2v Hi=0.5/Lo=1.0, CFG1.5, 480p)",
+   "color": "#432",
+   "bgcolor": "#653"
+  }
+ ],
+ "links": [
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+  ],
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+ "extra": {},
+ "version": 0.4
+}

custom_nodes/dolphin_nodes/_archive/workflows/slowmo_svi_simple_nag_test.json

@@ -0,0 +1,1243 @@
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+   ],
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custom_nodes/dolphin_nodes/_archive/workflows/wan22_v2v_high_denoise_compare.json

@@ -0,0 +1,1198 @@
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+      "LATENT"
+    ],
+    [
+      18,
+      15,
+      0,
+      16,
+      0,
+      "LATENT"
+    ],
+    [
+      19,
+      2,
+      0,
+      16,
+      1,
+      "VAE"
+    ],
+    [
+      20,
+      16,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      21,
+      7,
+      0,
+      18,
+      0,
+      "MODEL"
+    ],
+    [
+      22,
+      9,
+      0,
+      18,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      23,
+      10,
+      0,
+      18,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      24,
+      13,
+      0,
+      18,
+      3,
+      "LATENT"
+    ],
+    [
+      25,
+      8,
+      0,
+      19,
+      0,
+      "MODEL"
+    ],
+    [
+      26,
+      9,
+      0,
+      19,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      27,
+      10,
+      0,
+      19,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      28,
+      18,
+      0,
+      19,
+      3,
+      "LATENT"
+    ],
+    [
+      29,
+      19,
+      0,
+      20,
+      0,
+      "LATENT"
+    ],
+    [
+      30,
+      2,
+      0,
+      20,
+      1,
+      "VAE"
+    ],
+    [
+      31,
+      20,
+      0,
+      21,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

custom_nodes/dolphin_nodes/arch_logic.py

@@ -0,0 +1,141 @@
+import os
+import json
+import folder_paths
+import comfy.sd
+import comfy.utils
+from safetensors import safe_open
+import hashlib
+import urllib.request
+
+CACHE_FILE = os.path.join(os.path.dirname(__file__), "dolphin_tags_cache.json")
+
+def load_dolphin_cache():
+    if os.path.exists(CACHE_FILE):
+        try:
+            with open(CACHE_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except: return {}
+    return {}
+
+def save_dolphin_cache(cache_data):
+    try:
+        with open(CACHE_FILE, "w", encoding="utf-8") as f:
+            json.dump(cache_data, f, indent=4)
+    except: pass
+
+class DolphinArchitectV15:
+    @classmethod
+    def INPUT_TYPES(s):
+        loras = ["None"] + folder_paths.get_filename_list("loras")
+        
+        res = {
+            "required": {
+                "global_switch": ("BOOLEAN", {"default": True, "label_on": "GLOBAL ON", "label_off": "GLOBAL OFF (SKIP)"}),
+                "model_base": ("MODEL",), "clip_base": ("CLIP",),
+                "model_high": ("MODEL",), "clip_high": ("CLIP",),
+                "model_low": ("MODEL",), "clip_low": ("CLIP",),
+            },
+            "optional": {}
+        }
+
+        # [다이어트 완료] 오직 이름과 웨이트만 남겨서 가장 얇은 UI를 만듭니다.
+        res["optional"]["header_master"] = ("STRING", {"default": "─── [ MASTER: B+H ] ───", "multiline": False})
+        for i in range(1, 5): 
+            res["optional"][f"master_l_{i}"] = (loras, {"default": "None"})
+            res["optional"][f"master_b_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+            res["optional"][f"master_h_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+            
+        res["optional"]["header_low"] = ("STRING", {"default": "─── [ LOW ONLY ] ───", "multiline": False})
+        for i in range(1, 5): 
+            res["optional"][f"low_l_{i}"] = (loras, {"default": "None"})
+            res["optional"][f"low_w_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+
+        res["optional"]["manual_triggers"] = ("STRING", {"default": "", "multiline": True})
+        return res
+
+    RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "MODEL", "CLIP", "STRING")
+    RETURN_NAMES = ("M_BASE", "C_BASE", "M_HIGH", "C_HIGH", "M_LOW", "C_LOW", "triggers")
+    FUNCTION = "exec"
+    CATEGORY = "Dolphin"
+
+    def get_trig(self, lora_name):
+        if lora_name == "None": return []
+        path = folder_paths.get_full_path("loras", lora_name)
+        if not path or not os.path.exists(path): return []
+        
+        triggers = []
+        try:
+            with safe_open(path, framework="pt", device="cpu") as f:
+                m = f.metadata()
+                if m and "modelspec.trigger_words" in m:
+                    tw = json.loads(m["modelspec.trigger_words"])
+                    if isinstance(tw, list): triggers.extend(tw)
+                    elif isinstance(tw, str): triggers.append(tw)
+        except: pass
+        if triggers: return triggers 
+
+        cache = load_dolphin_cache()
+        if lora_name in cache and cache[lora_name]: return cache[lora_name]
+
+        try:
+            sha256_hash = hashlib.sha256()
+            with open(path, "rb") as f:
+                for byte_block in iter(lambda: f.read(4096 * 1024), b""):
+                    sha256_hash.update(byte_block)
+            
+            url = f"https://civitai.com/api/v1/model-versions/by-hash/{sha256_hash.hexdigest()}"
+            req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=5) as response:
+                data = json.loads(response.read().decode('utf-8'))
+                api_triggers = data.get("trainedWords", [])
+                if api_triggers:
+                    cache[lora_name] = api_triggers
+                    save_dolphin_cache(cache)
+                    return api_triggers
+        except: pass
+        return triggers
+
+    def exec(self, global_switch, model_base, clip_base, model_high, clip_high, model_low, clip_low, manual_triggers="", **kwargs):
+        mb, cb = model_base, clip_base
+        mh, ch = model_high, clip_high
+        ml, cl = model_low, clip_low
+        
+        if not global_switch:
+            trigs = [t.strip() for t in manual_triggers.split(',') if t.strip()]
+            ut = ", ".join(list(dict.fromkeys(trigs)))
+            return (mb, cb, mh, ch, ml, cl, ut)
+
+        applied_names = []
+
+        for i in range(1, 5): 
+            n = kwargs.get(f"master_l_{i}", "None")
+            w_b = kwargs.get(f"master_b_w_{i}", 1.0)
+            w_h = kwargs.get(f"master_h_w_{i}", 1.0)
+            
+            # [최적화] None이 아니면서, 웨이트가 0.0이 아닐 때만 적용 (0.0이면 스위치 OFF 효과)
+            if n != "None" and (w_b != 0.0 or w_h != 0.0):
+                applied_names.append(n)
+                lora_data = comfy.utils.load_torch_file(folder_paths.get_full_path("loras", n))
+                mb, cb = comfy.sd.load_lora_for_models(mb, cb, lora_data, w_b, w_b)
+                mh, ch = comfy.sd.load_lora_for_models(mh, ch, lora_data, w_h, w_h)
+
+        for i in range(1, 5): 
+            n = kwargs.get(f"low_l_{i}", "None")
+            w = kwargs.get(f"low_w_{i}", 1.0)
+            
+            if n != "None" and w != 0.0:
+                applied_names.append(n)
+                lora_data = comfy.utils.load_torch_file(folder_paths.get_full_path("loras", n))
+                ml, cl = comfy.sd.load_lora_for_models(ml, cl, lora_data, w, w)
+
+        trigs = []
+        for n in set(applied_names):
+            trigs.extend(self.get_trig(n))
+            
+        if manual_triggers.strip():
+            manual_list = [t.strip() for t in manual_triggers.split(',') if t.strip()]
+            trigs.extend(manual_list)
+        
+        unique_trigs = ", ".join(list(dict.fromkeys(trigs)))
+        
+        return (mb, cb, mh, ch, ml, cl, unique_trigs)

custom_nodes/dolphin_nodes/arch_logic_v16.py

@@ -0,0 +1,80 @@
+import os
+import comfy.sd
+import comfy.utils
+import folder_paths
+
+# civitai/civitaired 파일명 쿼리 방식은 개인 학습 로라(공개 DB 미등록)에 대해
+# 항상 빈 값으로 실패한다. DolphinVisionDirector가 이미 검증한 조회 순서
+# (safetensors 임베디드 메타데이터 -> LoRA Manager 로컬 SQLite -> 해시 기반 API)를
+# 그대로 재사용한다 - 중복 구현하면 두 노드의 트리거 조회 결과가 따로 놀게 됨.
+from .dolphin_vision_director import get_triggers, _load_cache, _save_cache
+
+_TRIPLE_LORA_CACHE = {}
+
+class DolphinTripleLoraMatrix:
+    @classmethod
+    def INPUT_TYPES(s):
+        loras = ["None"] + folder_paths.get_filename_list("loras")
+        inputs = {
+            "model_base": ("MODEL",), "clip_base": ("CLIP",),
+            "model_high": ("MODEL",), "clip_high": ("CLIP",),
+            "model_low": ("MODEL",),  "clip_low": ("CLIP",),
+        }
+        for i in range(1, 7):
+            inputs[f"lora_{i}"] = (loras, {"default": "None"})
+            # 토글을 이름 바로 옆(가중치 앞)에 붙임 - Vision Director(c{c}_lora_{n},
+            # c{c}_lora_{n}_on, c{c}_w_{n} 순서)와 동일한 배치. optional 위젯은 항상
+            # required 위젯 전부 뒤에 렌더링되므로 "이름 옆"이 되려면 required여야 한다.
+            # 이 재배치로 위젯 순서가 바뀌므로, 이미 저장된 워크플로우의 widgets_values는
+            # migrate_architect_v16_widgets.py로 재배치해야 한다 (구버전 노드는 이름이
+            # 아니라 위치로 값이 매핑됨).
+            inputs[f"lora_{i}_on"] = ("BOOLEAN", {"default": True,
+                                                   "label_on": "ON", "label_off": "OFF"})
+            inputs[f"weights_{i}"] = ("STRING", {"default": "0.0, 0.0, 0.0"})
+        opt = {
+            # civitai/civitaired에 등록 안 된 개인 학습 로라는 자동 트리거 조회가
+            # 항상 빈 값으로 실패한다. 그런 경우 여기에 콤마로 직접 적으면
+            # trigger_words 출력에 합쳐진다.
+            "manual_triggers": ("STRING", {"default": "", "multiline": True}),
+        }
+        return {"required": inputs, "optional": opt}
+
+    RETURN_TYPES = ("MODEL", "CLIP", "MODEL", "CLIP", "MODEL", "CLIP", "STRING")
+    RETURN_NAMES = ("M_BASE", "C_BASE", "M_HIGH", "C_HIGH", "M_LOW", "C_LOW", "trigger_words")
+    FUNCTION = "apply_matrix"
+    CATEGORY = "Dolphin"
+
+    def apply_matrix(self, model_base, clip_base, model_high, clip_high, model_low, clip_low, **kwargs):
+        m_b, c_b = model_base, clip_base
+        m_h, c_h = model_high, clip_high
+        m_l, c_l = model_low, clip_low
+        all_triggers = []
+        trigger_cache = _load_cache()
+
+        for i in range(1, 7):
+            lora_name = kwargs.get(f"lora_{i}")
+            w_str = kwargs.get(f"weights_{i}")
+
+            if lora_name == "None": continue
+            if not kwargs.get(f"lora_{i}_on", True): continue
+
+            # 트리거 워드 수집 (safetensors 메타데이터 -> LoRA Manager DB -> 해시 API)
+            all_triggers.extend(get_triggers(lora_name, trigger_cache))
+
+            try:
+                parts = [float(x.strip()) for x in w_str.split(',')]
+                wb, wh, wl = parts[0], parts[1], parts[2]
+            except: continue
+
+            if lora_name not in _TRIPLE_LORA_CACHE:
+                path = folder_paths.get_full_path("loras", lora_name)
+                _TRIPLE_LORA_CACHE[lora_name] = comfy.utils.load_torch_file(path)
+            data = _TRIPLE_LORA_CACHE[lora_name]
+
+            if wb != 0: m_b, c_b = comfy.sd.load_lora_for_models(m_b, c_b, data, wb, wb)
+            if wh != 0: m_h, c_h = comfy.sd.load_lora_for_models(m_h, c_h, data, wh, wh)
+            if wl != 0: m_l, c_l = comfy.sd.load_lora_for_models(m_l, c_l, data, wl, wl)
+
+        manual = [t.strip() for t in kwargs.get("manual_triggers", "").split(',') if t.strip()]
+        all_triggers = list(dict.fromkeys(all_triggers + manual))
+        return (m_b, c_b, m_h, c_h, m_l, c_l, ", ".join(all_triggers))

custom_nodes/dolphin_nodes/cloud_env/comfyui_env_snapshot.json

@@ -0,0 +1,598 @@
+{
+    "comfyui": "fe4195f7f4275f2626cbafc703acc3ddde1e5490",
+    "git_custom_nodes": {
+        "https://github.com/giriss/comfy-image-saver": {
+            "hash": "c6223965d45871d1ff2931b6ac9518a13947c920",
+            "disabled": false
+        },
+        "https://github.com/M1kep/ComfyLiterals": {
+            "hash": "bdddb08ca82d90d75d97b1d437a652e0284a32ac",
+            "disabled": false
+        },
+        "https://github.com/evanspearman/ComfyMath": {
+            "hash": "c01177221c31b8e5fbc062778fc8254aeb541638",
+            "disabled": false
+        },
+        "https://github.com/yolain/ComfyUI-Easy-Use": {
+            "hash": "595e0738a9e3f8d0d9c4d875461b2d2c9e7559c7",
+            "disabled": false
+        },
+        "https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context.git": {
+            "hash": "15fc6a7bf7b78efb27f33d7eef3818e7ed0e118a",
+            "disabled": false
+        },
+        "https://github.com/facok/comfyui-krea2-controlnet": {
+            "hash": "79ebfd3bd80d2180b334dd7ce57f3c9ddaa0848f",
+            "disabled": false
+        },
+        "https://github.com/ltdrdata/ComfyUI-Manager": {
+            "hash": "8c5e640f83a26f22b0c7f0e63e89f7bab7fa6084",
+            "disabled": false
+        },
+        "https://github.com/princepainter/Comfyui-PainterFLF2V": {
+            "hash": "c81a68feceb90b28557a979d6de44eac59c89123",
+            "disabled": false
+        },
+        "https://github.com/princepainter/ComfyUI-PainterI2V": {
+            "hash": "c46e480c35d9a54e70139e354b1fc2975d0b6805",
+            "disabled": false
+        },
+        "https://github.com/princepainter/ComfyUI-PainterI2Vadvanced": {
+            "hash": "a044ac7b2a33565343ddc75283c94a41371b4352",
+            "disabled": false
+        },
+        "https://github.com/princepainter/ComfyUI-PainterI2VforKJ": {
+            "hash": "56f6e8a849d24ede99f3000d2b0fa2c0bc733bd8",
+            "disabled": false
+        },
+        "https://github.com/Saganaki22/ComfyUI-sol-attn.git": {
+            "hash": "bd28909e6a152ba5db4e3590d2d9df1c249943f2",
+            "disabled": false
+        },
+        "https://github.com/un-seen/comfyui-tensorops": {
+            "hash": "d34488e3079ecd10db2fe867c3a7af568115faed",
+            "disabled": false
+        },
+        "https://github.com/jamesWalker55/comfyui-various": {
+            "hash": "5bd85aaf7616878471469c4ec7e11bbd0cef3bf2",
+            "disabled": false
+        },
+        "https://github.com/GACLove/ComfyUI-VFI": {
+            "hash": "6176a430f12cd16003f4664c1e3c6af8e96cc3c6",
+            "disabled": false
+        },
+        "https://github.com/kijai/ComfyUI-WanVideoWrapper": {
+            "hash": "088128b224242e110d3906c6750e9a3a348a659b",
+            "disabled": false
+        },
+        "https://github.com/TripleHeadedMonkey/ComfyUI-Zlycoris": {
+            "hash": "cc8ecb9952940aa642e71d9b123b2d0a768e84d8",
+            "disabled": false
+        },
+        "https://github.com/bjoo46/dolphin_nodes.git": {
+            "hash": "d1dee42586cc0f2bf0ef986c92d836052629c02a",
+            "disabled": false
+        },
+        "https://github.com/ClownsharkBatwing/RES4LYF": {
+            "hash": "26036f647ca15d3048a193daf99a40cecfc3820d",
+            "disabled": false
+        },
+        "https://github.com/Ltamann/ComfyUI-TBG-ETUR": {
+            "hash": "39e3a5f1225a817eea634d0c7ebbd7635cccf588",
+            "disabled": false
+        }
+    },
+    "cnr_custom_nodes": {
+        "10s-comfy-nodes": "1.9.7",
+        "cg-use-everywhere": "7.8.0",
+        "comfyui-af-find-nodes": "0.3.2",
+        "ComfyUI-Conditioning-Rebalance": "2.0.2",
+        "ComfyUI-Crystools": "1.27.4",
+        "comfyui-custom-scripts": "1.2.5",
+        "comfyui-easy-sam3": "1.0.4",
+        "comfyui-florence2": "1.1.0",
+        "ComfyUI-GGUF": "1.1.10",
+        "comfyui-image-saver": "1.24.0",
+        "comfyui-impact-pack": "8.28.3",
+        "comfyui-impact-subpack": "1.3.5",
+        "comfyui-inpaint-nodes": "1.4.3",
+        "comfyui-inpainteasy": "1.0.2",
+        "comfyui-jakeupgrade": "2.7.5",
+        "ComfyUI-JoyCaption": "2.0.2",
+        "comfyui-kjnodes": "1.5.0",
+        "comfyui-krea2-conditioning": "1.0.2",
+        "comfyui-krea2edit": "1.2.3",
+        "ComfyUI-Krea2T-Enhancer": "1.1.2",
+        "comfyui-lora-manager": "1.2.0",
+        "comfyui-minimax-h3-turbo": "1.2.3",
+        "comfyui-mmaudio": "1.0.3",
+        "comfyui-mxtoolkit": "0.9.92",
+        "ComfyUI-nhknodes": "1.1.0",
+        "comfyui-promptchain": "0.1.13",
+        "comfyui-rmbg": "3.1.0",
+        "comfyui-segment-anything-2": "1.0.2",
+        "comfyui-videohelpersuite": "1.7.9",
+        "comfyui-vrgamedevgirl": "9.1.1",
+        "comfyui-wd14-tagger": "1.0.1",
+        "ComfyUI-Woosh": "0.2.7",
+        "comfyui_controlnet_aux": "1.1.5",
+        "comfyui_essentials": "1.1.0",
+        "comfyui_layerstyle": "2.0.40",
+        "comfyui_memory_cleanup": "1.1.3",
+        "comfyui_nvidia_rtx_nodes": "0.1.3",
+        "comfyui_queue_manager": "0.0.17",
+        "comfyui_tinyterranodes": "2.0.11",
+        "controlaltai-nodes": "1.1.4",
+        "erosdiffusion-eulerflowmatchingdiscretescheduler": "1.0.8",
+        "LanPaint": "1.5.5",
+        "rgthree-comfy": "1.0.2607232129",
+        "seedvr2_videoupscaler": "2.5.24",
+        "stablediffusion-dpmpp_2m_alt-sampler": "1.0.0",
+        "tripleksampler": "0.10.5"
+    },
+    "file_custom_nodes": [
+        {
+            "filename": "websocket_image_save.py",
+            "disabled": false
+        }
+    ],
+    "pips": {
+        "absl-py==2.3.1": "",
+        "accelerate==1.12.0": "",
+        "addict==2.4.0": "",
+        "ai-edge-litert==2.1.4": "",
+        "aiofiles==24.1.0": "",
+        "aiohappyeyeballs": "file:///D:/a/ComfyUI/cu130_python_deps/aiohappyeyeballs-2.6.1-py3-none-any.whl#sha256=f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8",
+        "aiohttp==3.13.3": "",
+        "aiohttp_socks==0.11.0": "",
+        "aiosignal": "file:///D:/a/ComfyUI/cu130_python_deps/aiosignal-1.4.0-py3-none-any.whl#sha256=053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e",
+        "aiosqlite==0.22.1": "",
+        "albucore==0.0.24": "",
+        "albumentations==2.0.8": "",
+        "alembic": "file:///D:/a/ComfyUI/cu130_python_deps/alembic-1.17.2-py3-none-any.whl#sha256=f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6",
+        "aliyun-python-sdk-core==2.16.0": "",
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+        "setuptools": "file:///D:/a/ComfyUI/cu130_python_deps/setuptools-80.9.0-py3-none-any.whl#sha256=062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922",
+        "shapely==2.1.2": "",
+        "shellingham==1.5.4": "",
+        "shtab==1.8.0": "",
+        "simpleeval==1.0.3": "",
+        "simplejson==4.1.1": "",
+        "simsimd==6.5.12": "",
+        "six==1.17.0": "",
+        "smmap==5.0.2": "",
+        "sniffio==1.3.1": "",
+        "sortedcontainers==2.4.0": "",
+        "sounddevice==0.5.3": "",
+        "soundfile==0.13.1": "",
+        "soupsieve==2.8.1": "",
+        "soxr==1.0.0": "",
+        "spaces==0.51.1": "",
+        "spandrel": "file:///D:/a/ComfyUI/cu130_python_deps/spandrel-0.4.1-py3-none-any.whl#sha256=49a39aa979769749a42203428355bc4840452854d6334ce0d465af46098dd448",
+        "SQLAlchemy==2.0.45": "",
+        "stable-ts==2.19.1": "",
+        "standard-aifc==3.13.0": "",
+        "standard-chunk==3.13.0": "",
+        "standard-sunau==3.13.0": "",
+        "starlette==1.3.1": "",
+        "stringzilla==4.6.0": "",
+        "submitit==1.5.4": "",
+        "supervision==0.6.0": "",
+        "surrealist==2.0.2": "",
+        "svg.path==7.0": "",
+        "sympy": "file:///D:/a/ComfyUI/cu130_python_deps/sympy-1.14.0-py3-none-any.whl#sha256=e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5",
+        "tabulate==0.9.0": "",
+        "taichi==1.7.4": "",
+        "tenacity==9.1.2": "",
+        "tensorboardX==2.6.5": "",
+        "termcolor==3.3.0": "",
+        "terminaltables==3.1.10": "",
+        "text-unidecode==1.3": "",
+        "textsearch==0.0.24": "",
+        "texttable==1.7.0": "",
+        "threadpoolctl==3.6.0": "",
+        "tifffile==2025.12.20": "",
+        "tiktoken==0.12.0": "",
+        "timm==1.0.27": "",
+        "tokenizers==0.22.2": "",
+        "toml==0.10.2": "",
+        "tomli==2.4.1": "",
+        "tomli_w==1.2.0": "",
+        "tomlkit==0.14.0": "",
+        "torch": "file:///D:/a/ComfyUI/cu130_python_deps/torch-2.9.1%2Bcu130-cp313-cp313-win_amd64.whl#sha256=47cae56331fdec7f7082aad967b25868cc5f94b203fc273e40346a852aa009dd",
+        "torch-complex==0.4.4": "",
+        "torchaudio": "file:///D:/a/ComfyUI/cu130_python_deps/torchaudio-2.9.1%2Bcu130-cp313-cp313-win_amd64.whl#sha256=2e90a149a17a41676f5a2e4e95c3a46baf3a9389af205894c23b056c8ad06daf",
+        "torchcodec==0.9.1": "",
+        "torchdiffeq==0.2.5": "",
+        "torchlanc==1.1.1": "",
+        "torchmetrics==1.9.0": "",
+        "torchscale==0.3.0": "",
+        "torchsde": "file:///D:/a/ComfyUI/cu130_python_deps/torchsde-0.2.6-py3-none-any.whl#sha256=19bf7ff02eec7e8e46ba1cdb4aa0f9db1c51d492524a16975234b467f7fc463b",
+        "torchvision": "file:///D:/a/ComfyUI/cu130_python_deps/torchvision-0.24.1%2Bcu130-cp313-cp313-win_amd64.whl#sha256=d688426d623b8ba763087726faf5b34b1fe404f44741656978d855403dbc7444",
+        "tqdm": "file:///D:/a/ComfyUI/cu130_python_deps/tqdm-4.67.1-py3-none-any.whl#sha256=26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2",
+        "trampoline": "file:///D:/a/ComfyUI/cu130_python_deps/trampoline-0.1.2-py3-none-any.whl#sha256=36cc9a4ff9811843d177fc0e0740efbd7da39eadfe6e50c9e2937cbc06d899d9",
+        "transformers==5.12.1": "",
+        "translators==6.0.2": "",
+        "transparent-background==1.3.4": "",
+        "treetable==0.2.6": "",
+        "trimesh==4.11.0": "",
+        "triton-windows==3.5.1.post24": "",
+        "typeguard==4.6.0": "",
+        "typer==0.21.1": "",
+        "typer-config==1.4.3": "",
+        "typing-inspection": "file:///D:/a/ComfyUI/cu130_python_deps/typing_inspection-0.4.2-py3-none-any.whl#sha256=4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7",
+        "typing_extensions": "file:///D:/a/ComfyUI/cu130_python_deps/typing_extensions-4.15.0-py3-none-any.whl#sha256=f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548",
+        "tyro==0.8.5": "",
+        "tzdata==2025.3": "",
+        "ultralytics==8.4.5": "",
+        "ultralytics-thop==2.0.18": "",
+        "umap-learn==0.5.12": "",
+        "unpaddedbase64==2.1.0": "",
+        "uritemplate==4.2.0": "",
+        "urllib3": "file:///D:/a/ComfyUI/cu130_python_deps/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc",
+        "urllib3-future==2.15.901": "",
+        "uv==0.9.22": "",
+        "uvicorn==0.49.0": "",
+        "vhacdx==0.0.10": "",
+        "virtualenv==21.2.1": "",
+        "voxcpm==2.0.3": "",
+        "wandb==0.27.2": "",
+        "wassima==2.0.4": "",
+        "wcwidth==0.2.14": "",
+        "webdataset==1.0.2": "",
+        "websocket-client==1.9.0": "",
+        "websockets==15.0.1": "",
+        "wetext==0.1.6": "",
+        "wget==3.2": "",
+        "wheel==0.45.1": "",
+        "win32_setctime==1.2.0": "",
+        "word2number==1.1": "",
+        "wordcloud==1.9.6": "",
+        "xxhash==3.6.0": "",
+        "yacs==0.1.8": "",
+        "yapf==0.43.0": "",
+        "yarl": "file:///D:/a/ComfyUI/cu130_python_deps/yarl-1.22.0-cp313-cp313-win_amd64.whl#sha256=47743b82b76d89a1d20b83e60d5c20314cbd5ba2befc9cda8f28300c4a08ed4d",
+        "yt-dlp==2026.3.13": "",
+        "zhipuai==2.1.5.20250825": "",
+        "zipp==3.23.0": ""
+    }
+}

custom_nodes/dolphin_nodes/dataset_builder/README.md

@@ -0,0 +1,84 @@
+# Wan 2.2 LoRA Dataset Builder
+
+임의의 이미지/영상 폴더를 넣으면 피사체 중심으로 크롭하고 로컬 VLM으로 캡션을 달아,
+LoRA 학습 스크립트(musubi-tuner 등)에 바로 넣을 수 있는 `images/`, `videos/`,
+`.txt` 캡션, `dataset.toml` 초안을 생성하는 독립 CLI 도구.
+
+ComfyUI 노드가 아님 — `__init__.py`에서 import하지 않으므로 ComfyUI 노드 로딩에
+관여하지 않고, `python_embeded\python.exe`로 직접 실행한다.
+
+**사용 전제: 학습 대상 인물/캐릭터가 명백히 성인이어야 함.** 나이가 불명확하거나
+앳돼 보이는 인물의 성적 콘텐츠 데이터셋 구축에는 이 도구를 쓰지 말 것.
+
+## 사용법
+
+```
+D:\SD\ComfyUI_windows_portable\python_embeded\python.exe ^
+  dataset_builder\build.py ^
+  --input "D:\raw_media" ^
+  --output "D:\wan_lora_dataset" ^
+  --trigger "ohwx man" ^
+  --captioner joycaption
+```
+
+`--input` 폴더를 재귀적으로 스캔해 이미지(jpg/png/webp/bmp)와 영상(mp4/mov/mkv/avi/webm)을
+자동 분류하여 처리한다.
+
+## 파이프라인
+
+1. **피사체 감지**: mediapipe 얼굴 검출 → (실패 시) OpenCV Haar cascade 얼굴 검출 →
+   (그래도 실패 시) ultralytics YOLOv8n 사람 검출 → (그래도 없으면) 전체 프레임
+   그대로 사용. 사람이 아닌 사물/장면 LoRA는 자연스럽게 "전체 프레임" 경로로 빠진다.
+2. **버킷 매칭 + 크롭**: 감지된 bbox를 얼굴 2.2배 / 사람 1.3배로 패딩한 뒤, Wan
+   480p/720p/1080p × (가로/세로/정사각) 버킷 중 종횡비가 가장 가까운 것을 골라
+   그 해상도로 크롭+리사이즈(16 배수 정렬). 원본 해상도보다 커지는(업스케일) 버킷은
+   자동으로 건너뛴다 — `--max-tier`로 상한만 지정.
+3. **영상 씬 분할**: OpenCV 히스토그램 차분으로 컷 지점을 찾고, `--clip-min-sec`
+   ~ `--clip-max-sec` 범위로 클립을 나눈다. 클립마다 대표 프레임 몇 장의 bbox
+   중앙값으로 크롭을 고정하고(프레임 흔들림 방지), `--video-fps`로 리샘플, 4n+1
+   프레임 수로 트리밍, optical flow 평균 크기로 "static/subtle/moderate/fast
+   motion" 태그를 캡션에 덧붙인다.
+4. **캡셔닝** (`--captioner`):
+   - `joycaption` (기본) — `fancyfeast/llama-joycaption-beta-one-hf-llava`.
+     **주의**: HuggingFace 모델 카드에 "학습용 캡션" 전용 프롬프트 문자열이 공식
+     문서화되어 있지 않아, 여기 쓰인 지시문(`caption.py`의 `DEFAULT_PROMPT`)은
+     자체 작성한 기본값이다. 원하는 스타일이 있으면 `--caption-prompt`로 바꿔라.
+   - `florence2` — `microsoft/Florence-2-large`, 가볍고 빠름(~0.77B).
+   - `wd14` — `SmilingWolf/wd-vit-tagger-v3` (onnx), booru 태그 스타일.
+   - 공통: `--trigger`로 넘긴 트리거 워드를 캡션 맨 앞에 자동 삽입.
+
+## 출력
+
+```
+output_dir/
+  images/0001.jpg, 0001.txt, ...
+  videos/0001.mp4, 0001.txt, ...
+  dataset.toml   # musubi-tuner 스타일 초안 — 실제 트레이너 버전 문서와 대조 후 사용
+```
+
+## 모델 다운로드 / VRAM 참고
+
+- 최초 실행 시 HuggingFace에서 캡셔너 가중치(JoyCaption ~16GB, Florence-2-large
+  ~1.5GB, WD14 ~수백MB)와 ultralytics `yolov8n.pt`(~6MB)를 자동 다운로드한다.
+  네트워크/디스크 용량 확인 후 실행할 것.
+- RTX 5090 32GB 기준, ComfyUI가 이미 VRAM을 점유 중이면 JoyCaption(8B, bf16 기준
+  ~16GB)이 부족할 수 있다. 이 경우 `--load-in-4bit`(bitsandbytes)로 줄이거나,
+  ComfyUI 서버를 잠시 내려두고 실행할 것.
+
+## 알려진 환경 특이사항
+
+- 이 embedded Python 환경은 `import torch`만 해도 인터프리터 종료 시 비정상
+  종료 코드(예: `-1073740791`)를 낸다. `dataset_builder`와 무관한 기존 환경
+  이슈이며, 실제 작업(파일 저장)은 그 전에 이미 끝난 뒤 발생하는 종료 시점
+  크래시라 결과물에는 영향 없음. 배치 스크립트에서 종료 코드로 성공/실패를
+  판단하지 말고 콘솔 로그의 `Done.` 줄로 확인할 것.
+
+## 검증된 것 / 안 된 것
+
+- `crop_resize.py`(버킷 매칭 수학), `detect.py`(다단계 폴백), `scene_split.py`
+  (씬 컷 + 클립 추출 + 4n+1 트리밍 + 모션 태그)는 실제 샘플 이미지/영상으로
+  스모크 테스트 완료.
+- `caption.py`(JoyCaption/Florence-2/WD14 실제 추론 호출)는 모델 가중치 다운로드가
+  커서 이번 세션에서 end-to-end로 돌려보지 않았다. 클래스/전처리는 각 모델의
+  공식 문서(HF 모델 카드, wd-tagger Space 소스)를 대조해 작성했지만, 처음
+  실행할 때 캡셔너 쪽 에러가 나면 그 부분부터 확인할 것.

custom_nodes/dolphin_nodes/dataset_builder/build.py

@@ -0,0 +1,262 @@
+"""Wan 2.2 LoRA dataset builder.
+
+Feed it a folder of random images/videos; it detects the main subject, crops
+to a Wan resolution bucket, captions locally, and writes an
+images/ + videos/ + .txt caption dataset plus a starter dataset.toml.
+
+Usage:
+    python_embeded\\python.exe dataset_builder\\build.py --input ./raw --output ./dataset_out --trigger "ohwx man"
+
+Run from anywhere -- this script adds its own directory to sys.path so the
+sibling modules (crop_resize, detect, caption, scene_split) import cleanly
+whether invoked directly or via -m.
+"""
+import argparse
+import os
+import sys
+import traceback
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import cv2
+import numpy as np
+from PIL import Image
+
+import crop_resize
+import scene_split
+from caption import build_captioner, with_trigger
+from detect import detect_subject_bbox, subdivide_bbox
+
+IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+VIDEO_EXTS = {".mp4", ".mov", ".mkv", ".avi", ".webm"}
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Wan 2.2 LoRA dataset auto-builder")
+    p.add_argument("--input", required=True, help="Folder of raw images/videos (scanned recursively)")
+    p.add_argument("--output", required=True, help="Output dataset folder")
+    p.add_argument("--trigger", default="", help="Trigger word/phrase prepended to every caption")
+
+    p.add_argument("--captioner", choices=["joycaption", "florence2", "wd14"], default="joycaption")
+    p.add_argument("--device", default="cuda")
+    p.add_argument("--load-in-4bit", action="store_true", help="4-bit load for JoyCaption (bitsandbytes)")
+    p.add_argument("--caption-prompt", default=None, help="Override JoyCaption instruction text")
+    p.add_argument("--wd14-general-thresh", type=float, default=0.35)
+    p.add_argument("--wd14-character-thresh", type=float, default=0.75)
+
+    p.add_argument("--max-tier", choices=["480p", "720p", "1080p"], default="1080p",
+                    help="Highest resolution bucket to consider; auto-picks smaller tiers to avoid upscaling")
+    p.add_argument("--face-pad", type=float, default=2.2)
+    p.add_argument("--person-pad", type=float, default=1.3)
+    p.add_argument("--person-region-pad", type=float, default=1.7,
+                    help="padding for person_upper/person_lower crops (see --body-region)")
+    p.add_argument("--body-region", choices=["auto", "upper", "lower"], default="auto",
+                    help="for person-kind detections, narrow the crop to the top or bottom "
+                         "portion of the detected body instead of the whole figure -- use "
+                         "when most source frames are already close-ups of one body region. "
+                         "'auto' (default) keeps the full detected box. No effect on face-kind "
+                         "detections.")
+    p.add_argument("--body-region-frac", type=float, default=0.45,
+                    help="fraction of the person bbox's height kept when --body-region is upper/lower")
+    p.add_argument("--min-face-conf", type=float, default=0.5)
+    p.add_argument("--min-person-conf", type=float, default=0.35)
+    p.add_argument("--image-format", choices=["jpg", "png"], default="jpg")
+
+    p.add_argument("--video-fps", type=float, default=16.0)
+    p.add_argument("--clip-min-sec", type=float, default=2.0)
+    p.add_argument("--clip-max-sec", type=float, default=6.0)
+    p.add_argument("--scene-diff-thresh", type=float, default=0.45)
+    p.add_argument("--clips-per-video", type=int, default=5,
+                    help="cap clips extracted per source video, evenly sampled across its "
+                         "timeline (e.g. a 5-minute video with many scene cuts won't dump "
+                         "dozens of near-duplicate clips into the dataset); pass 0 for no cap")
+
+    return p.parse_args()
+
+
+def scan_inputs(input_dir):
+    images, videos = [], []
+    for root, _dirs, files in os.walk(input_dir):
+        for name in files:
+            ext = os.path.splitext(name)[1].lower()
+            path = os.path.join(root, name)
+            if ext in IMAGE_EXTS:
+                images.append(path)
+            elif ext in VIDEO_EXTS:
+                videos.append(path)
+    return sorted(images), sorted(videos)
+
+
+def process_image(path, idx, out_dir, args, captioner, pad_overrides):
+    pil_img = Image.open(path).convert("RGB")
+    bgr = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+
+    bbox, kind = detect_subject_bbox(bgr, args.min_face_conf, args.min_person_conf)
+    if kind == "person" and args.body_region != "auto":
+        bbox = subdivide_bbox(bbox, args.body_region, args.body_region_frac)
+        kind = f"person_{args.body_region}"
+    crop_box, (tw, th), bucket_name, tier = crop_resize.compute_crop(
+        pil_img.width, pil_img.height, bbox, kind, pad_overrides, args.max_tier
+    )
+    cropped = crop_resize.crop_resize_pil(pil_img, crop_box, tw, th)
+
+    stem = f"{idx:04d}"
+    images_dir = os.path.join(out_dir, "images")
+    os.makedirs(images_dir, exist_ok=True)
+    img_path = os.path.join(images_dir, f"{stem}.{args.image_format}")
+    if args.image_format == "jpg":
+        cropped.save(img_path, quality=95)
+    else:
+        cropped.save(img_path)
+
+    caption = captioner.caption([cropped])
+    caption = with_trigger(caption, args.trigger)
+    with open(os.path.join(images_dir, f"{stem}.txt"), "w", encoding="utf-8") as f:
+        f.write(caption)
+
+    print(f"  [image] {os.path.basename(path)} -> {stem}.{args.image_format} "
+          f"({kind}, {bucket_name}/{tier}, {tw}x{th})")
+
+
+def subsample_ranges(ranges, target):
+    """Evenly picks `target` ranges spread across the full list (by index, so
+    spread across the video's timeline) instead of just taking the first N --
+    keeps whatever diversity of scenes exists rather than favoring the start."""
+    if not target or len(ranges) <= target:
+        return ranges
+    if target <= 1:
+        return ranges[len(ranges) // 2 : len(ranges) // 2 + 1]
+    step = (len(ranges) - 1) / (target - 1)
+    picked_indices = sorted({round(i * step) for i in range(target)})
+    return [ranges[i] for i in picked_indices]
+
+
+def process_video(path, idx_start, out_dir, args, captioner, pad_overrides):
+    cuts = scene_split.find_scene_cuts(path, diff_thresh=args.scene_diff_thresh)
+    cap = cv2.VideoCapture(path)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    src_fps = cap.get(cv2.CAP_PROP_FPS) or args.video_fps
+    cap.release()
+
+    ranges = scene_split.build_clip_ranges(
+        total_frames, src_fps, cuts, args.clip_min_sec, args.clip_max_sec
+    )
+    detected_count = len(ranges)
+    ranges = subsample_ranges(ranges, args.clips_per_video)
+    if args.clips_per_video and detected_count > len(ranges):
+        print(f"  [video] {os.path.basename(path)}: {detected_count} scene(s) detected, "
+              f"sampling {len(ranges)} (--clips-per-video {args.clips_per_video})")
+
+    idx = idx_start
+    for start, end in ranges:
+        result = scene_split.extract_clip(
+            path, start, end, args.video_fps, args.max_tier, pad_overrides,
+            args.body_region, args.body_region_frac,
+        )
+        if result is None:
+            continue
+        frames, (tw, th), representative, motion_tag = result
+
+        stem = f"{idx:04d}"
+        videos_dir = os.path.join(out_dir, "videos")
+        os.makedirs(videos_dir, exist_ok=True)
+        clip_path = os.path.join(videos_dir, f"{stem}.mp4")
+        scene_split.write_clip_mp4(frames, clip_path, args.video_fps)
+
+        caption = captioner.caption([representative])
+        caption = f"{caption.strip()}, {motion_tag}" if caption.strip() else motion_tag
+        caption = with_trigger(caption, args.trigger)
+        with open(os.path.join(videos_dir, f"{stem}.txt"), "w", encoding="utf-8") as f:
+            f.write(caption)
+
+        print(f"  [video] {os.path.basename(path)} [{start}-{end}] -> {stem}.mp4 "
+              f"({len(frames)}f @ {args.video_fps}fps, {tw}x{th}, {motion_tag})")
+        idx += 1
+    return idx
+
+
+def write_dataset_toml(out_dir, args, has_images, has_videos):
+    """musubi-tuner dataset config (schema verified against docs/dataset_config.md).
+    Items are already pre-cropped to one of a handful of Wan resolution buckets
+    by this tool, so enable_bucket lets musubi-tuner group them without forcing
+    a single resolution; `resolution` below is just the nominal/reference size."""
+    ref_w, ref_h = crop_resize.bucket_set(args.max_tier)["landscape"]
+
+    def p(*parts):
+        return os.path.join(out_dir, *parts).replace("\\", "/")
+
+    lines = [
+        "[general]",
+        f"resolution = [{ref_w}, {ref_h}]",
+        'caption_extension = ".txt"',
+        "batch_size = 1",
+        "enable_bucket = true",
+        "bucket_no_upscale = true",
+        "",
+    ]
+    if has_images:
+        lines += [
+            "[[datasets]]",
+            f'image_directory = "{p("images")}"',
+            f'cache_directory = "{p("images", "cache")}"',
+            "num_repeats = 1",
+            "",
+        ]
+    if has_videos:
+        max_frames = int(args.video_fps * args.clip_max_sec) + 1
+        lines += [
+            "[[datasets]]",
+            f'video_directory = "{p("videos")}"',
+            f'cache_directory = "{p("videos", "cache")}"',
+            'frame_extraction = "full"',
+            f"max_frames = {max_frames}",
+            f"source_fps = {args.video_fps}",
+            "num_repeats = 1",
+            "",
+        ]
+    with open(os.path.join(out_dir, "dataset.toml"), "w", encoding="utf-8") as f:
+        f.write("\n".join(lines))
+
+
+def main():
+    args = parse_args()
+    os.makedirs(os.path.join(args.output, "images"), exist_ok=True)
+    os.makedirs(os.path.join(args.output, "videos"), exist_ok=True)
+
+    images, videos = scan_inputs(args.input)
+    print(f"Found {len(images)} image(s), {len(videos)} video(s) under {args.input}")
+    if not images and not videos:
+        print("Nothing to do.")
+        return
+
+    pad_overrides = {
+        "face": args.face_pad, "person": args.person_pad,
+        "person_upper": args.person_region_pad, "person_lower": args.person_region_pad,
+    }
+
+    print(f"Loading captioner: {args.captioner} ...")
+    captioner = build_captioner(args)
+
+    img_idx = 1
+    for path in images:
+        try:
+            process_image(path, img_idx, args.output, args, captioner, pad_overrides)
+            img_idx += 1
+        except Exception:
+            print(f"  [image] FAILED {path}:")
+            traceback.print_exc()
+
+    vid_idx = 1
+    for path in videos:
+        try:
+            vid_idx = process_video(path, vid_idx, args.output, args, captioner, pad_overrides)
+        except Exception:
+            print(f"  [video] FAILED {path}:")
+            traceback.print_exc()
+
+    write_dataset_toml(args.output, args, bool(images), bool(videos))
+    print(f"Done. {img_idx - 1} image(s), {vid_idx - 1} clip(s) -> {args.output}")
+
+
+if __name__ == "__main__":
+    main()