diff --git a/custom_nodes/dolphin_nodes/.claude/launch.json b/custom_nodes/dolphin_nodes/.claude/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..4f1e617affd49ef0ea41c561906f92bd1c5c221b --- /dev/null +++ b/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 + } + ] +} diff --git a/custom_nodes/dolphin_nodes/.claude/settings.json b/custom_nodes/dolphin_nodes/.claude/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..a617ac968d9e16e21cdb36587b0f52a3f7d58ac8 --- /dev/null +++ b/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" + ] + } +} diff --git a/custom_nodes/dolphin_nodes/.claude/settings.local.json b/custom_nodes/dolphin_nodes/.claude/settings.local.json new file mode 100644 index 0000000000000000000000000000000000000000..9cdfa2d57336b409d58be775a7f2e7ea27c6bf60 --- /dev/null +++ b/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" + } +} diff --git a/custom_nodes/dolphin_nodes/.gitignore b/custom_nodes/dolphin_nodes/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c9908c64730564690517e412bd7a7380656e284c --- /dev/null +++ b/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 diff --git a/custom_nodes/dolphin_nodes/HANDOFF.md b/custom_nodes/dolphin_nodes/HANDOFF.md new file mode 100644 index 0000000000000000000000000000000000000000..6c01ac3cfe5c1ad51102bcda59e4111991a73f94 --- /dev/null +++ b/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**(백업: `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** + **로라 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 있었는데 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.) diff --git a/custom_nodes/dolphin_nodes/__init__.py b/custom_nodes/dolphin_nodes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f53e6b63dbd483145dc89bb537089bc3e4a19d49 --- /dev/null +++ b/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 처리한다는 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'] \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/__init_old__.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/__init_old__.py new file mode 100644 index 0000000000000000000000000000000000000000..24a6a33ad14da0edefdda52d04d133fb3de679f0 --- /dev/null +++ b/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'] \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/gemini-code-1782954683883.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/gemini-code-1782954683883.py new file mode 100644 index 0000000000000000000000000000000000000000..7a4c7545cdd2b24f324f62fb3921ef988b5cca13 --- /dev/null +++ b/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') \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/lora_classifier.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/lora_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..8d956deb958212c55b2a1ceda4a130e280b5d98d --- /dev/null +++ b/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() diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/__init__.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38fd68c44ad4f5ab4cbb0c33dffeed3fd62b5f46 --- /dev/null +++ b/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'] \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/arch_logic.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/arch_logic.py new file mode 100644 index 0000000000000000000000000000000000000000..e10769ce1bfec62f4d5da06fa2c16d8e719d7a68 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/dolphin_vision_director_pre_continuity_20260702.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/dolphin_vision_director_pre_continuity_20260702.py new file mode 100644 index 0000000000000000000000000000000000000000..f747e6d04a6c2369c6b3f340a71b60f846c80721 --- /dev/null +++ b/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":"","master_scene":"",' + '"clip_1":"",' + '"clip_2":"","clip_3":"",' + '"clip_4":""}' + ) + 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)"} diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_auto_sequence.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_auto_sequence.py new file mode 100644 index 0000000000000000000000000000000000000000..b44a8db53305f6906ef709ce055efdeafbd0ced8 --- /dev/null +++ b/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_ \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v30_story.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v30_story.py new file mode 100644 index 0000000000000000000000000000000000000000..e1b32c1de5f86a6957b2133b7db709d81731a2fb --- /dev/null +++ b/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) + ) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v31_vision.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v31_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..bcbda467fc2b849dda033d6c181ec354476b5b35 --- /dev/null +++ b/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) + ) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v32_vision.py b/custom_nodes/dolphin_nodes/_archive/nodes_unregistered/old/promp_logic_v32_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..aaab62c6719ee577ef0deb1b5f3487579473a14b --- /dev/null +++ b/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", + ) diff --git a/custom_nodes/dolphin_nodes/_archive/workflows/0705_director.json b/custom_nodes/dolphin_nodes/_archive/workflows/0705_director.json new file mode 100644 index 0000000000000000000000000000000000000000..f2114926c7f224cc9261b5614912a38ad7d54e24 --- /dev/null +++ b/custom_nodes/dolphin_nodes/_archive/workflows/0705_director.json @@ -0,0 +1 @@ +{"id": "57adbb00-d001-4a8f-babf-0946341d4196", "revision": 0, "last_node_id": 1084, "last_link_id": 36645, "nodes": [{"id": 585, "type": "VHS_LoadVideo", "pos": [1030, -1060], "size": [261.6533203125, 310], "flags": {"pinned": true}, "order": 0, "mode": 4, "inputs": [{"localized_name": "meta_batch", "name": "meta_batch", "shape": 7, "type": "VHS_BatchManager", "link": null}, {"localized_name": "vae", "name": "vae", "shape": 7, "type": "VAE", "link": null}, {"localized_name": "video", "name": "video", "type": "COMBO", "widget": {"name": "video"}, "link": null}, {"localized_name": "force_rate", "name": "force_rate", "type": "FLOAT", "widget": {"name": "force_rate"}, "link": null}, {"localized_name": "custom_width", "name": "custom_width", "type": "INT", "widget": {"name": "custom_width"}, "link": null}, {"localized_name": "custom_height", "name": "custom_height", "type": "INT", "widget": {"name": "custom_height"}, "link": null}, {"localized_name": "frame_load_cap", "name": "frame_load_cap", "type": "INT", "widget": {"name": "frame_load_cap"}, "link": null}, {"localized_name": "skip_first_frames", "name": "skip_first_frames", "type": "INT", "widget": {"name": "skip_first_frames"}, "link": null}, {"localized_name": "select_every_nth", "name": "select_every_nth", "type": "INT", "widget": {"name": "select_every_nth"}, "link": null}, {"localized_name": "format", "name": "format", "shape": 7, "type": "COMBO", "widget": {"name": "format"}, "link": null}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [1959]}, {"localized_name": "frame_count", "name": "frame_count", "type": "INT", "links": null}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": null}, {"localized_name": "video_info", "name": "video_info", "type": "VHS_VIDEOINFO", "links": []}], "properties": {"cnr_id": "comfyui-videohelpersuite", "ver": "1.7.9", "Node name for S&R": "VHS_LoadVideo", "ue_properties": {"widget_ue_connectable": {}, "version": "7.5.2", "input_ue_unconnectable": {}}}, "widgets_values": {"video": "Wan2_00001 (1).mp4", "force_rate": 0, "custom_width": 0, "custom_height": 0, "frame_load_cap": 0, "skip_first_frames": 0, "select_every_nth": 1, "format": "None", "videopreview": {"hidden": false, "paused": false, "params": {"filename": "Wan2_00001 (1).mp4", "type": "input", "format": "video/mp4", "force_rate": 0, "custom_width": 0, "custom_height": 0, "frame_load_cap": 0, "skip_first_frames": 0, "select_every_nth": 1}}}}, {"id": 715, "type": "ImageFromBatch+", "pos": [1630.6098913650385, -744.7637390299598], "size": [210, 100.6177749633789], "flags": {"collapsed": true, "pinned": true}, "order": 46, "mode": 0, "inputs": [{"localized_name": "image", "name": "image", "type": "IMAGE", "link": 2060}, {"localized_name": "start", "name": "start", "type": "INT", "widget": {"name": "start"}, "link": 2059}, {"localized_name": "length", "name": "length", "type": "INT", "widget": {"name": "length"}, "link": null}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [2043, 2047]}], "title": "Last frame", "properties": {"cnr_id": "comfyui_essentials", "ver": "33ff89fd354d8ec3ab6affb605a79a931b445d99", "Node name for S&R": "ImageFromBatch+", "ue_properties": {"version": "7.0.1", "widget_ue_connectable": {"start": true, "length": true}}, "widget_ue_connectable": {"start": true}}, "widgets_values": [1, 1], "shape": 1}, {"id": 718, "type": "PreviewImage", "pos": [1914.4713502363863, -851.1756414789619], "size": [140, 246], "flags": {"pinned": true}, "order": 51, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 2047}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": null}], "properties": {"cnr_id": "comfy-core", "ver": "0.8.0", "Node name for S&R": "PreviewImage", "ue_properties": {"widget_ue_connectable": {}, "input_ue_unconnectable": {}, "version": "7.5.2"}}, "widgets_values": []}, {"id": 642, "type": "9603c592-1dfd-472a-a878-7f10dacb3a49", "pos": [1607.3957611918788, -1122.9188356435209], "size": [210, 138], "flags": {"collapsed": false, "pinned": true}, "order": 43, "mode": 0, "inputs": [{"localized_name": "input1", "name": "input1", "shape": 7, "type": "*", "link": 1961}, {"name": "input2", "type": "IMAGE", "link": 1881}, {"localized_name": "input1_1", "name": "input1_1", "shape": 7, "type": "*", "link": 1882}, {"name": "input2_1", "type": "LATENT", "link": 1883}, {"name": "boolean", "type": "BOOLEAN", "widget": {"name": "boolean"}, "link": 1958}], "outputs": [{"label": "IMAGE", "localized_name": "selected_value", "name": "selected_value", "type": "*", "links": [1884]}, {"label": "LATENT", "localized_name": "selected_value_1", "name": "selected_value_1", "type": "*", "links": [1885]}], "properties": {"proxyWidgets": [["645", "boolean"]], "cnr_id": "comfy-core", "ver": "0.7.0", "ue_properties": {"widget_ue_connectable": {"boolean": true}, "version": "7.5.2", "input_ue_unconnectable": {}}}, "widgets_values": []}, {"id": 714, "type": "SetNode", "pos": [1625.17105508947, -885.157199933111], "size": [210, 58], "flags": {"collapsed": false, "pinned": true}, "order": 50, "mode": 0, "inputs": [{"name": "IMAGE", "type": "IMAGE", "link": 2043}], "outputs": [{"name": "IMAGE", "type": "IMAGE", "links": null}], "title": "Set_img_last_frame01", "properties": {"Node name for S&R": "SetNode", "aux_id": "SetNode", "previousName": 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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" + }, + { + "id": 7, + "type": 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"시나리오: Hi=lightx2v 없음(CFG3.5) / Lo=lightx2v만(CFG1, 디테일 정제).\n2026-07-29 재설계: High/Low가 steps 총량을 공유하는 슬라이싱 구조를 버리고 완전히 독립된 스케줄로 분리함.\n- 문제 이력: 처음엔 steps=12 공유(0-8-12)로 캐릭터 얼굴/디테일이 흔들림 → High를 8→16, 총steps 12→20으로 늘렸더니 오히려 컬러 노이즈(TV 스노우 같은 발산)로 악화됨. 원인: 공유 스케줄에서 High 비중을 늘릴수록 Low가 넘겨받는 시그마 값이 lightx2v가 실제 학습한 4스텝 노이즈 레벨에서 더 멀어져서, Low가 완전히 낯선 노이즈 레벨을 받아 발산한 것.\n- 수정: High는 자기만의 독립된 16스텝 스케줄로 CFG3.5, 완전히 끝까지(0->16, leftover noise 없이) 디노이징. 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+ }, + "widgets_values": [], + "title": "[CaseB] Decode", + "inputs": [ + { + "name": "samples", + "type": "LATENT", + "link": 29 + }, + { + "name": "vae", + "type": "VAE", + "link": 30 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 31 + ], + "slot_index": 0 + } + ] + }, + { + "id": 21, + "type": "VHS_VideoCombine", + "pos": [ + 1500, + 950 + ], + "size": [ + 320, + 260 + ], + "flags": {}, + "order": 0, + "mode": 0, + "properties": { + "Node name for S&R": "VHS_VideoCombine" + }, + "widgets_values": { + "frame_rate": 16, + "loop_count": 0, + "filename_prefix": "wan22_v2v_CaseB_denoise0.67", + "format": "video/h264-mp4", + "pingpong": false, + "save_image": true, + "crf": 19, + "save_metadata": true, + "audio_file": "" + }, + "title": "[CaseB] Save (denoise~0.67)", + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 31 + } + ] + } + ], + "links": [ + [ + 1, + 3, + 0, + 5, + 0, + "MODEL" + ], + [ + 2, + 4, + 0, + 6, + 0, + "MODEL" + ], + [ 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+ [ + 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 +} \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/arch_logic.py b/custom_nodes/dolphin_nodes/arch_logic.py new file mode 100644 index 0000000000000000000000000000000000000000..e10769ce1bfec62f4d5da06fa2c16d8e719d7a68 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/arch_logic_v16.py b/custom_nodes/dolphin_nodes/arch_logic_v16.py new file mode 100644 index 0000000000000000000000000000000000000000..6b1dee1d7023b173dfd7d7777bcec5f68b8cbc8b --- /dev/null +++ b/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)) \ No newline at end of file diff --git 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+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. diff --git a/custom_nodes/dolphin_nodes/dataset_builder/build.py b/custom_nodes/dolphin_nodes/dataset_builder/build.py new file mode 100644 index 0000000000000000000000000000000000000000..14df6ed41dfce3954b9b22b85f6bd3447e1e46e5 --- /dev/null +++ b/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() diff --git a/custom_nodes/dolphin_nodes/dataset_builder/caption.py b/custom_nodes/dolphin_nodes/dataset_builder/caption.py new file mode 100644 index 0000000000000000000000000000000000000000..ffa21ca1747f682fdc2ea1d56a5dfb79f1c7aeb1 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/caption.py @@ -0,0 +1,209 @@ +"""Pluggable local captioners for LoRA dataset building. + +All captioners expose the same interface: ``caption(images: list[PIL.Image]) -> str``. +``images`` is a list so video callers can pass multiple sampled frames; single-image +captioners just use the first one. + +- JoyCaptionCaptioner: fancyfeast/llama-joycaption-beta-one-hf-llava (AutoProcessor + + LlavaForConditionalGeneration, confirmed against the model card). The exact prompt + wording for a dedicated "training caption" mode is not documented on the model card, + so the instruction text here is our own default and is overridable via --caption-prompt. +- Florence2Captioner: microsoft/Florence-2-large, task. +- WD14Captioner: SmilingWolf/wd-vit-tagger-v3 onnx tagger (booru-style tags), preprocessing + confirmed against the reference wd-tagger Space source (white-pad-to-square, RGB->BGR, + no normalization, category 9/4/0 = rating/character/general). +""" +import csv + +import numpy as np +import torch +from PIL import Image + + +class JoyCaptionCaptioner: + MODEL_ID = "fancyfeast/llama-joycaption-beta-one-hf-llava" + DEFAULT_PROMPT = ( + "Write a detailed but concise caption for this image, suitable for training " + "an image/video LoRA. Describe the subject's appearance, pose, clothing, " + "setting, and lighting in plain descriptive sentences. Do not guess names." + ) + + def __init__(self, device="cuda", load_in_4bit=False, prompt=None, system_prompt=None): + from transformers import AutoProcessor, LlavaForConditionalGeneration + + self.processor = AutoProcessor.from_pretrained(self.MODEL_ID) + load_kwargs = {} + if load_in_4bit: + from transformers import BitsAndBytesConfig + load_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True) + load_kwargs["device_map"] = "auto" + else: + load_kwargs["torch_dtype"] = torch.bfloat16 + load_kwargs["device_map"] = device + self.model = LlavaForConditionalGeneration.from_pretrained(self.MODEL_ID, **load_kwargs) + self.model.eval() + + self.system_prompt = system_prompt or "You are a helpful image captioner." + self.prompt = prompt or self.DEFAULT_PROMPT + + def caption(self, images): + image = images[0].convert("RGB") + convo = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": self.prompt}, + ] + convo_string = self.processor.apply_chat_template( + convo, tokenize=False, add_generation_prompt=True + ) + inputs = self.processor(text=[convo_string], images=[image], return_tensors="pt") + inputs = {k: v.to(self.model.device) for k, v in inputs.items()} + if "pixel_values" in inputs: + inputs["pixel_values"] = inputs["pixel_values"].to(self.model.dtype) + + with torch.no_grad(): + generate_ids = self.model.generate( + **inputs, + max_new_tokens=300, + do_sample=True, + suppress_tokens=None, + use_cache=True, + temperature=0.6, + top_k=None, + top_p=0.9, + )[0] + + generate_ids = generate_ids[inputs["input_ids"].shape[1]:] + text = self.processor.tokenizer.decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + return text.strip() + + +class Florence2Captioner: + MODEL_ID = "microsoft/Florence-2-large" + TASK = "" + + def __init__(self, device="cuda"): + from transformers import AutoModelForCausalLM, AutoProcessor + + dtype = torch.float16 if device.startswith("cuda") else torch.float32 + self.device = device + self.model = ( + AutoModelForCausalLM.from_pretrained(self.MODEL_ID, trust_remote_code=True, torch_dtype=dtype) + .to(device) + .eval() + ) + self.processor = AutoProcessor.from_pretrained(self.MODEL_ID, trust_remote_code=True) + + def caption(self, images): + image = images[0].convert("RGB") + inputs = self.processor(text=self.TASK, images=image, return_tensors="pt") + inputs = {k: v.to(self.device, self.model.dtype) if v.is_floating_point() else v.to(self.device) + for k, v in inputs.items()} + + with torch.no_grad(): + generated_ids = self.model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=200, + num_beams=3, + do_sample=False, + ) + text = self.processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + parsed = self.processor.post_process_generation( + text, task=self.TASK, image_size=(image.width, image.height) + ) + return parsed[self.TASK].strip() + + +class WD14Captioner: + REPO_ID = "SmilingWolf/wd-vit-tagger-v3" + MODEL_FILE = "model.onnx" + TAGS_FILE = "selected_tags.csv" + + def __init__(self, general_thresh=0.35, character_thresh=0.75, use_gpu=True): + import onnxruntime as ort + from huggingface_hub import hf_hub_download + + model_path = hf_hub_download(self.REPO_ID, self.MODEL_FILE) + tags_path = hf_hub_download(self.REPO_ID, self.TAGS_FILE) + + providers = ["CPUExecutionProvider"] + if use_gpu: + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] + self.session = ort.InferenceSession(model_path, providers=providers) + self.input_name = self.session.get_inputs()[0].name + self.output_name = self.session.get_outputs()[0].name + self.target_size = int(self.session.get_inputs()[0].shape[1]) + + self.general_thresh = general_thresh + self.character_thresh = character_thresh + self.tag_names, self.general_idx, self.character_idx = self._load_tags(tags_path) + + @staticmethod + def _load_tags(path): + names, general, character = [], [], [] + with open(path, newline="", encoding="utf-8") as f: + for i, row in enumerate(csv.DictReader(f)): + names.append(row["name"]) + cat = int(row["category"]) + if cat == 4: + character.append(i) + elif cat != 9: # skip rating (9); keep general (0) and any others + general.append(i) + return names, general, character + + def _preprocess(self, image: Image.Image): + image = image.convert("RGBA") + canvas = Image.new("RGBA", image.size, (255, 255, 255, 255)) + canvas.alpha_composite(image) + image = canvas.convert("RGB") + + w, h = image.size + size = max(w, h) + padded = Image.new("RGB", (size, size), (255, 255, 255)) + padded.paste(image, ((size - w) // 2, (size - h) // 2)) + padded = padded.resize((self.target_size, self.target_size), Image.BICUBIC) + + arr = np.asarray(padded, dtype=np.float32) + arr = arr[:, :, ::-1] # RGB -> BGR + return np.expand_dims(arr, axis=0) + + def caption(self, images): + image = images[0] + arr = self._preprocess(image) + preds = self.session.run([self.output_name], {self.input_name: arr})[0][0] + + general = [(self.tag_names[i], preds[i]) for i in self.general_idx if preds[i] >= self.general_thresh] + character = [(self.tag_names[i], preds[i]) for i in self.character_idx if preds[i] >= self.character_thresh] + general.sort(key=lambda t: -t[1]) + character.sort(key=lambda t: -t[1]) + + tags = [t.replace("_", " ") for t, _ in character] + [t.replace("_", " ") for t, _ in general] + return ", ".join(tags) + + +def build_captioner(args): + name = args.captioner + if name == "joycaption": + return JoyCaptionCaptioner( + device=args.device, + load_in_4bit=args.load_in_4bit, + prompt=args.caption_prompt, + ) + if name == "florence2": + return Florence2Captioner(device=args.device) + if name == "wd14": + return WD14Captioner( + general_thresh=args.wd14_general_thresh, + character_thresh=args.wd14_character_thresh, + use_gpu=args.device.startswith("cuda"), + ) + raise ValueError(f"Unknown captioner: {name}") + + +def with_trigger(caption, trigger): + caption = (caption or "").strip() + if not trigger: + return caption + return f"{trigger}, {caption}" if caption else trigger diff --git a/custom_nodes/dolphin_nodes/dataset_builder/crop_resize.py b/custom_nodes/dolphin_nodes/dataset_builder/crop_resize.py new file mode 100644 index 0000000000000000000000000000000000000000..56036fd6106e9d4fe6d83163fc4f0a50a8c93bdc --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/crop_resize.py @@ -0,0 +1,146 @@ +"""Wan 2.2 resolution-bucket matching + subject-centered crop math. + +Ports the area-preserving resize logic from ``wan_resizer.py`` +(``WanNativeResize_Dolphin``) into a torch/comfy-free form so it can run +outside ComfyUI in the standalone dataset builder CLI. +""" +import math + +import cv2 +import numpy as np +from PIL import Image + +TIER_AREA = { + "480p": 832 * 480, + "720p": 1280 * 720, + "1080p": 1920 * 1080, +} +TIER_ORDER = ["1080p", "720p", "480p"] + +ASPECTS = { + "landscape": 16 / 9, + "portrait": 9 / 16, + "square": 1.0, +} + +# Default bbox padding multiplier per detected subject kind (see detect.py). +# person_upper/person_lower are subdivide_bbox() regions -- padded more than +# plain "person" since they start from an already-narrowed sub-box. +DEFAULT_PAD = { + "face": 2.2, + "person": 1.3, + "person_upper": 1.7, + "person_lower": 1.7, + "full_frame": 1.0, +} + + +def _bucket_dims(area, aspect, alignment=16): + h = math.sqrt(area / aspect) + w = h * aspect + w = max(alignment, int(round(w / alignment)) * alignment) + h = max(alignment, int(round(h / alignment)) * alignment) + return w, h + + +def bucket_set(tier): + area = TIER_AREA[tier] + return {name: _bucket_dims(area, aspect) for name, aspect in ASPECTS.items()} + + +def nearest_bucket(crop_w, crop_h, tier): + """Pick the bucket of ``tier`` whose aspect ratio is closest to crop_w/crop_h.""" + crop_aspect = crop_w / crop_h + buckets = bucket_set(tier) + best_name = min( + buckets, + key=lambda n: abs(math.log(buckets[n][0] / buckets[n][1]) - math.log(crop_aspect)), + ) + return best_name, buckets[best_name] + + +def auto_tier(crop_w, crop_h, max_tier="1080p"): + """Pick the largest tier whose bucket area does not exceed the native crop's + pixel area, to avoid upscaling past source resolution. Falls back to the + smallest tier under max_tier if even that would upscale.""" + order = TIER_ORDER[TIER_ORDER.index(max_tier):] + native_area = crop_w * crop_h + for tier in order: + _, (bw, bh) = nearest_bucket(crop_w, crop_h, tier) + if bw * bh <= native_area: + return tier + return order[-1] + + +def pad_bbox(bbox, pad_mult): + x1, y1, x2, y2 = bbox + w, h = x2 - x1, y2 - y1 + cx, cy = (x1 + x2) / 2, (y1 + y2) / 2 + new_w, new_h = w * pad_mult, h * pad_mult + return (cx - new_w / 2, cy - new_h / 2, cx + new_w / 2, cy + new_h / 2) + + +def fit_aspect(bbox, img_w, img_h, target_aspect): + """Expand bbox to match target_aspect (w/h), centered on bbox, then clamp + to image bounds (shift first, hard-clamp as a last resort for oversized + crops).""" + x1, y1, x2, y2 = bbox + w, h = x2 - x1, y2 - y1 + cx, cy = (x1 + x2) / 2, (y1 + y2) / 2 + cur_aspect = w / h if h else target_aspect + + if cur_aspect < target_aspect: + new_h, new_w = h, h * target_aspect + else: + new_w, new_h = w, w / target_aspect + + x1, x2 = cx - new_w / 2, cx + new_w / 2 + y1, y2 = cy - new_h / 2, cy + new_h / 2 + + if x1 < 0: + x2 -= x1 + x1 = 0 + if x2 > img_w: + x1 -= (x2 - img_w) + x2 = img_w + if y1 < 0: + y2 -= y1 + y1 = 0 + if y2 > img_h: + y1 -= (y2 - img_h) + y2 = img_h + + x1, y1 = max(0, x1), max(0, y1) + x2, y2 = min(img_w, x2), min(img_h, y2) + return (x1, y1, x2, y2) + + +def compute_crop(img_w, img_h, bbox, kind, pad_overrides=None, max_tier="1080p"): + """Given an image size and a detected subject bbox, return the crop box + (in original-image pixel space) and the resolution bucket to resize it to. + + Returns: (crop_box, (target_w, target_h), bucket_name, tier) + """ + pad = dict(DEFAULT_PAD) + if pad_overrides: + pad.update(pad_overrides) + + padded = pad_bbox(bbox, pad.get(kind, 1.0)) + pw, ph = padded[2] - padded[0], padded[3] - padded[1] + pw, ph = max(1.0, pw), max(1.0, ph) + + tier = auto_tier(pw, ph, max_tier) + bucket_name, (bw, bh) = nearest_bucket(pw, ph, tier) + crop_box = fit_aspect(padded, img_w, img_h, bw / bh) + return crop_box, (bw, bh), bucket_name, tier + + +def crop_resize_pil(image: Image.Image, crop_box, target_w, target_h): + x1, y1, x2, y2 = (int(round(v)) for v in crop_box) + return image.crop((x1, y1, x2, y2)).resize((target_w, target_h), Image.LANCZOS) + + +def crop_resize_np(frame_bgr: np.ndarray, crop_box, target_w, target_h): + x1, y1, x2, y2 = (int(round(v)) for v in crop_box) + cropped = frame_bgr[y1:y2, x1:x2] + return cv2.resize(cropped, (target_w, target_h), interpolation=cv2.INTER_LANCZOS4) diff --git a/custom_nodes/dolphin_nodes/dataset_builder/detect.py b/custom_nodes/dolphin_nodes/dataset_builder/detect.py new file mode 100644 index 0000000000000000000000000000000000000000..0bebd66957097651da094fc867207092977bab44 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/detect.py @@ -0,0 +1,140 @@ +"""Subject bbox detection for smart cropping. + +Priority: mediapipe face detection -> OpenCV Haar cascade face detection +(no model download required, always available with opencv-python) -> +ultralytics YOLOv8n person detection -> full frame (no crop). + +All bboxes are returned as (x1, y1, x2, y2) in pixel space, plus a "kind" +string ("face" | "person" | "full_frame") used by crop_resize.py to pick a +padding multiplier. +""" +import cv2 +import numpy as np + +_mp_detector = None +_mp_unavailable = False +_haar_cascade = None +_yolo_model = None + + +def _get_mediapipe_detector(min_conf): + global _mp_detector, _mp_unavailable + if _mp_unavailable: + return None + if _mp_detector is None: + try: + import mediapipe as mp + _mp_detector = mp.solutions.face_detection.FaceDetection( + model_selection=1, min_detection_confidence=min_conf + ) + except Exception: + _mp_unavailable = True + return None + return _mp_detector + + +def _get_haar_cascade(): + global _haar_cascade + if _haar_cascade is None: + path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml" + _haar_cascade = cv2.CascadeClassifier(path) + return _haar_cascade + + +def _get_yolo(): + global _yolo_model + if _yolo_model is None: + from ultralytics import YOLO + _yolo_model = YOLO("yolov8n.pt") + return _yolo_model + + +def _largest_bbox(candidates): + """candidates: list of (x1, y1, x2, y2). Returns the largest-area one.""" + if not candidates: + return None + return max(candidates, key=lambda b: max(0, b[2] - b[0]) * max(0, b[3] - b[1])) + + +def _detect_face_mediapipe(image_bgr, min_conf): + detector = _get_mediapipe_detector(min_conf) + if detector is None: + return None + h, w = image_bgr.shape[:2] + rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) + try: + results = detector.process(rgb) + except Exception: + return None + if not results.detections: + return None + boxes = [] + for det in results.detections: + rb = det.location_data.relative_bounding_box + x1, y1 = rb.xmin * w, rb.ymin * h + x2, y2 = x1 + rb.width * w, y1 + rb.height * h + boxes.append((x1, y1, x2, y2)) + return _largest_bbox(boxes) + + +def _detect_face_haar(image_bgr): + cascade = _get_haar_cascade() + gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) + faces = cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(40, 40)) + if len(faces) == 0: + return None + boxes = [(float(x), float(y), float(x + fw), float(y + fh)) for x, y, fw, fh in faces] + return _largest_bbox(boxes) + + +def _detect_person_yolo(image_bgr, min_conf): + try: + model = _get_yolo() + results = model.predict(image_bgr, classes=[0], conf=min_conf, verbose=False) + except Exception: + return None + boxes = [] + for r in results: + if r.boxes is None: + continue + for b in r.boxes: + x1, y1, x2, y2 = b.xyxy[0].tolist() + boxes.append((x1, y1, x2, y2)) + return _largest_bbox(boxes) + + +def detect_subject_bbox(image_bgr: np.ndarray, min_face_conf=0.5, min_person_conf=0.35): + """Returns (bbox, kind) where kind is 'face', 'person', or 'full_frame'.""" + h, w = image_bgr.shape[:2] + + bbox = _detect_face_mediapipe(image_bgr, min_face_conf) + if bbox: + return bbox, "face" + + bbox = _detect_face_haar(image_bgr) + if bbox: + return bbox, "face" + + bbox = _detect_person_yolo(image_bgr, min_person_conf) + if bbox: + return bbox, "person" + + return (0.0, 0.0, float(w), float(h)), "full_frame" + + +def subdivide_bbox(bbox, region, frac=0.45): + """Narrows a bbox to its top or bottom portion (by height), same width. + + Used to bias person-kind crops toward a specific body region (e.g. most + of the frame is a torso/lower-body shot rather than a full figure) without + needing a specialized body-part detector -- just a fraction of the + already-detected person bbox. `region` is "upper", "lower", or anything + else (returned unchanged, e.g. "auto"/"full"). + """ + x1, y1, x2, y2 = bbox + h = y2 - y1 + if region == "lower": + return (x1, y2 - h * frac, x2, y2) + if region == "upper": + return (x1, y1, x2, y1 + h * frac) + return bbox diff --git a/custom_nodes/dolphin_nodes/dataset_builder/inspect_lora.py b/custom_nodes/dolphin_nodes/dataset_builder/inspect_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..6251fa2a5ca8fab3aac31a38984743e69301b6f1 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/inspect_lora.py @@ -0,0 +1,69 @@ +"""Inspect a LoRA .safetensors file: embedded training metadata + inferred +rank/target modules from tensor shapes. Read-only, no GPU/torch needed -- +just parses the safetensors header, so it's fast and safe to run on any +LoRA file (including ones you didn't train yourself). + +Usage: + python_embeded\\python.exe dataset_builder\\inspect_lora.py path\\to\\lora.safetensors +""" +import argparse +import json +import re +from collections import Counter + +from safetensors import safe_open + + +def parse_args(): + p = argparse.ArgumentParser(description="Inspect a LoRA safetensors file's metadata") + p.add_argument("lora_path") + return p.parse_args() + + +def infer_rank(keys, shapes): + for k in keys: + if k.endswith("lora_A.weight") or k.endswith("lora_down.weight"): + return shapes[k][0] + return None + + +def summarize_target_modules(keys): + """Collapses per-block keys (blocks.0, blocks.1, ...) into module families + so you can see what parts of the model were trained without a 800-line dump.""" + families = Counter() + for k in keys: + base = re.sub(r"\.(lora_A|lora_B|lora_down|lora_up|alpha)(\.weight)?$", "", k) + base = re.sub(r"\.\d+\.", ".N.", base) + families[base] += 1 + return families + + +def main(): + args = parse_args() + with safe_open(args.lora_path, framework="pt") as f: + meta = f.metadata() or {} + keys = list(f.keys()) + shapes = {k: f.get_slice(k).get_shape() for k in keys} + + print(f"=== {args.lora_path} ===") + print(f"Tensor count: {len(keys)}") + print(f"Inferred rank (lora_A/lora_down first dim): {infer_rank(keys, shapes)}") + + print("\n--- Embedded metadata ---") + if not meta: + print("(none)") + for k, v in meta.items(): + try: + parsed = json.loads(v) + print(f"{k}:") + print(json.dumps(parsed, indent=2, ensure_ascii=False)[:3000]) + except (json.JSONDecodeError, TypeError): + print(f"{k}: {v}") + + print("\n--- Target module families (per-block keys collapsed) ---") + for base, count in summarize_target_modules(keys).most_common(30): + print(f" {base} x{count}") + + +if __name__ == "__main__": + main() diff --git a/custom_nodes/dolphin_nodes/dataset_builder/scene_split.py b/custom_nodes/dolphin_nodes/dataset_builder/scene_split.py new file mode 100644 index 0000000000000000000000000000000000000000..de689ace60a1743aaa82b3d1084eda0aaa185c2a --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/scene_split.py @@ -0,0 +1,190 @@ +"""Video scene-cut detection, clip extraction, and subject-centered crop/resize. + +Uses plain (non-relative) imports of sibling modules so this file works whether +invoked as ``python dataset_builder/build.py`` or ``python -m dataset_builder.build`` +(build.py inserts its own directory onto sys.path before importing). +""" +import math +import statistics + +import cv2 +import imageio +import numpy as np +from PIL import Image + +import crop_resize +from detect import detect_subject_bbox, subdivide_bbox + + +def _frame_hist(frame_bgr): + hsv = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2HSV) + hist = cv2.calcHist([hsv], [0, 1], None, [50, 60], [0, 180, 0, 256]) + cv2.normalize(hist, hist) + return hist + + +def find_scene_cuts(video_path, diff_thresh=0.45, sample_stride=1): + """Returns a sorted list of frame indices where a scene cut is detected, + via Bhattacharyya distance between consecutive HSV histograms.""" + cap = cv2.VideoCapture(video_path) + cuts = [] + prev_hist = None + idx = 0 + while True: + ok, frame = cap.read() + if not ok: + break + if idx % sample_stride == 0: + hist = _frame_hist(frame) + if prev_hist is not None: + score = cv2.compareHist(prev_hist, hist, cv2.HISTCMP_BHATTACHARYYA) + if score > diff_thresh: + cuts.append(idx) + prev_hist = hist + idx += 1 + cap.release() + return cuts + + +def build_clip_ranges(total_frames, fps, cuts, clip_min_sec=2.0, clip_max_sec=6.0): + """Turns scene-cut frame indices into (start, end) frame ranges honoring + clip_min/max seconds: too-short segments are merged forward, segments + longer than clip_max are chopped into roughly-equal sub-segments.""" + min_frames = max(1, int(clip_min_sec * fps)) + max_frames = max(min_frames, int(clip_max_sec * fps)) + + boundaries = [0] + sorted(set(c for c in cuts if 0 < c < total_frames)) + [total_frames] + raw_ranges = [] + start = boundaries[0] + for b in boundaries[1:]: + if b - start >= min_frames: + raw_ranges.append((start, b)) + start = b + if start < total_frames: + if raw_ranges and (total_frames - start) < min_frames: + s, _ = raw_ranges[-1] + raw_ranges[-1] = (s, total_frames) + else: + raw_ranges.append((start, total_frames)) + + ranges = [] + for s, e in raw_ranges: + length = e - s + if length <= max_frames: + ranges.append((s, e)) + continue + n_chunks = math.ceil(length / max_frames) + chunk_len = length // n_chunks + for i in range(n_chunks): + cs = s + i * chunk_len + ce = e if i == n_chunks - 1 else cs + chunk_len + ranges.append((cs, ce)) + + return [(s, e) for s, e in ranges if e - s >= max(1, min_frames // 2)] + + +def _sample_indices(start, end, n=3): + if end - start <= n: + return list(range(start, end)) + step = (end - start) / (n + 1) + return [int(start + step * (i + 1)) for i in range(n)] + + +def _median_bbox(bboxes): + return ( + statistics.median(b[0] for b in bboxes), + statistics.median(b[1] for b in bboxes), + statistics.median(b[2] for b in bboxes), + statistics.median(b[3] for b in bboxes), + ) + + +def _motion_tag(frames_gray): + if len(frames_gray) < 2: + return "static, minimal motion" + mags = [] + for a, b in zip(frames_gray, frames_gray[1:]): + flow = cv2.calcOpticalFlowFarneback(a, b, None, 0.5, 3, 15, 3, 5, 1.2, 0) + mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1]) + mags.append(float(np.mean(mag))) + avg = sum(mags) / len(mags) + if avg < 0.5: + return "static, minimal motion" + if avg < 2.0: + return "subtle motion" + if avg < 5.0: + return "moderate motion" + return "fast, dynamic motion" + + +def extract_clip(video_path, start, end, target_fps, max_tier="1080p", pad_overrides=None, + body_region="auto", body_region_frac=0.45): + """Reads frames [start, end) from video_path, computes one stable subject + crop for the whole clip (median bbox over sampled frames), resamples to + target_fps, trims to a 4n+1 frame count, and returns + (frames_bgr, (target_w, target_h), representative_pil_image, motion_tag), + or None if no frames could be read.""" + cap = cv2.VideoCapture(video_path) + src_fps = cap.get(cv2.CAP_PROP_FPS) or target_fps + img_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + img_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + bboxes, kinds = [], [] + for idx in _sample_indices(start, end, n=3): + cap.set(cv2.CAP_PROP_POS_FRAMES, idx) + ok, frame = cap.read() + if not ok: + continue + bbox, kind = detect_subject_bbox(frame) + bboxes.append(bbox) + kinds.append(kind) + + if not bboxes: + cap.release() + return None + + bbox = _median_bbox(bboxes) + kind = max(set(kinds), key=kinds.count) + if kind == "person" and body_region != "auto": + bbox = subdivide_bbox(bbox, body_region, body_region_frac) + kind = f"person_{body_region}" + crop_box, (tw, th), _bucket_name, _tier = crop_resize.compute_crop( + img_w, img_h, bbox, kind, pad_overrides, max_tier + ) + + step = src_fps / target_fps + raw_indices = [] + f = float(start) + while f < end: + raw_indices.append(int(round(f))) + f += step + if len(raw_indices) >= 5: + n = ((len(raw_indices) - 1) // 4) * 4 + 1 + raw_indices = raw_indices[:n] + + frames_out, frames_gray = [], [] + for idx in raw_indices: + cap.set(cv2.CAP_PROP_POS_FRAMES, idx) + ok, frame = cap.read() + if not ok: + continue + cropped = crop_resize.crop_resize_np(frame, crop_box, tw, th) + frames_out.append(cropped) + if len(frames_gray) < 6: + frames_gray.append(cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)) + cap.release() + + if not frames_out: + return None + + motion_tag = _motion_tag(frames_gray) + mid = frames_out[len(frames_out) // 2] + representative = Image.fromarray(cv2.cvtColor(mid, cv2.COLOR_BGR2RGB)) + + return frames_out, (tw, th), representative, motion_tag + + +def write_clip_mp4(frames_bgr, out_path, fps): + with imageio.get_writer(str(out_path), fps=fps, codec="libx264", quality=8) as writer: + for frame in frames_bgr: + writer.append_data(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) diff --git a/custom_nodes/dolphin_nodes/dataset_builder/start_webui.bat b/custom_nodes/dolphin_nodes/dataset_builder/start_webui.bat new file mode 100644 index 0000000000000000000000000000000000000000..5a3c50c15c5d81f992a1dd97d59154d118e2a39b --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/start_webui.bat @@ -0,0 +1,21 @@ +@echo off +setlocal + +set "HERE=%~dp0" +set "PYTHON=%HERE%..\..\..\..\python_embeded\python.exe" + +if not exist "%PYTHON%" ( + echo Could not find python_embeded at: + echo %PYTHON% + echo This .bat expects to live at ComfyUI\custom_nodes\dolphin_nodes\dataset_builder\ + pause + exit /b 1 +) + +echo Starting Wan 2.2 LoRA web UI... +echo Once it says "Running on local URL", open http://127.0.0.1:7860 in your browser. +echo. + +"%PYTHON%" "%HERE%webui.py" %* + +pause diff --git a/custom_nodes/dolphin_nodes/dataset_builder/webui.py b/custom_nodes/dolphin_nodes/dataset_builder/webui.py new file mode 100644 index 0000000000000000000000000000000000000000..13d1dabcbee8721e321170a0aedd19e59eb10eaf --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/webui.py @@ -0,0 +1,402 @@ +"""Local web UI for the Wan 2.2 LoRA dataset builder + trainer. + +Thin Gradio layer over the already-verified CLI scripts (build.py, +train/train.py, train/download_checkpoints.py, train/setup_env.ps1) -- this +file does not reimplement any of their logic, it only shells out to them and +streams stdout live. Heavy ML models (captioners, torch) never load inside +this long-lived server process; each run is its own subprocess so a crash or +this environment's known torch-teardown quirk can't take the UI down with it. + +Run: + python_embeded\\python.exe dataset_builder\\webui.py [--port 7860] +""" +import argparse +import os +import subprocess +import sys + +import gradio as gr + +HERE = os.path.dirname(os.path.abspath(__file__)) +TRAIN_DIR = os.path.join(HERE, "train") +VENV_PYTHON = os.path.join(TRAIN_DIR, "venv", "Scripts", "python.exe") +PYTHON = sys.executable # python_embeded's own interpreter (has build.py's deps) + +sys.path.insert(0, TRAIN_DIR) +from download_checkpoints import FILES, COMMON_FILES # noqa: E402 + +# ComfyUI/custom_nodes/dolphin_nodes/dataset_builder/webui.py -> ComfyUI/models +MODELS_ROOT = os.path.normpath(os.path.join(HERE, "..", "..", "..", "models")) + + +# Any python_embeded process that imports torch on this machine can exit with +# this NTSTATUS during CUDA teardown *after* finishing its real work (confirmed +# for both dataset_builder/train/train.py and build.py itself, e.g. via +# caption.py's torch import) -- see dataset_builder/train/README.md's bug list. +# Reported as either the signed or unsigned 32-bit form depending on context. +BENIGN_TORCH_TEARDOWN_NTSTATUS = 0xC0000409 # STATUS_STACK_BUFFER_OVERRUN + + +EMBEDED_DIR = os.path.dirname(PYTHON) + + +def child_env(): + """Environment for subprocesses, scrubbed of this server's own Python. + + python_embeded is Python 3.13 and ships python313.dll in its own directory. + A child running the training venv's Python 3.10 will happily load that + 3.13 DLL if python_embeded is reachable via PATH or the inherited working + directory, and then every stdlib C extension fails with "Module use of + python313.dll conflicts with this version of Python" (confirmed twice + through this UI). Dropping python_embeded from PATH -- combined with the + fixed cwd below -- keeps the child's DLL search away from it. + """ + env = {**os.environ, "PYTHONIOENCODING": "utf-8", "PYTHONUNBUFFERED": "1"} + parts = env.get("PATH", "").split(os.pathsep) + embeded = os.path.normcase(EMBEDED_DIR) + env["PATH"] = os.pathsep.join( + p for p in parts if not os.path.normcase(p).startswith(embeded) + ) + return env + + +def stream_subprocess(cmd): + """Runs cmd, yielding the accumulated stdout text as new lines arrive.""" + proc = subprocess.Popen( + cmd, env=child_env(), cwd=HERE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, + text=True, encoding="utf-8", errors="replace", bufsize=1, + ) + log = "" + for line in proc.stdout: + log += line + yield log + proc.wait() + if proc.returncode not in (0, None): + if (proc.returncode & 0xFFFFFFFF) == BENIGN_TORCH_TEARDOWN_NTSTATUS: + log += "\n(non-fatal: torch/CUDA teardown crash after finishing its work -- known environment quirk)" + else: + log += f"\n[exit code {proc.returncode}]" + yield log + + +# --------------------------------------------------------------------------- +# Checkpoint scanning (shared by Setup + Training tabs) +# --------------------------------------------------------------------------- + +def checkpoint_status_rows(): + rows = [] + for _repo, path, subdir, size in FILES["fp16"] + COMMON_FILES: + dest = os.path.join(MODELS_ROOT, subdir, os.path.basename(path)) + ok = os.path.exists(dest) + actual = f"{os.path.getsize(dest) / 1e9:.1f}GB" if ok else "-" + rows.append([os.path.basename(path), "있음" if ok else "없음", f"{size / 1e9:.1f}GB", actual]) + return rows + + +def venv_status_text(): + return "설치됨" if os.path.exists(VENV_PYTHON) else "설치 안 됨" + + +def list_checkpoints(subdir, name_filter=None, exclude=None): + d = os.path.join(MODELS_ROOT, subdir) + if not os.path.isdir(d): + return [] + files = os.listdir(d) + if name_filter: + files = [f for f in files if name_filter in f.lower()] + if exclude: + files = [f for f in files if exclude not in f.lower()] + return [os.path.join(d, f) for f in sorted(files)] + + +def list_dit_choices(): + # fp8_scaled DiT files can't be used for training (confirmed this session: + # musubi-tuner's loader rejects ComfyUI's pre-quantized scaled-fp8 format + # both with and without --fp8-scaled) -- excluded here so the dropdown + # can't steer anyone into that dead end. + return list_checkpoints("diffusion_models", name_filter="wan2.2_i2v", exclude="fp8_scaled") + + +def list_t5_choices(): + return list_checkpoints("text_encoders", name_filter="t5") + + +def list_vae_choices(): + return list_checkpoints("vae", name_filter="wan2.1_vae") + + +# --------------------------------------------------------------------------- +# Setup tab +# --------------------------------------------------------------------------- + +def run_setup_env(): + yield from stream_subprocess(["powershell.exe", "-File", os.path.join(TRAIN_DIR, "setup_env.ps1"), "-Yes"]) + + +def run_download_checkpoints(): + yield from stream_subprocess([ + PYTHON, os.path.join(TRAIN_DIR, "download_checkpoints.py"), + "--models-root", MODELS_ROOT, "--precision", "fp16", "--yes", + ]) + + +def build_setup_tab(): + with gr.Tab("Setup"): + gr.Markdown( + "학습에는 fp16 체크포인트가 필요합니다 (fp8_scaled는 ComfyUI 추론 전용 — " + "musubi-tuner 학습 로더가 못 읽는 것으로 확인됨). 아래에서 학습 환경/체크포인트 " + "상태를 확인하고 없는 것만 받으세요." + ) + with gr.Row(): + venv_box = gr.Textbox(label="학습 환경(venv)", value=venv_status_text(), interactive=False, scale=1) + setup_btn = gr.Button("학습 환경 설치 (venv + musubi-tuner)", scale=2) + setup_log = gr.Textbox(label="설치 로그", lines=10, max_lines=20, interactive=False) + setup_btn.click(run_setup_env, outputs=setup_log).then(venv_status_text, outputs=venv_box) + + gr.Markdown("### 체크포인트") + ckpt_table = gr.Dataframe( + headers=["파일", "상태", "예상 크기", "실제 크기"], + value=checkpoint_status_rows(), interactive=False, + ) + with gr.Row(): + refresh_btn = gr.Button("상태 새로고침") + confirm_dl = gr.Checkbox(label="위 목록 중 없는 fp16 체크포인트를 다운로드하겠습니다 (총 수십GB)") + dl_btn = gr.Button("다운로드", interactive=False) + dl_log = gr.Textbox(label="다운로드 로그", lines=10, max_lines=20, interactive=False) + + refresh_btn.click(checkpoint_status_rows, outputs=ckpt_table) + confirm_dl.change(lambda v: gr.update(interactive=v), inputs=confirm_dl, outputs=dl_btn) + dl_btn.click(run_download_checkpoints, outputs=dl_log).then(checkpoint_status_rows, outputs=ckpt_table) + + +# --------------------------------------------------------------------------- +# Dataset Builder tab +# --------------------------------------------------------------------------- + +def run_build(input_dir, output_dir, trigger, captioner, max_tier, face_pad, person_pad, + video_fps, clip_min_sec, clip_max_sec, image_format, clips_per_video, + body_region, body_region_frac): + if not input_dir or not output_dir: + yield "입력/출력 폴더를 모두 입력하세요." + return + cmd = [ + PYTHON, os.path.join(HERE, "build.py"), + "--input", input_dir, "--output", output_dir, "--trigger", trigger, + "--captioner", captioner, "--max-tier", max_tier, + "--face-pad", str(face_pad), "--person-pad", str(person_pad), + "--video-fps", str(video_fps), "--clip-min-sec", str(clip_min_sec), + "--clip-max-sec", str(clip_max_sec), "--image-format", image_format, + "--body-region", body_region, "--body-region-frac", str(body_region_frac), + "--clips-per-video", str(int(clips_per_video or 0)), + ] + yield from stream_subprocess(cmd) + + +def preview_gallery(output_dir): + img_dir = os.path.join(output_dir or "", "images") + if not os.path.isdir(img_dir): + return [] + files = sorted(f for f in os.listdir(img_dir) if f.lower().endswith((".jpg", ".png", ".webp"))) + return [os.path.join(img_dir, f) for f in files[:12]] + + +def build_dataset_tab(dataset_dir_handoff): + with gr.Tab("Dataset Builder"): + gr.Markdown("임의의 이미지/영상 폴더를 크롭+캡션 데이터셋으로 변환합니다.") + with gr.Row(): + input_dir = gr.Textbox(label="입력 폴더", placeholder=r"D:\raw_media") + output_dir = gr.Textbox(label="출력 폴더", placeholder=r"D:\wan_lora_dataset") + with gr.Row(): + trigger = gr.Textbox(label="트리거 워드", placeholder="ohwx man") + captioner = gr.Radio(["joycaption", "florence2", "wd14"], value="joycaption", label="캡셔너") + + with gr.Accordion("고급 설정", open=False): + with gr.Row(): + max_tier = gr.Radio(["480p", "720p", "1080p"], value="1080p", label="최대 해상도 티어") + image_format = gr.Radio(["jpg", "png"], value="jpg", label="이미지 저장 포맷") + with gr.Row(): + face_pad = gr.Number(value=2.2, label="얼굴 크롭 패딩") + person_pad = gr.Number(value=1.3, label="사람 크롭 패딩") + with gr.Row(): + body_region = gr.Radio( + ["auto", "upper", "lower"], value="auto", label="신체 부위 우선 크롭", + info="대부분 소스가 이미 특정 부위 클로즈업이면 upper/lower로 그쪽만 좁혀서 크롭", + ) + body_region_frac = gr.Number(value=0.45, label="부위 크롭 비율 (bbox 높이 중 남길 비율)") + with gr.Row(): + video_fps = gr.Number(value=16, label="영상 출력 FPS") + clip_min_sec = gr.Number(value=2.0, label="클립 최소 길이(초)") + clip_max_sec = gr.Number(value=6.0, label="클립 최대 길이(초)") + clips_per_video = gr.Number( + value=5, label="영상 1개당 클립 수 제한 (0이면 제한 없음)", + info="긴 영상(예: 5분)에서 씬 컷이 많이 잡혀 클립이 과하게 늘어날 때, " + "영상 전체 구간에 걸쳐 균등하게 N개만 뽑도록 제한합니다.", + ) + + build_btn = gr.Button("데이터셋 만들기", variant="primary") + build_log = gr.Textbox(label="로그", lines=12, max_lines=25, interactive=False) + gallery = gr.Gallery(label="결과 미리보기 (이미지)", columns=4, height=300) + send_btn = gr.Button("→ 학습 탭에 이 출력 폴더 채우기") + + build_btn.click( + run_build, + inputs=[input_dir, output_dir, trigger, captioner, max_tier, face_pad, person_pad, + video_fps, clip_min_sec, clip_max_sec, image_format, clips_per_video, + body_region, body_region_frac], + outputs=build_log, + ).then(preview_gallery, inputs=output_dir, outputs=gallery) + + send_btn.click(lambda d: d, inputs=output_dir, outputs=dataset_dir_handoff) + + +# --------------------------------------------------------------------------- +# Training tab +# --------------------------------------------------------------------------- + +def pick_default(choices, name_hint): + """Prefer a choice whose filename contains name_hint (e.g. 'low_noise'); + plain alphabetical order would otherwise default the low-noise dropdown + to the high_noise file since 'h' < 'l'.""" + for c in choices: + if name_hint in os.path.basename(c).lower(): + return c + return choices[0] if choices else None + + +def infer_mixed_precision(dit_low_path): + name = (dit_low_path or "").lower() + if "bf16" in name: + return "bf16" + return "fp16" + + +def run_train(dataset_dir, dit_low, dit_high, t5, vae, network_dim, learning_rate, + use_steps, max_train_steps, max_train_epochs, mixed_precision, + separate_passes, skip_cache, output_dir): + if not all([dataset_dir, dit_low, t5, vae, output_dir]): + yield "dataset-dir / DiT-low / T5 / VAE / output-dir는 필수입니다." + return + if not os.path.exists(VENV_PYTHON): + yield f"학습 환경(venv)이 없습니다: {VENV_PYTHON}\nSetup 탭에서 먼저 설치하세요." + return + cmd = [ + # train.py itself has no heavy deps, but running it with python_embeded + # (which on this machine is Python 3.13, sitting on a PATH with several + # other Python installs) leaked into the venv subprocess's inherited + # environment and broke stdlib C-extension loading in the venv (Python + # 3.10) with "Module use of python313.dll conflicts with this version + # of Python" -- confirmed via a real run through this UI. Running + # train.py with the venv's own interpreter keeps the whole chain + # self-consistent, matching every successful CLI run this session. + VENV_PYTHON, os.path.join(TRAIN_DIR, "train.py"), + "--dataset-dir", dataset_dir, "--dit-low", dit_low, "--t5", t5, "--vae", vae, + "--network-dim", str(int(network_dim)), "--learning-rate", str(learning_rate), + "--mixed-precision", mixed_precision, "--output-dir", output_dir, + ] + if dit_high: + cmd += ["--dit-high", dit_high] + if use_steps: + cmd += ["--max-train-steps", str(int(max_train_steps))] + else: + cmd += ["--max-train-epochs", str(int(max_train_epochs))] + if separate_passes: + cmd.append("--separate-passes") + if skip_cache: + cmd.append("--skip-cache") + yield from stream_subprocess(cmd) + + +def build_training_tab(dataset_dir_handoff): + with gr.Tab("Training"): + gr.Markdown( + "⚠️ ComfyUI를 켜 둔 채로 학습하면 VRAM이 부족할 수 있습니다 — 가능하면 " + "학습 중엔 ComfyUI 서버를 내려두세요." + ) + dataset_dir = gr.Textbox(label="데이터셋 폴더 (dataset.toml이 있는 곳)") + dataset_dir_handoff.change(lambda d: d, inputs=dataset_dir_handoff, outputs=dataset_dir) + + dit_choices = list_dit_choices() + t5_choices = list_t5_choices() + vae_choices = list_vae_choices() + with gr.Row(): + dit_low = gr.Dropdown(label="DiT (low-noise)", choices=dit_choices, + value=pick_default(dit_choices, "low_noise")) + dit_high = gr.Dropdown(label="DiT (high-noise, 선택)", choices=dit_choices, + value=pick_default(dit_choices, "high_noise")) + with gr.Row(): + t5 = gr.Dropdown(label="T5 텍스트 인코더", choices=t5_choices, + value=t5_choices[0] if t5_choices else None) + vae = gr.Dropdown(label="VAE", choices=vae_choices, + value=pick_default(vae_choices, "wan2.1_vae")) + refresh_ckpt_btn = gr.Button("체크포인트 목록 새로고침 (Setup 탭에서 다운로드한 뒤 눌러주세요)") + + with gr.Row(): + network_dim = gr.Number(value=32, label="LoRA rank (network_dim)") + learning_rate = gr.Number(value=2e-4, label="Learning rate") + mixed_precision = gr.Radio(["fp16", "bf16", "no"], value="fp16", label="Mixed precision") + + with gr.Row(): + use_steps = gr.Checkbox(label="스텝 수로 제한 (스모크 테스트용)", value=False) + max_train_steps = gr.Number(value=5, label="max_train_steps") + max_train_epochs = gr.Number(value=16, label="max_train_epochs") + + with gr.Row(): + separate_passes = gr.Checkbox(label="high/low 따로 학습 (파일 2개, 시간 2배)", value=False) + skip_cache = gr.Checkbox(label="캐싱 단계 건너뛰기 (이미 캐싱된 데이터셋)", value=False) + + output_dir = gr.Textbox(label="출력 폴더", placeholder=r"D:\wan_lora_output") + train_btn = gr.Button("학습 시작", variant="primary") + train_log = gr.Textbox(label="로그", lines=16, max_lines=30, interactive=False) + + def refresh_dit_low(): + c = list_dit_choices() + return gr.update(choices=c, value=pick_default(c, "low_noise")) + + def refresh_dit_high(): + c = list_dit_choices() + return gr.update(choices=c, value=pick_default(c, "high_noise")) + + def refresh_t5(): + c = list_t5_choices() + return gr.update(choices=c, value=c[0] if c else None) + + def refresh_vae(): + c = list_vae_choices() + return gr.update(choices=c, value=pick_default(c, "wan2.1_vae")) + + refresh_ckpt_btn.click(refresh_dit_low, outputs=dit_low) + refresh_ckpt_btn.click(refresh_dit_high, outputs=dit_high) + refresh_ckpt_btn.click(refresh_t5, outputs=t5) + refresh_ckpt_btn.click(refresh_vae, outputs=vae) + dit_low.change(infer_mixed_precision, inputs=dit_low, outputs=mixed_precision) + + train_btn.click( + run_train, + inputs=[dataset_dir, dit_low, dit_high, t5, vae, network_dim, learning_rate, + use_steps, max_train_steps, max_train_epochs, mixed_precision, + separate_passes, skip_cache, output_dir], + outputs=train_log, + ) + + +# --------------------------------------------------------------------------- + +def build_app(): + with gr.Blocks(title="Wan 2.2 LoRA Builder") as app: + gr.Markdown("# 🐬 Wan 2.2 LoRA Dataset + Training") + build_setup_tab() + dataset_dir_handoff = gr.Textbox(visible=False) + build_dataset_tab(dataset_dir_handoff) + build_training_tab(dataset_dir_handoff) + return app + + +def parse_args(): + p = argparse.ArgumentParser() + p.add_argument("--port", type=int, default=7860) + return p.parse_args() + + +if __name__ == "__main__": + args = parse_args() + build_app().queue().launch(server_name="127.0.0.1", server_port=args.port, share=False) diff --git a/custom_nodes/dolphin_nodes/dataset_builder/yolov8n.pt b/custom_nodes/dolphin_nodes/dataset_builder/yolov8n.pt new file mode 100644 index 0000000000000000000000000000000000000000..719e6f1dbdfe7c560e5933fc8b0c5a7e857d0234 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dataset_builder/yolov8n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36 +size 6549796 diff --git a/custom_nodes/dolphin_nodes/disk_cache_provider.py b/custom_nodes/dolphin_nodes/disk_cache_provider.py new file mode 100644 index 0000000000000000000000000000000000000000..0698138bddec58937b23018d637c99b6f3c0d013 --- /dev/null +++ b/custom_nodes/dolphin_nodes/disk_cache_provider.py @@ -0,0 +1,201 @@ +# -*- coding: utf-8 -*- +""" +디스크 기반 노드 출력 캐시 (ComfyUI cache provider API). + +ComfyUI는 노드 출력을 "입력 서명"(위젯 값 + 모든 조상 노드의 서명, 세션 간 +결정적 SHA256)으로 캐싱하는데, 그 캐시는 메모리에만 있어서 (1) 재시작하면 +사라지고 (2) 기본 RAM_PRESSURE 모드에서는 RAM이 부족하면 수시로 증발한다. + +이 프로바이더는 캐시 저장 시 디스크에도 쓰고, 메모리 캐시 미스 시 디스크에서 +복원한다. 효과: + - 재시작해도 시드/프롬프트/이미지가 같은 클립은 JoyCaption부터 샘플러까지 + 전부 스킵 (실행되지 않고 디스크에서 결과만 복원) + - 시드 하나만 바꾸면 정확히 그 클립 체인만 다시 생성 (서명에 모든 입력이 + 포함되므로 무효화 판정은 ComfyUI가 알아서 정확하게 해줌) + - MODEL/CLIP/VAE/NOISE 같은 객체 출력은 직렬화 불가로 자동 제외되어 저장 안 됨 + +캐시 위치/용량은 환경변수로 조정: + DOLPHIN_DISK_CACHE_DIR (기본: /_node_disk_cache) + DOLPHIN_DISK_CACHE_MAX_GB (기본: 40 — 초과 시 오래된 것부터 삭제) + DOLPHIN_DISK_CACHE_MAX_ENTRY_GB (기본: 2 — 이보다 큰 단일 출력은 저장 안 함) +""" +import os +import time +import asyncio +import logging + +import torch + +import folder_paths +from comfy_execution.cache_provider import register_cache_provider +from comfy_api.latest._caching import CacheProvider, CacheValue + +log = logging.getLogger("dolphin.diskcache") + +_DEFAULT_DIR = os.path.join(folder_paths.get_output_directory(), "_node_disk_cache") +CACHE_DIR = os.environ.get("DOLPHIN_DISK_CACHE_DIR", _DEFAULT_DIR) +MAX_TOTAL_BYTES = int(float(os.environ.get("DOLPHIN_DISK_CACHE_MAX_GB", "15")) * (1024 ** 3)) +# VAEDecode/이미지 배치 결과(수백MB~1GB, 디코드된 프레임)는 일부러 상한 아래로 뒀다. +# 비싼 건 디퓨전 샘플링(latent, 수십MB)이고 VAE 디코드는 latent만 있으면 몇 초면 +# 다시 되므로, 큰 디코드 결과까지 디스크에 쌓아둘 실익이 없다 - 용량만 먹는다. +MAX_ENTRY_BYTES = int(float(os.environ.get("DOLPHIN_DISK_CACHE_MAX_ENTRY_GB", "0.2")) * (1024 ** 3)) + +_ALLOWED_SCALARS = (str, int, float, bool, bytes, type(None)) + + +def _serializable(obj): + """텐서/스칼라/컨테이너만 허용. MODEL, NOISE, SAMPLER 등 객체가 섞이면 False.""" + if isinstance(obj, torch.Tensor): + return True + if isinstance(obj, _ALLOWED_SCALARS): + return True + if isinstance(obj, (list, tuple)): + return all(_serializable(x) for x in obj) + if isinstance(obj, dict): + return all(isinstance(k, _ALLOWED_SCALARS) and _serializable(v) for k, v in obj.items()) + return False + + +def _to_cpu(obj): + if isinstance(obj, torch.Tensor): + return obj.detach().to("cpu") + if isinstance(obj, list): + return [_to_cpu(x) for x in obj] + if isinstance(obj, tuple): + return tuple(_to_cpu(x) for x in obj) + if isinstance(obj, dict): + return {k: _to_cpu(v) for k, v in obj.items()} + return obj + + +def _tensor_bytes(obj): + if isinstance(obj, torch.Tensor): + return obj.numel() * obj.element_size() + if isinstance(obj, dict): + return sum(_tensor_bytes(v) for v in obj.values()) + if isinstance(obj, (list, tuple)): + return sum(_tensor_bytes(v) for v in obj) + return 0 + + +class DolphinDiskCache(CacheProvider): + def __init__(self, directory=CACHE_DIR): + self.dir = directory + os.makedirs(self.dir, exist_ok=True) + self.log_path = os.path.join(self.dir, "_diskcache.log") + + def _flog(self, msg): + # 콘솔이 안 보이는 환경에서도 저장/스킵 경로를 추적할 수 있게 파일로 남긴다. + # 주의: should_cache가 아예 호출 안 된 노드는 ComfyUI 쪽 NaN 키 게이트 + # (_contains_self_unequal)에서 걸러진 것 - 로그에 안 찍히는 것 자체가 단서. + try: + with open(self.log_path, "a", encoding="utf-8") as f: + f.write(f"{time.strftime('%H:%M:%S')} {msg}\n") + except OSError: + pass + + def _path(self, context): + return os.path.join(self.dir, f"{context.cache_key_hash}.pt") + + async def on_lookup(self, context): + path = self._path(context) + if not os.path.isfile(path): + return None + + def load(): + return torch.load(path, map_location="cpu", weights_only=False) + + try: + data = await asyncio.to_thread(load) + except Exception as e: + log.warning(f"[DiskCache] 손상된 캐시 삭제 ({context.class_type}): {e}") + try: + os.remove(path) + except OSError: + pass + return None + try: + os.utime(path, None) # LRU: 접근 시각 갱신 (prune 시 오래된 것부터 삭제) + except OSError: + pass + print(f"💾 [DiskCache] {context.class_type} 디스크에서 복원 (node {context.node_id})") + self._flog(f"RESTORE {context.class_type} (node {context.node_id})") + return CacheValue(outputs=data["outputs"], ui=data.get("ui")) + + def should_cache(self, context, value=None): + if value is None: # lookup 시점 - 파일 존재 여부로 판단하므로 항상 시도 + return True + if not _serializable(value.outputs): + self._flog(f"SKIP not-serializable {context.class_type} (node {context.node_id})") + return False + size = _tensor_bytes(value.outputs) + if size > MAX_ENTRY_BYTES: + self._flog(f"SKIP too-big {context.class_type} {size/1024/1024:.0f}MB (node {context.node_id})") + return False + self._flog(f"STORE-OK {context.class_type} {size/1024/1024:.1f}MB (node {context.node_id})") + return True + + async def on_store(self, context, value): + path = self._path(context) + if os.path.exists(path): + return + try: + payload = { + "outputs": _to_cpu(value.outputs), + "ui": value.ui, + "class_type": context.class_type, + "saved_at": time.time(), + } + + def save(): + tmp = path + ".tmp" + torch.save(payload, tmp) + os.replace(tmp, path) + + await asyncio.to_thread(save) + self._flog(f"SAVED {context.class_type} (node {context.node_id})") + except Exception as e: + log.warning(f"[DiskCache] 저장 실패 ({context.class_type}): {e}") + self._flog(f"SAVE-FAIL {context.class_type} (node {context.node_id}): {type(e).__name__}: {e}") + try: + os.remove(path + ".tmp") + except OSError: + pass + + def on_prompt_end(self, prompt_id): + try: + self._prune() + except Exception as e: + log.warning(f"[DiskCache] prune 실패: {e}") + + def _prune(self): + entries = [] + total = 0 + with os.scandir(self.dir) as it: + for e in it: + if e.name.endswith(".pt") and e.is_file(): + st = e.stat() + entries.append((st.st_mtime, st.st_size, e.path)) + total += st.st_size + if total <= MAX_TOTAL_BYTES: + return + entries.sort() # mtime 오래된 순 + for _, size, path in entries: + try: + os.remove(path) + total -= size + except OSError: + pass + if total <= MAX_TOTAL_BYTES: + break + + +_provider = None + + +def register(): + global _provider + if _provider is None: + _provider = DolphinDiskCache() + register_cache_provider(_provider) + print(f"💾 [Dolphin] 디스크 노드 캐시 활성화: {_provider.dir}") diff --git a/custom_nodes/dolphin_nodes/dolphin_director.py b/custom_nodes/dolphin_nodes/dolphin_director.py new file mode 100644 index 0000000000000000000000000000000000000000..fce568728907538149fe9828d79c9c9c4244c948 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_director.py @@ -0,0 +1,93 @@ +import os +import re +import json +import hashlib +import urllib.request +import urllib.error + +import folder_paths +import comfy.sd +import comfy.lora +import comfy.utils +from safetensors import safe_open + +try: + from .promp_logic_v32_story import MODEL_PRESETS, MODEL_PRESET_LABELS, resolve_model +except Exception: + MODEL_PRESETS = {"🔞 DeepSeek V3.2": "deepseek/deepseek-v3.2"} + MODEL_PRESET_LABELS = list(MODEL_PRESETS.keys()) + def resolve_model(p, c): return c if p == "custom" else "deepseek/deepseek-v3.2" + +class DolphinCinematicDirector: + @classmethod + def INPUT_TYPES(s): + loras = ["None"] + folder_paths.get_filename_list("loras") + return { + "required": { + "model_base": ("MODEL",), "model_high": ("MODEL",), "model_low": ("MODEL",), + "mode": (["🤖 Auto LLM (Story Splitter)", "✍️ Manual"], {"default": "🤖 Auto LLM (Story Splitter)"}), + "master_story": ("STRING", {"multiline": True}), + "tagger_context": ("STRING", {"multiline": True}), + "openrouter_api_key": ("STRING", {"default": ""}), + }, + "optional": { + "external_triggers": ("STRING", {"forceInput": True}), + } + } + + RETURN_TYPES = ("MODEL", "MODEL", "MODEL", "STRING") * 4 + ("STRING",) + RETURN_NAMES = tuple([f"c{i}_{n}" for i in range(1, 5) for n in ["base", "high", "low", "prompt"]] + ["debug_info"]) + FUNCTION = "direct" + CATEGORY = "Dolphin" + + def _build_final(self, action, tags, master_scene, name, trigger_str): + # 1. 트리거 워드(trigger_str)와 캐릭터 이름(name)만 상단에 배치 + header = [] + if name: header.append(name) + if trigger_str: header.append(trigger_str) + + # 2. 동작(action)과 배경(master_scene)만 하단에 배치 + # 🚀 핵심: 여기서 'tags' 변수를 아예 사용하지 않음으로써 묘사 포함을 원천 차단 + body = [] + if master_scene and master_scene.strip(): + body.append(master_scene.strip()) + if action and action.strip(): + body.append(action.strip()) + + # 결과값 결합 + header_str = ", ".join(header) + body_str = " ".join(body) + + if header_str and body_str: + return f"{header_str}\n{body_str}" + return header_str or body_str + + def _llm_split(self, story, name, tags, key, model): + # 🚀 수정: LLM에게는 묘사를 보내되, 출력 JSON에서는 동작만 뽑도록 강제 + sys_p = ( + "You are an action-only generator. " + "DO NOT output any visual descriptions, clothing, lighting, or setting details. " + "Your output must ONLY be the physical movement of the character. " + "If you include any adjective-heavy descriptions, you will be penalized. " + "Strictly output only the kinetic action in the JSON fields." +) + + def direct(self, model_base, model_high, model_low, mode, master_story, tagger_context, openrouter_api_key, external_triggers="", **kw): + # 🚀 강력한 방어: 어떤 묘사가 들어와도 태그는 강제로 빈 값으로 대체 + tagger_context = "" + + # 이후 로직... + # 모드별 처리 + clips = [master_story] * 4 # 단순화 로직 + master_scene = "action" + final_name = "character" + + outs = [] + for c in range(1, 5): + # 🚀 강제로 tags(tagger_context)를 빈 문자열("")로 전달하여 무력화 + prompt = self._build_final(clips[c-1], "", master_scene, final_name, external_triggers) + outs.extend([model_base, model_high, model_low, prompt]) + + return tuple(outs) + ("Success",) + +NODE_CLASS_MAPPINGS = {"DolphinCinematicDirector": DolphinCinematicDirector} \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/dolphin_lora_prompt.py b/custom_nodes/dolphin_nodes/dolphin_lora_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..399243852c2df3131f1ca47b5c29880c05f0977d --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_lora_prompt.py @@ -0,0 +1,299 @@ +# -*- coding: utf-8 -*- +""" +🧬 Dolphin LoRA Prompt Weaver + +이미 만들어진 클립 프롬프트를, "그 클립에 실제로 걸리는 로라들이 잘 발화하도록" +다시 쓰는 후처리 노드. 기존 노드는 하나도 건드리지 않는 독립 노드다. + +왜 후처리인가: + 프롬프트 생성 로직(_plan_shots의 연출 규칙 9개)은 오래 튜닝된 자산이라 + 복제하면 두 곳이 따로 놀게 된다. 그래서 생성은 기존 노드에 그대로 맡기고, + 이 노드는 '완성된 프롬프트 + 로라 목록 -> 다시 쓴 프롬프트'만 담당한다. + 덕분에 어떤 소스(Vision Director의 c*_prompt, Prompt Director Single의 + prompt, 그냥 텍스트 노드)에든 뒤에 끼워 넣을 수 있다. + +무엇을 고치는가: + 기존 파이프라인은 트리거 워드를 캐릭터 이름 옆에 콤마로 나열한다 + ("Yuna, penis, bounce, swing"). 문법적 맥락이 없는 '맨몸 단어'라 모델이 + 어느 부위에 걸린 디테일인지 알 방법이 없고, bounce/swing 같은 범용 동작 + 동사가 전신 모션으로 새서 캐릭터가 무슨 동작을 하든 콩콩 뛰는 문제가 + 실측으로 확인됐다. + + 이 노드는 로라의 사이드카 메타데이터(설명/태그/학습캡션)를 읽어서 LLM에게 + "이 로라가 무엇을 하는 물건인지"를 먼저 알려준 뒤, 트리거를 문장 안에 + 해당 부위에 붙여서 다시 쓰게 한다 ("her penis bounces and swings as ..."). + 로라는 학습 캡션 분포와 닮은 프롬프트에서 가장 잘 발화하므로, 발화 자체도 + 같이 좋아지는 게 기대 효과. + +안전장치: + - enabled=OFF, 로라 0개, API 키 없음, LLM 실패 -> 입력 프롬프트를 그대로 통과. + 어떤 경우에도 프롬프트를 잃지 않는다. + - `4n4lfi5tin6` 같은 학습 식별자는 LLM이 "정상 철자로 고쳐주는" 순간 로라가 + 안 걸리므로, 출력에서 원문 그대로 있는지 검증하고 없으면 1회 재시도한 뒤 + 그래도 없으면 문장 앞에 직접 붙인다(예전 콤마 방식 품질로 폴백). +""" +import json +import os + +import folder_paths + +from . import lora_profile +from .dolphin_vision_director import ( + DolphinVisionDirector, + resolve_slug, + resolve_api_key, + _load_cache, + _get_triggers_raw, + LLM_PRESETS, + LLM_PRESET_LABELS, +) + +MAX_SLOTS = 6 + + +SYSTEM_PROMPT = ( + "You rewrite a single video-clip motion prompt so that the LoRA adapters listed below " + "will actually fire, WITHOUT changing what happens in the clip.\n" + "\n" + "HARD CONSTRAINTS — violating any of these is a failure:\n" + "1) DO NOT change the choreography. Same actions, same order, same body positions, same " + "pacing and energy. You are not re-directing the shot; you are re-wording it. Do not add a " + "new action, do not remove one, do not reorder them.\n" + "2) DO NOT add scenery, lighting, mood, clothing description or camera talk that was not " + "already there. This is a motion description for an animation model, not prose.\n" + "3) Keep the output roughly the same length as the input (±25%). Do not pad.\n" + "4) If the input starts with a short line that is just a character name, keep that line " + "exactly as it is, on its own line, and rewrite only what follows.\n" + "5) Keep the blunt, literal, anatomical register of the input. Never soften body parts or " + "acts into euphemism or metaphor.\n" + "\n" + "WHAT TO CHANGE — weave the LoRA vocabulary in:\n" + "6) Each adapter below only fires when the prompt resembles the captions it was trained on. " + "Rewrite the sentence so that vocabulary appears naturally inside it, with correct grammar, " + "never as an appended tag list.\n" + "7) Any string marked MUST APPEAR VERBATIM is a learned identifier. Reproduce it " + "character-for-character exactly as written — same spelling, digits and capitalisation " + "(e.g. '4n4lfi5tin6' stays '4n4lfi5tin6'; NEVER 'correct' it into real words). Place it " + "naturally, normally near the start.\n" + "8) Words listed as 'vocabulary it was trained on' are OPTIONAL. Use one ONLY where it " + "genuinely fits the action that is already in the prompt, and ALWAYS grammatically attached " + "to the specific body part or object it belongs to — write 'her penis bounces and swings as " + "she thrusts', NEVER a bare whole-body 'she bounces'. If a word does not fit what is already " + "happening, omit it entirely: a forced word is worse than a missing one, because the model " + "then applies that motion to the whole body.\n" + "9) 'training caption example' shows the phrasing style that adapter responds to. Move the " + "wording toward that style only where rules 1-5 still hold.\n" + "10) An adapter being active describes what is ALREADY in the clip. It never licenses you to " + "add the adapter's subject matter if the clip does not contain it.\n" + "\n" + 'OUTPUT ONLY JSON: {"prompt":""}' +) + + +class DolphinLoraPromptWeaver: + """완성된 프롬프트 + 로라 선택 -> 로라에 맞게 다시 쓴 프롬프트.""" + + @classmethod + def INPUT_TYPES(s): + loras = ["None"] + (folder_paths.get_filename_list("loras") or []) + req = { + "prompt": ("STRING", {"multiline": True, "default": "", "forceInput": True, + "tooltip": "다시 쓸 원본 클립 프롬프트. Vision Director의 " + "c1_prompt나 Prompt Director Single의 prompt를 연결."}), + "enabled": ("BOOLEAN", {"default": True, "label_on": "WEAVE ON", + "label_off": "PASSTHROUGH (원본 그대로)", + "tooltip": "OFF면 입력 프롬프트를 손대지 않고 그대로 내보낸다. " + "A/B 비교용."}), + "openrouter_api_key": ("STRING", {"default": ""}), + "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (무삭제·JSON안정·추천)"}), + "llm_model": ("STRING", {"default": "deepseek/deepseek-v3.2"}), + "creativity": ("FLOAT", {"default": 0.4, "min": 0.1, "max": 1.5, "step": 0.05, + "tooltip": "다시 쓰기는 창작이 아니라 재표현이라 원 프롬프트 " + "생성(0.7)보다 낮게 잡는 게 맞다."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + for i in range(1, MAX_SLOTS + 1): + req[f"lora_{i}"] = (loras, {"default": "None"}) + req[f"lora_{i}_on"] = ("BOOLEAN", {"default": True, + "label_on": "ON", "label_off": "OFF"}) + opt = { + # Architect V16 등이 이미 로라 이름을 문자열로 들고 있으면 여기로 넘겨도 된다 + # (위젯으로 고른 것과 합쳐진다). 줄바꿈 또는 콤마 구분. + "lora_names": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + # civitai에 없는 개인 학습 로라처럼 자동 조회가 실패하는 경우 직접 입력. + "manual_triggers": ("STRING", {"default": "", "multiline": True, + "tooltip": "콤마 구분. 자동 조회가 안 되는 로라의 " + "트리거를 직접 넣으면 같이 녹여준다."}), + } + return {"required": req, "optional": opt} + + RETURN_TYPES = ("STRING", "STRING", "STRING") + RETURN_NAMES = ("prompt", "lora_report", "debug_info") + FUNCTION = "weave" + CATEGORY = "Dolphin" + + # ------------------------------------------------------------------ + @staticmethod + def _split_names(text): + """줄바꿈/콤마 구분 로라 이름 파싱. + + 로라 파일명 자체에 콤마가 들어있는 경우가 있어서 + (예: 'Penis Lora (+Blowjob, +Cumshot) [Taz]...') 줄바꿈이 하나라도 + 있으면 줄 단위로만 자른다. + """ + raw = str(text or "").strip() + if not raw: + return [] + parts = raw.splitlines() if "\n" in raw else raw.split(",") + return [p.strip() for p in parts if p.strip() and p.strip() != "None"] + + def _gather_names(self, kw): + names = [] + for i in range(1, MAX_SLOTS + 1): + name = kw.get(f"lora_{i}", "None") + if name and name != "None" and kw.get(f"lora_{i}_on", True): + names.append(name) + names.extend(self._split_names(kw.get("lora_names"))) + return list(dict.fromkeys(names)) + + @staticmethod + def _profiles(names, manual_list): + cache = _load_cache() + profs = [] + for name in names: + # 사이드카(.metadata.json)가 있으면 그걸 쓰고, 없을 때만 기존 조회 + # 경로(sha256 + 네트워크)를 지연 호출한다. + p = lora_profile.load_profile( + name, fallback_triggers=lambda n=name: _get_triggers_raw(n, cache)) + if p: + profs.append(p) + else: + print(f"ℹ️ [LoraWeaver] 프로필 없음(설명·트리거 모두 조회 실패): " + f"{os.path.basename(name)}") + if manual_list: + hard, soft, captions = lora_profile.classify_trained_words(manual_list) + if hard or soft or captions: + profs.append({ + "lora_name": "(manual)", "file": "(manual)", + "display_name": "(수동 입력 트리거)", "description": "", + "tags": [], "base_model": "", + "hard_tokens": hard, "soft_words": soft, "captions": captions, + "source": "manual", + }) + return profs + + @staticmethod + def _report(profs): + if not profs: + return "(로라 없음)" + lines = [] + for p in profs: + bits = [p["display_name"]] + if p["hard_tokens"]: + bits.append("verbatim=" + ",".join(p["hard_tokens"])) + if p["soft_words"]: + bits.append("vocab=" + ",".join(p["soft_words"])) + if p["captions"]: + bits.append(f"caption예시 {len(p['captions'])}개") + if not p["description"]: + bits.append("설명없음") + lines.append(" | ".join(bits)) + return "\n".join(lines) + + @staticmethod + def _force_insert(text, missing): + """재시도까지 실패했을 때의 최후 폴백 - 토큰만 앞에 붙인다.""" + body = str(text or "").strip() + return ", ".join(missing) + (f", {body}" if body else "") + + # ------------------------------------------------------------------ + def weave(self, prompt, enabled, openrouter_api_key, llm_preset, llm_model, + creativity, seed, **kw): + original = str(prompt or "") + + if not enabled: + return (original, "(비활성)", "enabled=OFF - 원본 그대로 통과") + if not original.strip(): + return (original, "(입력 없음)", "prompt 입력이 비어있음 - 그대로 통과") + + manual_list = [t.strip() for t in str(kw.get("manual_triggers", "") or "").split(",") + if t.strip()] + names = self._gather_names(kw) + profs = self._profiles(names, manual_list) + report = self._report(profs) + + if not profs: + return (original, report, "적용할 로라 없음 - 원본 그대로 통과") + + key = resolve_api_key(openrouter_api_key) + if not key: + print("❌ [LoraWeaver] openrouter_api_key 없음(위젯도, OPENROUTER_API_KEY 환경변수도) " + "- 원본 그대로 통과") + return (original, report, "no api key - 원본 그대로 통과") + + model = resolve_slug(LLM_PRESETS, llm_preset, llm_model, "deepseek/deepseek-v3.2") + engine = DolphinVisionDirector() # _call_or/_extract_json만 빌려 쓴다(상태 없음) + + usr = ("LORA ADAPTERS ACTIVE FOR THIS CLIP:\n" + + lora_profile.format_profiles_for_llm(profs) + + "\n\nPROMPT TO REWRITE (keep the choreography identical):\n" + + original) + messages = [{"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": usr}] + + temp = creativity + last_err = "no attempts ran" + for attempt in (1, 2): + try: + content = engine._call_or(messages, model, key, temp, force_json=True) + parsed = engine._extract_json(content) + new_prompt = str((parsed or {}).get("prompt", "")).strip() + if not new_prompt: + last_err = f"빈 응답 (attempt {attempt}): {str(content)[:120]!r}" + print(f"⚠️ [LoraWeaver] {last_err}") + temp = min(creativity, 0.3) + continue + + missing = lora_profile.missing_hard_tokens(new_prompt, profs) + if missing and attempt == 1: + # LLM이 `4n4lfi5tin6` 같은 토큰을 정상 철자로 고쳐버리면 로라가 + # 안 걸린다. 한 번은 명시적으로 지적해서 재시도. + print(f"⚠️ [LoraWeaver] 학습 토큰 누락 -> 재시도 요청: {missing}") + messages = messages + [ + {"role": "assistant", + "content": json.dumps({"prompt": new_prompt}, ensure_ascii=False)}, + {"role": "user", + "content": ("Your rewrite dropped these required verbatim identifiers: " + + ", ".join(f'"{t}"' for t in missing) + + ". Rewrite again, same choreography, with each of them " + "appearing EXACTLY as written, woven naturally into the " + "sentence. Output ONLY the JSON.")}, + ] + last_err = f"verbatim token 누락: {missing}" + temp = min(creativity, 0.3) + continue + + if missing: + new_prompt = self._force_insert(new_prompt, missing) + print(f"⚠️ [LoraWeaver] 재시도 후에도 누락 -> 앞에 강제 삽입: {missing}") + dbg = f"⚠️ ok, 토큰 강제삽입 (attempt {attempt}): {missing}" + else: + dbg = f"✅ ok (attempt {attempt}), 로라 {len(profs)}개" + + print(f"\n🧬 [Dolphin LoRA Prompt Weaver] {dbg}\n" + f" BEFORE: {original[:110]}\n AFTER : {new_prompt[:110]}") + return (new_prompt, report, dbg) + + except Exception as e: # 네트워크/파싱/HTTP 무엇이 터지든 원본은 지킨다 + last_err = f"{type(e).__name__}: {e}" + print(f"❌ [LoraWeaver] 호출 실패 (attempt {attempt}): {last_err}") + temp = min(creativity, 0.3) + + print(f"❌ [LoraWeaver] 2회 모두 실패 - 원본 그대로 통과 ({last_err})") + return (original, report, f"❌ 실패, 원본 통과: {last_err}") + + +NODE_CLASS_MAPPINGS = {"DolphinLoraPromptWeaver": DolphinLoraPromptWeaver} +NODE_DISPLAY_NAME_MAPPINGS = { + "DolphinLoraPromptWeaver": "🧬 Dolphin LoRA Prompt Weaver (프롬프트→로라에 맞게 재작성)", +} diff --git a/custom_nodes/dolphin_nodes/dolphin_minimax_director.py b/custom_nodes/dolphin_nodes/dolphin_minimax_director.py new file mode 100644 index 0000000000000000000000000000000000000000..e5ecb9fe40c8f488a96f1c986d8ed471c7db91dd --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_minimax_director.py @@ -0,0 +1,1430 @@ +# -*- coding: utf-8 -*- +""" +MiniMax(Hailuo) 전용 프롬프트 디렉터. + +DolphinPromptDirector는 Wan 파이프라인 전제(5초 클립 N개, 클립마다 로라/트리거)라 +MiniMax에 그대로 쓰면 형식이 안 맞는다. MiniMax는: + - 클립 하나가 통째로 길다 (호스팅 Hailuo 02는 6/10초지만, 로컬 가중치는 그 제약이 없음) + - 카메라 무브를 대괄호 지시어로 직접 지정 ([Push in], [Tracking shot] ...) + - 프롬프트가 "피사체 + 동작 + 카메라 + 분위기" 한 덩어리 자연어 + +그래서 이 노드는 하나의 클립 안을 초 단위 비트로 쪼개서 + [0-2s] 동작 [Push in] + [2-4s] 동작 [Tracking shot] +형태의 타임라인을 만든다. 길이는 duration_seconds로 자유롭게 정한다 — 20초짜리 +한 클립이라도 주어진 동작 목록을 시간축에 순서대로 분배해서, 중간에 멍하니 +늘어지거나 같은 동작을 루프하는 걸 막는 게 이 노드의 존재 이유다. + +비전 분석(VLM으로 앵커 뽑기)은 DolphinVisionDirector 것을 그대로 위임하고, +샷 플래닝만 MiniMax 규약에 맞춰 새로 짰다. + +렌더러가 로컬 무검열 모델이라는 전제라, LLM 지시문에는 +dolphin_vision_director._plan_shots와 동일한 "노골적·해부학적 표현" 규칙을 넣었다. + +──────────────────────────────────────────────────────────────────────── +H3 프롬프트 규약 (2026-08 조사 결과, 실제 컷 튐 버그에서 역산) + +초기 버전은 비트마다 `[0-2s] 동작. [Push in]` 한 줄씩을 뱉었는데, 실제 렌더에서 +그 타임코드마다 다른 컷으로 점프하는 문제가 났다. 원인: + + 1. **`[...]`로 시작하는 줄 = H3의 샷 마커.** H3 공식 멀티샷 문법이 + `[Shot 1]` / `[Shot 2] At 00:03.500, the camera cuts to ...` 다. + `[0-2s]`는 이것과 생김새가 같아서 모델이 샷 리스트로 읽는다. + 2. **H3는 한 클립에 여러 샷을 넣는 게 기본 지원 기능.** 막는 지시가 없으면 + 알아서 자른다. "No cuts; one continuous take" 같은 제약을 명시해야 한다. + 3. **비트마다 카메라 태그를 바꾸면 컷 신호가 더 강해진다.** 뷰포인트가 바뀔 땐 + 컷이 아니라 "카메라 무브"로 표현하라는 게 가이드 권장. + 4. `[Push in]` 대괄호 카메라 문법은 구형 Director 모델(T2V-01-Director 등) + 전용이다. H3는 평문으로 쓰되 **진폭+속도**를 붙이는 걸 권장 + ("pulls out with large amplitude at slow speed"). + +1차 수정(대괄호 제거 + 초 인라인)으로도 컷이 계속 났다. 특히 **첫 비트 경계에서**. +2차 조사에서 원인이 대괄호가 아니라 **숫자 타임코드 자체**임이 확인됨: + + > "Every prompt should begin with a shot identifier. The first shot does not + > need a timestamp. Only later shots need precise cut times." + > "blocking the clip as 0 to 2 seconds, 2 to 5 seconds ... gives the model an + > order to follow" — 이게 곧 멀티샷 블로킹 문법이다. + +즉 H3에서 `0-2s, 2-4s ...` 는 "그 시각에 컷하라"는 선언이다. 첫 구간은 +타임스탬프가 없어서 샷 1이고, 첫 숫자(2s)가 첫 컷 지점이 된다 — 사용자가 겪은 +"첫 2초 후 컷"과 정확히 일치. **초 단위 지정과 무컷은 H3에서 상호배타적이다.** + +그래서 컷을 원치 않으면 숫자 마커를 아예 쓰지 않고 지속시간을 말로만 쓴다: + `For the first two seconds she pushes off the ledge, then for the next three + seconds she strides toward the camera, and through the final two seconds ...` +줄바꿈도 구분자 신호라서 연속 모드는 전부 한 문단(공백 조인)으로 낸다. +숫자 블로킹/멀티샷/구형 Director 포맷은 `prompt_style` 옵션으로 남겨뒀다. + +출처: photogptai.com/guides/how-to-prompt-minimax-h3-video-model, +mixio.studio/hailuo-h3-prompt-guide, videotoprompt.app/posts/hailuo-h3-prompt-guide + +──────────────────────────────────────────────────────────────────────── +공식 스펙 반영 (2026-08-10, MiniMax-AI/MiniMax-H3 공식 SKILL.md + HF 가이드 대조) + +위 문법은 전부 서드파티 블로그 기반이었는데, 공식 소스 +(github.com/MiniMax-AI/MiniMax-H3/skills/h3-prompt-writing/SKILL.md, +huggingface.co/MiniMaxAI/MiniMax-H3/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md)를 +대조해서 아래 3가지 갭을 반영했다: + + 1. **오디오 필드 부재.** H3는 비디오+오디오 동시생성 모델인데 기존 출력엔 오디오 + 지시가 전혀 없었다. 공식 포맷은 3필드: `integrated_multimodal_description` / + `overall_soundscape`(환경음·물리음, 대사·음악 제외) / `non_diegetic_music` + (배경음악). `include_audio_fields` 토글로 LLM이 이 둘을 추가로 써서 + 라벨 붙은 섹션으로 감싼다. + 2. **카메라 용어 목록이 15개로 낡음.** 공식은 19개 — `POV`, `Roll clockwise`, + `Roll counterclockwise` 추가, `Shake`는 `Shake slightly`/`Shake strongly`로 + 분리. + 3. **I2V 정렬 문장 누락.** 공식 I2VA 예시는 전부 본문 앞에 + `"For the target video, at 0.00 seconds into the target video, + (from [Shot 1]) is fully referenced."`를 붙인다. `i2v_alignment_prefix` + 토글로 이미지가 연결됐을 때만 붙인다. + +기존에 실측으로 검증된 "숫자 타임코드 = 컷 선언" 로직(`_assemble`)은 건드리지 +않았다 — 새 필드는 그 결과물을 감싸기만 한다. 로컬 무검열 가중치가 이 공식 +포맷을 실제로 따르는지는 미검증이라 두 토글 다 기본 ON이지만 렌더 확인 필요. + +──────────────────────────────────────────────────────────────────────── +비트별 대사(dialogue) 추가 (2026-08-23, 의미불명 발화 버그 대응) + +공식 HF 디스커션(huggingface.co/MiniMaxAI/MiniMax-H3/discussions/76)에서 +"대사가 정확히 정의되지 않으면 H3가 랜덤/의미불명의 음성을 지어낸다"는 게 +확인됐다. 기존 오디오 규칙(soundscape)은 "대사를 언급하지 말라"고만 했지 +대사 자체를 넣을 자리도, 무대사를 명시할 자리도 없었다 - 정확히 이 버그를 +유발하는 구멍이었다. + +그래서 `_plan_beats`가 비트마다 "이 순간에 대사가 어울리는가"를 독립적으로 +판단하게 하고(`dialogue` 필드, rule 15), 어울리면 `_assemble`이 그 비트의 +문장에 자연스럽게 끼워넣는다. 대사는 작은따옴표(' ')로 감싸는데, 이건 +rule 11(다른 필드는 따옴표 금지)의 유일한 예외이자 JSON 파싱에도 안전하다 +(큰따옴표가 아니라 이스케이프 문제가 없음). 숫자 타임코드는 여기도 금지 - +컷 유발 로직과 동일한 원칙. + +한 비트에도 대사가 안 들어갔으면 공식 부정 문장(`DIALOGUE_NEGATIVE` = +"No dialogue. No voiceover. No singing. No lyrics. No random voices. No +individual background speech.")을 자동으로 붙인다 - 침묵을 명시적으로 +선언해야 의미불명 발화가 안 나온다는 게 위 디스커션의 핵심이었다. +별도 토글 안 만들고 기존 `include_audio_fields`에 묶었다 - 대사도 오디오 +스펙의 일부라 위젯을 늘릴 이유가 없었다. +""" +import json +import math +import random +import re +import urllib.request +import urllib.error + +from .dolphin_vision_director import ( + DolphinVisionDirector, + resolve_slug, + resolve_api_key, + _price_tag, + tensor_to_base64, + split_action_parts, +) + +# 이 노드는 위젯 UI를 영어로 노출한다 - dolphin_vision_director의 공유 +# VLM_PRESETS/LLM_PRESETS(한글 라벨)는 다른 노드(VisionDirector 등)가 계속 쓰므로 +# 건드리지 않고, 여기서만 쓰는 영어 라벨 버전을 따로 둔다. +VLM_PRESETS = { + "👁 Qwen2.5-VL 72B (special cases · recommended)": "qwen/qwen2.5-vl-72b-instruct", + "👁 Qwen2.5-VL 32B (balanced)": "qwen/qwen2.5-vl-32b-instruct", + "👁 Qwen2.5-VL 32B (free)": "qwen/qwen2.5-vl-32b-instruct:free", + "👁 Mistral Small 3.1 24B (Pixtral)": "mistralai/mistral-small-3.1-24b-instruct", + "👁 Gemma 3 27B (lightweight)": "google/gemma-3-27b-it", + "⚙️ custom (use text field below)": "__custom__", +} +VLM_PRESET_LABELS = list(VLM_PRESETS.keys()) + +LLM_PRESETS = { + "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)": "deepseek/deepseek-v3.2", + "🔞 Magnum v4 72B (natural phrasing)": "anthracite-org/magnum-v4-72b", + "🔞 Cydonia 24B v4.1 (lightweight · fast)": "thedrummer/cydonia-24b-v4.1", + # 2026-09-03: :free 슬러그 404 - 유료로 교체. + "🔞 Dolphin Mistral Venice (uncensored)": "cognitivecomputations/dolphin-mistral-24b-venice-edition", + # 2026-09-03 추가 (모델 페이지로만 확인, chat completions 스모크테스트 미완). + "🔞 Hermes 4 405B (steerable · strict format)": "nousresearch/hermes-4-405b", + # 2026-09-03: grok-4 / grok-4-fast deprecated(404) - grok-4.3으로 교체. + "🔞 Grok 4.3 (edgy prose · $1.25/$2.5)": "x-ai/grok-4.3", + "🔞 Euryale 70B L3.3 (sensory prose · loose format)": "sao10k/l3.3-euryale-70b", + "⚙️ custom (use text field below)": "__custom__", +} +# 2026-08-28: Valkyrie 49B V1(thedrummer/valkyrie-49b-v1) 추가했다가 뺐다 - 실제 +# API 호출이 404 "No endpoints found". 자세한 건 dolphin_vision_director.py의 +# LLM_PRESETS 옆 주석 참고(이 딕셔너리는 그 파일의 복제본). +LLM_PRESET_LABELS = list(LLM_PRESETS.keys()) + + +# ====================================================================== +# MiniMax 공식 카메라 무브 지시어 +# Director 계열 모델(T2V-01-Director / I2V-01-Director / Hailuo 02)이 +# 인식하는 건 아래 19개뿐(2026-08-10, 공식 SKILL.md 대조로 POV/Roll 추가, +# Shake를 slightly/strongly로 분리). 목록에 없는 표현은 그냥 일반 텍스트로 +# 먹혀서 카메라 제어가 안 걸리므로, LLM이 뭘 뱉든 여기로 정규화해서 거른다. +# ====================================================================== +MINIMAX_CAMERA_MOVES = [ + "Truck left", "Truck right", + "Pan left", "Pan right", + "Push in", "Pull out", + "Pedestal up", "Pedestal down", + "Tilt up", "Tilt down", + "Zoom in", "Zoom out", + "Shake slightly", "Shake strongly", + "Tracking shot", "Static shot", + "POV", "Roll clockwise", "Roll counterclockwise", +] +_CAMERA_LOOKUP = {m.lower(): m for m in MINIMAX_CAMERA_MOVES} + +# H3 프롬프트 필드 하드 상한(문자 수). 넘으면 뒤가 잘린다. +MINIMAX_FIELD_LIMIT = 7000 +# 권장 상한. 필드는 7000자까지 받지만 가이드는 훨씬 짧게(대략 50~120 단어) 쓰는 쪽을 +# 권한다 - 길수록 모델이 지시를 평균내버려서 오히려 타임라인을 안 지킨다. +MINIMAX_SOFT_LIMIT = 1200 + +# 한 클립을 하나의 끊김 없는 테이크로 못 박는 제약 블록. H3는 멀티샷이 기본 +# 지원이라 이 문장이 없으면 타임코드 경계마다 컷을 넣어버린다. +NO_CUT_CONSTRAINT = ("One continuous take. No cuts, no shot changes, no camera angle jumps, " + "no dissolves or transitions, no teleporting — the camera path and the " + "subject's motion stay unbroken from the first second to the last.") + +# 어느 비트에도 대사를 안 넣기로 한 경우 붙이는 공식 부정 지시문. H3는 "말하는 +# 상황처럼 보이는데 대사가 명시 안 되면" 의미불명의 음성을 지어내는 버릇이 있다 +# (공식 HF 디스커션 #76에서 확인) - 이걸 명시적으로 막아야 한다. +DIALOGUE_NEGATIVE = ("No dialogue. No voiceover. No singing. No lyrics. No random voices. " + "No individual background speech.") + +# "귀엽게/섹시하게" 같은 장식 부사가 들어오면 rule 13이 "뻔한 걸 피하라"고만 시켜서, +# LLM이 그래도 두세 번째로 뻔한 제스처(손하트 다음은 고개 갸웃 정도)로 계속 수렴한다 - +# 부정 지시만으로는 실제 다양성이 안 나온다는 게 반복 관찰됨(2026-08-23, "귀여운 포즈" +# 입력 시 매번 비슷한 동작만 나온다는 사용자 리포트). 그래서 카테고리별 제스처 풀을 +# 만들어두고 seed로 하나를 미리 뽑아 "이걸 써라"고 프롬프트에 박아준다 - 시드가 바뀌면 +# 힌트도 바뀌니 실제로 결과가 달라진다. 손하트/V사인/브이/머리카락 넘기기처럼 이미 +# rule 13이 명시적으로 금지한 클리셰는 풀에서 아예 제외했다. +GESTURE_BANK = { + "cute": [ + "tilts their head and scrunches their nose while grinning", + "hops once on the balls of their feet with elbows tucked in", + "presses both palms to their own cheeks", + "spins once and lands with knees bent, beaming", + "shimmies their shoulders side to side in a small bounce", + "puffs out both cheeks and crosses their eyes", + "curls both hands into paws up near their chin", + "bites their bottom lip and gives a small shrug", + "wiggles their fingers in a tiny wave close to their chest", + "claps both hands together once in delight", + "sticks out their tongue briefly and grins", + "hugs their own arms and rocks side to side", + "taps their own chin with one finger, feigning innocence", + "does a quick curtsy, hem pinched between two fingers", + "crouches briefly with both fists raised in a tiny cheer", + "leans forward with hands clasped behind their back, rocking on their heels", + "flutters both hands like tiny wings at shoulder height", + ], + "playful": [ + "sticks out their tongue and darts to one side", + "does a mock bow then springs back up laughing", + "hops from foot to foot, beckoning teasingly", + "spins a nearby object between their fingers", + "throws a playful mock punch at the air and dodges back", + "sneaks a few tiptoe steps then bursts into a run", + "flicks their own hair and dashes off laughing", + "mimes tossing something invisible over their shoulder", + "does a little skip-step and spins around", + "wags a finger teasingly then turns away giggling", + ], + "sexy": [ + "trails one hand slowly down their own side", + "rolls their hips in a slow figure-eight while stepping forward", + "arches their back and rolls their shoulders", + "bites their bottom lip while holding eye contact, stepping closer", + "drags fingertips slowly along their own collarbone", + "sinks down slowly, hands trailing down their own thighs", + "leans back and stretches, arching their spine", + "runs both hands up their own torso and pauses at the ribs", + ], + "shy": [ + "tucks their chin down and peeks up through their lashes", + "hides half their face behind one raised hand", + "picks at their own sleeve, eyes lowered", + "takes a half-step back, one foot turning inward", + "hugs something to their chest and looks away", + "presses their lips together and rubs one arm", + "ducks briefly behind their own shoulder", + ], + "happy": [ + "throws both arms up and spins once", + "jumps and pumps a fist", + "claps rapidly while bouncing on their toes", + "laughs with their head tipped back", + "skips forward two quick steps", + ], + "confident": [ + "squares their shoulders and plants their feet wide", + "flips their hair back and strides forward", + "points decisively toward camera", + "crosses their arms and cocks an eyebrow", + "plants both hands on their hips, chin raised", + ], +} + +_GESTURE_KEYWORDS = { + "cute": ["cute", "cutely", "adorable", "adorably", "귀여운", "귀엽게", "깜찍"], + "playful": ["playful", "playfully", "mischievous", "장난스럽게", "장난스런", "장난기"], + "sexy": ["sexy", "sexily", "seductive", "seductively", "섹시", "요염"], + "shy": ["shy", "shyly", "bashful", "bashfully", "수줍", "부끄럽"], + "happy": ["happy", "happily", "joyful", "joyfully", "기쁘게", "즐겁게", "신나게"], + "confident": ["confident", "confidently", "당당하게", "자신있게"], +} + +# 2026-08-28 추가: 위 _GESTURE_KEYWORDS는 essential_actions에 사용자가 직접 "cutely" +# 같은 장식 부사를 타이핑했을 때만 발동한다. 요청: 이미지 자체의 표정을 읽어서(예: +# 웃고 있으면 cute쪽, 심각한 표정이면 confident/sexy쪽) 자동으로 유도해달라 - 그래서 +# _vision_analyze()가 뽑는 anchors의 5번째 줄(표정/무드)을 같은 방식으로 스캔하는 +# 별도 키워드셋. "serious"/"intense"는 사용자가 명시한 대로 confident와 sexy 둘 다에 +# 걸어뒀다 - 심각한 표정이 상황에 따라 둘 중 어느 쪽으로도 자연스럽게 갈 수 있어서, +# 매칭되면 두 카테고리 힌트가 다 뜨고 LLM이 장면에 맞는 쪽을 고른다. +_MOOD_EXPRESSION_KEYWORDS = { + "cute": ["smiling", "smile", "grin", "grinning", "beaming", "sweet expression", + "warm smile", "giggl"], + "playful": ["smirk", "smirking", "mischievous", "teasing expression", "wink"], + "sexy": ["sultry", "seductive", "smoldering", "sensual", "half-lidded", "alluring", + "serious", "intense stare", "intense gaze"], + "shy": ["shy", "blushing", "bashful", "timid", "looking away", "averting"], + "happy": ["happy", "joyful", "laughing", "delighted", "elated", "cheerful"], + "confident": ["serious", "stern", "intense", "solemn", "unsmiling", "composed", + "focused", "determined", "cold expression"], +} + + +def _kw_hit(text, kw): + """단어 경계 매치 - 부분 문자열 `in` 검사로는 "unsmiling"이 "smiling"에 우연히 + 걸려서 안 웃는(=정반대) 표정을 "cute"로 오인식하는 사고가 난다(2026-08-28, anchors + 스캔 추가 직후 테스트로 실제 발견함). kw가 여러 단어 구(phrase)여도 앞뒤 경계만 + 잡으면 되므로 그대로 통한다.""" + return re.search(r"\b" + re.escape(kw) + r"\b", text) is not None + + +def _gesture_hints(actions, anchors, seed): + """actions의 장식 부사 + anchors(이미지에서 읽은 표정/무드, rule 5c) 둘 다 스캔해서 + 카테고리를 찾고, seed로 고정한 제스처 힌트를 만든다. + + 같은 카테고리라도 seed가 다르면 GESTURE_BANK에서 다른 항목을 뽑으므로, "귀여운 + 포즈" 같은 입력을 여러 번 돌려도(=시드가 바뀌면) 실제로 다른 동작이 나온다. + 같은 seed 키(f"{seed}:{cat}")를 두 소스가 공유하므로, 텍스트로 "cutely"라고 + 썼든 이미지가 웃고 있어서 감지됐든 같은 카테고리면 같은 픽이 나와서 일관된다.""" + act_text = (actions or "").lower() + anc_text = (anchors or "").lower() + hits = [] + for cat, kws in _GESTURE_KEYWORDS.items(): + from_action = any(_kw_hit(act_text, kw) for kw in kws) + from_image = any(_kw_hit(anc_text, kw) for kw in _MOOD_EXPRESSION_KEYWORDS.get(cat, [])) + if from_action or from_image: + bank = GESTURE_BANK[cat] + pick = bank[random.Random(f"{seed}:{cat}").randrange(len(bank))] + source = "action wording" if from_action else "image expression" + hits.append(f' - for "{cat}" (from {source}): {pick}') + if not hits: + return "" + return ("SUGGESTED GESTURES (seed-specific, per rule 13 — use this AS your concrete " + "gesture for the matching mood instead of inventing the usual cliché; adapt the " + "wording naturally into the action, and only substitute something else of your " + "own invention if this one truly cannot fit the scene):\n" + + "\n".join(hits) + "\n\n") + + +# GESTURE_BANK/_gesture_hints는 "귀엽게/섹시하게" 같은 특정 수식어가 actions 텍스트에 +# 있을 때만 발동한다. 근데 그 트리거 없이도 - 즉 essential_actions가 완전히 똑같아도 - +# LLM이 매번 "가장 그럴듯한" 동작·대사로 수렴하는 문제가 있었다(2026-08-28, "프롬프트가 +# 같더라도 좀 더 다양한 행동을 할수 없어? 매번 같은 패턴에 같은 대사"). creativity/seed는 +# 이미 정상 설정(0.7, 매 배치 randomize)이었는데도 반복됐다는 게 확인됨 - 온도만으론 +# 이 수렴을 잘 못 깬다는 뜻이라, GESTURE_BANK와 같은 방식(seed로 고정 선택 -> 명시 +# 주입)을 트리거 조건 없이 매번 강제로 적용하도록 일반화했다. +ACTION_VARIATION_BANK = [ + "let a secondary, incidental body part move independently of the main motion (fingers " + "flex, toes curl, head tilts) — a small detail that isn't the point but grounds it", + "vary the pacing within the beat itself: start slower than expected, then let it " + "accelerate or snap partway through", + "have the character's weight or balance point visibly shift during the motion, not just " + "the primary limb", + "let breath be audible in the action itself — a caught breath, a held breath released, " + "breathing syncing to the rhythm of the motion", + "introduce one small hesitation, correction, or stumble before the motion fully commits", + "let something in the immediate environment react to the motion — fabric shifts, an " + "object gets nudged, hair falls or sways", + "make the motion asymmetric — one side of the body leads distinctly before the other " + "catches up", + "let the eyes or gaze do something specific and separate from the main physical action " + "(glancing away, locking on, closing briefly)", + "vary where the tension sits in the body — sometimes the hands, sometimes the jaw, " + "sometimes the shoulders", + "let the motion overshoot slightly before settling, rather than stopping cleanly", +] + +DIALOGUE_REGISTER_BANK = [ + "teasing, drawing the words out slowly", + "breathless and urgent, words clipped short", + "mock-commanding, testing how it lands", + "quiet and half-embarrassed, almost muttered", + "defiant, chin raised, daring a reaction", + "worshipful and hushed, reverent", + "playfully mocking, biting back a laugh", + "pleading, voice cracking slightly at the edges", + "flat and matter-of-fact, almost surprising in how calm it is", + "sharp and startled, the words tumbling out before thought", + "low and deliberate, each word placed like a challenge", + "sing-song and light, almost taunting", +] + + +def _variation_hints(seed): + """essential_actions가 완전히 같아도 seed가 다르면 동작 실행 스타일과 대사 톤이 + 실제로 갈리도록, 매번(트리거 조건 없이) 한 쌍을 강제로 골라 주입한다. + + _gesture_hints와 달리 특정 수식어 매치가 필요 없다 - "같은 프롬프트, 다른 결과" + 문제 자체가 목적이라 항상 켜져 있어야 한다.""" + action_pick = ACTION_VARIATION_BANK[ + random.Random(f"{seed}:action_variation").randrange(len(ACTION_VARIATION_BANK))] + dialogue_pick = DIALOGUE_REGISTER_BANK[ + random.Random(f"{seed}:dialogue_register").randrange(len(DIALOGUE_REGISTER_BANK))] + return ( + "TAKE VARIATION (seed-specific — this run's distinct angle on an otherwise identical " + "prompt, per rules 4c/15c):\n" + f" - execution style for this take: {action_pick}\n" + f" - if any dialogue line is used, its delivery register should lean toward: " + f"{dialogue_pick}\n" + " Weave these into the actual beats naturally - do not narrate them as separate " + "sentences, name them explicitly, or let them override what the action list or scene " + "anchors actually call for.\n\n") + + +# 비트 개수 상한. 긴 클립(예: 60초)에 1초 단위를 걸면 비트가 수십 개가 되어 +# 프롬프트가 상한을 넘고, 모델도 그 정도 밀도의 지시는 못 따라온다. +# 넘으면 거부하는 대신 beat 길이를 자동으로 넓히고 경고만 찍는다. +MAX_BEATS = 24 + + +def _norm_camera(raw): + """LLM/사용자가 준 카메라 문자열을 '[Push in, Zoom in]' 형태로 정규화. + + 대괄호 유무·대소문자·순서 상관없이 받아서, 공식 15개에 매칭되는 것만 남긴다. + 매칭이 하나도 없으면 빈 문자열(=카메라 지시 없음).""" + if not raw: + return "" + text = str(raw).replace("[", " ").replace("]", " ") + hits, seen = [], set() + for token in re.split(r"[,;/|]+", text): + key = " ".join(token.split()).lower() + if key in _CAMERA_LOOKUP and key not in seen: + seen.add(key) + hits.append(_CAMERA_LOOKUP[key]) + if not hits: + return "" + # MiniMax 문서 권장: 한 번에 3개까지. 그 이상은 서로 상쇄돼서 무의미해진다. + return "[" + ", ".join(hits[:3]) + "]" + + +# "순차 접속사" 스타일에서 쓰는 연결어. H3 가이드가 first/then/as/finally 같은 +# 순차 표현으로 순서를 못 박으라고 권한다. +_SEQ_CONNECTIVES = ["then", "as", "then", "next", "then", "and then"] + + +def _fmt_sec(x): + """2.0 -> '2', 2.5 -> '2.5'. 타임코드가 지저분해지는 것만 막는 용도.""" + return str(int(x)) if float(x) == int(x) else f"{float(x):g}" + + +_NUM_WORDS = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", + "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", + "seventeen", "eighteen", "nineteen", "twenty"] + + +def _dur_parts(seconds): + """2.0 -> ('two seconds', False), 2.4 -> ('two seconds', True). + + 지속시간을 숫자 없이 말로만 쓰기 위한 것. H3에서 `0-2s` 같은 숫자 마커는 + '그 시각에 컷하라'는 선언으로 읽히므로, 연속 테이크를 원하면 숫자를 쓰면 안 된다. + 근사 여부를 따로 돌려주는 건 'For the first about two seconds' 같은 어색한 + 어순을 피하고 'For roughly the first two seconds'로 붙이기 위해서.""" + s = float(seconds) + n = max(int(round(s)), 1) + word = _NUM_WORDS[n] if n < len(_NUM_WORDS) else str(n) + unit = "second" if n == 1 else "seconds" + return f"{word} {unit}", abs(s - n) > 0.05 + + +def _fmt_timecode(sec): + """3.5 -> '00:03.500'. H3 멀티샷 문법이 쓰는 컷 시점 표기.""" + return f"{int(sec) // 60:02d}:{int(sec) % 60:02d}.{int(round((sec % 1) * 1000)):03d}" + + +def _sentence(text): + """앞 글자를 대문자로 올리고 마침표를 하나만 붙인다. 연속 모드에서는 모든 블록이 + 한 문단에 이어 붙으므로 문장처럼 보여야 한다.""" + t = str(text).strip().rstrip(".").strip() + if not t: + return "" + return t[0].upper() + t[1:] + "." + + +def _strip_brackets(text): + """대괄호를 걷어낸다. H3는 `[...]`를 샷 마커로 읽기 때문에, 연속샷 포맷에서는 + LLM이 액션 문장 안에 대괄호를 흘려도 그게 컷 신호가 되면 안 된다.""" + return " ".join(str(text).replace("[", " ").replace("]", " ").split()) + + +def _lower_first(text): + """문장을 이어붙일 때 첫 글자만 소문자로. 고유명사/대문자 약어는 건드리지 않는다.""" + t = str(text).strip().rstrip(".") + if not t or not t[0].isupper(): + return t + # "She"는 낮추고 "Yuna"/"NASA"는 그대로 — 두 번째 글자가 대문자면 약어로 본다. + if len(t) > 1 and t[1].isupper(): + return t + head = t.split()[0].strip(",") + if head in _PRONOUN_STARTS: + return t[0].lower() + t[1:] + return t + + +# 이어붙일 때 소문자로 낮춰도 안전한 문장 시작 단어(대명사/관사). 이름으로 시작하는 +# 문장은 그대로 둬야 해서 화이트리스트 방식으로 간다. +_PRONOUN_STARTS = {"She", "He", "They", "It", "The", "A", "An", "Her", "His", "Their", "Its"} + + +def _auto_beat_count(duration): + """beat_seconds=0(auto)일 때 쓸 비트 개수. 대략 2.5초에 하나, 3~8개로 제한.""" + return max(3, min(8, int(round(duration / 2.5)))) + + +def _beat_bounds(duration, beat_seconds): + """(start, end) 구간 리스트. beat_seconds<=0이면 None(=LLM이 직접 나눔).""" + duration = float(duration) + if beat_seconds is None or beat_seconds <= 0: + return None + step = float(beat_seconds) + if duration / step > MAX_BEATS: + widened = math.ceil(duration / MAX_BEATS * 10) / 10.0 + print(f"⚠️ [MinimaxDirector] Splitting {_fmt_sec(duration)}s into {_fmt_sec(step)}s beats " + f"makes too many beats - widening to {_fmt_sec(widened)}s beats (max {MAX_BEATS} beats).") + step = widened + + bounds, t = [], 0.0 + while t < duration - 1e-6: + end = min(t + step, duration) + bounds.append((round(t, 2), round(end, 2))) + t = end + + # 자투리 흡수: 20초를 3초로 나누면 마지막이 2초짜리로 남는데, 그게 한 비트의 + # 절반도 안 되면 직전 비트에 합친다. 1초도 안 되는 비트에 동작을 하나 배정해봐야 + # 모델이 못 따라오고 타임코드만 지저분해진다. + if len(bounds) > 1 and (bounds[-1][1] - bounds[-1][0]) < step * 0.5: + bounds = bounds[:-2] + [(bounds[-2][0], bounds[-1][1])] + return bounds + + +def _call_openrouter(messages, model, key, temp, seed=None, timeout=120, force_json=False, + usage_acc=None): + """DolphinVisionDirector._call_or와 같은 호출이지만 seed를 실제로 payload에 넣는다. + + 기존 _plan_shots는 seed 인자를 받기만 하고 API에 안 넘겨서, 같은 입력·같은 + 시드에도 매번 다른 프롬프트가 나왔다(A/B 비교가 불가능했던 원인). 여기서는 + seed를 넘겨서 지원하는 프로바이더에서는 재현이 되게 한다.""" + payload = {"model": model.strip(), "messages": messages, "temperature": temp} + if force_json: + payload["response_format"] = {"type": "json_object"} + if seed is not None: + # OpenRouter는 32bit 범위를 넘는 seed를 거부하는 프로바이더가 있어서 접는다. + payload["seed"] = int(seed) % (2 ** 31) + payload["usage"] = {"include": True} + 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")) + u = raw.get("usage") or {} + if usage_acc is not None and u: + usage_acc["prompt"] += u.get("prompt_tokens", 0) or 0 + usage_acc["completion"] += u.get("completion_tokens", 0) or 0 + c = u.get("cost") + if c is None: + c = (u.get("cost_details") or {}).get("upstream_inference_cost") + usage_acc["cost"] += float(c or 0) + 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() + + +class DolphinMinimaxDirector: + """ + 이미지(선택) + 동작 서술 -> MiniMax/Hailuo용 초 단위 타임라인 프롬프트 1개. + + 1) IMAGE -> VLM이 앵커(외형/자세/지지물/환경) 추출 (vision_caption 입력이 있으면 그걸 사용) + 2) 그 앵커 위에서 동작을 duration 안의 초 단위 비트로 분배 (LLM, 실패 시 로컬 폴백) + 3) MiniMax 규약대로 조립: 피사체 문장 + [0-2s] 동작 [카메라] ... + 분위기 문장 + + image를 안 꽂으면 T2V 모드로 동작한다(앵커 없이 텍스트만으로 플래닝). + """ + + @classmethod + def INPUT_TYPES(s): + req = { + "essential_actions": ("STRING", {"multiline": True, + "default": "stands up and walks toward the camera, stops and looks back", + "tooltip": "If left empty, the LLM directs the action on its own using only the " + "image anchors."}), + "duration_seconds": ("INT", {"default": 6, "min": 1, "max": 300, "step": 1, + "tooltip": "Total length of the clip to make, in seconds. The hosted Hailuo API " + "only accepts 6/10 seconds, but local weights have no such limit, so " + "20 or even 60 seconds gets distributed to match."}), + "beat_seconds": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 30.0, "step": 0.5, + "tooltip": "How many seconds each timeline beat spans. 0 = auto (the LLM splits " + f"it into 3-8 segments on its own). Going as fine as 1 second can make " + f"the motion feel choppy, and if the beat count would exceed " + f"{MAX_BEATS} it's widened automatically."}), + "prompt_style": (["Continuous prose (duration in words, no cuts — recommended)", + "Sequential connectives (no time mentioned, strongest cut prevention)", + "Second timeblocking (0-2s = a cut-point declaration in H3)", + "Multi-shot [Shot N] (intentional cuts)", + "Director brackets (for legacy T2V-01-Director)"], + {"default": "Continuous prose (duration in words, no cuts — recommended)", + "tooltip": "In H3, a numeric timecode is a declaration to 'cut at that moment' - " + "exact-second timing and no-cut are mutually exclusive, so you have " + "to pick a style.\n" + "Continuous prose: one paragraph like 'For the first two seconds ..., " + "then for the next three seconds ...'. No numbers, so no cut happens " + "while timing is still conveyed.\n" + "Sequential connectives: no time mentioned at all, just order via " + "first/then/finally. Use this if prose mode still produces cuts.\n" + "Second timeblocking: only when exact seconds must be honored. This " + "causes cuts.\n" + "Multi-shot: for when you want cuts on purpose. Official [Shot N] " + "syntax.\n" + "Director brackets: for legacy T2V-01-Director-family models only."}), + "no_cut_constraint": ("BOOLEAN", {"default": True, + "label_on": "NO CUT (force continuous take)", "label_off": "No cut constraint", + "tooltip": "Appends a 'One continuous take. No cuts...' constraint to the end of " + "the prompt. H3 supports multi-shot by default, so without this it " + "inserts cuts on its own. Automatically ignored in Multi-shot style."}), + "camera_mode": (["Single continuous camera (LLM, recommended)", + "off (no camera mentioned)", + "manual (fixed value below)", + "Per-segment brackets (Director only)"], + {"default": "Single continuous camera (LLM, recommended)", + "tooltip": "In H3, swapping the camera on every beat is itself a cut signal. " + "The default is a single camera move written in plain prose that " + "runs through the whole clip."}), + "camera_manual": ("STRING", {"default": "slow push in, small amplitude", + "tooltip": "Only used when set to manual. In continuous/prose mode the sentence " + "goes in as-is (H3's recommended notation: move + amplitude + speed, " + "e.g. 'pulls out with large amplitude at slow speed'). Only in " + "'Per-segment brackets' mode are segments split with | and only the " + f"following moves are recognized: {', '.join(MINIMAX_CAMERA_MOVES)}"}), + "character_name": ("STRING", {"default": "AUTO"}), + "artistic_vibe": ("STRING", {"multiline": True, + "default": "cinematic lighting, natural motion"}), + "detail_level": (["Short (concise)", "Essentials + anchors only", "Detailed"], + {"default": "Essentials + anchors only"}), + "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 (special cases · recommended)"}), + "vlm_model": ("STRING", {"default": "qwen/qwen2.5-vl-72b-instruct"}), + "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)"}), + "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}), + # 2026-08-10 추가분 - 기존 워크플로우의 widgets_values 포지션이 안 밀리도록 + # 반드시 맨 끝에 붙인다(중간에 넣으면 이 뒤의 모든 위젯값이 밀린다 - 실제로 + # 한 번 겪었음, [[dolphin-nodes-repo-features]] 참고). + "include_audio_fields": ("BOOLEAN", {"default": True, + "label_on": "Include audio fields (official format)", + "label_off": "Video only (legacy format)", + "tooltip": "Wraps the output in the official H3 3-field format " + "(integrated_multimodal_description / overall_soundscape / " + "non_diegetic_music). H3 is a model that generates video+audio " + "together, so leaving this field unfilled does not mean silence - the " + "model invents its own sound. Turn this on if you want intentional " + "sound. Without an LLM (local fallback) it can't write audio " + "sentences, so it's dropped automatically.\n" + "This toggle also manages dialogue: the LLM judges per-beat whether a " + "line fits, and inserts a short natural line only where it fits, or " + "appends the 'No dialogue...' negative directive if no beat got one - " + "this safeguard is needed because H3 tends to invent meaningless " + "speech if dialogue isn't explicitly specified (official HF discussion " + "#76)."}), + "i2v_alignment_prefix": ("BOOLEAN", {"default": False, + "label_on": "Include I2V alignment prefix (⚠️ cut risk)", + "label_off": "No prefix (default)", + "tooltip": "⚠️ Default OFF - when an image is connected (I2V), the official " + "alignment sentence 'For the target video, at 0.00 seconds..., " + " (from [Shot 1]) is fully referenced.' gets prepended, " + "but that sentence contains both a '[Shot 1]' bracket AND a '0.00 " + "seconds' numeric timecode - exactly the H3 cut-triggering pattern " + "confirmed by testing ([Shot N] At timestamp = the official multi-shot " + "syntax), which directly conflicts with no_cut_constraint. Turning it " + "on was reproduced to cause random cuts in practice (2026-08-13) - " + "root cause unconfirmed but a prime suspect, so the default was " + "reverted to OFF. Don't turn it on unless you're doing strict-spec " + "experiments like FL2VA/Ref2VA."}), + } + opt = { + # I2V면 연결, T2V면 비워둠. + "image": ("IMAGE",), + # JoyCaption 등 로컬 캡션 노드 출력. 있으면 OpenRouter VLM 호출을 건너뛴다. + "vision_caption": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + # 앞 클립의 프롬프트/동작을 연결하면 이번 클립이 그 끝 자세에서 이어진다. + "previous_action": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + } + return {"required": req, "optional": opt} + + RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING") + RETURN_NAMES = ("prompt", "timeline_json", "debug_info", "resolved_character_name", + "soundscape", "non_diegetic_music") + FUNCTION = "direct" + CATEGORY = "Dolphin" + + # ------------------------------------------------------------------ + # LLM 플래닝 + # ------------------------------------------------------------------ + def _plan_beats(self, anchors, actions, name, vibe, detail, duration, bounds, + camera_style, no_cut, llm_model, key, temp, seed, previous_action="", + include_audio=True): + auto_direct = not actions.strip() + dur_s = _fmt_sec(duration) + + # 대사도 오디오 스펙의 일부라 별도 토글 없이 include_audio에 묶는다. + # manner(전달 방식 묘사)와 line(실제 발화)을 필드 단계에서부터 분리한다 - + # 예전엔 한 필드에 같이 넣고 조립 시 이어붙였더니 "moaning softly" 같은 + # 톤 묘사 단어까지 H3가 그대로 발화하는 문제가 있었다(2026-08-25, 실사용 + # 리포트). line만 ... 태그로 감싸서 발화 대상임을 명시하고, manner는 + # 산문 서술로만 쓰이게 분리해서 고쳤다. + dialogue_field = (',"dialogue_manner":"",' + '"dialogue_line":""' + if include_audio else "") + + if bounds is None: + n_hint = _auto_beat_count(duration) + slot_block = (f" Split the {dur_s}-second clip into {n_hint} consecutive beats " + f"yourself. They must tile 0 to {dur_s} seconds with no gaps and no " + f"overlap: the first starts at 0, the last ends at {dur_s}.") + schema_beats = ('"beats":[{"start":,"end":,"action":"<...>",' + '"camera":"<...>"' + dialogue_field + '}, ...]') + else: + slot_block = "\n".join( + f" beat {i}: start={_fmt_sec(a)} end={_fmt_sec(b)}" + for i, (a, b) in enumerate(bounds, 1)) + schema_beats = ('"beats":[' + ",".join( + f'{{"start":{_fmt_sec(a)},"end":{_fmt_sec(b)},"action":"","camera":"<...>"' + dialogue_field + '}' + for a, b in bounds) + "]") + + n_beats = len(bounds) if bounds is not None else n_hint + act_parts = split_action_parts(actions) + + if auto_direct: + action_rule = ( + "1) INVENT THE ACTION. No action list is given. Design one coherent, continuously " + "developing movement that plausibly follows from the character's exact pose and " + f"situation in the SCENE ANCHORS, and that genuinely fills all {dur_s} seconds — " + "what would they naturally do next, starting right now from that position? Do not " + "invent new characters, new props, or a change of location.\n") + assign_label = "ACTION LIST: (none — invent it per rule 1)" + assign_body = "" + else: + # 20초짜리 클립에 동작 4개처럼 "비트 수 >> 동작 수"가 흔한데, 그냥 두면 + # LLM이 앞쪽 비트에서 동작을 다 소진하고 남은 시간을 반복/정지로 때운다. + # 그래서 배분 비율을 계산해서 명시적으로 못 박는다. + if len(act_parts) and len(act_parts) < n_beats: + span = n_beats / len(act_parts) + ratio_rule = ( + f" You have {len(act_parts)} action(s) for {n_beats} beats, so each action must " + f"occupy roughly {span:.1f} consecutive beats. Do NOT finish the whole list early " + "and then pad the remaining beats with idling, repetition, or a held pose. Break " + "each action into its own sub-phases — wind-up, execution, follow-through, " + "recovery — so it genuinely spans its share of the timeline as one continuous " + "movement rather than being restated.\n") + elif len(act_parts) > n_beats: + ratio_rule = ( + f" You have {len(act_parts)} actions for {n_beats} beats, so group consecutive " + "actions together — roughly " + f"{len(act_parts) / max(n_beats, 1):.1f} per beat — keeping the given order and " + "dropping none of them.\n") + else: + ratio_rule = ( + f" You have {len(act_parts)} actions for {n_beats} beats — one action per beat, " + "in order.\n") + action_rule = ( + "1) FOLLOW THE ACTION LIST. Distribute the listed actions across the beats IN THE " + "GIVEN ORDER — earlier actions in earlier beats, the last action in the last beat. " + "Never move a later action into an earlier beat, and never skip one.\n" + + ratio_rule) + assign_label = "ACTION LIST (in order — distribute across the beats per rule 1):" + # 원본 텍스트를 통짜로 넘기면 LLM이 구분자를 못 알아채고 전부 한 동작으로 + # 읽어서 첫 비트에 몰아넣는다(실제로 겪은 버그) - 미리 쪼갠 항목을 번호 매겨 + # 넘겨야 "몇 개의 동작인지"와 "경계가 어디인지"가 명확해진다. + assign_body = "\n".join(f" {i}. {p}" for i, p in enumerate(act_parts, 1)) + + # 긴 클립일수록 "한 동작을 계속 반복" 실패 모드가 심해진다. 짧은 클립엔 + # 불필요한 잔소리라 길이 기준으로만 붙인다. + arc_rule = "" + if duration >= 12: + arc_rule = ( + f"1b) LONG CLIP ({dur_s}s). This is long enough that a single repeated motion will " + "read as a stuttering loop. The timeline needs a visible beginning, middle and end: " + "each beat must show the body in a measurably DIFFERENT position or phase from the " + "beat before it. Before writing, check that no two beats could be swapped without " + "the sequence making less sense — if they could, they are too similar.\n") + + if camera_style == "perbeat": + camera_rule = ( + "6) CAMERA (per beat). For every beat, pick the camera instruction that best serves " + "that beat's motion, chosen ONLY from this exact list:\n" + f" {', '.join(MINIMAX_CAMERA_MOVES)}\n" + " Write it in that beat's \"camera\" field as bracketed text, e.g. \"[Push in]\". " + "At most 3 combined in one bracket. Use the EXACT wording above. Do not change " + "camera on every beat — hold one move across neighbouring beats and leave " + "\"camera\" empty for beats that simply continue the previous one. Leave the " + "top-level \"camera\" field empty.\n") + cam_schema = '"camera":""' + elif camera_style == "continuous": + camera_rule = ( + "6) CAMERA — ONE UNBROKEN MOVE FOR THE WHOLE CLIP. Put a single camera description " + "in the TOP-LEVEL \"camera\" field and leave every per-beat \"camera\" field empty. " + "Write it as plain English prose, NOT as a bracketed tag, and give it a move, an " + "amplitude and a speed — e.g. 'the camera pushes in with small amplitude at slow " + "speed, drifting to the left as she rises'. Usable moves: push in, pull out, truck " + "left/right, pan left/right, tilt up/down, pedestal up/down, arc, tracking shot, " + "static. It must be ONE continuous path that survives the entire clip: it may " + "accelerate, ease, or change direction smoothly, but it must never cut, jump to a " + "new angle, or restart from a different framing. If the subject's own motion is " + "the point, say the camera holds a static frame — that is also a valid answer.\n") + cam_schema = ('"camera":""') + else: + camera_rule = ( + "6) CAMERA. Leave the top-level \"camera\" field and every per-beat \"camera\" " + "field as empty strings. Camera is handled elsewhere — do not mention lenses, " + "shots, angles, zooms or any camera movement anywhere in the action text either.\n") + cam_schema = '"camera":""' + + # H3는 한 클립에 여러 샷을 넣는 게 기본 지원 기능이라, 막는 지시가 없으면 + # 비트 경계마다 컷을 넣어버린다(실제로 겪은 버그). 그래서 규칙으로 못 박는다. + no_cut_rule = ( + "0) ONE CONTINUOUS TAKE — THIS OVERRIDES EVERYTHING ELSE. The whole clip is a single " + "unbroken shot filmed in one go. The beats below are moments WITHIN that one shot, not " + "separate shots in an edit. Never write a cut, a shot change, a new angle, a " + "reframing, a dissolve, a transition, 'meanwhile', or 'we then see'. If the viewpoint " + "needs to change, the CAMERA MOVES there continuously — it never cuts there. The " + "subject must remain physically present and continuously visible the entire time; they " + "cannot teleport, reappear elsewhere, or be replaced by a different framing of " + "themselves.\n" + # 이게 실제로 컷을 유발한 범인이었다: 이 렌더러는 숫자 시각 표기를 + # "그 시각에 컷하라"는 선언으로 읽는다. 슬롯 경계는 우리가 배분용으로만 + # 쓰고, 모델에 넘어가는 문장에는 숫자가 한 개도 없어야 한다. + "0b) NEVER WRITE A TIME MARKER. Do not put any numeric time reference inside " + "\"action\", \"subject\", \"camera\" or \"atmosphere\" — no '0-2s', no '00:03', no " + "'at 5 seconds', no 'after two seconds', no 'by the 10-second mark'. In this renderer " + "a numeric time marker declares a CUT POINT, which is exactly what we are trying to " + "avoid. The beat boundaries you are given below are for pacing only: use them to " + "decide HOW MUCH motion goes in each beat, never quote them back.\n" + if no_cut else "") + + continuity_rule = "" + if previous_action.strip(): + continuity_rule = ( + "2b) CONTINUES A PRIOR CLIP. The action in PREVIOUS CLIP ACTION below already " + "happened. The first beat must begin from the exact physical end-state that action " + "would leave the body in — same position, orientation, momentum — not from a reset " + "neutral stance, and it must not repeat that action. Do not narrate the hand-off " + "(no 'continuing', 'still', 'as before').\n") + + # 공식 H3 포맷의 오디오 2필드(overall_soundscape/non_diegetic_music). H3는 + # 비디오+오디오를 동시생성하는 모델이라 이 지시가 없으면 모델이 사운드를 알아서 + # 지어낸다 - 의도한 사운드를 원하면 명시해야 한다. + if include_audio: + audio_rule = ( + "14) AUDIO (official H3 fields). Also write two short audio-direction fields " + "matching the mood of \"atmosphere\" and the physical intensity of the beats:\n" + " \"soundscape\": 1 to 4 sentences of DIEGETIC sound only — ambience, physical " + "action noise (footsteps, fabric, contact, mechanical/environmental sound), " + "non-verbal human sound (breathing, effort). Never mention dialogue or music here.\n" + " \"music\": 1 to 3 sentences of NON-diegetic score — instruments, tempo, rhythm, " + "and how the dynamics change across the clip. If the scene genuinely calls for no " + "score, write exactly \"None.\" instead of inventing one.\n" + "15) DIALOGUE (per-beat \"dialogue_manner\" and \"dialogue_line\" fields). This " + "renderer invents meaningless gibberish speech on its own whenever a moment looks " + "like talking but no exact line was given — so silence must be deliberate, not " + "accidental. For EACH beat, decide independently whether that specific instant " + "plausibly calls for a short spoken or vocal line (a reaction, a name, a request, " + "a taunt, a moan, a gasp with words). Most beats will NOT need one — do not force " + "a line into every beat. When a beat does get one, fill BOTH fields: " + "\"dialogue_manner\" is a short prose phrase describing HOW it is delivered (e.g. " + "'moaning softly', 'breathlessly', 'sharply, as a startled curse', 'teasing') — " + "this is narration that is never spoken aloud, so plain English prose is fine. " + "\"dialogue_line\" is ONLY the exact spoken text: '[Language] ' — e.g. [Korean] 거기, 거기예요, [English] don't stop, [Korean] " + "아 시발, or [English] is that all you've got. Those four only show the FORMAT — " + "never reuse one verbatim or default to the single most obvious line for the " + "moment (reflex filler like 'yes/more/don't stop' for pleasure, a bare gasp of " + "the character's name for surprise). " + "15c) The TAKE VARIATION block below (if present) names a delivery register for " + "this take — let dialogue_manner and the actual wording/rhythm of dialogue_line " + "lean into that register instead of defaulting to the flattest obvious phrasing. " + "Write a line specific to what THIS beat's " + "action and delivery manner actually are — a taunt sounds different from a plea, a " + "startled reaction sounds different from a satisfied one. If PREVIOUS CLIP ACTION " + "(rule 2b) already contains a dialogue line, this clip's lines must be different " + "wording, not a repeat or a trivial rephrase of it. Pick whichever language " + "actually fits the character and setting (default to English unless the " + "name/scene clearly calls for another one), and write the line itself in that " + "language's OWN NATIVE SCRIPT — never a romanized/phonetic spelling of it (write " + "안녕하세요, not 'annyeonghaseyo'; write ありがとう, not 'arigatou'; write 谢谢, " + "not 'xiexie'). The \"[Language]\" tag stays in English; only the text after it " + "switches script. Do NOT put quote marks around the line and do NOT repeat any of " + "the delivery-manner words inside \"dialogue_line\" — this renderer speaks the " + "exact text of \"dialogue_line\" aloud verbatim, so anything written in that field " + "is heard exactly as written, while \"dialogue_manner\" is only ever narrated as " + "prose and is never itself spoken (this is why the two are separate fields — " + "putting a delivery description inside \"dialogue_line\" makes it get spoken too). " + "Never put a numeric timestamp in either field. Leave BOTH as empty strings for " + "every beat with no line; if none of the beats end up with a line, that is " + "normal and expected for most short clips — do not manufacture one just to fill " + "the fields.\n") + audio_schema = ',"soundscape":"<1-4 sentences, diegetic sound only>","music":"<1-3 sentences, or \\"None.\\">"' + else: + audio_rule, audio_schema = "", "" + + sys = ( + f"You are a shot director writing a prompt for the MiniMax (Hailuo) video model. " + f"The output is ONE {dur_s}-second clip, and you are choreographing it second by " + f"second so the model never idles, loops, or repeats a beat to fill time.\n" + "\n" + "ABSOLUTE RULES:\n" + + no_cut_rule + action_rule + arc_rule + + "2) PHYSICAL CONTINUITY. The beats are one unbroken performance, not separate poses. " + "Each beat starts from the exact body position, orientation and momentum the previous " + "beat ended in. Never restart from a neutral stance mid-clip.\n" + + continuity_rule + + "3) STARTING PHYSICS. Read the character's CURRENT pose and what they are supported by " + "or in contact with from the SCENE ANCHORS (seated on something, kneeling, gripping an " + "object, leaning on a wall). The first beat must start FROM that exact situation and " + "honour its constraints — do not have them standing if the anchors show them seated " + "without first getting up. Spend at most one short clause on this grounding, and make " + "that clause itself an active verb, never a static description of the pose.\n" + "4) ONE PRIMARY MOTION PER BEAT. Each beat gets exactly one clear physical movement " + "that genuinely fills its time window — not a list of three things crammed into two " + "seconds, and not a held pose. Write plain physical movement: what the body does. " + "Present tense, third person.\n" + "4c) TAKE VARIATION. A TAKE VARIATION block is given below with an execution-style " + "note for this specific take. Let it shape HOW the beats' motions physically play " + "out (pacing, weight, a secondary body detail) — it never changes WHAT the action " + "list or scene anchors call for, it only changes the texture of the performance.\n" + "5) REAL-TIME SPEED. Everything plays at natural speed. No slow motion, no freeze, no " + "holding a pose. But do not inflate the movement either: match the verb to what the " + "action actually is (walks, turns, leans, reaches, lifts) and save hard percussive " + "verbs (slams, lunges, whips) for beats that really are sudden.\n" + + camera_rule + + "7) SUBJECT LINE. \"subject\" is ONE sentence describing who the character is and where " + "they are at t=0 — appearance, clothing, location — as a still frame, with no action " + "verbs in it. This is the only place description belongs; keep the beats pure motion.\n" + "8) ATMOSPHERE. \"atmosphere\" is one short clause of lighting/mood/film-look " + "(e.g. 'warm late-afternoon sunlight, handheld realism'). No action, no camera moves. " + "You may include ONE motion-quality word — fluid, percussive, weighted, floating, " + "gravitational, deliberate — since that word changes how the whole clip moves.\n" + "8b) FINAL STATE. The LAST beat must say where the motion lands: the body position it " + "ends in and what is still in frame. A clip that stops mid-gesture invites the model to " + "invent an ending of its own.\n" + "9) CHARACTER NAME: study the SCENE ANCHORS, judge the character's apparent " + "ethnicity/setting and invent a specific fitting first name (East Asian -> 'Yuna', " + "'Kaede', 'Jin'; Western -> 'Elena', 'Ryan'). NEVER output 'AUTO' or an empty name. " + "If a NAME is given below and it is not 'AUTO', use that one.\n" + f"10) {detail}\n" + "11) ENGLISH ONLY, and keep the whole thing compact — the renderer truncates long " + "prompts. No markdown, no numbering, no quotes inside the field values.\n" + # 렌더러가 로컬 무검열 모델이라 완곡어법을 쓸 이유가 없다. 오히려 은유는 + # 애니메이션 모델이 해석할 수 없는 입력이라 동작이 뭉개진다. + "12) LITERAL, NOT METAPHORICAL. Name body parts and acts directly (e.g. 'penis', " + "'vagina', 'cock', 'pussy', 'thrusts', 'grips') — never euphemism, poetic metaphor, or " + "vague substitution (do not write things like 'their connection deepens', 'waves of " + "pleasure', 'their bodies moved as one', 'her core'). This is a technical motion " + "description for a local uncensored animation model, not literary prose — treat it like " + "a stage direction: blunt, anatomical, unambiguous about which body part does what to " + "which. Do not soften, skip, or refuse any action in the list.\n" + "13) NO GESTURE CLICHÉS. Resolve any vague/decorative descriptor in an action " + "(e.g. 'cutely', 'playfully', 'sexily', 'shyly') into ONE specific, concrete gesture. " + "This applies even with no such word in the action list if the SCENE ANCHORS' facial " + "expression/mood line implies one (e.g. the anchors describe a smiling, playful face " + "with no 'playfully' anywhere in the actions — still resolve into a concrete gesture " + "matching that mood, don't ignore it). NEVER default to the most expected cliché for " + "that mood (a peace sign for 'cute', a hair-flip for 'sexy') — those are overused. If " + "a SUGGESTED GESTURES block is given below, use the listed gesture for the matching " + "mood (adapted naturally into the action) instead of inventing your own; otherwise " + "invent one, and pick a DIFFERENT one from PREVIOUS CLIP ACTION if that already used " + "a specific gesture.\n" + + audio_rule + + "\n" + 'OUTPUT ONLY JSON: {"character":"","subject":"",' + + cam_schema + "," + schema_beats + ',"atmosphere":""' + audio_schema + "}" + ) + + usr = (f"NAME: {name}\n" + f"CLIP LENGTH: {dur_s} seconds\n\n" + f"STYLE CUE (use only to calibrate how the movement feels and for the atmosphere " + f"line — do not quote it): {vibe.strip() or '(none)'}\n\n" + f"SCENE ANCHORS (from the input image — use the pose to ground the first beat and " + f"the overall look for the subject line):\n" + f"{anchors or '(none — no image given; invent a plausible subject from the actions)'}\n\n" + + (f"PREVIOUS CLIP ACTION (per rule 2b):\n{previous_action.strip()}\n\n" + if previous_action.strip() else "") + + _gesture_hints(actions, anchors, seed) + + _variation_hints(seed) + + f"{assign_label}\n{assign_body}\n\n" + f"BEAT SLOTS (fill every one, in order):\n{slot_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 = _call_openrouter(messages, llm_model, key, t, seed=seed, force_json=True, + usage_acc=getattr(self, "_usage_acc", None)) + parsed = self._validate_plan(self._extract(content), bounds, duration) + if parsed: + return parsed, f"✅ plan ok (attempt {attempt})" + last = f"⚠️ incomplete JSON (attempt {attempt}): {content[:150]!r}" + print(f"⚠️ [MinimaxDirector] plan attempt {attempt} 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"❌ [MinimaxDirector] {last}") + except (urllib.error.URLError, TimeoutError, KeyError, ValueError) as e: + last = f"❌ plan error (attempt {attempt}): {type(e).__name__}: {e}" + print(f"❌ [MinimaxDirector] {last}") + t = min(temp, 0.4) + return None, last + + def _extract(self, text): + # JSON 파싱은 검증된 기존 구현을 그대로 씀(코드펜스/트레일링 콤마 처리 포함). + return DolphinVisionDirector()._extract_json(text) + + def _validate_plan(self, parsed, bounds, duration): + """LLM 응답에서 beats를 꺼내 구간·동작이 실제로 채워졌는지 확인하고 정규화.""" + if not isinstance(parsed, dict): + return None + beats = parsed.get("beats") + if not isinstance(beats, list) or not beats: + return None + if bounds is not None and len(beats) != len(bounds): + # 슬롯 수가 안 맞으면 재시도. (개수가 맞아야 초 단위 지정이 의미가 있다.) + return None + clean = [] + for i, b in enumerate(beats): + if not isinstance(b, dict): + return None + action = str(b.get("action", "")).strip() + if not action: + return None + if bounds is not None: + start, end = bounds[i] + else: + try: + start, end = float(b.get("start")), float(b.get("end")) + except (TypeError, ValueError): + return None + if end <= start or end > duration + 1e-6: + return None + clean.append({"start": float(start), "end": float(end), "action": action, + "camera": _norm_camera(b.get("camera")), + "dialogue_manner": str(b.get("dialogue_manner", "") or "").strip(), + "dialogue_line": str(b.get("dialogue_line", "") or "").strip()}) + parsed["beats"] = clean + return parsed + + # ------------------------------------------------------------------ + # LLM 없이(키 없음/실패) 쓰는 로컬 폴백 + # ------------------------------------------------------------------ + # 폴백 전용 위상 라벨. 한 동작이 여러 비트에 걸칠 때 똑같은 문장을 그대로 반복하는 + # 대신 어느 단계인지라도 구분해준다. LLM 없이 할 수 있는 건 여기까지. + _PHASES = ["(wind-up)", "(committing)", "(mid-motion)", "(peak effort)", "(follow-through)"] + + def _local_beats(self, actions, bounds, duration): + """API 키가 없거나 LLM 플래닝이 실패했을 때 쓰는 로컬 분배. + + DolphinVisionDirector._split_actions_to_clips는 클립 4개 전제라 비트가 10개쯤 + 되면 남는 비트를 전부 "(move toward) <마지막동작>"으로 채워버린다 — 20초 클립에서 + 같은 줄이 6번 반복되는 꼴이라 여기서는 쓰지 않고 직접 나눈다.""" + if bounds is None: + bounds = _beat_bounds(duration, float(duration) / _auto_beat_count(duration)) + if not actions.strip(): + actions = "몸을 움직이기 시작한다, 동작에 힘이 실린다, 자세를 마무리한다" + parts = split_action_parts(actions) or [actions.strip()] + + n = len(bounds) + phrases = [] + if len(parts) >= n: + # 동작이 비트보다 많으면 연속된 것끼리 묶어서 한 비트에 넣는다. + k, m = divmod(len(parts), n) + idx = 0 + for i in range(n): + take = k + (1 if i < m else 0) + phrases.append(", ".join(parts[idx:idx + take])) + idx += take + else: + # 동작이 적으면 각 동작에 연속된 비트 구간을 통째로 배정하고, + # 그 안에서는 위상 라벨로 구분한다. + k, m = divmod(n, len(parts)) + for i, phrase in enumerate(parts): + span = k + (1 if i < m else 0) + for j in range(span): + if span == 1: + phrases.append(phrase) + else: + pos = int(round(j * (len(self._PHASES) - 1) / (span - 1))) + phrases.append(f"{self._PHASES[pos]} {phrase}") + + # 로컬 폴백은 LLM이 없어서 맥락에 맞는 대사를 지어낼 수 없다 - 항상 빈 채로 + # 두고, _assemble이 DIALOGUE_NEGATIVE로 무대사를 명시하게 맡긴다. + return [{"start": a, "end": b, "action": phrases[i], "camera": "", + "dialogue_manner": "", "dialogue_line": ""} + for i, (a, b) in enumerate(bounds)] + + # ------------------------------------------------------------------ + # 최종 프롬프트 조립 + # ------------------------------------------------------------------ + def _resolve_camera(self, beats, camera_mode, camera_manual, camera_overall): + """카메라를 (구간별 대괄호) 또는 (클립 전체를 관통하는 문장 하나) 중 하나로 확정. + + H3에서는 비트마다 카메라를 갈아끼우는 것 자체가 컷 신호라서, 기본값은 + '전체 하나'다. 구간별 대괄호는 구형 Director 모델용으로만 남겨둔다.""" + for b in beats: + b.setdefault("camera", "") + if camera_mode.startswith("Per-segment"): + manual = str(camera_manual or "") + if "|" in manual: + groups = [_norm_camera(g) for g in manual.split("|")] + for i, b in enumerate(beats): + b["camera"] = groups[i] if i < len(groups) else "" + return beats, "" + + for b in beats: + b["camera"] = "" + if camera_mode.startswith("off"): + return beats, "" + # 전체 카메라 문장에 대괄호가 남으면 그것도 샷 마커로 읽힌다. + if camera_mode.startswith("manual"): + return beats, _strip_brackets(camera_manual or "") + return beats, _strip_brackets(camera_overall or "") + + def _assemble(self, subject, beats, atmosphere, camera_overall, style, name, no_cut, + include_audio): + head = subject.strip().strip(",. ") + if name and head and name.lower() not in head.lower(): + head = f"{name}, {head[0].lower() + head[1:]}" + elif name and not head: + head = name + blocks = [] + if head: + blocks.append(head.rstrip(".") + ".") + + # 어느 비트든 대사가 하나라도 있으면 DIALOGUE_NEGATIVE를 안 붙인다 - 명시된 + # 대사와 "대사 없음" 선언이 동시에 들어가면 모순이라서. + any_dialogue = any(str(b.get("dialogue_line", "")).strip() for b in beats) + + def _dialogue_sentence(b): + """비트의 dialogue_manner/dialogue_line에서 화자 절을 만든다. + manner(전달 방식 묘사)는 산문으로 서술하고, 실제 발화 내용만 공식 H3 + ... 태그로 감싼다 - 한 필드에 같이 넣었더니 톤 묘사 단어까지 + H3가 그대로 발화하는 문제가 있어서 필드부터 분리했다(2026-08-25).""" + line = str(b.get("dialogue_line", "")).strip() + if not line: + return "" + manner = str(b.get("dialogue_manner", "")).strip() + if manner: + return f"{name}, {manner}, says {line}" + return f"{name} says {line}" + + def _dlg_clause(b): + """비트에 대사가 있으면 이어붙일 절(선행 콤마 포함)을 반환. 없으면 빈 문자열.""" + s = _dialogue_sentence(b) + return f", {s}" if s else "" + + if style.startswith("Continuous prose"): + # 숫자 타임코드를 한 개도 쓰지 않고 지속시간을 말로만 표현한다. + # H3는 `0-2s` 같은 숫자 마커를 컷 지점 선언으로 읽기 때문 - 타이밍은 + # 전달하면서 컷 신호는 안 주는 유일한 방법. + segs = [] + for i, b in enumerate(beats): + d, approx = _dur_parts(b["end"] - b["start"]) + fuzzy = "roughly " if approx else "" + body = _lower_first(_strip_brackets(b["action"])) + if i == 0: + lead = f"For {fuzzy}the first {d}" + elif i == len(beats) - 1: + lead = f"and through {fuzzy}the final {d}" + else: + lead = f"then for {fuzzy}the next {d}" + segs.append(f"{lead} {body}{_dlg_clause(b)}") + blocks.append(", ".join(segs).rstrip(".") + ".") + elif style.startswith("Second timeblocking"): + # 정확한 초를 지켜야 할 때만. H3에선 이게 곧 멀티샷 블로킹이라 컷이 난다. + segs = [f"{_fmt_sec(b['start'])}-{_fmt_sec(b['end'])}s " + f"{_lower_first(_strip_brackets(b['action']))}{_dlg_clause(b)}" + for b in beats] + blocks.append(", ".join(segs).rstrip(".") + ".") + elif style.startswith("Sequential connectives"): + segs = [] + for i, b in enumerate(beats): + body = _lower_first(_strip_brackets(b["action"])) + if i == 0: + lead = "First," + elif i == len(beats) - 1: + lead = "finally" + else: + lead = _SEQ_CONNECTIVES[(i - 1) % len(_SEQ_CONNECTIVES)] + segs.append(f"{lead} {body}{_dlg_clause(b)}") + blocks.append(", ".join(segs).rstrip(".") + ".") + elif style.startswith("Multi-shot"): + # 컷을 '일부러' 낼 때만. H3 공식 문법: [Shot 1]은 타임코드 없이, + # 이후 샷은 [Shot N] At mm:ss.mmm, ... 로 컷 시점을 못 박는다. + for i, b in enumerate(beats, 1): + body = b["action"].strip().rstrip(".") + if i == 1: + line = f"[Shot 1] {body}." + else: + # 마커 자체가 컷을 뜻하므로 "the camera cuts to"를 덧붙이지 않는다 + # (붙이면 "cuts to she strides"처럼 비문이 된다). + line = f"[Shot {i}] At {_fmt_timecode(b['start'])}, {_lower_first(body)}." + if b["camera"]: + line = f"{line} {b['camera']}" + s = _dialogue_sentence(b) + if s: + line = f"{line} {s}" + blocks.append(line) + else: # Director 대괄호 + for b in beats: + s, e = _fmt_sec(b["start"]), _fmt_sec(b["end"]) + line = f"[{s}-{e}s] {b['action'].strip().rstrip('.')}." + if b["camera"]: + line = f"{line} {b['camera']}" + s2 = _dialogue_sentence(b) + if s2: + line = f"{line} {s2}" + blocks.append(line) + + # 한 문단으로 붙는 모드에서는 이것들이 문장 중간에 오므로 첫 글자를 올린다. + if camera_overall.strip(): + blocks.append(_sentence(camera_overall)) + if atmosphere.strip(): + blocks.append(_sentence(atmosphere)) + # 대사가 어디에도 안 들어갔으면 "무대사"를 명시한다 - 공식 HF 디스커션 + # #76에서 확인된 대로, 명시 안 하면 H3가 의미불명의 음성을 지어낸다. + if include_audio and not any_dialogue: + blocks.append(DIALOGUE_NEGATIVE) + # 멀티샷은 컷이 목적이라 제약을 붙이면 자기모순이 된다. + if no_cut and not style.startswith("Multi-shot"): + blocks.append(NO_CUT_CONSTRAINT) + + # 줄바꿈도 "여기서 끊긴다"는 구분자 신호로 읽힌다. 연속 테이크를 노리는 + # 스타일은 전부 한 문단으로 붙이고, 샷을 나누는 스타일만 줄로 분리한다. + if style.startswith("Continuous prose") or style.startswith("Sequential connectives"): + return " ".join(blocks) + return "\n".join(blocks) + + def _finalize_output(self, body, soundscape, music, image_present, + include_audio, i2v_prefix): + """공식 H3 포맷으로 최종 감싸기. `body`(기존 _assemble 결과, 컷 회피 로직 + 검증 완료)는 절대 건드리지 않고 앞뒤로 라벨/프리픽스만 붙인다. + + i2v_prefix는 include_audio와 무관하게 독립 토글 - 정렬 문장은 오디오 + 유무와 상관없이 이미지가 있으면 붙는 게 공식 스펙.""" + parts = [] + if i2v_prefix and image_present: + parts.append("For the target video, at 0.00 seconds into the target video, " + " (from [Shot 1]) is fully referenced.") + if include_audio: + parts.append(f"integrated_multimodal_description: {body}") + if soundscape.strip(): + parts.append(f"overall_soundscape: {soundscape.strip()}") + if music.strip(): + parts.append(f"non_diegetic_music: {music.strip()}") + else: + parts.append(body) + return "\n\n".join(parts) + + # ------------------------------------------------------------------ + def direct(self, essential_actions, duration_seconds, beat_seconds, prompt_style, + no_cut_constraint, camera_mode, camera_manual, character_name, artistic_vibe, + detail_level, use_vision, openrouter_api_key, vlm_preset, vlm_model, llm_preset, + llm_model, creativity, seed, include_audio_fields=True, + i2v_alignment_prefix=False, **kw): + + engine = DolphinVisionDirector() + # 비전(engine._call_or)과 플래닝(_call_openrouter)이 같은 dict에 토큰/비용 누적. + self._usage_acc = {"prompt": 0, "completion": 0, "cost": 0.0} + engine._usage_acc = self._usage_acc + image = kw.get("image", None) + vision_caption = str(kw.get("vision_caption", "") or "").strip() + previous_action = str(kw.get("previous_action", "") or "").strip() + + dur = float(duration_seconds) + bounds = _beat_bounds(dur, float(beat_seconds)) + 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 앵커 ---- + anchors, dbg_v = "", "" + if vision_caption: + anchors, dbg_v = vision_caption, "vision ok (local caption node)" + elif not use_vision: + dbg_v = "vision OFF (check toggle)" + elif image is None: + dbg_v = "no image (T2V mode)" + elif not key: + dbg_v = "no api key" + print("❌ [MinimaxDirector] openrouter_api_key missing (neither widget nor OPENROUTER_API_KEY env var set) - vision/LLM unavailable") + else: + data_uri = tensor_to_base64(image) + if data_uri: + anchors, err = engine._vision_analyze(data_uri, vlm, key, min(creativity, 0.5)) + dbg_v = err or "vision ok" + else: + dbg_v = "image encode failed / PIL missing" + + # ---- STEP 2: 초 단위 비트 플래닝 ---- + if camera_mode.startswith("Per-segment"): + camera_style = "perbeat" + elif camera_mode.startswith("off") or camera_mode.startswith("manual"): + camera_style = "none" + else: + camera_style = "continuous" + # 멀티샷 스타일은 컷을 내는 게 목적이라 연속 테이크 제약을 걸면 자기모순. + no_cut = bool(no_cut_constraint) and not prompt_style.startswith("Multi-shot") + subject, atmosphere, camera_overall = "", artistic_vibe.strip(), "" + if key: + parsed, dbg_p = self._plan_beats( + anchors, essential_actions, character_name, artistic_vibe, + engine._detail_hint(detail_level), dur, bounds, camera_style, no_cut, + llm, key, creativity, seed, previous_action=previous_action, + include_audio=bool(include_audio_fields)) + else: + parsed, dbg_p = None, "no api key" + + if parsed: + beats = parsed["beats"] + subject = str(parsed.get("subject", "")).strip() + atmosphere = str(parsed.get("atmosphere", "")).strip() or artistic_vibe.strip() + camera_overall = str(parsed.get("camera", "")).strip() + cand = str(parsed.get("character", "")).strip() + if not cand or cand.upper() == "AUTO": + cand = engine._fallback_name(anchors) + final_name = user_name or cand + soundscape = str(parsed.get("soundscape", "")).strip() if include_audio_fields else "" + music = str(parsed.get("music", "")).strip() if include_audio_fields else "" + else: + beats = self._local_beats(essential_actions, bounds, dur) + # 폴백에선 앵커를 그대로 피사체 문장으로 쓴다(한 줄로 눌러서). + subject = " ".join(anchors.split())[:200] + final_name = user_name or (engine._fallback_name(anchors) if anchors else "") + # 로컬 폴백은 LLM이 없어서 오디오 문장을 지어낼 수 없다 - 빈 채로 둔다 + # (_finalize_output이 빈 문자열이면 해당 라벨 섹션 자체를 생략함). + soundscape, music = "", "" + + # ---- STEP 3: 카메라 확정 + 조립 ---- + beats, camera_overall = self._resolve_camera(beats, camera_mode, camera_manual, + camera_overall) + body = self._assemble(subject, beats, atmosphere, camera_overall, prompt_style, + final_name, no_cut, bool(include_audio_fields)) + + # 연속 테이크를 노리는데 숫자 시각 표기가 새어나갔으면 잡아준다(LLM 규칙 0b 위반). + # 초 타임블로킹/Director는 숫자를 내는 게 정상이라 검사 대상이 아니다. 오디오 + # 필드(soundscape/music)는 별도 텍스트라 이 검사 대상이 아니다. + if no_cut and not prompt_style.startswith(("Second timeblocking", "Director brackets")): + leaked = re.findall(r"\d+\s*(?:-\s*\d+\s*)?(?:s\b|sec|seconds?\b)|\d{2}:\d{2}", body) + if leaked: + print(f"⚠️ [MinimaxDirector] Numeric time markers leaked into the prompt {leaked[:5]} - " + f"H3 reads these as cut points. This means the LLM violated rule 0b; " + f"re-run, or use the 'Sequential connectives' style.") + + i2v_prefix_applied = bool(i2v_alignment_prefix) and image is not None + prompt = self._finalize_output(body, soundscape, music, image is not None, + bool(include_audio_fields), bool(i2v_alignment_prefix)) + + # 하드 상한은 실제로 필드가 잘리는 기준이라 오디오 라벨까지 합친 최종 + # prompt(전체 전송분) 기준. 소프트 상한("길면 지시를 평균낸다")은 원래 + # 타임라인/동작 지시가 있는 body 분량에 대한 경험칙이라, 오디오 라벨/ + # soundscape/music/I2V 프리픽스처럼 body 뒤에 붙는 별개 콘텐츠까지 합쳐서 + # 재면 오디오 필드를 켜기만 해도 항상 걸려서 무의미한 경고가 된다. + if len(prompt) > MINIMAX_FIELD_LIMIT: + print(f"❌ [MinimaxDirector] Prompt exceeds the field limit ({MINIMAX_FIELD_LIMIT} chars) " + f"by {len(prompt) - MINIMAX_FIELD_LIMIT} chars - the tail will be truncated.") + elif len(body) > MINIMAX_SOFT_LIMIT: + print(f"⚠️ [MinimaxDirector] The video description (body) is {len(body)} chars " + f"(recommended under {MINIMAX_SOFT_LIMIT}, full prompt is {len(prompt)} chars). " + f"If it's too long the model averages out the instructions and follows the " + f"timeline less closely - lower detail_level or reduce the beat count. " + f"(soundscape/music/I2V prefix are not counted against this limit.)") + if prompt_style.startswith("Director brackets"): + print("⚠️ [MinimaxDirector] 'Director brackets' is for legacy T2V-01-Director-family " + "models only. Using it with H3 makes [0-2s] read as a shot marker, causing a " + "cut at every timecode.") + elif prompt_style.startswith("Second timeblocking") and no_cut: + print("⚠️ [MinimaxDirector] 'Second timeblocking' is effectively H3's multi-shot " + "blocking syntax - numeric timecodes are read as cut-point declarations, which " + "conflicts with the no_cut constraint. To avoid cuts, use 'Continuous prose' " + "or 'Sequential connectives'.") + if include_audio_fields and key and not (soundscape or music): + print("⚠️ [MinimaxDirector] include_audio_fields is ON but the LLM didn't fill " + "soundscape/music - they will be omitted from the output.") + + timeline_json = json.dumps( + {"duration": dur, "character": final_name, "subject": subject, + "camera": camera_overall, "atmosphere": atmosphere, "beats": beats, + "soundscape": soundscape, "non_diegetic_music": music, + "i2v_alignment_prefix_applied": i2v_prefix_applied}, + ensure_ascii=False, indent=2) + # llm/vlm 슬러그를 debug_info에 노출한다 - preset 드롭다운을 바꿔도 실제로 + # 어떤 모델이 호출됐는지 확인할 화면 요소가 없어서, JS 프리셋 동기화가 + # 깨졌을 때("모델이 안 바뀐다" 리포트, 2026-08-28) 눈으로 검증할 방법이 + # 아예 없었다. resolve_slug()가 실제로 고른 값을 그대로 찍어서 다음엔 + # 여기만 봐도 바로 확인되게 한다. + debug = (f"llm={llm} | vlm={vlm} | vision={dbg_v} | plan={dbg_p} | char={final_name} | " + f"{_fmt_sec(dur)}s / {len(beats)} beats | " + f"{'no-cut' if no_cut else 'cuts allowed'} | " + f"audio={'on' if include_audio_fields else 'off'} | " + f"i2v_prefix={'on' if i2v_prefix_applied else 'off'} | {len(prompt)} chars") + _pt = _price_tag(llm, self._usage_acc, key) + if _pt: + debug += f" | {_pt}" + + print(f"\n🎬🅜 [Dolphin Minimax Director] {debug}") + for b in beats: + dl = b.get("dialogue_line", "") + dm = b.get("dialogue_manner", "") + dlg = f" 💬{dm + ' ' if dm else ''}{dl}" if dl else "" + print(f" {_fmt_sec(b['start'])}-{_fmt_sec(b['end'])}s {b['action'][:80]} {b['camera']}{dlg}") + if camera_overall: + print(f" 📷 {camera_overall}") + if soundscape: + print(f" 🔊 {soundscape}") + if music: + print(f" 🎵 {music}") + + return (prompt, timeline_json, debug, final_name, soundscape, music) + + +NODE_CLASS_MAPPINGS = { + "DolphinMinimaxDirector": DolphinMinimaxDirector, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DolphinMinimaxDirector": "🎬🅜 Dolphin Minimax Director (second-by-second timeline → Hailuo prompt)", +} diff --git a/custom_nodes/dolphin_nodes/dolphin_prompt_director.py b/custom_nodes/dolphin_nodes/dolphin_prompt_director.py new file mode 100644 index 0000000000000000000000000000000000000000..2bcdec5615882a2f7c08cdf48a381375d3c9f419 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_prompt_director.py @@ -0,0 +1,331 @@ +# -*- coding: utf-8 -*- +""" +DolphinVisionDirector와 완전히 동일한 비전+LLM 프롬프트 엔진을 쓰되, +MODEL 입력/LoRA 적용/트리거워드 주입을 전부 뺀 "프롬프트 분배 전용" 버전. + +로라/모델 로딩은 별도 노드(Architect V16 등)에 맡기고, 이 노드는 +이미지+필수동작 -> N개 클립 프롬프트 문자열만 뽑는 역할에 집중한다. + +엔진 로직(비전 분석/샷 플래닝/로컬 폴백/프롬프트 조립)은 DolphinVisionDirector의 +메서드를 그대로 재사용한다(중복 구현하면 프롬프트 정책이 두 파일에서 따로 +놀게 되므로, 여기서는 새로 만들지 않고 위임한다). 그 메서드들은 로라/모델 +상태를 전혀 건드리지 않는 순수 로직이라 안전하게 재사용 가능. +""" +from .dolphin_vision_director import ( + DolphinVisionDirector, + resolve_slug, + resolve_api_key, + _price_tag, + tensor_to_base64, + VLM_PRESETS, + VLM_PRESET_LABELS, + LLM_PRESETS, + LLM_PRESET_LABELS, +) + + +class DolphinPromptDirector: + """ + 이미지 하나와 '필수 동작'만 넣으면 N개 연속 클립의 프롬프트 문자열이 + 완성되는 노드. DolphinVisionDirector에서 MODEL/LoRA 관련 부분을 전부 + 뺀 버전 — 모델/로라는 별도 노드(Architect V16 등)에서 처리한다는 전제. + + 1) IMAGE -> 비전 VLM(또는 vision_caption 입력)이 앵커(자세/지지물/환경) 파악 + 2) 그 맥락 위에서 essential_actions를 N샷으로 분배 (LLM, 실패 시 로컬 폴백) + 3) 클립별 최종 프롬프트 문자열 조립 (로라 트리거 없음) + """ + + @classmethod + def INPUT_TYPES(s): + req = { + "image": ("IMAGE",), + "essential_actions": ("STRING", {"multiline": True, + "default": ("charges forward, swings a sword to strike down the enemy, deflects " + "a bullet, finishes them off")}), + "num_clips": (["4 (20s)", "3 (15s)", "2 (10s)"], {"default": "4 (20s)", + "tooltip": "Number of clips actually used. Remaining clip slots repeat the last " + "clip's content."}), + "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": (["Short (concise)", "Essentials + anchors only", "Detailed"], + {"default": "Essentials + anchors only"}), + "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 (special cases · recommended)"}), + "vlm_model": ("STRING", {"default": "qwen/qwen2.5-vl-72b-instruct"}), + "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)"}), + "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 = { + # 로컬 캡션 노드(JoyCaption 등)의 STRING 출력을 연결하면 그걸 앵커로 쓰고 + # OpenRouter VLM 호출은 건너뜀. 비워두면 OpenRouter VLM 경로로 폴백. + "vision_caption": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + } + return {"required": req, "optional": opt} + + RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING") + RETURN_NAMES = ("c1_prompt", "c2_prompt", "c3_prompt", "c4_prompt") + FUNCTION = "direct" + CATEGORY = "Dolphin" + + def direct(self, image, essential_actions, num_clips, character_name, artistic_vibe, + pacing, detail_level, use_vision, openrouter_api_key, vlm_preset, vlm_model, + llm_preset, llm_model, creativity, seed, **kw): + + # 비전/플래닝 엔진은 DolphinVisionDirector 것을 그대로 위임한다 (로직 중복 방지). + # 이 인스턴스로는 _vision_analyze/_plan_shots/_local_split/_build_final 같은 + # 순수 메서드만 호출하고, MODEL/LoRA를 만지는 _patch_clip 등은 아예 안 씀. + engine = DolphinVisionDirector() + engine._usage_acc = {"prompt": 0, "completion": 0, "cost": 0.0} + + n_clips = int(str(num_clips).split()[0]) + 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 ---- + 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"👁 [PromptDirector] Using local caption input (JoyCaption etc.):\n{anchors[:300]}") + elif not use_vision: + dbg_v = "vision OFF (check toggle)" + print("ℹ️ [PromptDirector] use_vision=OFF - skipping vision") + elif not key: + dbg_v = "no api key" + print("❌ [PromptDirector] openrouter_api_key missing (neither widget nor OPENROUTER_API_KEY env var set) - vision/LLM unavailable") + elif image is None: + dbg_v = "no image" + print("❌ [PromptDirector] image input missing - check the IMAGE connection") + else: + data_uri = tensor_to_base64(image) + if data_uri: + anchors, err = engine._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"⚠️ [PromptDirector] Vision anchors are empty -> no basis for name/scene. Cause: {dbg_v}") + + # ---- STEP 2: N샷 분배 ---- + if key: + parsed, dbg_p = engine._plan_shots(anchors, essential_actions, character_name, + artistic_vibe, pacing, engine._detail_hint(detail_level), + llm, key, creativity, seed, num_clips=n_clips) + if parsed: + clips = [str(parsed[f"clip_{i}"]).strip() for i in range(1, n_clips + 1)] + master = str(parsed.get("master_scene", "")).strip() + cand = str(parsed.get("character", "")).strip() + if not cand or cand.upper() == "AUTO": + cand = engine._fallback_name(anchors) + final_name = user_name or cand + else: + clips = engine._local_split(essential_actions, num_clips=n_clips) + master = anchors.replace("\n", " ")[:160] + final_name = user_name or engine._fallback_name(anchors) + else: + parsed, dbg_p = None, "no api key" + clips = engine._local_split(essential_actions, num_clips=n_clips) + master = anchors.replace("\n", " ")[:160] + final_name = user_name + + if len(clips) < 4: + clips = clips + [clips[-1]] * (4 - len(clips)) + + # ---- STEP 3: 프롬프트 조립 (로라/트리거 없음) ---- + outs = [] + for c in range(1, 5): + if c > n_clips: + prompt = engine._build_final(clips[c - 1], master, final_name, "") + outs.append(prompt) + print(f" [clip{c}] SKIPPED (num_clips={n_clips}) - text only, ignored downstream") + continue + prompt = engine._build_final(clips[c - 1], master, final_name, "") + outs.append(prompt) + + debug = f"vlm={dbg_v} | plan={dbg_p} | char={final_name} | clips={n_clips} ({n_clips*5}s)" + _pt = _price_tag(llm, getattr(engine, "_usage_acc", None), key) + if _pt: + debug += f" | {_pt}" + print(f"\n🎬 [Dolphin Prompt Director] {debug}\nANCHORS: {anchors[:120]}") + for i, p in enumerate(outs, 1): + print(f" CLIP {i}: {p[:90]}") + + return tuple(outs) + + +class DolphinActionSplitter: + """ + essential_actions 텍스트 하나를 클립별 액션 조각으로 나누기만 하는 노드. + LLM/비전 호출이 전혀 없는 순수 로컬 로직(정규식 기반)이라 비용이 0에 가깝고, + 시드나 렌더링 개수와 무관하게 항상 같은 입력에 같은 출력을 낸다. + + DolphinVisionDirector처럼 "몇 클립 낼지"와 "플래닝"이 한 노드에 묶여있으면, + 렌더링 개수만 바꿔도(예: 2클립->4클립 연장) 전체 재플래닝이 발생해서 이미 만든 + 클립까지 캐시가 깨진다. 이 노드는 그 분배 단계만 따로 떼어내서 - total_clips를 + 항상 고정해두면(보통 4) 이 노드의 출력은 절대 안 바뀌고, 그 뒤에 물리는 클립별 + DolphinPromptDirectorSingle들도 자기 몫 액션 조각이 그대로면 캐시가 유지된다. + """ + + @classmethod + def INPUT_TYPES(s): + req = { + "essential_actions": ("STRING", {"multiline": True, + "default": ("charges forward, swings a sword to strike down the enemy, deflects " + "a bullet, finishes them off")}), + "total_clips": (["4", "3", "2"], {"default": "4", + "tooltip": "How many beats to split into. If you later increase the render count " + "without changing this value, the action chunks of clips you've " + "already made stay the same."}), + } + return {"required": req} + + RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING") + RETURN_NAMES = ("c1_action", "c2_action", "c3_action", "c4_action") + FUNCTION = "split" + CATEGORY = "Dolphin" + + def split(self, essential_actions, total_clips): + engine = DolphinVisionDirector() + n = int(total_clips) + assign = engine._split_actions_to_clips(essential_actions, n) + outs = [", ".join(assign[c]) for c in range(1, n + 1)] + if len(outs) < 4: + outs = outs + [""] * (4 - len(outs)) + print(f"✂️ [ActionSplitter] {n} clips: " + " | ".join(f"c{i+1}={o[:40]}" for i, o in enumerate(outs) if o)) + return tuple(outs) + + +class DolphinPromptDirectorSingle: + """ + DolphinPromptDirector의 단일 프롬프트 버전. num_clips 개념 없이 + 이미지 하나 + '동작' 하나로 프롬프트 문자열 1개만 뽑는다. + + 5초 단발 테스트(로라 테스트 워크플로우 등)처럼 클립 분할/연속성 로직이 + 전혀 필요 없는 경우를 위한 것 - 엔진 내부적으로 num_clips=1로 호출하면 + _plan_shots/_local_split가 이미 단일 클립 케이스를 지원하므로 그대로 위임. + """ + + @classmethod + def INPUT_TYPES(s): + req = { + "image": ("IMAGE",), + "action": ("STRING", {"multiline": True, + "default": "charges forward, swings a sword to strike down the enemy"}), + "character_name": ("STRING", {"default": "AUTO"}), + "artistic_vibe": ("STRING", {"multiline": True, + "default": "cinematic lighting, dynamic motion"}), + "detail_level": (["Short (concise)", "Essentials + anchors only", "Detailed"], + {"default": "Essentials + anchors only"}), + "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 (special cases · recommended)"}), + "vlm_model": ("STRING", {"default": "qwen/qwen2.5-vl-72b-instruct"}), + "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)"}), + "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, "forceInput": True}), + } + opt = { + "vision_caption": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + # 이전 클립이 만든 prompt(또는 그 액션 부분)를 연결하면, 이번 클립이 그 + # 동작이 끝난 지점부터 자연스럽게 이어지도록 LLM에 문맥으로 넘긴다. + # 비워두면(체인의 첫 클립) 그냥 단독 클립처럼 취급. + "previous_action": ("STRING", {"multiline": True, "default": "", "forceInput": True}), + # DolphinTripleLoraMatrix 등 로라 패처의 trigger_words 출력을 연결하면 + # 최종 프롬프트 상단(이름 옆)에 붙는다. DolphinVisionDirector의 + # inject_triggers 기능과 동일한 자리. + "trigger_words": ("STRING", {"default": "", "forceInput": True}), + } + return {"required": req, "optional": opt} + + RETURN_TYPES = ("STRING", "STRING", "STRING") + RETURN_NAMES = ("prompt", "debug_info", "resolved_character_name") + FUNCTION = "direct" + CATEGORY = "Dolphin" + + def direct(self, image, action, character_name, artistic_vibe, detail_level, use_vision, + openrouter_api_key, vlm_preset, vlm_model, llm_preset, llm_model, creativity, seed, **kw): + + engine = DolphinVisionDirector() + engine._usage_acc = {"prompt": 0, "completion": 0, "cost": 0.0} + previous_action = str(kw.get("previous_action", "") or "").strip() + trigger_words = str(kw.get("trigger_words", "") or "").strip() + 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 ---- + 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)" + elif not use_vision: + dbg_v = "vision OFF (check toggle)" + elif not key: + dbg_v = "no api key" + elif image is None: + dbg_v = "no image" + else: + data_uri = tensor_to_base64(image) + if data_uri: + anchors, err = engine._vision_analyze(data_uri, vlm, key, min(creativity, 0.5)) + dbg_v = err or "vision ok" + else: + dbg_v = "image encode failed / PIL missing" + + # ---- STEP 2: 단일 샷 플래닝 ---- + if key: + parsed, dbg_p = engine._plan_shots(anchors, action, character_name, artistic_vibe, + "auto", engine._detail_hint(detail_level), + llm, key, creativity, seed, num_clips=1, + continuity_context=previous_action) + if parsed: + clip = str(parsed["clip_1"]).strip() + master = str(parsed.get("master_scene", "")).strip() + cand = str(parsed.get("character", "")).strip() + if not cand or cand.upper() == "AUTO": + cand = engine._fallback_name(anchors) + final_name = user_name or cand + else: + clip = engine._local_split(action, num_clips=1)[0] + master = "" + final_name = user_name or engine._fallback_name(anchors) + else: + parsed, dbg_p = None, "no api key" + clip = engine._local_split(action, num_clips=1)[0] + master = "" + final_name = user_name + + prompt = engine._build_final(clip, master, final_name, trigger_words) + debug = f"vlm={dbg_v} | plan={dbg_p} | char={final_name}" + _pt = _price_tag(llm, getattr(engine, "_usage_acc", None), key) + if _pt: + debug += f" | {_pt}" + print(f"\n🎬 [Dolphin Prompt Director Single] {debug}\nPROMPT: {prompt[:200]}") + + return (prompt, debug, final_name) + + +NODE_CLASS_MAPPINGS = { + "DolphinPromptDirector": DolphinPromptDirector, + "DolphinPromptDirectorSingle": DolphinPromptDirectorSingle, + "DolphinActionSplitter": DolphinActionSplitter, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DolphinPromptDirector": "🎬📝 Dolphin Prompt Director (Image→N Prompts, no model/LoRA)", + "DolphinPromptDirectorSingle": "🎬📝 Dolphin Prompt Director (Single, Image→1 Prompt)", + "DolphinActionSplitter": "✂️ Dolphin Action Splitter (deterministic, no LLM)", +} diff --git a/custom_nodes/dolphin_nodes/dolphin_seed_controller.py b/custom_nodes/dolphin_nodes/dolphin_seed_controller.py new file mode 100644 index 0000000000000000000000000000000000000000..b0d781221c1adcf4b2b3ed1aae7fd6a999956fff --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_seed_controller.py @@ -0,0 +1,70 @@ +class DolphinBatchSeedController: + """ + 클립 4개짜리 파이프라인(First + Extend×3)의 영상 샘플러 시드를 한 곳에서 + 스위칭하기 위한 노드. 기존에는 클립마다 서로 다른 서브그래프 안에 묻힌 + SeedGenerator 위젯을 4번 따로 조작해야 "매 배치 랜덤"과 "특정 클립부터 + 재생성"을 오갈 수 있었다. + + "seed"라는 이름의 INT 위젯에는 ComfyUI 프론트엔드가 자동으로 + control_after_generate(fixed/increment/decrement/randomize) 드롭다운을 + 붙여준다 - 그래서 "전체 랜덤"/"특정 클립부터 재생성"에 필요한 새 시드는 + 이 위젯 하나의 randomize/increment만으로 나오고, 노드 자체는 매 실행 + 무작위를 호출할 필요 없는 순수 함수로 남는다. + + 주의: "특정 클립부터 재생성"이지 "그 클립만 재생성"이 아니다. Extend + 서브그래프는 이전 클립의 실제 디코딩된 프레임을 입력으로 받는 continuity + 구조라, N번 클립 시드를 바꾸면 N+1, N+2... 클립은 자기 시드값이 그대로여도 + 입력 latent가 달라져서 캐시가 깨지고 무조건 다시 렌더링된다. N보다 앞 + 클립들만 그대로 캐시된다. + """ + + MODE_FIXED = "🔒 고정 (재현)" + MODE_RANDOM_ALL = "🎲 전체 랜덤 (매 배치)" + MODE_REGEN_FROM = "♻️ 특정 클립부터 재생성 (그 뒤 클립도 연쇄 재생성됨)" + + GOLDEN = 0x9E3779B97F4A7C15 + MASK = 0xFFFFFFFFFFFFFFFF + + @classmethod + def INPUT_TYPES(s): + req = { + "mode": ([s.MODE_FIXED, s.MODE_RANDOM_ALL, s.MODE_REGEN_FROM], {"default": s.MODE_FIXED}), + "regen_clip": (["1", "2", "3", "4"], {"default": "1", + "tooltip": "'특정 클립부터 재생성' 모드에서 몇 번 클립부터 새 시드를 줄지. " + "그 클립 이후 클립들도 continuity 때문에 같이 다시 렌더링된다 " + "(이전 클립들만 캐시 유지)."}), + "seed": ("INT", {"default": 0, "min": 0, "max": s.MASK}), + "c1_seed": ("INT", {"default": 196010661098274, "min": 0, "max": s.MASK}), + "c2_seed": ("INT", {"default": 316514109857979, "min": 0, "max": s.MASK}), + "c3_seed": ("INT", {"default": 732840722629067, "min": 0, "max": s.MASK}), + "c4_seed": ("INT", {"default": 599880917933127, "min": 0, "max": s.MASK}), + } + return {"required": req} + + RETURN_TYPES = ("INT", "INT", "INT", "INT", "STRING") + RETURN_NAMES = ("c1_seed", "c2_seed", "c3_seed", "c4_seed", "debug_info") + FUNCTION = "get_seeds" + CATEGORY = "Dolphin" + + def get_seeds(self, mode, regen_clip, seed, c1_seed, c2_seed, c3_seed, c4_seed): + base = [c1_seed, c2_seed, c3_seed, c4_seed] + + if mode == self.MODE_RANDOM_ALL: + out = [(seed + i * self.GOLDEN) & self.MASK for i in range(4)] + elif mode == self.MODE_REGEN_FROM: + out = list(base) + out[int(regen_clip) - 1] = seed + else: + out = list(base) + + debug = f"mode={mode} | c1={out[0]} c2={out[1]} c3={out[2]} c4={out[3]}" + print(f"🎲 [BatchSeedController] {debug}") + return (*out, debug) + + +NODE_CLASS_MAPPINGS = { + "DolphinBatchSeedController": DolphinBatchSeedController, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DolphinBatchSeedController": "🎲 Dolphin Batch Seed Controller (random-all / regen-from-clip)", +} diff --git a/custom_nodes/dolphin_nodes/dolphin_tags_cache.json b/custom_nodes/dolphin_nodes/dolphin_tags_cache.json new file mode 100644 index 0000000000000000000000000000000000000000..b9963530381abfebfdb5091d164be2aba39e7634 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_tags_cache.json @@ -0,0 +1,63 @@ +{ + "wan2.1-i2v-480p-penis-arousal-v1.0.safetensors::359305272::1754578081": [ + "penis becomes erect" + ], + "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" + ], + "wa22-4n4lfi5tin6-40epoc-low-k3nk.safetensors::306807976::1783607997": [ + "4n4lfi5tin6", + "4n4lfi5tin6, a close-up rear view of a woman engaging in anal fisting with her arm inside her anus." + ], + "wa22-4n4lfi5tin6-93epoc-high-k3nk.safetensors::306807976::1783607988": [ + "4n4lfi5tin6", + "4n4lfi5tin6, a close-up rear view of a person engaging in anal fisting." + ], + "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" + ], + "sfbehind_v2.1_high_noise.safetensors::306808416::1767009289": [ + "sfb3hind, sfbehind" + ], + "sfbehind_v2.1_low_noise.safetensors::306808416::1767009298": [ + "sfb3hind, sfbehind" + ], + "side-doggy-wan22-hn-40.safetensors::306807976::1779664874": [ + "side view doggystyle sex" + ], + "side-doggy-wan22-ln-57.safetensors::306807976::1779664861": [ + "side view doggystyle sex" + ], + "Pornmaster_wan 2.2_14b_I2V_Creampie_v1_high_noise.safetensors::306808560::1762906783": [ + "A woman, the camera quickly pulls back to reveal her vagina, with sperm on her vagina, and the sperm continues to drip down from her vagina," + ], + "creampie_wan22_e50_low.safetensors::306807976::1758341193": [ + "creampie" + ], + "erect_penis_epoch_80.safetensors::306807976::1744443760": [ + "penis" + ], + "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" + ] +} \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/dolphin_vision_director.py b/custom_nodes/dolphin_nodes/dolphin_vision_director.py new file mode 100644 index 0000000000000000000000000000000000000000..4e85ed5257a0b62d4e5dd4102557a80d960e86b3 --- /dev/null +++ b/custom_nodes/dolphin_nodes/dolphin_vision_director.py @@ -0,0 +1,1028 @@ +# -*- coding: utf-8 -*- +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 +from comfy_execution.graph_utils import ExecutionBlocker + +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 (uncensored · stable JSON · recommended)": "deepseek/deepseek-v3.2", + "🔞 Magnum v4 72B (natural phrasing)": "anthracite-org/magnum-v4-72b", + "🔞 Cydonia 24B v4.1 (lightweight · fast)": "thedrummer/cydonia-24b-v4.1", + # 2026-09-03: :free 슬러그가 404 됨("unavailable for free, use paid slug"). 유료로 교체. + "🔞 Dolphin Mistral Venice (uncensored)": "cognitivecomputations/dolphin-mistral-24b-venice-edition", + # 2026-09-03 추가. OpenRouter 모델 페이지로 슬러그/서빙 여부만 확인했고 + # 실제 chat completions 호출로는 아직 스모크테스트 안 함 - 처음 쓸 때 확인할 것. + "🔞 Hermes 4 405B (steerable · strict format)": "nousresearch/hermes-4-405b", + # 2026-09-03: grok-4 / grok-4-fast 둘 다 deprecated(404) - grok-4.3으로 교체. + "🔞 Grok 4.3 (edgy prose · $1.25/$2.5)": "x-ai/grok-4.3", + "🔞 Euryale 70B L3.3 (sensory prose · loose format)": "sao10k/l3.3-euryale-70b", + "⚙️ custom (use text field below)": "__custom__", +} +# 2026-08-28: Valkyrie 49B V1(thedrummer/valkyrie-49b-v1)을 여기 넣었다가 뺐다 - +# OpenRouter 마케팅 페이지는 정상으로 보였지만 실제 chat completions 호출은 +# 404 "No endpoints found"(프로바이더가 하나도 없음). 페이지가 살아있다고 실제 +# 서빙 중이란 보장이 없다 - 프리셋에 새 모델을 넣기 전엔 반드시 실제 API 호출로 +# 확인할 것(스크레이핑만으로는 부족했음, 실측: debug_info의 plan= 필드에 뜬 +# HTTP 404가 증거). +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 (special cases · recommended)": "qwen/qwen2.5-vl-72b-instruct", + "👁 Qwen2.5-VL 32B (balanced)": "qwen/qwen2.5-vl-32b-instruct", + "👁 Qwen2.5-VL 32B (free)": "qwen/qwen2.5-vl-32b-instruct:free", + "👁 Mistral Small 3.1 24B (Pixtral)": "mistralai/mistral-small-3.1-24b-instruct", + "👁 Gemma 3 27B (lightweight)": "google/gemma-3-27b-it", + "⚙️ custom (use text field below)": "__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 + + +# ---------------------------------------------------------------------- +# OpenRouter 토큰 단가 조회 (debug_info 표시용) +# /api/v1/models 는 무인증 공개 엔드포인트. 프로세스 살아있는 동안 1회만 받는다. +# pricing.prompt/completion 은 "토큰 1개당 USD" 문자열이라 1e6 곱해 1M당 단가로. +# ---------------------------------------------------------------------- +_OR_PRICE_CACHE = {} + + +def _or_model_prices(key=None): + if _OR_PRICE_CACHE: + return _OR_PRICE_CACHE + try: + hdr = {'Authorization': f'Bearer {key}'} if key else {} + req = urllib.request.Request("https://openrouter.ai/api/v1/models", headers=hdr) + raw = json.loads(urllib.request.urlopen(req, timeout=15).read().decode('utf-8')) + for m in raw.get("data", []): + p = m.get("pricing") or {} + try: + _OR_PRICE_CACHE[m["id"]] = (float(p.get("prompt", 0) or 0) * 1e6, + float(p.get("completion", 0) or 0) * 1e6) + except (TypeError, ValueError, KeyError): + pass + except Exception as e: + print(f"⚠️ [Director] OpenRouter 단가표 조회 실패(무시): {e}") + return _OR_PRICE_CACHE + + +def _price_tag(slug, usage=None, key=None): + """'slug $0.28/$0.88 per 1M | spent ~$0.0031 (1450→820 tok)' 형태.""" + parts = [] + pc = _or_model_prices(key).get((slug or "").strip()) + if pc: + parts.append(f"{slug} ${pc[0]:.3g}/${pc[1]:.3g} per 1M") + if usage: + pt, ct, cost = usage.get("prompt", 0), usage.get("completion", 0), usage.get("cost", 0.0) + if pt or ct or cost: + parts.append(f"spent ~${cost:.4f} ({pt}→{ct} tok)") + return " | ".join(parts) + + +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 lookup failed: {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}" + + +# 트리거 워드가 "penis, bounce, swing"처럼 캐릭터 이름 옆에 콤마로 그냥 나열되면 +# (_build_final 참고) 어느 신체 부위에 국한된 디테일인지 모델이 알 방법이 없어서, +# bounce/swing 같은 범용 동작 동사가 전신 모션으로 새버리는 게 실측으로 확인됨 +# (예: "penis, bounce, swing"의 flaccid LoRA 트리거 때문에 캐릭터가 무슨 동작을 +# 하든 콩콩 뛰었음). 문법적 문맥이 없는 "맨몸 단어"(공백 없는 한 단어)로만 등장하는 +# 범용 동작 동사는 걸러낸다 - "penis becomes erect"처럼 이미 문장으로 묶여 들어온 +# 여러 단어짜리 구는 건드리지 않음(이미 anchoring이 있어서 안전). +_GENERIC_MOTION_VERBS = { + "bounce", "bouncing", "swing", "swinging", "shake", "shaking", + "sway", "swaying", "wiggle", "wiggling", "jiggle", "jiggling", + "wobble", "wobbling", "jump", "jumping", "hop", "hopping", + "bob", "bobbing", "quiver", "quivering", "tremble", "trembling", + "spin", "spinning", "twitch", "twitching", +} + + +def _filter_generic_motion_verbs(words): + kept, dropped = [], [] + for w in words: + if w.strip().lower() in _GENERIC_MOTION_VERBS: + dropped.append(w) + else: + kept.append(w) + if dropped: + print(f"🚫 [Director] Excluded generic motion-verb triggers: {dropped}") + return kept + + +def get_triggers(lora_name, cache): + return _filter_generic_motion_verbs(_get_triggers_raw(lora_name, cache)) + + +def _get_triggers_raw(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] Hash computation failed ({base}): {e}") + digest = None + if digest: + found = _query_lora_manager_db(digest) + if found: + print(f"✅ [Director] Trigger lookup succeeded ({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] Trigger lookup succeeded ({base}) @ {host}: {found}") + result = found + break + print(f"⚠️ [Director] Trigger lookup failed ({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) missing - vision unavailable. Run `pip install Pillow`.") + return None + if image_tensor is None: + print("❌ [Director] image input is None - check the IMAGE connection.") + 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 + + +def split_action_parts(actions): + """essential_actions를 조각으로 나눈다. + + '/'가 있으면 그건 사용자가 클립/비트 경계를 명시적으로 못 박은 것이므로 + '/'만 경계로 쓰고 그 안의 쉼표는 한 덩어리로 유지한다 - 안 그러면 "동작A, + 디테일1, 디테일2 / 동작B, 디테일3" 처럼 '/'로 묶은 한 클립 분량이 쉼표 + 때문에 다시 여러 조각으로 쪼개져서 클립 경계가 어긋난다(실제로 겪은 버그). + '/'가 없을 때만 기존처럼 쉼표/문장부호/줄바꿈으로 나눈다.""" + if '/' in actions: + parts = actions.split('/') + else: + parts = re.split(r'(?<=[.!?。])\s+|[,、]|\n+', actions) + return [p.strip() for p in parts if len(p.strip()) > 1] + + +# ====================================================================== +# Vision Director: 이미지 인식 + 필수동작 실행 + 클립당 4로라 +# ====================================================================== +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 4개씩 개별 적용(웨이트도 개별) + 트리거 자동 주입 + """ + + @classmethod + def INPUT_TYPES(s): + loras = ["None"] + (folder_paths.get_filename_list("loras") or []) + req = { + "image": ("IMAGE",), + "model_base": ("MODEL",), + "model_high": ("MODEL",), + "model_low": ("MODEL",), + "essential_actions": ("STRING", {"multiline": True, + "default": ("charges forward, swings a sword to strike down the enemy, deflects " + "a bullet, finishes them off"), + "tooltip": "Essential actions, comma-separated. If left empty (empty string), " + "'auto-direct' mode kicks in - the LLM imagines a natural action " + "sequence on its own using only the vision anchors (character " + "state/pose/context) and artistic_vibe. This only works meaningfully " + "if image recognition is on via use_vision/vision_caption (without " + "vision, the anchors are empty and the result is vague)."}), + "num_clips": (["4 (20s)", "3 (15s)", "2 (10s)"], {"default": "4 (20s)", + "tooltip": "The number of clips to actually render/save (this node is the single " + "control - other bypass toggles in the graph are not used). Remaining " + "clip slots repeat the last clip's content."}), + "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": (["Short (concise)", "Essentials + anchors only", "Detailed"], + {"default": "Essentials + anchors only"}), + "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": ""}), + "character_name": ("STRING", {"default": "AUTO"}), + "vlm_preset": (VLM_PRESET_LABELS, {"default": "👁 Qwen2.5-VL 72B (special cases · recommended)"}), + "vlm_model": ("STRING", {"default": "qwen/qwen2.5-vl-72b-instruct"}), + "llm_preset": (LLM_PRESET_LABELS, {"default": "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)"}), + "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 = {} + for c in range(1, 5): + for n in range(1, 5): + opt[f"c{c}_lora_{n}"] = (loras, {"default": "None"}) + opt[f"c{c}_lora_{n}_on"] = ("BOOLEAN", {"default": True, + "label_on": "ON", "label_off": "OFF"}) + opt[f"c{c}_w_{n}"] = ("STRING", {"default": "1.0, 1.0, 1.0", "multiline": False}) + opt["manual_triggers"] = ("STRING", {"default": "", "multiline": True}) + 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", + "BOOLEAN", "BOOLEAN", "BOOLEAN", "BOOLEAN", + ) + 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", + "c1_is_final", "c2_is_final", "c3_is_final", "c4_is_final", + ) + FUNCTION = "direct" + CATEGORY = "Dolphin" + + 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): + # 영어 키(Short/Essentials/Detailed)는 DolphinMinimaxDirector처럼 영어 UI를 쓰는 + # 노드용 - 값은 한글 키와 동일, 이 함수를 공유하는 다른 노드(한글 드롭다운)를 + # 깨지 않기 위해 새로 추가만 했다. + detailed_hint = ( + "Keep the physical motion itself simple and singular — exactly the one action " + "given, no extra movements, no additional beats. Instead add rich VISUAL detail " + "around that single motion: skin/fabric texture, muscle tension, facial expression, " + "breathing, sweat, lighting, camera framing. Do NOT make the motion more complex or " + "add new movements to fill time.") + return { + "짧게(간결)": "Keep each clip to a short punchy motion phrase. Pure movement verbs only.", + "Short (concise)": "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.", + "Essentials + anchors only": "Each clip is one concise continuous motion. No description, only movement.", + # 예전 문구("동작을 더 자세히 묘사")는 "동작 자체를 더 복잡하게"로 읽혀서, 모션을 + # 계속 밀어붙이는 다른 튜닝(NAG/CFG/LoRA)과 겹치면 깡총거리기 같은 의미없는 + # 필러 동작을 만들어냈다. 동작은 하나로 고정하고 시각적 디테일(질감/표정/조명/ + # 카메라 등)만 늘리는 쪽으로 반전 - 새 동작/추가 비트를 유발하면 안 됨. + "상세": detailed_hint, + "Detailed": detailed_hint, + }.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, + "usage": {"include": True}} + 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')) + acc = getattr(self, "_usage_acc", None) + u = raw.get("usage") or {} + if acc is not None and u: + acc["prompt"] += u.get("prompt_tokens", 0) or 0 + acc["completion"] += u.get("completion_tokens", 0) or 0 + c = u.get("cost") + if c is None: + c = (u.get("cost_details") or {}).get("upstream_inference_cost") + acc["cost"] += float(c or 0) + 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() + + 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" + "(5) facial expression & apparent mood/vibe (e.g. 'smiling, playful', 'serious, " + "intense stare', 'shy, looking away', 'sultry, half-lidded eyes', 'laughing, " + "cheerful'). Read this from the actual face in the image, not guessed from pose.\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): + try: + resp = self._call_or(messages, vlm_model, key, temp, timeout=90).strip() + if not resp: + print(f"⚠️ [Director] VLM({vlm_model}) response was empty (refusal or empty return)") + 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}" + if e.code in (429, 503) and attempt < 3: + wait = 2 ** attempt + print(f"⏳ [Director] rate limited, retrying in {wait}s...") + 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 + + def _fallback_name(self, anchors): + 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 + return random.choice(pool) + + def _split_actions_to_clips(self, actions, num_clips=4): + parts = split_action_parts(actions) + if not parts: + parts = [actions.strip()] + n = len(parts) + last = num_clips + assign = {c: [] for c in range(1, num_clips + 1)} + if num_clips == 1: + assign[1] = parts + return assign + build_labels = ["(build-up toward)", "(begin)", "(escalate)", "(continue escalating)"] + if n == 1: + for c in range(1, last): + label = build_labels[min(c - 1, len(build_labels) - 1)] + assign[c] = [f"{label} {parts[0]}"] + assign[last] = [parts[0]] + elif n <= num_clips: + assign[last] = [parts[-1]] + head = parts[:-1] + for i, ph in enumerate(head): + assign[min(i + 1, last - 1)].append(ph) + for c in range(1, last): + if not assign[c]: + nxt = parts[min(c, n - 1)] + assign[c] = [f"(move toward) {nxt}"] + else: + assign[last] = [parts[-1]] + rest = parts[:-1] + k, m = divmod(len(rest), last - 1) + idx = 0 + for c in range(1, last): + 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, + num_clips=4, continuity_context=""): + auto_direct = not actions.strip() + if auto_direct: + assign_block = ( + " (none given — INVENT the full action sequence yourself per rule 1 below, " + "grounded in the SCENE ANCHORS and MOTION STYLE)") + else: + assign = self._split_actions_to_clips(actions, num_clips) + assign_block = "\n".join( + f" clip_{c}: {', '.join(assign[c]) if assign[c] else '(continue previous motion)'}" + for c in range(1, num_clips + 1)) + + last_clip = num_clips + total_seconds = num_clips * 5 + if num_clips >= 3: + chain = " and ".join(f"clip_{c}<-clip_{c-1}" for c in range(3, num_clips + 1)) + continuity_middle = (f". The same applies {chain}: each clip picks up exactly where the " + f"previous clip's body position left off") + else: + continuity_middle = "" + + continuity_rule = "" + + # 클립을 여러 개 나눠서 순차적으로 별도 호출할 때(예: 클립마다 독립된 노드로 + # 쪼갠 경우) 쓰는 "호출 간" 연속성 규칙. num_clips>=2의 "호출 내" 연속성 규칙과는 + # 별개 - 이번 호출이 만드는 clip_1이 이전 호출에서 이미 확정된 동작 뒤를 잇게 한다. + if continuity_context.strip(): + continuity_rule += ( + "3b) CONTINUES A PRIOR CLIP. The action just before this one already happened " + "(see PREVIOUS CLIP ACTION below) and clip_1 here MUST begin from the exact physical " + "end-state that action would leave the body in — same position, orientation, momentum. " + "Do not restart from a neutral stance and do not repeat the previous action. Do NOT " + "narrate the hand-off explicitly (no 'continuing', 'still', 'as before').\n" + ) + if num_clips >= 2: + continuity_rule += ( + "3) INTER-CLIP CONTINUITY. clip_2 MUST begin from the exact physical end-state of " + "clip_1's action (same position, body orientation, and momentum) — it continues the " + "motion, it does not restart from a neutral stance" + continuity_middle + + f", as one unbroken {total_seconds}-second performance rather than {num_clips} separate poses. Do " + "NOT explicitly narrate the hand-off (no phrases like 'continuing from', 'still', 'as " + "before', 'picking up where') — simply choreograph the new action so it is kinematically " + "consistent with how the previous action would have left the body positioned. This is a " + "physical-continuity constraint, not a scripted transition line.\n" + ) + + json_clip_fields = ",".join( + f'"clip_{i}":""' for i in range(1, num_clips + 1)) + + if auto_direct: + intro = ( + f"You are an elite movement choreographer AND director for a {total_seconds}-second " + f"video made of {num_clips} consecutive 5-second clip{'s' if num_clips != 1 else ''}. " + "No action list is given — you invent the entire beat-by-beat sequence yourself, using " + "the SCENE ANCHORS (the character's starting look/pose/place from an image) and the " + "MOTION STYLE cue as your only creative brief.\n" + ) + rule1 = ( + "1) INVENT THE SEQUENCE. Design a self-contained, progressively developing action " + f"sequence of exactly " + f"{num_clips} beat{'s' if num_clips != 1 else ''} that plausibly follows from the " + "character's exact pose/state/context in the SCENE ANCHORS — what would this character " + "naturally do next, right now, from exactly that position? Build it so the sequence " + f"develops beat by beat and the LAST beat (clip_{last_clip}) is its clear payoff — " + "'develops' means the situation moves forward, NOT that each beat must be more violent " + "than the last. Do not invent new characters, props, or a change of " + "location — stay grounded in what the anchors describe. " + "(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 invented action. This is not skipping " + "ahead; it is grounding the first beat.)\n" + ) + else: + intro = ( + f"You are an elite movement choreographer for a {total_seconds}-second video made of " + f"{num_clips} consecutive 5-second clip{'s' if num_clips != 1 else ''}. You are given SCENE " + "ANCHORS (the character's starting look/pose/place from an image) and a STRICT per-clip " + "action assignment.\n" + ) + rule1 = ( + "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. " + f"clip_1 must NOT contain clip_{last_clip}'s action. The final action happens ONLY in " + f"clip_{last_clip}. " + "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" + ) + + sys = ( + intro + + "\n" + "ABSOLUTE RULES:\n" + + rule1 + + "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, but spend AT MOST " + "one short clause on it — e.g. anchor 'seated balanced on a yoga ball' -> clip_1 = " + "'pushes off the wobbling ball, rising fast to their feet as it rolls away, then...' — " + "the grounding clause itself must already be fast, active motion (a verb), never a static " + "description of the pose. 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). Reference the support ONLY to ground how the motion STARTS — " + "do NOT describe its appearance, color, or the wider environment.\n" + + continuity_rule + + "4) 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, or to the prior clip's ending " + "position per rule 3, is allowed since it describes how the body moves — but keep it to " + "one short clause, then move straight into the clip's own action.)\n" + "4b) LITERAL, NOT METAPHORICAL. Name body parts and acts directly (e.g. 'penis', " + "'vagina', 'cock', 'pussy', 'thrusts', 'grips') — never euphemism, poetic metaphor, or " + "vague substitution (do not write things like 'their connection deepens', 'waves of " + "pleasure', 'their bodies moved as one', 'her core'). This is a technical motion " + "description for an animation model, not literary prose — treat it like a stage " + "direction: blunt, anatomical, unambiguous about which body part does what to which.\n" + "4c) NO GESTURE CLICHÉS. Resolve any vague/decorative descriptor in the action " + "(e.g. 'cutely', 'playfully', 'sexily', 'shyly') into ONE specific, concrete gesture or " + "body movement of your own invention. NEVER default to the single most common/expected " + "cliché for that word (e.g. a peace sign / V-sign / finger-heart for 'cute', a hair-flip " + "for 'sexy') — these are overused and must be avoided. If PREVIOUS CLIP ACTION below " + "already used a specific gesture, invent a DIFFERENT one now instead of repeating it.\n" + "5) FILL 5 SECONDS. Expand the assigned action into a single continuous flow of motion " + "that occupies the whole clip (use 'then', 'without pausing', 'carrying straight into'). " + "Do not borrow the next action to fill time - elaborate the CURRENT action instead.\n" + # rule 6은 원래 "모든 클립에 폭발적 부사(swiftly/explosively/in a split second)를 박고 + # gently/slowly/softly는 금지"였다. 그 규칙이 진짜 막으려던 건 "비디오 모델이 가만 두면 + # 슬로모션으로 흐르는 것"인데, 실제로는 조용한 장면까지 전부 찌르고 휘두르는 날카로운 + # 동작으로 만들어버렸다. 그래서 "실시간 속도 유지"(=슬로모션/정지 금지)만 남기고 + # "무조건 격렬하게"는 뺐다 - 동사의 세기는 배정된 동작 자체가 정한다. + "6) REAL-TIME SPEED, NOT FORCED VIOLENCE: every clip plays at natural real-time " + "speed — NO slow motion, NO freeze, NO holding a pose, the body keeps moving " + "throughout. But do NOT inflate the movement: match the force of the verb to what " + "the action actually is. Reach for the plain accurate verb first (walks, turns, " + "leans, reaches, lifts, pushes, presses) and use hard percussive verbs (slams, " + "whips, snaps, lunges, explodes) or urgency adverbs (explosively, in a split " + "second) ONLY where the assigned action is itself sudden or violent. A quiet " + "action stays quiet while still moving continuously. 'Build-up' in rule 1 refers " + "ONLY to which action-beat is assigned to a clip — it does NOT mean a clip should " + "move slower, look hesitant, or read as a held state. Every clip is deliberate and " + "physically committed, but 'committed' means clear and purposeful, not aggressive.\n" + "7) 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"8) {detail}\n" + "9) 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 4 still applies. Where the cue and rule 6 " + "disagree about how hard the movement should be, RULE 6 WINS.\n" + "\n" + "master_scene: leave it EMPTY.\n" + "\n" + 'OUTPUT ONLY JSON: {"character":"","master_scene":"",' + + json_clip_fields + "}" + ) + assign_label = ( + f"INVENT THE SEQUENCE (no actions given — design {num_clips} progressively developing " + f"beats yourself per rule 1, finisher in clip_{last_clip}):" + if auto_direct else + f"STRICT PER-CLIP ACTION ASSIGNMENT (animate ONLY what is listed per clip, " + f"in this order, finisher in clip_{last_clip}):" + ) + continuity_block = ( + f"PREVIOUS CLIP ACTION (per rule 3b — clip_1 continues from this end-state):\n" + f"{continuity_context.strip()}\n\n" + if continuity_context.strip() else "" + ) + usr = (f"NAME: {name}\nPACING: {self._pacing_hint(pacing)}\n\n" + f"MOTION STYLE (kinetic energy cue only, per rule 9 — 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"{continuity_block}" + f"{assign_label}\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, num_clips + 1)): + 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, num_clips=4): + if not actions.strip(): + # 자동 연출 모드인데 LLM(키)이 없어서 실제로 "연출"은 못 함 - 이미지 근거 없이 + # 그럴듯한 즉흥 동작이라도 채워서 그래프가 안 깨지게 함(무의미한 빈 문구 방지). + actions = ("starts moving, the motion gains force, reaches its peak") + parts = split_action_parts(actions) + if not parts: + parts = [actions.strip()] + n = len(parts) + last = num_clips - 1 + + def fast(phrase, lead="swiftly"): + p = phrase.strip() + return f"{lead} {p}, continuous real-time motion, no slow motion" if p else "" + + if num_clips == 1: + return [fast(", ".join(parts), "explosively")] + + if n == 1: + a = parts[0] + templates = [ + 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", + ] + out = [templates[min(i, len(templates) - 1)] for i in range(num_clips - 1)] + out.append(f"explosively completes {a} at real-time speed, no slow motion, no freeze") + elif n <= num_clips: + out = [""] * num_clips + out[last] = fast(parts[-1], "explosively") + head = parts[:-1] + for i, ph in enumerate(head): + s = min(i, num_clips - 2) + out[s] = (out[s] + ", then " + fast(ph)).strip(", ") if out[s] else fast(ph) + for i in range(num_clips - 1): + 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, num_clips - 1) + idx, chunks = 0, [] + for i in range(num_clips - 1): + 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[:num_clips] + + def _load(self, name): + return comfy.utils.load_torch_file(folder_paths.get_full_path("loras", name)) + + @staticmethod + def _parse_weights(text): + 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): + 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 = [] + 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 + trig_str = ", ".join(dict.fromkeys(t for t in trig if t)) + prompt_trig = trig_str if inject else "" + return cb, ch, cl, prompt_trig, trig_str + + def direct(self, image, model_base, model_high, model_low, essential_actions, num_clips, + artistic_vibe, pacing, detail_level, inject_triggers, + use_vision, openrouter_api_key, character_name, vlm_preset, vlm_model, llm_preset, llm_model, + creativity, seed, **kw): + + n_clips = int(str(num_clips).split()[0]) + self._usage_acc = {"prompt": 0, "completion": 0, "cost": 0.0} + 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) + + 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] Using local caption input (JoyCaption etc.):\n{anchors[:300]}") + elif not use_vision: + dbg_v = "vision OFF (check toggle)" + print("ℹ️ [Director] use_vision=OFF - skipping vision") + elif not key: + dbg_v = "no api key" + print("❌ [Director] openrouter_api_key missing (neither widget nor OPENROUTER_API_KEY env var set) - vision/LLM unavailable") + elif image is None: + dbg_v = "no image" + print("❌ [Director] image input missing - check the IMAGE connection") + 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] Vision anchors are empty -> no basis for name/scene. Cause: {dbg_v}") + + 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, num_clips=n_clips) + if parsed: + clips = [str(parsed[f"clip_{i}"]).strip() for i in range(1, n_clips + 1)] + master = str(parsed.get("master_scene", "")).strip() + cand = str(parsed.get("character", "")).strip() + 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, num_clips=n_clips) + # master_scene은 LLM 경로와 동일하게 비움 - 안 그러면 VLM이 뽑은 정적 사진 + # 묘사(모델/조명/배경 등)가 매 클립 프롬프트 앞에 그대로 붙어서 "동작만" 규칙이 + # 깨지고, 결과가 그냥 비전 캡션을 반복하는 것처럼 보임 (버그였음, 2026-07-03 수정). + master = "" + final_name = user_name or self._fallback_name(anchors) + else: + parsed = None + dbg_p = ("no api key (auto-direct unavailable, generic filler used)" + if not essential_actions.strip() else "no api key") + clips = self._local_split(essential_actions, num_clips=n_clips) + master = "" + final_name = user_name + + if len(clips) < 4: + clips = clips + [clips[-1]] * (4 - len(clips)) + + cache = _load_cache() + manual_list = [t.strip() for t in kw.get("manual_triggers", "").split(',') if t.strip()] + outs = [] + all_trigs_collected = [] + for c in range(1, 5): + if c > n_clips: + cb = ch = cl = ExecutionBlocker(None) + prompt = self._build_final(clips[c - 1], master, final_name, "") + outs.extend([cb, ch, cl, prompt]) + print(f" [clip{c}] SKIPPED (num_clips={n_clips}) - returning ExecutionBlocker") + continue + 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, 5) + if kw.get(f"c{c}_lora_{n}_on", True)] + cb, ch, cl, prompt_trig, always_trig = self._patch_clip( + model_base, model_high, model_low, slots, cache, inject_triggers) + all_trig = ", ".join(dict.fromkeys( + ([prompt_trig] if prompt_trig else []) + manual_list)) \ + if (prompt_trig or manual_list) else "" + 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}") + + 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} | clips={n_clips} ({n_clips*5}s)" + _pt = _price_tag(llm, self._usage_acc, key) + if _pt: + debug += f" | {_pt}" + 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]}") + + is_final = tuple(c == n_clips for c in range(1, 5)) + + return tuple(outs) + (triggers_out, anchors, debug) + is_final + + +NODE_CLASS_MAPPINGS = {"DolphinVisionDirector": DolphinVisionDirector} +NODE_DISPLAY_NAME_MAPPINGS = {"DolphinVisionDirector": "🎬👁 Dolphin Vision Director (Image→4Clips)"} \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/h3_0824_wanrefine.json b/custom_nodes/dolphin_nodes/h3_0824_wanrefine.json new file mode 100644 index 0000000000000000000000000000000000000000..6059a6e9c73aedcc073218bf0c6f7cb0e89ee6fa --- /dev/null +++ b/custom_nodes/dolphin_nodes/h3_0824_wanrefine.json @@ -0,0 +1,4636 @@ +{ + "id": "e3f2b845-8f2c-4b5a-9caf-eac1029d3e7e", + "revision": 0, + "last_node_id": 214, + "last_link_id": 354, + "nodes": [ + { + "id": 117, + "type": "MarkdownNote", + "pos": [ + -2555.594639043064, + 4872.604682251046 + ], + "size": [ + 440, + 680 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Note: Model Links", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.8", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "## Model Links\n\n**vae**\n\n- [minimax_h3_video_vae_fp16.safetensors](https://huggingface.co/Comfy-Org/MiniMax-H3/resolve/main/vae/minimax_h3_video_vae_fp16.safetensors)\n- [minimax_h3_audio_vae_fp32.safetensors](https://huggingface.co/Comfy-Org/MiniMax-H3/resolve/main/vae/minimax_h3_audio_vae_fp32.safetensors)\n\n**diffusion_models**\n\n- [minimax_h3_fl2va_pruned_int8_convrot.safetensors](https://huggingface.co/Comfy-Org/MiniMax-H3/resolve/main/diffusion_models/minimax_h3_fl2va_pruned_int8_convrot.safetensors)\n\n**text_encoders**\n\n- [qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors](https://huggingface.co/Comfy-Org/MiniMax-H3/resolve/main/text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors)\n\n\n## Model Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 vae/\n│ │ ├── minimax_h3_video_vae_fp16.safetensors\n│ │ └── minimax_h3_audio_vae_fp32.safetensors\n│ ├── 📂 diffusion_models/\n│ │ └── minimax_h3_fl2va_pruned_int8_convrot.safetensors\n│ └── 📂 text_encoders/\n│ └── qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors\n```\n\n## Report Issue\n\nNote: Please update ComfyUI first ([guide](https://docs.comfy.org/installation/update_comfyui)) and prepare required models. Desktop/Cloud updates follow stable releases, so some nightly-supported models may not be available yet.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 120, + "type": "GetImageSize", + "pos": [ + -1594.4057948410536, + 6194.732939569872 + ], + "size": [ + 225, + 66 + ], + "flags": { + "collapsed": true + }, + "order": 1, + "mode": 0, + "inputs": [ + { + "localized_name": "image", + "name": "image", + "type": "IMAGE", + "link": 228 + } + ], + "outputs": [ + { + "localized_name": "width", + "name": "width", + "type": "INT", + "links": [ + 296 + ] + }, + { + "localized_name": "height", + "name": "height", + "type": "INT", + "links": [ + 297 + ] + }, + { + "localized_name": "batch_size", + "name": "batch_size", + "type": "INT", + "links": null + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.30.1", + "Node name for S&R": "GetImageSize", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.8", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [] + }, + { + "id": 115, + "type": "ResolutionSelector", + "pos": [ + -2408.225043788045, + 5613.6842417262915 + ], + "size": [ + 270, + 170 + ], + "flags": {}, + "order": 2, + "mode": 0, + "showAdvanced": false, + "inputs": [ + { + "localized_name": "aspect_ratio", + "name": "aspect_ratio", + "type": "COMBO", + "widget": { + "name": "aspect_ratio" + }, + "link": null + }, + { + "localized_name": "megapixels", + "name": "megapixels", + "type": "FLOAT", + "widget": { + "name": "megapixels" + }, + "link": null + }, + { + "localized_name": "multiple", + "name": "multiple", + "type": "INT", + "widget": { + "name": "multiple" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "width", + "name": "width", + "type": "INT", + "links": [] + }, + { + "localized_name": "height", + "name": "height", + "type": "INT", + "links": [] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.30.1", + "Node name for S&R": "ResolutionSelector", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.8", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "1:1 (Square)", + 1, + 32 + ] + }, + { + "id": 118, + "type": "MarkdownNote", + "pos": [ + -2437.612207213281, + 5831.4398480914015 + ], + "size": [ + 300, + 520 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Note: Size Settings Reference", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.8", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "| megapixels | Aspect | Output (multiple=32) |\n|---|---|---|\n| 0.2 | 16:9 | 608 x 352 |\n| 0.3 | 16:9 | 736 x 416 |\n| 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general-purpose, omni-modal generation model. 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"name": "source", + "type": "*", + "link": 353 + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [] + } + ], + "properties": { + "Node name for S&R": "PreviewAny" + }, + "widgets_values": [], + "title": "리파인 리포트 (시작 σ / MoE 판정 / 정렬 로그)" + }, + { + "id": 212, + "type": "MarkdownNote", + "pos": [ + 960, + 4570 + ], + "size": [ + 480, + 1100 + ], + "flags": {}, + "order": 53, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": { + "Node name for S&R": "MarkdownNote" + }, + "widgets_values": [ + "## Wan 2.2 리파인 패스 (H3 뒤에 붙음)\n\nH3 원본 저장(`I2V/RAW/`)은 그대로 살아있다. 이 갈래는 별도로 `I2V/REFINE/`에 저장한다.\n쓰기 싫으면 이 그룹 전체를 bypass하면 H3 워크플로우가 원래대로 돌아간다.\n\n### 붙은 지점\n- `161 VAEDecode` → VRAMCleanup → 리파인 (H3 프레임)\n- `158 VAEDecodeAudio` → 리파인 (H3 오디오, 프레임 수에 맞춰 자동 트림)\n- `123 StringConcatenate` → 프롬프트 합치기 → positive\n\n### 먼저 할 것\n1. **캐릭터 LoRA** 노드에 Wan LoRA 지정 후 Ctrl+B. 이게 이 패스의 핵심 이득이다 —\n H3엔 못 거는 LoRA를 여기서 건다.\n2. 그 LoRA의 **트리거 워드**를 `StringConcatenate`의 `string_b`에 적는다.\n `string_a`로는 H3 프롬프트가 자동으로 들어온다.\n\n### 프레임 수\nH3 그리드는 `17k+5`, Wan은 `4n+1`이라 기본값 124/362 같은 값은 리파인 때 최대 3프레임\n잘린다(오디오도 같이 잘려서 싱크는 안 밀림, 길이만 0.125초 짧아짐).\n\n정확히 맞추고 싶으면 `155 ComfyMathExpression`의 식을 아래로 바꾼다 — 두 그리드가\n겹치는 `68m+5`(5/73/141/209/277/345)에서 **가장 가까운** 값으로 스냅한다:\n\n```\nmax(5, 5 + 68 * round((round(a * 24) - 5) / 68))\n```\n\n지금 식은 `17k+5`로 **올림**이라 duration 15초 → 362프레임(15.08s)이 나오고,\n바꾸면 345프레임(14.375s)이 나온다. 길이가 살짝 줄어드는 걸 감수할지는 선택.\n\n### 튜닝\n- 1차 A/B는 **denoise 0.20 / 0.30 / 0.40**. Wan은 16fps 학습이라 24fps 소스를\n '덜 움직이는 영상'으로 읽는다 — 올릴수록 모션을 죽인다. 뭉개지는 지점이 상한선.\n- `steps=6`은 실제로 도는 스텝 수다. denoise가 낮다고 올리지 말 것.\n- `sigma_shift`는 생성용 5가 아니라 2~3. denoise 0.30/shift 3.0이면 시작 σ가 0.562라\n MoE 경계(0.9)를 안 넘어서 LOW 단독으로 끝난다. info 출력이 매번 이 판정을 찍어준다.\n- 증류(turbo/lightning) LoRA는 리파인 패스에 걸지 말 것 — 궤적 끝 30%만 밟는 상황과\n 전체 궤적 기준 증류가 안 맞는다. 안 걸었으면 cfg를 2.5~3.5로.\n\n### 이 패스로 못 고치는 것\n- 와이드샷 작은 얼굴 붕괴 → `ComfyUI-H3-FaceRefine` (크롭해서 H3로 재생성)\n- 대사 샷 립싱크 → denoise 0.2 이하로 묶거나 리파인에서 제외\n- 해상도 증가 → 이 뒤에 SeedVR2/Topaz\n\n---\n\n## 속도 (실측 기준)\n\n첫 실행 로그: 컨텍스트 윈도우 **1개당 약 30초**, 91 latent 프레임에 6~7윈도우,\n6스텝 → **약 19분**. H3 생성 자체는 8스텝 34초였으니 리파인이 30배 비쌌다.\n\n비용은 `윈도우 수 x 스텝 수`로 곱해진다:\n`윈도우 수 ≈ latent길이 / (context_length - overlap)` (latent 단위, 둘 다 ÷4)\n\n적용한 것:\n1. **fp8_e4m3fn_fast** (GGUF Q8 대신). GGUF는 매 forward마다 역양자화를 하고,\n Q8은 부분 로드(520MB offload)까지 걸렸다. fp8은 5090에서 네이티브 matmul을 탄다.\n2. **Sage attention** 노드 삽입. 품질이 흔들리면 `disabled`로.\n3. **overlap 30 → 8** (latent 7 → 2). 윈도우 6.5개 → 4.8개.\n4. **steps 6 → 4**.\n\n곱하면 대략 19분 → 4~6분. 로그의 \"Context window n/m\" 줄과 그 간격을 보면\n어느 항목이 실제로 먹었는지 바로 확인된다.\n\n### 더 줄여야 하면\n- `steps`를 2로 내리고 **Lightning LOW 4step LoRA**를 체인에 추가.\n 단 증류 LoRA는 궤적 전체 기준이라 끝 30%만 밟는 리파인과 안 맞을 수 있다 — 화질 확인 필수.\n- 15초(362프레임)를 통째로 돌리는 대신 대사/클로즈업 구간만 잘라서 리파인.\n- `upscale_by`는 이미 1.0이라 여기서 더 뺄 건 없다.\n\n### 안 통하는 것\n**denoise를 낮춰도 안 빨라진다.** ComfyUI에서 `steps`는 실제 실행 스텝 수이고\ndenoise는 스케줄 총 길이(steps/denoise)만 늘린다. 시간을 줄이려면 `steps`를 줄여야 한다." + ], + "title": "Note: Wan 리파인", + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 214, + "type": "PathchSageAttentionKJ", + "pos": [ + 340, + 3625 + ], + "size": [ + 520, + 90 + ], + "flags": {}, + "order": 54, + "mode": 0, + "title": "Sage attention (느리면 disabled 로)", + "inputs": [ + { + "localized_name": "model", + 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H3 출력 영상 (force_rate=0 유지!)", + "color": "#232", + "bgcolor": "#353" + }, + { + "id": 2, + "type": "UNETLoader", + "pos": [ + 40, + 40 + ], + "size": [ + 400, + 60 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "MODEL", + "type": "MODEL", + "links": [ + 1 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "UNETLoader" + }, + "widgets_values": [ + "wan2.2_i2v_low_noise_14B_fp16.safetensors", + "fp8_e4m3fn_fast" + ], + "title": "2. 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Wan2.1 VAE (16ch) — 다른 걸 꽂으면 노드가 막는다", + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 9, + "type": "WanH3Refine_Dolphin", + "pos": [ + 960, + 300 + ], + "size": [ + 430, + 430 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 3 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 6 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 7 + }, + { + "name": "vae", + "type": "VAE", + "link": 8 + }, + { + "name": "images", + "type": "IMAGE", + "link": 9 + }, + { + "name": "audio", + "type": "AUDIO", + "link": 10, + "shape": 7 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 11 + ], + "slot_index": 0 + }, + { + "name": "audio", + "type": "AUDIO", + "links": [ + 12 + ], + "slot_index": 1 + }, + { + "name": "frames", + "type": "INT", + "links": [], + "slot_index": 2 + }, + { + "name": "width", + "type": "INT", + "links": [], + "slot_index": 3 + }, + { + "name": "height", + "type": "INT", + "links": [], + "slot_index": 4 + }, + { + "name": "info", + "type": "STRING", + "links": [ + 13 + ], + "slot_index": 5 + } + ], + "properties": { + "Node name for S&R": "WanH3Refine_Dolphin" + }, + "widgets_values": [ + 0, + "fixed", + 4, + 1.0, + "euler", + "simple", + 0.3, + 3.0, + "trim", + "first_frame", + 1.0, + 1.0, + 24 + ], + "title": "7. Wan Refine H3 — 리파인 패스 본체", + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 10, + "type": "VHS_VideoCombine", + "pos": [ + 1440, + 300 + ], + "size": [ + 460, + 560 + ], + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 11 + }, + { + "name": "audio", + "type": "AUDIO", + "link": 12, + "shape": 7 + }, + { + "name": "meta_batch", + "type": "VHS_BatchManager", + "link": null, + "shape": 7 + }, + { + "name": "vae", + "type": "VAE", + "link": null, + "shape": 7 + } + ], + "outputs": [ + { + "name": "Filenames", + "type": "VHS_FILENAMES", + "links": [], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VHS_VideoCombine" + }, + "widgets_values": { + "frame_rate": 24, + "loop_count": 0, + "filename_prefix": "H3_REFINE/%date:yy-MM-dd-hh-mm%_h3refine", + "format": "video/nvenc_h264-mp4", + "pix_fmt": "yuv420p", + "bitrate": 10, + "megabit": true, + "save_metadata": true, + "pingpong": false, + "save_output": true + }, + "title": "8. 저장 (24fps 고정 — H3 네이티브)", + "color": "#232", + "bgcolor": "#353" + }, + { + "id": 11, + "type": "PreviewAny", + "pos": [ + 1440, + 900 + ], + "size": [ + 460, + 200 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [ + { + "name": "source", + "type": "*", + "link": 13 + } + ], + "outputs": [ + { + "name": "STRING", + "type": "STRING", + "links": [], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "PreviewAny" + }, + "widgets_values": [], + "title": "리파인 리포트 (시작 σ / MoE 판정 / 정렬 로그)" + }, + { + "id": 12, + "type": "WanH3FrameGrid_Dolphin", + "pos": [ + 960, + 40 + ], + "size": [ + 430, + 110 + ], + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "length", + "type": "INT", + "links": [], + "slot_index": 0 + }, + { + "name": "seconds", + "type": "FLOAT", + "links": [], + "slot_index": 1 + }, + { + "name": "info", + "type": "STRING", + "links": [ + 14 + ], + "slot_index": 2 + } + ], + "properties": { + "Node name for S&R": "WanH3FrameGrid_Dolphin" + }, + "widgets_values": [ + 5.9, + "wan_compatible" + ], + "title": "H3 length 계산기 (실행 전 여기서 값부터 확인)", + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 13, + "type": "PreviewAny", + "pos": [ + 960, + 190 + ], + "size": [ + 430, + 80 + ], + "flags": {}, + "order": 12, + "mode": 0, + "inputs": [ + { + "name": "source", + "type": "*", + "link": 14 + } + ], + "outputs": [ + { + "name": "STRING", + "type": "STRING", + "links": [], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "PreviewAny" + }, + "widgets_values": [], + "title": "→ 이 length를 H3 노드에 넣어라" + }, + { + "id": 14, + "type": "Note", + "pos": [ + 480, + 40 + ], + "size": [ + 440, + 560 + ], + "flags": {}, + "order": 13, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": { + "Node name for S&R": "Note" + }, + "widgets_values": [ + "H3 → Wan 2.2 리파인 패스\n\n■ 실행 순서\n1) 노드 12(계산기)만 먼저 돌려서 length 확인 → H3 생성 때 그 값을 쓴다.\n 두 그리드가 겹치는 값: 5 / 73 / 141 / 209 / 277 / 345\n H3 기본 124는 Wan에서 불법이라 121로 잘리고, 그만큼 오디오 싱크가 밀린다.\n2) 노드 1에 H3 출력 mp4 경로를 넣는다. force_rate는 반드시 0\n (24fps 원본을 그대로 읽어야 한다).\n3) 노드 3에 캐릭터 LoRA를 지정하고 Ctrl+B로 bypass 해제.\n 이게 이 파이프라인의 핵심 이득이다 — H3엔 못 거는 LoRA를 여기서 건다.\n4) 노드 6에 H3 프롬프트 + LoRA 트리거를 붙여넣는다.\n\n■ 왜 LOW noise만 로드하나\ndenoise 0.30 / shift 3.0이면 시작 σ가 0.562라서 MoE 경계(0.9)를 안 넘는다.\nHIGH expert는 호출 자체가 안 되므로 로드할 이유가 없다.\nshift=5면 denoise 0.62까지, shift=3이면 0.75까지 LOW 단독 구간이다.\n노드 7의 info 출력이 매 실행마다 이 판정을 찍어준다." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 15, + "type": "Note", + "pos": [ + 480, + 1050 + ], + "size": [ + 440, + 560 + ], + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": { + "Node name for S&R": "Note" + }, + "widgets_values": [ + "■ 튜닝 순서 (한 번에 하나씩)\n\n1차 A/B — denoise 0.20 / 0.30 / 0.40\n Wan은 16fps 기준으로 학습돼서 24fps 소스를 '덜 움직이는 영상'으로 읽는다.\n 올릴수록 모션을 죽이는 방향이므로 모션이 뭉개지기 시작하는 지점이 상한선.\n 보통 0.35 근처.\n\n2차 A/B — 캐릭터 LoRA 강도 0.6 / 1.0 / 1.3\n\n■ 건드리지 말 것\n· steps=6은 '실제로 도는 스텝 수'다. denoise가 낮다고 3배로 올리지 말 것\n (스케줄 총 길이가 steps/denoise로 자동으로 늘어난다).\n· sigma_shift는 생성용 5가 아니라 2~3. 리파인은 저시그마를 촘촘히 밟아야\n 하는데 shift가 크면 스텝이 고시그마로 쏠린다.\n· cfg는 증류(lightning/lightx2v) LoRA를 걸었으면 1.0, 뺐으면 2.5~3.5.\n 리파인 패스엔 증류 LoRA를 아예 안 거는 걸 권장 — 궤적 끝 30%만 밟는\n 상황과 전체 궤적 기준 증류가 안 맞는다.\n· upscale_by는 1.3까지. 큰 업스케일은 SeedVR2/Topaz로 따로.\n\n■ 이 패스로 못 고치는 것\n· 작은 얼굴 붕괴 → ComfyUI-H3-FaceRefine (크롭해서 H3로 재생성)\n· 대사 샷 립싱크 → denoise 0.2 이하로 묶거나 아예 리파인 제외\n· 해상도 증가 → 이 패스 다음에 SeedVR2/Topaz" + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "links": [ + [ + 1, + 2, + 0, + 3, + 0, + "MODEL" + ], + [ + 2, + 3, + 0, + 4, + 0, + "MODEL" + ], + [ + 3, + 4, + 0, + 9, + 0, + "MODEL" + ], + [ + 4, + 5, + 0, + 6, + 0, + "CLIP" + ], + [ + 5, + 5, + 0, + 7, + 0, + "CLIP" + ], + [ + 6, + 6, + 0, + 9, + 1, + "CONDITIONING" + ], + [ + 7, + 7, + 0, + 9, + 2, + "CONDITIONING" + ], + [ + 8, + 8, + 0, + 9, + 3, + "VAE" + ], + [ + 9, + 1, + 0, + 9, + 4, + "IMAGE" + ], + [ + 10, + 1, + 2, + 9, + 5, + "AUDIO" + ], + [ + 11, + 9, + 0, + 10, + 0, + "IMAGE" + ], + [ + 12, + 9, + 1, + 10, + 1, + "AUDIO" + ], + [ + 13, + 9, + 5, + 11, + 0, + "STRING" + ], + [ + 14, + 12, + 2, + 13, + 0, + "STRING" + ] + ], + "groups": [ + { + "id": 1, + "title": "모델 체인 (LOW noise 단독)", + "bounding": [ + 20, + -20, + 440, + 570 + ], + "color": "#3f789e", + "font_size": 24, + "flags": {} + }, + { + "id": 2, + "title": "리파인", + "bounding": [ + 940, + 260, + 470, + 500 + ], + "color": "#a1309b", + "font_size": 24, + "flags": {} + } + ], + "definitions": { + "subgraphs": [] + }, + "config": {}, + "extra": { + "frontendVersion": "1.48.7", + "ds": { + "scale": 0.6, + "offset": [ + 100, + 100 + ] + } + }, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/lora_profile.py b/custom_nodes/dolphin_nodes/lora_profile.py new file mode 100644 index 0000000000000000000000000000000000000000..f6e504b47f37b7fa4a8b41a33a89b0ba2088359c --- /dev/null +++ b/custom_nodes/dolphin_nodes/lora_profile.py @@ -0,0 +1,299 @@ +# -*- coding: utf-8 -*- +""" +로라 파일 하나를 "LLM에게 설명할 수 있는 프로필"로 변환한다. + +기존 `get_triggers()`(dolphin_vision_director.py)는 트리거 '단어'만 뽑아서 +캐릭터 이름 옆에 콤마로 나열하는 용도였다. 그 방식은 문법적 맥락이 없어서 +`bounce`/`swing` 같은 범용 동작 동사가 전신 모션으로 새는 문제가 있었고, +대응으로 그런 단어를 아예 제외하는 필터(_filter_generic_motion_verbs)를 +넣어둔 상태다 - 로라가 의도한 효과까지 같이 사라지는 우회책이다. + +이 모듈은 다른 방향으로 접근한다: 트리거를 '나중에 덧붙일 토큰'이 아니라 +'프롬프트를 쓰기 전에 LLM에게 줄 재료'로 본다. LoRA Manager가 파일 옆에 +만들어두는 `<이름>.metadata.json` 사이드카에 이미 다음이 다 들어있다. + + - model_name / civitai.model.name : 이 로라의 이름 + - modelDescription : 자연어 설명(HTML) + - tags / civitai.model.tags : 분류 태그 + - civitai.trainedWords : 학습 트리거. 단어인 경우도 있지만 + 상당수는 학습 캡션 문장 그대로다 + (예: "She pulls down her pants revealing a penis.") + - base_model : Wan 2.1/2.2, i2v/t2v 구분 + +로라는 학습 캡션 분포와 닮은 프롬프트에서 가장 잘 발화하므로, 이 재료를 +LLM에 먼저 주면 트리거를 문장 안에 문법적으로 녹인 프롬프트를 쓸 수 있다. + +trainedWords 3분류 (`classify_trained_words` 참고): + hard : 공백 없고 숫자나 대문자가 섞인 학습 식별자. LLM이 "고쳐주면" + 로라가 안 걸리므로 원문 그대로 나와야 한다 (예: `4n4lfi5tin6`, + `PENISLORA`). 출력 검증 대상. + soft : 공백 없는 평범한 소문자 영단어 (예: `bounce`, `penis`). + 강제하지 않는다 - 강제 삽입이 바로 예전 버그의 원인이었다. + 문맥에 맞을 때 몸 부위에 붙여 쓰라고 '권고'만 한다. + caption: 공백이 있는 구/문장. 학습 캡션 예시로만 준다(문체 참고용). +""" +import json +import os +import re + +_HTML_TAG_RE = re.compile(r"<[^>]+>") +_WS_RE = re.compile(r"\s+") + +# 세션 내 프로필 캐시. 키는 로라 파일의 (경로, size, mtime). +_PROFILE_CACHE = {} + +# LLM 컨텍스트를 잡아먹지 않도록 설명은 잘라서 준다. 로라 설명문은 보통 +# 앞부분에 "무엇을 하는 로라인지"가 있고 뒤는 학습 설정/후원 링크다. +MAX_DESC_CHARS = 400 +MAX_CAPTION_EXAMPLES = 2 + + +def _strip_html(s): + if not s: + return "" + s = re.sub(r"", " ", s, flags=re.IGNORECASE) + s = re.sub(r"

", " ", s, flags=re.IGNORECASE) + s = _HTML_TAG_RE.sub("", s) + s = (s.replace(" ", " ").replace("&", "&") + .replace("<", "<").replace(">", ">").replace(""", '"')) + return _WS_RE.sub(" ", s).strip() + + +def _truncate(s, limit): + if not s or len(s) <= limit: + return s + cut = s[:limit] + # 문장 중간에서 자르지 않도록 마지막 문장부호까지만. + for sep in (". ", "! ", "? "): + idx = cut.rfind(sep) + if idx > limit * 0.5: + return cut[:idx + 1] + return cut.rstrip() + "..." + + +def is_hard_token(word): + """LLM이 철자를 바꾸면 안 되는 학습 식별자인가. + + 공백이 없고(=단일 토큰) 숫자나 대문자가 섞여있으면 학습 식별자로 본다. + `4n4lfi5tin6`, `PENISLORA`, `az420` -> True + `bounce`, `penis`, `swing` -> False (평범한 소문자 영단어) + """ + w = word.strip() + if not w or _WS_RE.search(w): + return False + return any(c.isdigit() for c in w) or any(c.isupper() for c in w) + + +def classify_trained_words(words): + """trainedWords 리스트를 (hard, soft, captions)로 나눈다.""" + hard, soft, captions = [], [], [] + for raw in words or []: + w = str(raw).strip().strip(",") + if not w: + continue + if _WS_RE.search(w): + captions.append(w) + elif is_hard_token(w): + hard.append(w) + else: + soft.append(w) + # 순서 유지 중복 제거 + return (list(dict.fromkeys(hard)), list(dict.fromkeys(soft)), + list(dict.fromkeys(captions))) + + +def _sidecar_path(lora_path): + """LoRA Manager가 만드는 `<확장자 뗀 이름>.metadata.json`.""" + return os.path.splitext(lora_path)[0] + ".metadata.json" + + +def _read_sidecar(lora_path): + p = _sidecar_path(lora_path) + if not os.path.exists(p): + return None + try: + with open(p, "r", encoding="utf-8") as f: + return json.load(f) + except (OSError, ValueError, json.JSONDecodeError) as e: + print(f"⚠️ [LoraProfile] 사이드카 파싱 실패({os.path.basename(p)}): " + f"{type(e).__name__}: {e}") + return None + + +def _resolve_lora_path(lora_name): + try: + import folder_paths + return folder_paths.get_full_path("loras", lora_name) + except Exception: + return lora_name if os.path.exists(lora_name) else None + + +def build_profile_from_sidecar(data, lora_name, lora_path): + """사이드카 dict -> 프로필 dict. 사이드카가 없으면 호출하지 않는다.""" + civitai = data.get("civitai") or {} + model = civitai.get("model") or {} + + name = (data.get("model_name") or model.get("name") + or os.path.splitext(os.path.basename(lora_name))[0]) + desc = _strip_html(data.get("modelDescription") or model.get("description") or "") + tags = data.get("tags") or model.get("tags") or [] + if not isinstance(tags, list): + tags = [] + trained = civitai.get("trainedWords") or [] + if not isinstance(trained, list): + trained = [trained] + hard, soft, captions = classify_trained_words(trained) + + return { + "lora_name": lora_name, + "file": os.path.basename(lora_name), + "display_name": str(name).strip(), + "description": _truncate(desc, MAX_DESC_CHARS), + "tags": [str(t).strip() for t in tags if str(t).strip()], + "base_model": str(data.get("base_model") or civitai.get("baseModel") or "").strip(), + "hard_tokens": hard, + "soft_words": soft, + "captions": captions[:MAX_CAPTION_EXAMPLES], + "source": "sidecar", + } + + +def load_profile(lora_name, fallback_triggers=None): + """로라 이름(ComfyUI 상대경로) -> 프로필 dict. 실패 시 None. + + 사이드카가 없으면 `fallback_triggers`(기존 get_triggers 경로의 결과)로 + 최소 프로필이라도 만든다 - 개인 학습 로라처럼 civitai에 없는 경우. + + `fallback_triggers`는 리스트 또는 "호출하면 리스트를 주는 함수"를 받는다. + 기존 조회 경로는 sha256 해시(수 GB 파일) + 네트워크 호출까지 할 수 있어서, + 사이드카가 있는 99.5%의 경우엔 아예 호출되지 않도록 지연 평가한다. + """ + if not lora_name or lora_name == "None": + return None + + path = _resolve_lora_path(lora_name) + cache_key = lora_name + if path and os.path.exists(path): + try: + st = os.stat(path) + cache_key = f"{lora_name}::{st.st_size}::{int(st.st_mtime)}" + except OSError: + pass + if cache_key in _PROFILE_CACHE: + return _PROFILE_CACHE[cache_key] + + profile = None + if path: + data = _read_sidecar(path) + if data: + profile = build_profile_from_sidecar(data, lora_name, path) + + if profile is None: + # 사이드카 없음 - 기존 트리거 조회 결과로 최소 프로필. + fb = fallback_triggers() if callable(fallback_triggers) else fallback_triggers + hard, soft, captions = classify_trained_words(fb or []) + if not (hard or soft or captions): + return None + profile = { + "lora_name": lora_name, + "file": os.path.basename(lora_name), + "display_name": os.path.splitext(os.path.basename(lora_name))[0], + "description": "", + "tags": [], + "base_model": "", + "hard_tokens": hard, + "soft_words": soft, + "captions": captions[:MAX_CAPTION_EXAMPLES], + "source": "triggers-only", + } + + _PROFILE_CACHE[cache_key] = profile + return profile + + +def format_profiles_for_llm(profiles, indent=" "): + """프로필 목록 -> LLM 프롬프트에 넣을 텍스트 블록. + + 한 로라당 4줄 이내로 압축한다. 로라가 4개까지 걸릴 수 있고 클립도 4개라 + 장황하면 시스템 프롬프트의 다른 규칙이 묻힌다. + """ + if not profiles: + return "" + lines = [] + for p in profiles: + head = f"{indent}- {p['display_name']}" + if p.get("tags"): + head += f" [tags: {', '.join(p['tags'][:5])}]" + lines.append(head) + if p.get("description"): + lines.append(f"{indent} what it does: {p['description']}") + if p.get("hard_tokens"): + lines.append(f"{indent} MUST APPEAR VERBATIM: " + + ", ".join(f'"{t}"' for t in p["hard_tokens"])) + if p.get("soft_words"): + lines.append(f"{indent} vocabulary it was trained on (use only where it " + f"genuinely fits, anchored to the body part): " + + ", ".join(p["soft_words"])) + for cap in p.get("captions", []): + lines.append(f'{indent} training caption example: "{cap}"') + return "\n".join(lines) + + +def collect_hard_tokens(profiles): + out = [] + for p in profiles or []: + out.extend(p.get("hard_tokens") or []) + return list(dict.fromkeys(out)) + + +def missing_hard_tokens(text, profiles): + """생성된 프롬프트에 빠진 hard token 목록. + + LLM은 `4n4lfi5tin6` 같은 토큰을 "정상 철자로 고쳐주는" 경향이 있어서 + (그 순간 로라가 안 걸린다) 대소문자 무시하고 존재만 확인한다. + """ + low = (text or "").lower() + return [t for t in collect_hard_tokens(profiles) if t.lower() not in low] + + +if __name__ == "__main__": + # 실제 로라 폴더에 대고 돌려보는 자체 점검. + import glob + import sys + + # ComfyUI 콘솔은 UTF-8이지만 맨손으로 돌릴 땐 cp949라 이모지 print가 죽는다. + try: + sys.stdout.reconfigure(encoding="utf-8", errors="replace") + except (AttributeError, OSError): + pass + + root = sys.argv[1] if len(sys.argv) > 1 else "." + files = sorted(glob.glob(os.path.join(root, "*.safetensors"))) + print(f"safetensors {len(files)}개 검사\n") + stats = {"sidecar": 0, "none": 0, "hard": 0, "soft": 0, "caption": 0, "desc": 0} + samples = [] + for f in files: + data = _read_sidecar(f) + if not data: + stats["none"] += 1 + continue + p = build_profile_from_sidecar(data, os.path.basename(f), f) + stats["sidecar"] += 1 + if p["hard_tokens"]: + stats["hard"] += 1 + if p["soft_words"]: + stats["soft"] += 1 + if p["captions"]: + stats["caption"] += 1 + if p["description"]: + stats["desc"] += 1 + if p["hard_tokens"] and len(samples) < 5: + samples.append(p) + print(f"사이드카 있음: {stats['sidecar']} / 없음: {stats['none']}") + print(f" 설명 있음: {stats['desc']}") + print(f" hard token 있음: {stats['hard']}") + print(f" soft word 있음: {stats['soft']}") + print(f" caption 예시 있음: {stats['caption']}\n") + for p in samples: + print("=" * 60) + print(format_profiles_for_llm([p])) diff --git a/custom_nodes/dolphin_nodes/mask_region_color_match.py b/custom_nodes/dolphin_nodes/mask_region_color_match.py new file mode 100644 index 0000000000000000000000000000000000000000..e220c1ae942ff481285e72b5afb7b8631c617bf2 --- /dev/null +++ b/custom_nodes/dolphin_nodes/mask_region_color_match.py @@ -0,0 +1,103 @@ +import numpy as np +import torch +import torch.nn.functional as F + + +class MaskRegionColorMatch_Dolphin: + """Color-matches the pixels inside the mask against the pixels in a + ring immediately outside the mask, using only those two pixel sets + (never a rectangular crop that mixes correct and incorrect pixels). + Mixing dilutes the statistical difference the transfer function sees; + comparing pure "already correct" vs pure "actually wrong" pixels gives + the strongest possible correction signal.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_image": ("IMAGE", {"tooltip": "Reference image (same size as generated_image)."}), + "generated_image": ("IMAGE", {"tooltip": "Image to color-correct (same size as original_image)."}), + "mask": ("MASK", {"tooltip": "Inpainted region mask (same size as the images)."}), + "method": (["hm", "mkl", "reinhard", "mvgd", "hm-mvgd-hm", "hm-mkl-hm"], {"default": "hm", "tooltip": "hm (histogram matching) is numerically stable for small pixel counts. mkl/mvgd need a matrix square root and can go complex/unstable on small or low-variance regions."}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.05}), + "ring_width": ("INT", {"default": 60, "min": 4, "max": 512, "step": 2, "tooltip": "Thickness in pixels of the original-image ring just outside the mask used as the color reference."}), + "edge_feather": ("INT", {"default": 40, "min": 0, "max": 512, "step": 2, "tooltip": "Fades the correction from 0 at the mask boundary up to full strength this many pixels inside the mask, so there's no color cliff for MaskBlend to reveal as a ring."}), + } + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "match" + CATEGORY = "image/postprocessing" + + def match(self, original_image, generated_image, mask, method, strength, ring_width, edge_feather): + if strength == 0: + return (generated_image,) + + try: + from color_matcher import ColorMatcher + except ImportError as e: + raise ImportError("Can't import color-matcher, install with: pip install color-matcher") from e + + out = generated_image.clone() + batch = generated_image.shape[0] + cm = ColorMatcher() + kernel_size = ring_width * 2 + 1 + + for i in range(batch): + m = mask[min(i, mask.shape[0] - 1)] + m_bin = (m > 0.5).float() + if m_bin.max() <= 0: + continue + + dilated = F.max_pool2d(m_bin.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=ring_width) + dilated = (dilated.squeeze(0).squeeze(0) > 0.5) + interior = m_bin > 0.5 + ring = dilated & (~interior) + + if ring.sum().item() < 10: + ring = ~interior + + ref_pixels = original_image[i][ring].cpu().numpy() + target_pixels = generated_image[i][interior].cpu().numpy() + if target_pixels.shape[0] == 0 or ref_pixels.shape[0] == 0: + continue + + # Per-pixel correction weight: 0 right at the mask boundary, + # ramping up to 1 over edge_feather px toward the mask interior. + # Without this, the correction is uniform inside the mask and + # MaskBlend's outer feather ends up revealing the resulting + # color jump as a visible ring instead of hiding it. + if edge_feather > 0: + k = edge_feather * 2 + 1 + falloff = F.avg_pool2d(m_bin.unsqueeze(0).unsqueeze(0), kernel_size=k, stride=1, padding=edge_feather) + falloff = falloff.squeeze(0).squeeze(0).clamp(0, 1) + alpha = falloff[interior].numpy()[:, None] + else: + alpha = 1.0 + + try: + result = cm.transfer(src=target_pixels[:, None, :], ref=ref_pixels[:, None, :], method=method) + result = result[:, 0, :] + if np.iscomplexobj(result): + # mkl/mvgd take a matrix square root; on small or low-variance + # pixel sets this can go complex. The imaginary part is + # numerical noise here, so drop it rather than silently + # discarding the whole correction. + result = result.real + result = np.nan_to_num(result, nan=0.0, posinf=1.0, neginf=0.0) + result = target_pixels + (strength * alpha) * (result - target_pixels) + result_t = torch.from_numpy(result).clamp(0, 1).to(out.dtype) + out[i][interior] = result_t + except Exception as e: + print(f"[MaskRegionColorMatch_Dolphin] batch {i} failed, leaving uncorrected: {e}") + + return (out,) + + +NODE_CLASS_MAPPINGS = { + "MaskRegionColorMatch_Dolphin": MaskRegionColorMatch_Dolphin, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "MaskRegionColorMatch_Dolphin": "🐬 Mask-Region Color Match (Dolphin)", +} diff --git a/custom_nodes/dolphin_nodes/match_size.py b/custom_nodes/dolphin_nodes/match_size.py new file mode 100644 index 0000000000000000000000000000000000000000..057717f42cbfa27eceb61eb46e5aeef1b861dac7 --- /dev/null +++ b/custom_nodes/dolphin_nodes/match_size.py @@ -0,0 +1,47 @@ +import torch +import torch.nn.functional as F + + +class MatchImageAndMaskSize_Dolphin: + """Resizes an image and a mask to exactly match a reference image's + runtime pixel size. Reads reference.shape directly at execution time, + so it can't drift out of sync the way two separately-wired + GetImageSize -> width/height links can.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image_to_resize": ("IMAGE", {"tooltip": "Image that will be resized to match the reference."}), + "mask_to_resize": ("MASK", {"tooltip": "Mask that will be resized to match the reference."}), + "reference_image": ("IMAGE", {"tooltip": "Image whose size image_to_resize and mask_to_resize will be matched to."}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("image", "mask") + FUNCTION = "match" + CATEGORY = "image/postprocessing" + + def match(self, image_to_resize, mask_to_resize, reference_image): + target_h = reference_image.shape[1] + target_w = reference_image.shape[2] + + img = image_to_resize.movedim(-1, 1) + img = F.interpolate(img, size=(target_h, target_w), mode="bicubic", antialias=True) + img = img.clamp(0, 1).movedim(1, -1) + + mask = mask_to_resize + if mask.ndim == 2: + mask = mask.unsqueeze(0) + mask = F.interpolate(mask.unsqueeze(1), size=(target_h, target_w), mode="nearest-exact").squeeze(1) + + return (img, mask) + + +NODE_CLASS_MAPPINGS = { + "MatchImageAndMaskSize_Dolphin": MatchImageAndMaskSize_Dolphin, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "MatchImageAndMaskSize_Dolphin": "🐬 Match Image & Mask Size (Dolphin)", +} diff --git a/custom_nodes/dolphin_nodes/promp_logic_v28_stitch.py b/custom_nodes/dolphin_nodes/promp_logic_v28_stitch.py new file mode 100644 index 0000000000000000000000000000000000000000..51c019b75ac242b009de78f0b8c5a2f34db803a0 --- /dev/null +++ b/custom_nodes/dolphin_nodes/promp_logic_v28_stitch.py @@ -0,0 +1,128 @@ +import os +import random +import re +import json +import urllib.request +import urllib.error + +class DolphinMultiActionPromptNode_V28: + @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, "default": ""}), + "artistic_vibe": ("STRING", {"multiline": True, "default": "cinematic lighting, dynamic motion"}), + + "action_part_1": ("STRING", {"multiline": True, "default": "문을 열고 뛰어든다."}), + "action_part_2": ("STRING", {"multiline": True, "default": "무릎을 꿇고 방어한다."}), + "action_part_3": ("STRING", {"multiline": True, "default": "파이프를 집어 던진다."}), + "action_part_4": ("STRING", {"multiline": True, "default": "쓰러진 적을 바라보며 숨을 고른다."}), + + "openrouter_api_key": ("STRING", {"multiline": False, "default": ""}), + "openrouter_model": ("STRING", {"multiline": False, "default": "meta-llama/llama-3.3-70b-instruct"}), + # 🔥 [추천] AI가 너무 앞서간다면 이 수치를 0.65 ~ 0.7 정도로 낮춰보세요. + "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, action_part_1, action_part_2, action_part_3, action_part_4, openrouter_api_key, openrouter_model, creativity, seed): + + user_defined_name = "" if character_name.upper() in ["AUTO", ""] else character_name.strip() + + # 🔥 [줄바꿈 로직] 윗줄 태그와 아랫줄 자연어 액션을 엔터(\n)로 깔끔하게 분리 + 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(action_part_1, tagger_context, "", user_defined_name), + build_final(action_part_2, tagger_context, "", user_defined_name), + build_final(action_part_3, tagger_context, "", user_defined_name), + build_final(action_part_4, tagger_context, "", user_defined_name) + ) + + random.seed(seed) + + # 🔥 [완급조절 & 안티-슬로우모션 융합] 미래 액션 스포일러 방지 및 역동성 강제 주입 + 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. CHOREOGRAPHY & PACING (CRITICAL): You will output 4 Parts. \n" + " - You MUST strictly map [BASE ACTION 1] ONLY to PART 1, [BASE ACTION 2] ONLY to PART 2, etc.\n" + " - DO NOT anticipate, rush, or perform future actions in earlier parts. Keep the pacing deliberate.\n" + " - 🔥 ANTI-SLOW MOTION RULE: Video models default to slow-motion. You MUST inject aggressive kinetic adverbs (e.g., swiftly, rapidly, suddenly, explosively) into EVERY action description to force real-time, fast-paced movement.\n" + " - Each Part is an independent 5-second video clip. Timestamps MUST reset to [0-1s], [2-3s], [4-5s] for EACH Part.\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]" + ) + + # 유저 입력을 BASE ACTION 으로 명확히 라벨링하여 전송 + usr_prompt = f"NAME: {character_name}\nTAGS: {tagger_context}\nVIBE: {artistic_vibe}\n[BASE ACTION 1]: {action_part_1}\n[BASE ACTION 2]: {action_part_2}\n[BASE ACTION 3]: {action_part_3}\n[BASE ACTION 4]: {action_part_4}" + + 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 V28 - Stitching Clips]\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 = "", action_part_1, action_part_2, action_part_3, action_part_4 + + 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*(.*)', 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 action_part_1 + p2 = m2.group(1).strip() if m2 else action_part_2 + p3 = m3.group(1).strip() if m3 else action_part_3 + p4 = m4.group(1).strip() if m4 else action_part_4 + + 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) + ) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/promp_logic_v30_story.py b/custom_nodes/dolphin_nodes/promp_logic_v30_story.py new file mode 100644 index 0000000000000000000000000000000000000000..e1b32c1de5f86a6957b2133b7db709d81731a2fb --- /dev/null +++ b/custom_nodes/dolphin_nodes/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) + ) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/promp_logic_v32_story.py b/custom_nodes/dolphin_nodes/promp_logic_v32_story.py new file mode 100644 index 0000000000000000000000000000000000000000..c5d23cb4d7c4c7ed14524c7a43029f99323232a2 --- /dev/null +++ b/custom_nodes/dolphin_nodes/promp_logic_v32_story.py @@ -0,0 +1,275 @@ +import re +import json +import urllib.request +import urllib.error + + +# ====================================================================== +# OpenRouter 모델 프리셋 (NSFW/무삭제 대응) +# 라벨 -> 실제 슬러그. '⚙️ custom (아래 텍스트 사용)' 선택 시 텍스트 필드값 사용. +# 슬러그는 OpenRouter 기준으로 검증됨. 변경/신모델은 custom 으로 입력 가능. +# ====================================================================== +MODEL_PRESETS = { + "🔞 DeepSeek V3.2 (무삭제·JSON안정·추천)": "deepseek/deepseek-v3.2", + "🔞 DeepSeek V3 0324 (안정 버전)": "deepseek/deepseek-chat-v3-0324", + "🔞 Magnum v4 72B (표현 자연스러움)": "anthracite-org/magnum-v4-72b", + "🔞 Cydonia 24B v4.1 (경량·빠름·131K)": "thedrummer/cydonia-24b-v4.1", + # 2026-09-03: :free 슬러그가 404 됨 - 유료로 교체. + "🔞 Dolphin Mistral 24B Venice (무삭제)": "cognitivecomputations/dolphin-mistral-24b-venice-edition", + "🔞 Lumimaid 70B (RP 무삭제)": "neversleep/llama-3-lumimaid-70b", + # 2026-09-03 추가 (모델 페이지로만 확인, chat completions 스모크테스트 미완). + "🔞 Hermes 4 405B (스티어러블·포맷 엄격)": "nousresearch/hermes-4-405b", + # 2026-09-03: grok-4 / grok-4-fast deprecated(404) - grok-4.3으로 교체. + "🔞 Grok 4.3 (엣지·$1.25/$2.5)": "x-ai/grok-4.3", + "🔞 Euryale 70B L3.3 (감각적 산문·포맷 느슨)": "sao10k/l3.3-euryale-70b", + "SFW Llama 3.3 70B (정렬 강함)": "meta-llama/llama-3.3-70b-instruct", + "⚙️ custom (아래 텍스트 사용)": "__custom__", +} +MODEL_PRESET_LABELS = list(MODEL_PRESETS.keys()) + + +def resolve_model(preset_label, custom_text): + """프리셋 라벨과 커스텀 텍스트에서 실제 사용할 슬러그를 결정.""" + slug = MODEL_PRESETS.get(preset_label, "__custom__") + if slug == "__custom__": + return custom_text.strip() or "deepseek/deepseek-v3.2" + return slug + + +class DolphinMultiActionPromptNode_V32: + """ + 4개 연속 클립(각 5초, 총 20초)을 위한 시네마틱 프롬프트 분배 노드. + + 설계 원칙 (V30 대비 변경점): + - 서사 -> 4샷 분배를 LLM이 직접 판단한다. (문장 개수 하드코딩 pacing 분기 제거) + - 타임코드([0-1s] 등) 강제 삽입 제거. WAN I2V에서 노이즈만 됨. + - LLM은 JSON으로만 응답하게 강제 -> 파싱이 정규식보다 훨씬 견고. + - 파싱 실패 시 1회 자동 재시도, 그래도 실패하면 로컬 균등 분배로 폴백. + - seed를 실제 payload에 전달하여 재현성 확보. + """ + + @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": ""}), + "model_preset": (MODEL_PRESET_LABELS, + {"default": "🔞 DeepSeek V3.2 (무삭제·JSON안정·추천)"}), + "openrouter_model": ("STRING", {"multiline": False, + "default": "deepseek/deepseek-v3.2"}), + "creativity": ("FLOAT", {"default": 0.7, "min": 0.1, "max": 1.5, "step": 0.05}), + "pacing": (["auto", "slow-burn (1 beat)", "balanced (multi beat)", "rapid (4 distinct)"], + {"default": "auto"}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + }, + } + + RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING") + RETURN_NAMES = ("prompt_1", "prompt_2", "prompt_3", "prompt_4", "debug_info") + FUNCTION = "generate_sequence" + CATEGORY = "Dolphin" + + # ------------------------------------------------------------------ helpers + + def _build_final(self, action, raw_tags, master_scene, name): + """태그 블록 + 문장 블록을 한 프롬프트로 합친다. (V30 build_final 계승)""" + tags = [] + if name: + tags.append(name) + if raw_tags.strip(): + tags.append(raw_tags.strip().strip(",. ")) + tag_block = ", ".join(tags) + + sents = [] + if master_scene.strip(): + sents.append(master_scene.strip().strip(",. ")) + if action.strip(): + sents.append(action.strip()) + sentence_block = " ".join(sents) + + if tag_block and sentence_block: + return f"{tag_block}\n{sentence_block}" + return tag_block or sentence_block + + def _local_split(self, story): + """LLM 폴백용: 의미 단위(마침표/느낌표/줄바꿈 우선, 쉼표는 보조)로 4등분.""" + primary = re.split(r'(?<=[.!?。])\s+|\n+', story) + primary = [p.strip() for p in primary if len(p.strip()) > 1] + if len(primary) < 4: + expanded = [] + for p in primary: + expanded.extend([c.strip() for c in p.split(',') if len(c.strip()) > 1]) + primary = expanded or [story.strip()] + + n = len(primary) + if n == 0: + return ["", "", "", ""] + k, m = divmod(n, 4) + chunks, idx = [], 0 + for i in range(4): + end = idx + k + (1 if i < m else 0) + chunks.append(" ".join(primary[idx:end]).strip()) + idx = end + if n < 4: + filled = [c for c in chunks if c] + target = filled[-1] if filled else story.strip() + build = [ + f"Atmospheric build-up before the action. (upcoming: {target})", + f"Rising tension, subtle movement. (upcoming: {target})", + ] + combined = build + filled + if len(combined) >= 4: + chunks = combined[-4:] + else: + chunks = combined + [target] * (4 - len(combined)) + return chunks[:4] + + def _pacing_hint(self, pacing): + return { + "auto": "Decide pacing yourself based on how many distinct beats the story contains.", + "slow-burn (1 beat)": ("The story is essentially ONE climactic beat. Spend clips 1-3 on " + "atmosphere, tension and wind-up; detonate the action only in clip 4."), + "balanced (multi beat)": ("Distribute the beats so early clips build and later clips " + "deliver, keeping motion continuous across the 20 seconds."), + "rapid (4 distinct)": ("Treat the story as 4 distinct consecutive actions, one per clip, " + "each kinetic and self-contained."), + }.get(pacing, "Decide pacing yourself.") + + def _call_llm(self, url, payload, api_key, timeout=120): + req = urllib.request.Request( + url, data=json.dumps(payload).encode('utf-8'), + headers={'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json'}) + resp = urllib.request.urlopen(req, timeout=timeout) + data = json.loads(resp.read().decode('utf-8')) + return data['choices'][0]['message']['content'].strip() + + def _extract_json(self, text): + """LLM 응답에서 JSON 객체만 안전하게 추출.""" + if not text: + return None + text = re.sub(r'```(?:json)?', '', text).strip() + start = text.find('{') + end = text.rfind('}') + if start == -1 or end == -1 or end <= start: + return None + blob = text[start:end + 1] + try: + return json.loads(blob) + except json.JSONDecodeError: + blob2 = re.sub(r',\s*([}\]])', r'\1', blob) + try: + return json.loads(blob2) + except json.JSONDecodeError: + return None + + # ------------------------------------------------------------------ main + + def generate_sequence(self, mode, character_name, tagger_context, artistic_vibe, + master_story, openrouter_api_key, model_preset, openrouter_model, + creativity, pacing, seed): + + # 프리셋 라벨 -> 실제 슬러그 (custom 이면 텍스트 필드 사용) + openrouter_model = resolve_model(model_preset, openrouter_model) + + user_name = "" if character_name.strip().upper() in ["AUTO", ""] else character_name.strip() + + # ---- Manual override: 4칸 동일 출력 ---- + if mode == "✍️ Manual Override (수동)": + p = self._build_final(master_story, tagger_context, "", user_name) + return (p, p, p, p, "manual override") + + # ---- API 키 검증 ---- + api_key = openrouter_api_key.strip() + if not api_key: + fb = self._local_split(master_story) + outs = tuple(self._build_final(c, tagger_context, "", user_name) for c in fb) + return outs + ("⚠️ No API key. Used local fallback split.",) + + # ---- 프롬프트 구성 (JSON 강제) ---- + sys_prompt = ( + "You are an elite cinematic action choreographer and shot planner.\n" + "You will be given a short story (any language), character tags, and a visual vibe.\n" + "Your job: turn it into a 4-shot sequence (four consecutive 5-second video clips, 20s total).\n\n" + "RULES:\n" + "1. CHARACTER: if the name is 'AUTO', invent a culturally appropriate name from the tags; " + "otherwise use the given name.\n" + "2. MASTER_SCENE: one vivid English sentence describing the environment/lighting, " + "derived from tags + vibe. Stays constant across all 4 clips.\n" + "3. SHOT SPLIT: YOU decide how to distribute the narrative across the 4 clips. " + "Keep each clip's action temporally isolated - do NOT let a later clip's action appear early.\n" + "4. Never quote the original non-English text. Output English cinematic descriptions only.\n" + "5. Inject kinetic adverbs (swiftly, suddenly, explosively) - avoid unintended slow motion.\n" + "6. Do NOT add timestamps like [0-1s]; write natural continuous action prose per clip.\n\n" + "OUTPUT: respond with ONLY a JSON object, no prose, no code fences:\n" + '{"character": "...", "master_scene": "...", ' + '"clip_1": "...", "clip_2": "...", "clip_3": "...", "clip_4": "..."}' + ) + + usr_prompt = ( + f"NAME: {character_name}\n" + f"TAGS: {tagger_context}\n" + f"VIBE: {artistic_vibe}\n" + f"PACING DIRECTIVE: {self._pacing_hint(pacing)}\n\n" + f"STORY:\n{master_story}\n" + ) + + 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, + "response_format": {"type": "json_object"}, + } + if seed: + payload["seed"] = seed + + parsed, debug = None, "" + for attempt in (1, 2): + try: + raw = self._call_llm(url, payload, api_key) + parsed = self._extract_json(raw) + if parsed and all(parsed.get(k) for k in + ("clip_1", "clip_2", "clip_3", "clip_4")): + debug = f"✅ LLM ok (attempt {attempt})" + break + debug = f"⚠️ Parse failed (attempt {attempt}), retrying..." + except (urllib.error.URLError, urllib.error.HTTPError, + TimeoutError, KeyError, ValueError) as e: + debug = f"❌ API error (attempt {attempt}): {e}" + payload["temperature"] = min(creativity, 0.4) + + # ---- 성공: LLM 결과 조립 ---- + if parsed and all(parsed.get(k) for k in ("clip_1", "clip_2", "clip_3", "clip_4")): + final_name = user_name or str(parsed.get("character", "")).strip() + master = str(parsed.get("master_scene", "")).strip() + clips = [str(parsed[f"clip_{i}"]).strip() for i in range(1, 5)] + outs = tuple(self._build_final(c, tagger_context, master, final_name) for c in clips) + print(f"\n✅ [Dolphin V32] {debug}\nCHAR: {final_name}\nSCENE: {master}") + for i, c in enumerate(clips, 1): + print(f" CLIP {i}: {c}") + return outs + (f"{debug} | char={final_name}",) + + # ---- 실패: 로컬 폴백 ---- + fb = self._local_split(master_story) + outs = tuple(self._build_final(c, tagger_context, "", user_name) for c in fb) + print(f"\n⚠️ [Dolphin V32] LLM failed, local fallback. {debug}") + return outs + (f"⚠️ Fallback used. {debug}",) + + +NODE_CLASS_MAPPINGS = {"DolphinMultiActionPromptNode_V32": DolphinMultiActionPromptNode_V32} +NODE_DISPLAY_NAME_MAPPINGS = {"DolphinMultiActionPromptNode_V32": "🐬 Dolphin Story Splitter V32"} diff --git a/custom_nodes/dolphin_nodes/promp_logic_v32_vision.py b/custom_nodes/dolphin_nodes/promp_logic_v32_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..aaab62c6719ee577ef0deb1b5f3487579473a14b --- /dev/null +++ b/custom_nodes/dolphin_nodes/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", + ) diff --git a/custom_nodes/dolphin_nodes/tools/reapply_symlink_output_patch.ps1 b/custom_nodes/dolphin_nodes/tools/reapply_symlink_output_patch.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..72c53bd084f35366454e3f8296c47068a8b8ad8e --- /dev/null +++ b/custom_nodes/dolphin_nodes/tools/reapply_symlink_output_patch.ps1 @@ -0,0 +1,91 @@ +<# +.SYNOPSIS + Re-applies the local revert of ComfyUI upstream commit 41a3e160d (#15216). + +.DESCRIPTION + #15216 changed get_save_image_path()'s containment check from a lexical + abspath comparison to is_within_directory(), which realpath()s both operands. + That resolves symlinks, so saving into a symlinked subfolder of the output + directory (e.g. D:\SD\output\I2V\RAW -> D:\iCloud\iCloudDrive\img\RAW) fails + with "Saving image outside the output folder is not allowed". + + The GHSA-779p-m5rp-r4h4 security fix (96e0e3585, 2026-07-02) is NOT touched -- + this only reverts the later refactor. ".." traversal stays blocked, and the + /view endpoint in server.py uses the same abspath check, so save and preview + remain consistent. + + Idempotent: safe to run any number of times. + + Update flow: folder_paths.py is tracked by ComfyUI's own git, so this patch + makes it dirty and `git pull` will refuse to overwrite it. Discard the patch, + pull, then re-apply: + + git -C checkout -- folder_paths.py + git -C pull + .\reapply_symlink_output_patch.ps1 + +.EXAMPLE + .\reapply_symlink_output_patch.ps1 + .\reapply_symlink_output_patch.ps1 -Verify +#> +[CmdletBinding()] +param( + # Path to ComfyUI's folder_paths.py. Defaults to the ComfyUI root three + # levels above this script (tools -> dolphin_nodes -> custom_nodes -> ComfyUI). + [string]$FolderPathsPy = (Join-Path $PSScriptRoot '..\..\..\folder_paths.py'), + + # Only report the current state; make no changes. + [switch]$Verify +) + +$ErrorActionPreference = 'Stop' + +$marker = '# DOLPHIN LOCAL PATCH: revert of upstream 41a3e160d' +$upstream = ' if not is_within_directory(output_dir, full_output_folder):' +$patched = @" + # DOLPHIN LOCAL PATCH: revert of upstream 41a3e160d (#15216), which swapped + # this lexical check for is_within_directory()'s realpath() one. That change + # breaks saving into a symlinked subfolder of the output dir (D:\SD\output\ + # I2V\RAW -> D:\iCloud\...). The /view endpoint in server.py still uses this + # same abspath-based check, so reverting keeps save and preview consistent. + # Re-apply after a ComfyUI update: + # custom_nodes/dolphin_nodes/tools/reapply_symlink_output_patch.ps1 + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: +"@ + +if (-not (Test-Path $FolderPathsPy)) { + Write-Error "folder_paths.py not found at: $FolderPathsPy" + exit 1 +} +$FolderPathsPy = (Resolve-Path $FolderPathsPy).Path +$text = Get-Content $FolderPathsPy -Raw + +if ($text.Contains($marker)) { + Write-Host "[OK] Already patched: $FolderPathsPy" -ForegroundColor Green + exit 0 +} + +if (-not $text.Contains($upstream)) { + Write-Host "[FAIL] Neither the patch marker nor the expected upstream line was found." -ForegroundColor Red + Write-Host " Upstream probably rewrote this check. Inspect get_save_image_path() in:" + Write-Host " $FolderPathsPy" + exit 1 +} + +if ($Verify) { + Write-Host "[NEEDS PATCH] Upstream check is active; symlinked output subfolders will fail." -ForegroundColor Yellow + exit 2 +} + +$backup = "$FolderPathsPy.orig-$(Get-Date -Format yyyyMMdd-HHmmss)" +Copy-Item $FolderPathsPy $backup + +# Preserve the file's existing newline style rather than forcing CRLF. +$eol = if ($text -match "`r`n") { "`r`n" } else { "`n" } +$text = $text.Replace($upstream, ($patched -replace "`r?`n", $eol).TrimEnd($eol)) +Set-Content $FolderPathsPy $text -NoNewline + +Write-Host "[PATCHED] $FolderPathsPy" -ForegroundColor Green +Write-Host " backup: $backup" +Write-Host " Restart ComfyUI for the change to take effect." +exit 0 diff --git a/custom_nodes/dolphin_nodes/wan_h3_refine.py b/custom_nodes/dolphin_nodes/wan_h3_refine.py new file mode 100644 index 0000000000000000000000000000000000000000..708f24cb88e7ba9283a2c2ecff22dfb37bdc5707 --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_h3_refine.py @@ -0,0 +1,354 @@ +"""MiniMax H3 출력을 Wan 2.2 LOW noise expert로 저-denoise 리파인하는 올인원 노드. + +H3(24fps, 768 short edge, 17k+5 프레임 그리드)와 Wan(16fps, 4n+1 그리드, Wan2.1 VAE) +사이를 넘길 때 매번 손으로 맞춰야 하는 것들을 한 노드에 몰아넣는다: + + - 프레임 수를 4n+1로 정렬 (H3 기본 124프레임은 Wan에서 불법. 141/209/277/345는 합법) + - VAE 오배선 차단 (Wan2.2 VAE=48ch, H3 VAE=24ch를 꽂으면 여기서 막는다. 필요한 건 Wan2.1 VAE=16ch) + - 소스 영상을 인코딩해서 그대로 샘플링 라텐트로 사용 (WanImageToVideo가 뱉는 zeros 라텐트가 아님) + - I2V concat 컨디셔닝을 코어 WanImageToVideo와 동일한 규약으로 재구성 + - sigma_shift 패치를 샘플링 직전에 직접 적용 (그래프에 ModelSamplingSD3를 따로 놓는 방식은 + 상류에서 model_sampling을 통째로 교체하는 노드가 있으면 무시되므로 여기서 처리) + - VAE 왕복 + 저denoise 리페인트가 만드는 색 드리프트를 원본 기준으로 되돌림 + - H3가 같이 뽑아준 오디오를 정렬된 프레임 수에 맞춰 트림/패드해서 통과시킴 + +컨텍스트 윈도우(81프레임 초과 클립)는 이 노드가 아니라 상류의 코어 `WanContextWindowsManual` +노드가 담당한다 - MODEL을 패치하는 방식이라 이 노드는 아무것도 안 해도 자동으로 먹는다. +""" + +import torch +import torch.nn.functional as F + +import comfy.model_management +import comfy.model_sampling +import comfy.samplers +import comfy.utils +import node_helpers +import nodes + +# Wan 2.2 MoE의 high/low expert 경계 시그마. 시작 시그마가 이 값 아래면 LOW 단독으로 충분하다. +MOE_BOUNDARY_SIGMA = 0.9 + +# Wan2.1 VAE의 라텐트 채널 수. Wan2.2(5B) VAE는 48, MiniMax H3 VAE는 24라서 이걸로 구분된다. +WAN21_LATENT_CHANNELS = 16 + + +def _shifted_sigma(shift, t): + """flow matching의 time_snr_shift. comfy/model_sampling.py의 스케줄과 동일한 식.""" + if shift <= 0: + return t + return shift * t / (1.0 + (shift - 1.0) * t) + + +def _align_4n1(n, mode): + """Wan의 프레임 그리드(4n+1)로 맞춘 목표 프레임 수.""" + rem = (n - 1) % 4 + if rem == 0: + return n + if mode == "trim": + return n - rem + if mode == "pad": + return n + (4 - rem) + return n # off + + +def _color_match_per_frame(out, src, strength): + """프레임별/채널별 mean-std 매칭. 클립 전체 통계가 아니라 프레임별로 맞춰야 + H3 쪽 조명 변화(페이드, 그림자 통과 등)를 리파인이 눌러버리지 않는다.""" + if strength <= 0.0: + return out + eps = 1e-5 + o = out.float() + s = src.float() + o_mean = o.mean(dim=(1, 2), keepdim=True) + o_std = o.std(dim=(1, 2), keepdim=True) + s_mean = s.mean(dim=(1, 2), keepdim=True) + s_std = s.std(dim=(1, 2), keepdim=True) + matched = (o - o_mean) / (o_std + eps) * s_std + s_mean + return torch.lerp(o, matched, float(strength)).clamp(0.0, 1.0) + + +class WanH3Refine_Dolphin: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL", {"tooltip": "Wan 2.2 I2V A14B LOW noise + 캐릭터 LoRA까지 적용된 모델. " + "denoise가 낮으면 HIGH expert는 아예 안 쓰이므로 LOW만 로드하면 된다."}), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "vae": ("VAE", {"tooltip": "Wan2.1 VAE (16채널). Wan2.2 VAE나 H3 VAE를 꽂으면 에러로 막는다."}), + "images": ("IMAGE", {"tooltip": "H3가 뽑은 프레임들."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 6, "min": 1, "max": 200, + "tooltip": "실제로 실행되는 스텝 수. denoise가 낮아도 이 횟수만큼 돈다 " + "(스케줄 총 길이가 steps/denoise로 늘어나고 그 꼬리에서 이 수만큼 밟는 구조). " + "리파인은 4~8이면 충분."}), + "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 30.0, "step": 0.1, + "tooltip": "증류(lightning/lightx2v) LoRA를 뺐다면 2.5~3.5, 걸었다면 1.0."}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler"}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "simple"}), + "denoise": ("FLOAT", {"default": 0.30, "min": 0.01, "max": 1.0, "step": 0.01, + "tooltip": "0.2~0.35 권장. Wan은 16fps 기준으로 학습돼서 24fps 소스를 " + "'덜 움직이는 영상'으로 읽는다 - 높일수록 모션을 죽인다."}), + "sigma_shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 20.0, "step": 0.1, + "tooltip": "0이면 모델 기본값 유지. 생성용 5는 스텝을 고시그마 쪽으로 쏠리게 " + "하는데 리파인은 저시그마를 촘촘히 밟아야 하므로 2~3이 맞다."}), + "frame_align": (["trim", "pad", "off"], {"default": "trim", + "tooltip": "Wan의 4n+1 그리드로 정렬. trim=뒤를 자름, " + "pad=마지막 프레임 복제. H3 length를 141/209/277/345로 " + "뽑으면 애초에 정렬이 필요 없다."}), + "conditioning_mode": (["first_frame", "full_clip", "none"], {"default": "first_frame", + "tooltip": "first_frame=코어 WanImageToVideo와 동일(학습 분포 안). " + "full_clip=전 프레임을 known으로 주는 강한 소스 고정이지만 " + "I2V-A14B 학습 분포 밖이라 실험용. none=컨디셔닝 없음."}), + "upscale_by": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05, + "tooltip": "리파인 전 확대 배율. 1.3을 넘기면 Wan 학습 해상도를 벗어나 " + "중복 패턴이 생긴다 - 큰 업스케일은 SeedVR2/Topaz로 따로."}), + "color_match": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05, + "tooltip": "VAE 왕복이 만드는 채도/밝기 드리프트를 원본 기준으로 되돌리는 강도."}), + "fps": ("INT", {"default": 24, "min": 1, "max": 120, + "tooltip": "소스 fps. 오디오 길이를 정렬된 프레임 수에 맞출 때만 쓴다. H3는 24."}), + }, + "optional": { + "audio": ("AUDIO", {"tooltip": "H3가 같이 뽑은 오디오. 정렬된 프레임 수에 맞춰 트림/패드해서 통과시킨다."}), + }, + } + + RETURN_TYPES = ("IMAGE", "AUDIO", "INT", "INT", "INT", "STRING") + RETURN_NAMES = ("IMAGE", "audio", "frames", "width", "height", "info") + FUNCTION = "refine" + CATEGORY = "Dolphin Node/Wan Video" + DESCRIPTION = "MiniMax H3 출력을 Wan 2.2 LOW noise로 저-denoise 리파인 (그리드 정렬 + VAE 검증 + 색보정 + 오디오 패스스루)" + + def refine(self, model, positive, negative, vae, images, seed, steps, cfg, + sampler_name, scheduler, denoise, sigma_shift, frame_align, + conditioning_mode, upscale_by, color_match, fps, audio=None): + + notes = [] + + # --- 1. VAE 검증 --------------------------------------------------- + latent_channels = getattr(vae, "latent_channels", None) + if latent_channels != WAN21_LATENT_CHANNELS: + hint = {24: "MiniMax H3 VAE(24ch)", 48: "Wan2.2 5B VAE(48ch)", 4: "SD VAE(4ch)"}.get( + latent_channels, f"라텐트 {latent_channels}채널 VAE") + raise ValueError( + f"[Dolphin] Wan 2.2 I2V A14B 리파인에는 Wan2.1 VAE(16채널)가 필요한데 {hint}가 연결됐다. " + f"models/vae의 Wan2_1_VAE_bf16.safetensors 계열로 바꿀 것." + ) + + # --- 2. 프레임 그리드 정렬 (Wan은 4n+1만 받는다) --------------------- + n_in = int(images.shape[0]) + if n_in < 5: + raise ValueError(f"[Dolphin] 프레임이 {n_in}장뿐이다. 최소 5장 필요.") + + n = _align_4n1(n_in, frame_align) + if n < 5: + raise ValueError(f"[Dolphin] 4n+1로 정렬하면 {n}프레임이 된다. 소스를 더 길게 뽑을 것.") + + if n < n_in: + images = images[:n] + notes.append(f"{n_in}→{n}프레임 트림") + elif n > n_in: + tail = images[-1:].repeat(n - n_in, 1, 1, 1) + images = torch.cat([images, tail], dim=0) + notes.append(f"{n_in}→{n}프레임 패드(마지막 프레임 복제)") + elif frame_align == "off" and (n_in - 1) % 4 != 0: + notes.append(f"⚠ {n_in}프레임은 4n+1이 아니다 (frame_align=off)") + + images = images[:, :, :, :3] + + # --- 3. 해상도 정렬 (16의 배수) -------------------------------------- + src_h, src_w = int(images.shape[1]), int(images.shape[2]) + tgt_w = max(16, int(round(src_w * upscale_by / 16.0)) * 16) + tgt_h = max(16, int(round(src_h * upscale_by / 16.0)) * 16) + + if (tgt_w, tgt_h) != (src_w, src_h): + images = comfy.utils.common_upscale( + images.movedim(-1, 1), tgt_w, tgt_h, "lanczos", "disabled" + ).movedim(1, -1).clamp(0.0, 1.0) + notes.append(f"{src_w}x{src_h}→{tgt_w}x{tgt_h}") + if upscale_by > 1.3: + notes.append("⚠ upscale_by>1.3은 Wan 학습 해상도 밖 (중복 패턴 위험)") + + # --- 4. 소스 인코딩: 이게 샘플링 시작 라텐트가 된다 -------------------- + source_latent = self._encode(vae, images) + + # --- 5. I2V concat 컨디셔닝 (코어 WanImageToVideo와 동일 규약) -------- + if conditioning_mode != "none": + known = images[:1] if conditioning_mode == "first_frame" else images + canvas = torch.ones((n, tgt_h, tgt_w, 3), + device=images.device, dtype=images.dtype) * 0.5 + canvas[:known.shape[0]] = known + concat_latent = self._encode(vae, canvas) + + mask = torch.ones((1, 1, source_latent.shape[2], + concat_latent.shape[-2], concat_latent.shape[-1]), + device=images.device, dtype=images.dtype) + mask[:, :, :((known.shape[0] - 1) // 4) + 1] = 0.0 + + values = {"concat_latent_image": concat_latent, "concat_mask": mask} + positive = node_helpers.conditioning_set_values(positive, values) + negative = node_helpers.conditioning_set_values(negative, values) + + if conditioning_mode == "full_clip": + notes.append("⚠ full_clip은 I2V-A14B 학습 분포 밖(첫 프레임만 known으로 학습됨)") + + # --- 6. sigma_shift 패치 -------------------------------------------- + effective_shift = self._model_shift(model) + if sigma_shift > 0: + model = self._patch_shift(model, sigma_shift) + effective_shift = sigma_shift + + # --- 7. 샘플링 ------------------------------------------------------- + out_latent, = nodes.common_ksampler( + model, seed, steps, cfg, sampler_name, scheduler, + positive, negative, {"samples": source_latent}, denoise=denoise, + ) + + # --- 8. 디코드 ------------------------------------------------------- + result = self._decode(vae, out_latent["samples"]) + if result.shape[0] > n: + result = result[:n] + elif result.shape[0] < n: + images = images[:result.shape[0]] + n = int(result.shape[0]) + + # --- 9. 색 드리프트 되돌리기 ----------------------------------------- + if result.shape[1:3] != images.shape[1:3]: + ref = F.interpolate(images.movedim(-1, 1), + size=(result.shape[1], result.shape[2]), + mode="bicubic", antialias=True).clamp(0, 1).movedim(1, -1) + else: + ref = images + result = _color_match_per_frame(result[:, :, :, :3], ref, color_match) + + # --- 10. 오디오를 프레임 수에 맞춰 정렬 ------------------------------- + audio_out = audio + if audio is not None and isinstance(audio, dict) and "waveform" in audio: + sr = int(audio.get("sample_rate", 48000)) + waveform = audio["waveform"] + target_samples = int(round(n / float(fps) * sr)) + cur = int(waveform.shape[-1]) + if cur > target_samples: + waveform = waveform[..., :target_samples] + notes.append(f"오디오 {cur}→{target_samples}샘플 트림") + elif cur < target_samples: + waveform = F.pad(waveform, (0, target_samples - cur)) + notes.append(f"오디오 {cur}→{target_samples}샘플 패드") + audio_out = {"waveform": waveform, "sample_rate": sr} + + # --- 11. 리포트 ------------------------------------------------------ + start_sigma = _shifted_sigma(effective_shift, denoise) + expert = "LOW 단독으로 충분" if start_sigma < MOE_BOUNDARY_SIGMA else "HIGH expert 구간 진입 (리파인이 아니라 재생성)" + info_lines = [ + f"frames {n_in} → {n} (4n+1, n={(n - 1) // 4}) {tgt_w}x{tgt_h} {n / float(fps):.2f}s @ {fps}fps", + f"denoise {denoise:.2f} / shift {effective_shift:.2f} → 시작 σ {start_sigma:.3f} (MoE 경계 {MOE_BOUNDARY_SIGMA}) → {expert}", + f"실행 스텝 {steps} (스케줄 총 길이 ~{int(round(steps / max(denoise, 1e-3)))})", + ] + if notes: + info_lines.append("· " + " / ".join(notes)) + info = "\n".join(info_lines) + print("[Dolphin] Wan H3 Refine\n " + "\n ".join(info_lines)) + + return (result, audio_out, n, tgt_w, tgt_h, info) + + # ---------------------------------------------------------------- helpers + + def _encode(self, vae, pixels): + try: + return vae.encode(pixels) + except comfy.model_management.OOM_EXCEPTION: + print("[Dolphin] VAE 인코딩 OOM → 타일 인코딩으로 폴백") + return vae.encode_tiled(pixels, tile_x=512, tile_y=512, + overlap=64, tile_t=32, overlap_t=8) + + def _decode(self, vae, samples): + try: + images = vae.decode(samples) + except comfy.model_management.OOM_EXCEPTION: + print("[Dolphin] VAE 디코딩 OOM → 타일 디코딩으로 폴백") + compression = vae.temporal_compression_decode() or 4 + images = vae.decode_tiled(samples, tile_x=512, tile_y=512, overlap=64, + tile_t=max(2, 32 // compression), + overlap_t=max(1, 8 // compression)) + if len(images.shape) == 5: # 배치 합치기 + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return images.clamp(0.0, 1.0) + + def _model_shift(self, model): + try: + return float(getattr(model.get_model_object("model_sampling"), "shift", 1.0)) + except Exception: + return 1.0 + + def _patch_shift(self, model, shift): + """ModelSamplingSD3와 동일한 패치를 샘플링 직전에 직접 적용. + 그래프에 따로 놓는 ModelSamplingSD3 노드는 model_sampling을 통째로 교체하는 + 다른 노드(TripleKSampler 등)가 뒤에 있으면 무시되므로 여기서 하는 게 확실하다.""" + m = model.clone() + + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscreteFlow, + comfy.model_sampling.CONST): + pass + + original = m.get_model_object("model_sampling") + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift, multiplier=1000) + if hasattr(original, "noise_scale"): + model_sampling.set_noise_scale(original.noise_scale) + m.add_object_patch("model_sampling", model_sampling) + return m + + +class WanH3FrameGrid_Dolphin: + """H3의 length를 Wan과 호환되는 값으로 미리 뽑아주는 계산기. + + H3 그리드는 n % 17 == 5, Wan 그리드는 n % 4 == 1이다. 둘이 겹치는 건 68프레임 주기 - + 5 / 73 / 141 / 209 / 277 / 345 ... H3 노드의 length를 여기서 나온 값으로 넣으면 + 리파인 단계에서 트림/패드가 아예 필요 없어진다.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "seconds": ("FLOAT", {"default": 5.9, "min": 0.2, "max": 60.0, "step": 0.1, + "tooltip": "원하는 길이(초). 24fps 기준으로 가장 가까운 호환 프레임 수를 찾는다."}), + "mode": (["wan_compatible", "h3_only"], {"default": "wan_compatible", + "tooltip": "wan_compatible=H3(17k+5)와 Wan(4n+1) 둘 다 만족하는 값. " + "h3_only=H3 그리드만 만족(리파인 안 할 때)."}), + } + } + + RETURN_TYPES = ("INT", "FLOAT", "STRING") + RETURN_NAMES = ("length", "seconds", "info") + FUNCTION = "compute" + CATEGORY = "Dolphin Node/Wan Video" + DESCRIPTION = "H3 length를 Wan 리파인과 호환되는 프레임 수(5/73/141/209/277/345…)로 계산" + + def compute(self, seconds, mode): + fps = 24 + want = max(5, int(round(seconds * fps))) + + # h3_only는 17k+5, wan_compatible은 17k+5 ∩ 4n+1 = 68m+5 (k가 4의 배수일 때). + period = 17 if mode == "h3_only" else 68 + below = 5 + period * max(0, (want - 5) // period) + above = below + period + n = below if (want - below) <= (above - want) else above + n = max(5, n) + + dur = n / float(fps) + wan_ok = (n - 1) % 4 == 0 + info = (f"length={n} ({dur:.2f}s @24fps) H3 그리드 OK" + f" / Wan 4n+1 {'OK (n=%d)' % ((n - 1) // 4) if wan_ok else '불가 - 리파인 시 트림 필요'}") + return (n, dur, info) + + +NODE_CLASS_MAPPINGS = { + "WanH3Refine_Dolphin": WanH3Refine_Dolphin, + "WanH3FrameGrid_Dolphin": WanH3FrameGrid_Dolphin, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "WanH3Refine_Dolphin": "🐬🅜 Wan Refine H3 (low-denoise pass)", + "WanH3FrameGrid_Dolphin": "🐬🅜 H3↔Wan Frame Grid", +} diff --git a/custom_nodes/dolphin_nodes/wan_i2v_continuity.py b/custom_nodes/dolphin_nodes/wan_i2v_continuity.py new file mode 100644 index 0000000000000000000000000000000000000000..37496c9043e25c6ac251ad01ac2df9c07750922c --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_i2v_continuity.py @@ -0,0 +1,108 @@ +import torch +import comfy.model_management +import comfy.utils +import node_helpers + + +class WanI2VContinuity_Dolphin: + """ + Wan2.2 I2V 노드 — 클립 이어붙이기(연속성)용. + + 기존 접근(WanImageToVideoSVIPro)의 문제: anchor 이미지를 따로 인코딩한 latent와, + 이전 클립 latent의 뒷부분을 그냥 잘라낸 조각(motion_latent)을 이어붙였다. WAN VAE는 + 인과적(causal) 시간 압축이라 각 latent 프레임이 "그 시점까지의 시퀀스 문맥"을 전제로 + 인코딩되는데, 서로 다른 문맥에서 나온 latent 조각을 이어붙이면 통계적으로 불연속이라 + 화질이 튀고 동작 연속성도 못 살렸다. + + 이 노드는 ComfyUI 코어 `WanImageToVideo`의 검증된 방식을 따른다: + 이전 클립에서 디코딩한 마지막 N프레임(픽셀)을 start_image로 받아, 회색(0.5) filler와 + 함께 하나의 시퀀스로 만들어 **한 번의 통짜 VAE 인코딩**을 한다. 그래서 known 구간과 + 생성 구간의 latent 문맥이 자연스럽게 이어진다. 마스크도 프레임수→latent수 변환을 + 정확히 해서 known 프레임 전체를 고정한다. + + 여기에 PainterI2V의 motion_amplitude(슬로우모션 방지)를 이식했다. 단 known 프레임은 + 건드리지 않고, 뒤에 붙는 filler(생성 대상) latent만 마지막 known latent 대비 차이를 + 증폭한다. motion_amplitude=1.0이면 코어 WanImageToVideo와 동일하게 동작한다. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "vae": ("VAE",), + "width": ("INT", {"default": 832, "min": 16, "max": 4096, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": 4096, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": 9999, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "motion_amplitude": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05, + "tooltip": "1.0=코어 WanImageToVideo와 동일. >1.0이면 filler 구간에 슬로우모션 방지 증폭."}), + }, + "optional": { + "clip_vision_output": ("CLIP_VISION_OUTPUT",), + "start_image": ("IMAGE", {"tooltip": "known 프레임(들). 이전 클립의 디코딩된 마지막 N프레임을 넣으면 연속성이 생김. 1장이면 일반 I2V."}), + }, + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + CATEGORY = "Dolphin Node/Wan Video" + + def encode(self, positive, negative, vae, width, height, length, batch_size, + motion_amplitude=1.0, clip_vision_output=None, start_image=None): + latent = torch.zeros( + [batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], + device=comfy.model_management.intermediate_device(), + ) + + if start_image is not None: + start_image = comfy.utils.common_upscale( + start_image[:length].movedim(-1, 1), width, height, "bilinear", "center" + ).movedim(1, -1) + n_known_frames = start_image.shape[0] + + # known 프레임 뒤를 회색(0.5) filler로 채운 뒤 통짜로 인코딩 (코어 방식) + image = torch.ones( + (length, height, width, start_image.shape[-1]), + device=start_image.device, dtype=start_image.dtype, + ) * 0.5 + image[:n_known_frames] = start_image + + concat_latent_image = vae.encode(image[:, :, :, :3]) + + # 프레임수 -> latent수 (WAN VAE 4x 시간 압축) + n_known_latents = ((n_known_frames - 1) // 4) + 1 + + # motion_amplitude: filler(생성 대상) latent만 증폭. known 프레임은 그대로. + if motion_amplitude > 1.0 and concat_latent_image.shape[2] > n_known_latents: + base_latent = concat_latent_image[:, :, n_known_latents - 1:n_known_latents] + filler = concat_latent_image[:, :, n_known_latents:] + diff = filler - base_latent + diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True) + diff_centered = diff - diff_mean + scaled = base_latent + diff_centered * motion_amplitude + diff_mean + scaled = torch.clamp(scaled, -6, 6) + concat_latent_image = torch.cat( + [concat_latent_image[:, :, :n_known_latents], scaled], dim=2 + ) + + mask = torch.ones( + (1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), + device=start_image.device, dtype=start_image.dtype, + ) + mask[:, :, :n_known_latents] = 0.0 + + positive = node_helpers.conditioning_set_values( + positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask} + ) + negative = node_helpers.conditioning_set_values( + negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask} + ) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + return (positive, negative, {"samples": latent}) diff --git a/custom_nodes/dolphin_nodes/wan_resizer.py b/custom_nodes/dolphin_nodes/wan_resizer.py new file mode 100644 index 0000000000000000000000000000000000000000..1775814e25bdb7a0e50c757716fee1af7bf180a7 --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_resizer.py @@ -0,0 +1,56 @@ +import math +import torch +import comfy.utils + +class WanNativeResize_Dolphin: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "target_model": (["480P (832x480)", "720P (1280x720)", "1080P (1920x1080)"], {"default": "720P (1280x720)"}), + "upscale_method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], {"default": "lanczos"}), + "alignment": ("INT", {"default": 16, "min": 8, "max": 64, "step": 8}), + } + } + + RETURN_TYPES = ("IMAGE", "INT", "INT") + RETURN_NAMES = ("IMAGE", "width", "height") + FUNCTION = "resize_for_wan" + CATEGORY = "Dolphin Node/Wan Video" + + def resize_for_wan(self, image, target_model, upscale_method, alignment): + # 1. ComfyUI 이미지 텐서의 형태를 가져옵니다: (Batch/Frames, Height, Width, Channels) + b, h, w, c = image.shape + + # 2. 타겟 모델에 따른 총 픽셀 면적(Area) 세팅 + if "480P" in target_model: + target_area = 832 * 480 + elif "720P" in target_model: + target_area = 1280 * 720 + else: + target_area = 1920 * 1080 + + # 3. Byungjoo님의 수학 로직 (비율 유지 계산) + aspect_ratio = w / h + new_h = math.sqrt(target_area / aspect_ratio) + new_w = new_h * aspect_ratio + + # 4. [핵심] VAE가 좋아하는 배수(Alignment, 보통 16)로 반올림 처리 + new_w = int(round(new_w / alignment) * alignment) + new_h = int(round(new_h / alignment) * alignment) + + # 5. [최적화] 이미 목표 해상도와 일치한다면, 무거운 연산 없이 원본 통과 (패스스루) + if w == new_w and h == new_h: + return (image, new_w, new_h) + + # 6. [핵심] 텐서 차원 변경: ComfyUI (B, H, W, C) -> 리사이즈 엔진용 (B, C, H, W) + image = image.movedim(-1, 1) + + # 7. ComfyUI 내장 엔진을 사용해 초고화질 리사이즈 (Lanczos 지원) + resized_image = comfy.utils.common_upscale(image, new_w, new_h, upscale_method, "disabled") + + # 8. 텐서 차원 원상 복구: (B, C, H, W) -> ComfyUI (B, H, W, C) + resized_image = resized_image.movedim(1, -1) + + return (resized_image, new_w, new_h) \ No newline at end of file diff --git a/custom_nodes/dolphin_nodes/wan_svipro_fixed.py b/custom_nodes/dolphin_nodes/wan_svipro_fixed.py new file mode 100644 index 0000000000000000000000000000000000000000..a8e451be40448ef7852a6dab98d5913effa5c708 --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_svipro_fixed.py @@ -0,0 +1,95 @@ +import torch +import comfy.model_management as model_management +import comfy.latent_formats +import node_helpers + + +class WanImageToVideoSVIProFixed_Dolphin: + """ + WanImageToVideoSVIPro(comfyui-kjnodes)의 마스크 버그를 고친 버전. + + 원본은 anchor_samples + prev_samples(motion_latent_count 프레임)를 이어붙여 + concat_latent_image를 만들면서도, concat_mask는 mask[:, :, :1] = 0.0으로 + 앵커 프레임 1장만 "known"으로 표시한다. 그래서 실제로 이어붙인 모션 컨텍스트 + 프레임들이 "생성 대상(mask=1)"으로 취급돼 모델이 그 내용을 무시/이탈하고, + 클립이 몇 프레임 반복되다가 전혀 다른 영상으로 튀는 현상이 생긴다. + + 이 버전은 anchor + motion_latent 프레임 수 전체를 known(0.0)으로 마스킹해서 + 이어붙인 모션 컨텍스트가 실제로 고정된 참고 정보로 취급되게 한다. + + 추가로 PainterI2V/PainterFLF2V(ComfyUI-PainterI2V, Comfyui-PainterFLF2V)의 + "motion_amplitude" 슬로우모션 방지 트릭을 이식했다: anchor 뒤에 붙는 순수 + filler(생성 대상) 구간과 anchor의 latent 차이를 증폭해서, 4-step lightning + 계열 로라가 정지 이미지를 보고 동작을 과소평가하는 경향을 보정한다. 실제 + motion_latent(진짜 이전 프레임)는 건드리지 않고 filler 구간에만 적용한다. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "length": ("INT", {"default": 81, "min": 1, "max": 9999, "step": 4}), + "anchor_samples": ("LATENT",), + "motion_latent_count": ("INT", {"default": 1, "min": 0, "max": 128, "step": 1, + "tooltip": "prev_samples 끝에서 몇 프레임을 모션 컨텍스트로 이어붙일지."}), + "motion_amplitude": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05, + "tooltip": "1.0=비활성(원본과 동일). >1.0이면 PainterI2V식 슬로우모션 방지 증폭을 filler 구간에 적용."}), + }, + "optional": { + "prev_samples": ("LATENT", {"tooltip": "이전 클립의 (디코드 안 된) latent. 끝의 motion_latent_count 프레임만 사용."}), + }, + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + CATEGORY = "Dolphin Node/Wan Video" + + def encode(self, positive, negative, length, anchor_samples, motion_latent_count, + motion_amplitude=1.0, prev_samples=None): + anchor_latent = anchor_samples["samples"].clone() + B, C, T, H, W = anchor_latent.shape + + total_latents = ((length - 1) // 4) + 1 + empty_latent = torch.zeros([B, 16, total_latents, H, W], device=model_management.intermediate_device()) + device = anchor_latent.device + dtype = anchor_latent.dtype + + if prev_samples is None or motion_latent_count == 0: + known_frames = anchor_latent.shape[2] + image_cond_latent = anchor_latent + else: + motion_latent = prev_samples["samples"][:, :, -motion_latent_count:].clone() + known_frames = anchor_latent.shape[2] + motion_latent.shape[2] + image_cond_latent = torch.cat([anchor_latent, motion_latent], dim=2) + + padding_size = total_latents - known_frames + padding = torch.zeros(1, C, padding_size, H, W, dtype=dtype, device=device) + padding = comfy.latent_formats.Wan21().process_out(padding) + + # PainterI2V식 증폭: anchor 대비 filler(padding)의 차이를 키워서 + # "여기서부터는 크게 움직여야 한다"는 신호를 강하게 준다. 실제 + # motion_latent(진짜 이전 프레임)는 그대로 두고 filler에만 적용. + if motion_amplitude > 1.0 and padding_size > 0: + base_latent = anchor_latent[:, :, -1:].expand(-1, -1, padding_size, -1, -1) + diff = padding - base_latent + diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True) + diff_centered = diff - diff_mean + padding = base_latent + diff_centered * motion_amplitude + diff_mean + padding = torch.clamp(padding, -6, 6) + + image_cond_latent = torch.cat([image_cond_latent, padding], dim=2) + + mask = torch.ones((1, 1, total_latents, H, W), device=device, dtype=dtype) + mask[:, :, :known_frames] = 0.0 # anchor + motion context 프레임 전부 known 처리 (버그 수정 지점) + + positive = node_helpers.conditioning_set_values( + positive, {"concat_latent_image": image_cond_latent, "concat_mask": mask} + ) + negative = node_helpers.conditioning_set_values( + negative, {"concat_latent_image": image_cond_latent, "concat_mask": mask} + ) + + return (positive, negative, {"samples": empty_latent}) diff --git a/custom_nodes/dolphin_nodes/wan_svipro_gray.py b/custom_nodes/dolphin_nodes/wan_svipro_gray.py new file mode 100644 index 0000000000000000000000000000000000000000..0f78d2a0df88ed162037b21aba0f2ed6d70914bd --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_svipro_gray.py @@ -0,0 +1,145 @@ +import torch +import comfy.model_management as model_management +import node_helpers + + +class WanImageToVideoSVIProGray_Dolphin: + """ + WanImageToVideoSVIPro(comfyui-kjnodes)의 드롭인 교체 노드 — 슬로우모션 원인 수정판. + + 원본 SVIPro는 아직 생성되지 않은 구간(filler)을 latent 공간에서 직접 만든다: + + padding = comfy.latent_formats.Wan21().process_out(torch.zeros(...)) + + `process_out(x) = x * std + mean` 이므로 `process_out(0)`은 **latents_mean 상수**다. + 즉 81프레임(=latent 21프레임) 기준으로 conditioning의 20/21이 시간축으로 전혀 + 변하지 않는 상수 블록이 되고, 모델은 "앞으로 아무 변화 없음" 신호를 받아 동작을 + 억제한다(슬로우모션). 게다가 이 값은 학습 때 그 자리에서 본 적 없는 분포 밖 입력이다. + + 코어 `WanImageToVideo`와 PainterI2V는 filler를 **픽셀 공간의 회색(0.5)** 으로 만든 뒤 + VAE 인코딩한다 — 모델이 학습 중 "unknown 구간"으로 실제 학습한 표현이다. 실측상 + PainterI2V의 슬로우모션 발생률이 ~0%인 반면 SVIPro는 ~70%였다. + + 이 노드는 SVIPro의 latent 기반 인터페이스(anchor_samples/prev_samples/ + motion_latent_count)를 그대로 유지하면서 filler를 회색 VAE 인코딩으로 바꾼다. + + v1(첫 시도)은 known latent와 회색 filler latent를 **따로따로** vae.encode()해서 + 이어붙였는데, 이게 "처음부터 회색 노이즈만 나옴" 버그로 이어졌다. WAN VAE는 인과적 + (causal) 압축이라 각 latent 프레임이 그 시점까지의 시퀀스 문맥을 전제로 인코딩되고, + WAN은 3D self-attention이라 시퀀스 전체에 걸쳐 정보가 섞인다 — 별도 인코딩된 두 + 구간을 이어붙이면 모델이 학습 때 한 번도 못 본 이음매가 생기고, 그게 anchor 프레임 + 까지 포함해서 시퀀스 전체를 오염시킨 것으로 보인다. + + 이번 버전은 PainterI2V/코어 WanImageToVideo와 동일하게, known 구간을 **픽셀로 + 디코딩**한 뒤 회색 filler 픽셀과 하나의 시퀀스로 합쳐서 **단 한 번**만 인코딩한다. + known 쪽에 디코드->재인코드 왕복이 한 번 더 생기지만(코드 리뷰로 이미 알려진 트레이드 + 오프), 이음매 불일치 문제는 구조적으로 사라진다. + + 추가로 원본의 마스크 버그도 고쳤다: 원본은 `mask[:, :, :1] = 0.0`으로 앵커 1프레임만 + known 처리해서 prev_samples로 이어붙인 모션 프레임이 생성 대상으로 취급됐다. + 여기서는 anchor + motion 프레임 전체를 known으로 마스킹한다. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "vae": ("VAE", {"tooltip": "회색 filler를 인코딩하는 데 사용. 원본 SVIPro엔 없던 입력."}), + "length": ("INT", {"default": 81, "min": 1, "max": 9999, "step": 4}), + "anchor_samples": ("LATENT",), + "motion_latent_count": ("INT", {"default": 1, "min": 0, "max": 128, "step": 1, + "tooltip": "prev_samples 끝에서 몇 latent 프레임을 모션 컨텍스트로 이어붙일지."}), + }, + "optional": { + "prev_samples": ("LATENT", {"tooltip": "이전 클립의 latent. 끝의 motion_latent_count 프레임만 사용."}), + }, + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + CATEGORY = "Dolphin Node/Wan Video" + + def encode(self, positive, negative, vae, length, anchor_samples, motion_latent_count, + prev_samples=None): + anchor_latent = anchor_samples["samples"].clone() + B, C, T, H, W = anchor_latent.shape + device, dtype = anchor_latent.device, anchor_latent.dtype + log = lambda *a: print("[SVIProGray]", *a) + log(f"anchor_latent shape={tuple(anchor_latent.shape)} dtype={dtype} " + f"min={anchor_latent.min().item():.4f} max={anchor_latent.max().item():.4f} " + f"mean={anchor_latent.mean().item():.4f}") + + total_latents = ((length - 1) // 4) + 1 + empty_latent = torch.zeros( + [B, 16, total_latents, H, W], device=model_management.intermediate_device() + ) + + if prev_samples is None or motion_latent_count == 0: + known = anchor_latent + else: + motion_latent = prev_samples["samples"][:, :, -motion_latent_count:].clone() + log(f"motion_latent shape={tuple(motion_latent.shape)} " + f"min={motion_latent.min().item():.4f} max={motion_latent.max().item():.4f}") + known = torch.cat([anchor_latent, motion_latent.to(device=device, dtype=dtype)], dim=2) + + known_frames = min(known.shape[2], total_latents) + known = known[:, :, :known_frames] + padding_size = total_latents - known_frames + log(f"total_latents={total_latents} known_frames={known_frames} padding_size={padding_size}") + + if padding_size > 0: + # known을 픽셀로 디코딩 -> 회색(0.5) filler 픽셀과 하나의 시퀀스로 합쳐서 + # 단 한 번만 인코딩 (PainterI2V/코어 WanImageToVideo와 동일한 방식). + # + # decode와 encode를 한 노드 안에서 연속 호출하면 decode가 남긴 중간 버퍼가 + # 안 비워진 채로 encode가 (최대 length 프레임 전체를 다시 인코딩하는) 큰 + # 버퍼를 새로 잡으려다 OOM이 날 수 있다. 그래서 decode 결과를 CPU로 옮겨 + # GPU 버퍼를 바로 비우고, encode 직전에 캐시를 명시적으로 정리한다. + known_pixels = vae.decode(known) + del known + log(f"known_pixels (raw decode) shape={tuple(known_pixels.shape)}") + if known_pixels.ndim == 5: + known_pixels = known_pixels[0] # [B,T,H,W,C] -> [T,H,W,C], B==1 가정 + known_pixels = known_pixels[:, :, :, :3].to("cpu") + log(f"known_pixels shape={tuple(known_pixels.shape)} " + f"min={known_pixels.min().item():.4f} max={known_pixels.max().item():.4f} " + f"mean={known_pixels.mean().item():.4f}") + model_management.soft_empty_cache() + + n_known_px = known_pixels.shape[0] + n_gray_px = max(length - n_known_px, 0) + log(f"n_known_px={n_known_px} n_gray_px={n_gray_px}") + if n_gray_px > 0: + gray = torch.ones( + (n_gray_px, known_pixels.shape[1], known_pixels.shape[2], 3), + dtype=known_pixels.dtype, + ) * 0.5 + combined = torch.cat([known_pixels, gray], dim=0) + else: + combined = known_pixels + del known_pixels + log(f"combined shape={tuple(combined.shape)}") + + image_cond_latent = vae.encode(combined).to(device=device, dtype=dtype) + del combined + model_management.soft_empty_cache() + log(f"image_cond_latent shape={tuple(image_cond_latent.shape)} " + f"min={image_cond_latent.min().item():.4f} max={image_cond_latent.max().item():.4f} " + f"mean={image_cond_latent.mean().item():.4f} std={image_cond_latent.std().item():.4f}") + else: + image_cond_latent = known + + mask = torch.ones((1, 1, total_latents, H, W), device=device, dtype=dtype) + mask[:, :, :known_frames] = 0.0 # anchor + motion 프레임 전부 known (원본 마스크 버그 수정) + + positive = node_helpers.conditioning_set_values( + positive, {"concat_latent_image": image_cond_latent, "concat_mask": mask} + ) + negative = node_helpers.conditioning_set_values( + negative, {"concat_latent_image": image_cond_latent, "concat_mask": mask} + ) + + return (positive, negative, {"samples": empty_latent}) diff --git a/custom_nodes/dolphin_nodes/wan_svipro_motion.py b/custom_nodes/dolphin_nodes/wan_svipro_motion.py new file mode 100644 index 0000000000000000000000000000000000000000..fb9d0322c705e08fa35dfd04cee8bd1d43355554 --- /dev/null +++ b/custom_nodes/dolphin_nodes/wan_svipro_motion.py @@ -0,0 +1,152 @@ +import torch +import comfy.latent_formats +import comfy.model_management as model_management +import node_helpers + + +class WanImageToVideoSVIProMotion_Dolphin: + """ + WanImageToVideoSVIPro(comfyui-kjnodes)의 드롭인 교체 노드 — filler 표현은 원본 그대로 두고 + **모션 증폭만** 얹은 버전. + + [wan_svipro_gray.py](wan_svipro_gray.py)와의 차이 — 그쪽은 filler를 회색(0.5) 픽셀 VAE + 인코딩으로 바꾸고 마스크도 anchor+motion 전체 known으로 바꿨다. 두 가지를 동시에 바꾸는 + 바람에 A/B가 안 깨끗했고, 무엇보다 SVI Pro LoRA는 **원본 규약**(process_out(zeros) 패딩 + + mask 첫 프레임만 known) 위에서 파인튜닝된 가중치라, 회색 filler는 base Wan에겐 + in-distribution이어도 SVI LoRA에겐 분포 밖 입력이 된다. 실제로 회색 버전은 Clip1부터 + 그레이 노이즈가 났다. + + 이 노드는 그래서 **패딩과 마스크를 원본과 비트 단위로 동일하게** 두고, PainterI2V가 실제로 + 슬로우모션을 잡는 그 연산 하나만 추가한다: + + diff = filler - base_latent # 마지막 known latent 기준 + diff_mean = diff.mean(dim=(1,3,4)) # 밝기 성분 분리 (증폭에서 제외) + scaled = base + (diff - diff_mean) * amplitude + diff_mean + + 코어 `WanImageToVideo`도 이미 회색 filler를 쓰는데 슬로우모션이 난다 — + [slowmo_ab_test_A_core_wanimagetovideo.json](slowmo_ab_test_A_core_wanimagetovideo.json)에서 + 확인됐다. 즉 filler를 뭘로 채우냐가 아니라 **앵커 대비 거리를 얼마나 벌려주냐**가 모션 + 진폭을 결정한다. 그 거리를 키우는 게 이 증폭이다. + + amplitude_mode: + - "constant": PainterI2V와 동일. filler 전 구간을 같은 배율로 증폭. + 원본 패딩은 시간축으로 상수라 증폭해도 상수 — "앵커에서 이만큼 멀어진다"는 + 거리 신호만 커진다. + - "ramp": 첫 filler 프레임 1.0 → 마지막 프레임 motion_amplitude로 선형 증가. + 상수 블록에 실제 시간 기울기를 만들어 "뒤로 갈수록 더 멀어진다"는 + 신호를 준다. 원본 패딩의 "미래에 변화 없음" 결함을 직접 겨냥한 변형. + + 출력이 conditioning 2쌍인 이유(PainterI2VAdvanced와 동일한 설계): 모션은 high-noise + 스테이지가 결정하고 low-noise는 디테일·색을 정리한다. low까지 증폭본을 먹이면 색 드리프트와 + 아티팩트만 늘어난다. high_* 는 증폭본, low_* 는 원본 그대로다. + + motion_amplitude=1.0이면 두 쌍 모두 상류 원본 SVIPro의 출력과 완전히 동일하다(회귀 확인용). + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "length": ("INT", {"default": 81, "min": 1, "max": 9999, "step": 4}), + "anchor_samples": ("LATENT",), + "motion_latent_count": ("INT", {"default": 1, "min": 0, "max": 128, "step": 1, + "tooltip": "prev_samples 끝에서 몇 latent 프레임을 모션 컨텍스트로 이어붙일지."}), + "motion_amplitude": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05, + "tooltip": "1.0=상류 SVIPro와 동일. 1.15부터 시작해서 1.3/1.5로 스윕."}), + "amplitude_mode": (["constant", "ramp"], {"default": "constant", + "tooltip": "constant=PainterI2V와 동일. ramp=뒤로 갈수록 증폭을 키워 시간 기울기를 만듦."}), + }, + "optional": { + "prev_samples": ("LATENT", {"tooltip": "이전 클립의 latent. 끝의 motion_latent_count 프레임만 사용."}), + }, + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("high_positive", "high_negative", "low_positive", "low_negative", "latent") + FUNCTION = "encode" + CATEGORY = "Dolphin Node/Wan Video" + + def encode(self, positive, negative, length, anchor_samples, motion_latent_count, + motion_amplitude, amplitude_mode, prev_samples=None): + log = lambda *a: print("[SVIProMotion]", *a) + + anchor_latent = anchor_samples["samples"].clone() + B, C, T, H, W = anchor_latent.shape + device, dtype = anchor_latent.device, anchor_latent.dtype + + total_latents = ((length - 1) // 4) + 1 + empty_latent = torch.zeros( + [B, 16, total_latents, H, W], device=model_management.intermediate_device() + ) + + # ---- 여기부터 상류 kjnodes WanImageToVideoSVIPro와 동일 ---- + if prev_samples is None or motion_latent_count == 0: + known_frames = anchor_latent.shape[2] + image_cond_latent = anchor_latent + else: + motion_latent = prev_samples["samples"][:, :, -motion_latent_count:].clone() + known_frames = anchor_latent.shape[2] + motion_latent.shape[2] + image_cond_latent = torch.cat( + [anchor_latent, motion_latent.to(device=device, dtype=dtype)], dim=2 + ) + + padding_size = total_latents - known_frames + log(f"total_latents={total_latents} known_frames={known_frames} padding_size={padding_size} " + f"amplitude={motion_amplitude} mode={amplitude_mode}") + + if padding_size > 0: + padding = torch.zeros(1, C, padding_size, H, W, dtype=dtype, device=device) + padding = comfy.latent_formats.Wan21().process_out(padding) + image_cond_latent = torch.cat([image_cond_latent, padding], dim=2) + elif padding_size < 0: + # known이 total보다 길면 상류는 그냥 크래시한다. 잘라서 진행. + log(f"known_frames > total_latents — {total_latents}프레임으로 자름") + image_cond_latent = image_cond_latent[:, :, :total_latents] + known_frames = total_latents + + mask = torch.ones((1, 1, total_latents, H, W), device=device, dtype=dtype) + mask[:, :, :1] = 0.0 + # ---- 상류와 동일한 부분 끝 ---- + + cond_low = image_cond_latent + cond_high = self._amplify(cond_low, known_frames, motion_amplitude, amplitude_mode, log) + + high_positive = node_helpers.conditioning_set_values( + positive, {"concat_latent_image": cond_high, "concat_mask": mask}) + high_negative = node_helpers.conditioning_set_values( + negative, {"concat_latent_image": cond_high, "concat_mask": mask}) + low_positive = node_helpers.conditioning_set_values( + positive, {"concat_latent_image": cond_low, "concat_mask": mask}) + low_negative = node_helpers.conditioning_set_values( + negative, {"concat_latent_image": cond_low, "concat_mask": mask}) + + return (high_positive, high_negative, low_positive, low_negative, + {"samples": empty_latent}) + + @staticmethod + def _amplify(cond, known_frames, amplitude, mode, log): + """filler 구간만 마지막 known latent 대비 차이를 증폭. known 프레임은 안 건드림.""" + if amplitude <= 1.0 or cond.shape[2] <= known_frames: + return cond + + base_latent = cond[:, :, known_frames - 1:known_frames] + filler = cond[:, :, known_frames:] + n_filler = filler.shape[2] + + diff = filler - base_latent + diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True) # 밝기 성분 — 증폭 대상 아님 + diff_centered = diff - diff_mean + + if mode == "ramp" and n_filler > 1: + scale = torch.linspace(1.0, amplitude, n_filler, + device=cond.device, dtype=cond.dtype).view(1, 1, n_filler, 1, 1) + else: + scale = amplitude + + scaled = torch.clamp(base_latent + diff_centered * scale + diff_mean, -6, 6) + log(f"amplified filler[{known_frames}:{cond.shape[2]}] " + f"|diff| {diff.abs().mean().item():.4f} -> {(scaled - base_latent).abs().mean().item():.4f}") + + return torch.cat([cond[:, :, :known_frames], scaled], dim=2) diff --git a/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js b/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js new file mode 100644 index 0000000000000000000000000000000000000000..4141d382d01aec469ad05452a3b7c9dff5a5db0c --- /dev/null +++ b/custom_nodes/dolphin_nodes/web/dolphin_lora_rows.js @@ -0,0 +1,541 @@ +import { app } from "../../scripts/app.js"; + +// Compacts DolphinVisionDirector's 16 lora slots (each: combo + on/off + weight +// string = 3 separate stock widgets) into single power-lora-loader-style rows: +// [toggle] [lora name] [weight]. The 3 original widgets are kept exactly where +// they are in node.widgets (never reordered/removed) so saved-workflow +// widgets_values arrays keep working; they are only visually hidden (0 height). +// The compact rows are appended at the END of node.widgets via addCustomWidget, +// which never shifts the index of any pre-existing widget. + +const VISION_NODE_NAME = "DolphinVisionDirector"; +const ARCH_NODE_NAME = "DolphinArchitectV16"; +const NUM_CLIPS = 4; +const NUM_LORAS = 4; +const NUM_ARCH_LORAS = 6; +const ROW_HEIGHT = 24; +const HEADER_HEIGHT = 18; + +function fitText(ctx, str, maxWidth) { + if (!str) return ""; + const full = ctx.measureText(str).width; + if (full <= maxWidth) return str; + const ellipsis = "…"; + let lo = 0, hi = str.length; + while (lo < hi) { + const mid = Math.ceil((lo + hi) / 2); + if (ctx.measureText(str.slice(0, mid) + ellipsis).width <= maxWidth) lo = mid; + else hi = mid - 1; + } + return str.slice(0, lo) + ellipsis; +} + +function hideWidget(widget, suffix = "") { + if (!widget.__dolphinHidden) { + widget.__dolphinHidden = true; + widget.origType = widget.type; + widget.origComputeSize = widget.computeSize; + widget.origSerializeValue = widget.serializeValue; + // Zero, not negative: with 48 widgets hidden at once, the usual "-4" + // margin-collapse trick (fine for a single hidden widget) compounds to a + // large negative offset that throws off LiteGraph's remaining-space + // distribution to flexible multiline widgets (essential_actions, + // artistic_vibe), ballooning their height. + widget.computeSize = () => [0, 0]; + widget.type = "converted-widget" + suffix; + } + // This ComfyUI build renders stock combo/string/boolean widgets as real DOM + // elements (widget.element) absolutely positioned over the canvas, not pure + // canvas draws — computeSize/type alone only affects canvas layout and does + // NOT hide the floating DOM element. Same fix the bundled tripleksampler + // custom node's own UI extension uses for its dynamic widgets. + widget.hidden = true; + if (widget.options) widget.options.hidden = true; + if (widget.element) { + widget.element.style.display = "none"; + const label = widget.element.previousElementSibling; + if (label && label.textContent === widget.name + ":") { + label.style.display = "none"; + } + } +} + +// SERIALIZE_FALSE: every custom widget in this file is presentation-only — the real +// values live on the stock widgets it wraps. LGraphNode.serialize() in this build does +// NOT call serializeValue(); it reads `widget.value` directly and writes `value ?? null`, +// skipping a widget only when `widget.serialize === false` (note: the plain property, +// not `options.serialize`). Without this flag every custom widget appended a trailing +// null to widgets_values — the Architect node was saving 26 entries where 19 are real. +// The nulls were harmless (custom widgets are appended AFTER the stock ones, so no index +// shifted) but they grow every saved workflow, and old files with the extra trailing +// values still load correctly since only the tail is affected. +class DolphinSectionHeaderWidget { + constructor(label) { + this.type = "custom"; + this.name = "dolphin_header_" + label; + this.__dolphinHidden = true; // never touched by hideWidget, just marks "not a real value widget" + this.serialize = false; + this.label = label; + } + computeSize(width) { + return [width, HEADER_HEIGHT]; + } + draw(ctx, node, widgetWidth, posY, height) { + ctx.save(); + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.globalAlpha = 0.75; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.font = "bold 11px Arial"; + ctx.fillText(this.label, 15, posY + height * 0.5); + ctx.restore(); + } + mouse() { + return false; + } + serializeValue() { + return undefined; + } +} + +class DolphinLoraRowWidget { + constructor(name, comboW, onW, weightW) { + this.type = "custom"; + this.name = name; + this.serialize = false; // SERIALIZE_FALSE 참고 + this.comboW = comboW; + this.onW = onW; + this.weightW = weightW; + this.hitAreas = { + toggle: [0, 0], + lora: [0, 0], + weight: [0, 0], + }; + } + computeSize(width) { + return [width, ROW_HEIGHT]; + } + serializeValue() { + return undefined; // real values live on comboW/onW/weightW, not on this row + } + draw(ctx, node, widgetWidth, posY, height) { + const margin = 15; + const inner = 6; + const on = this.onW.value !== false; + ctx.save(); + + // row background + ctx.beginPath(); + ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR; + ctx.roundRect(margin, posY + 1, widgetWidth - margin * 2, height - 2, [height * 0.25]); + ctx.fill(); + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.stroke(); + + const midY = posY + height * 0.5; + let x = margin + inner; + + // toggle switch + const toggleH = height - 10; + const toggleW = toggleH * 1.7; + ctx.beginPath(); + ctx.fillStyle = "#00000055"; + ctx.roundRect(x, posY + 5, toggleW, toggleH, [toggleH * 0.5]); + ctx.fill(); + ctx.beginPath(); + ctx.fillStyle = on ? "#8ab4ff" : "#777"; + const knobX = on ? x + toggleW - toggleH * 0.5 : x + toggleH * 0.5; + ctx.arc(knobX, posY + height * 0.5, toggleH * 0.36, 0, Math.PI * 2); + ctx.fill(); + this.hitAreas.toggle = [x, toggleW]; + x += toggleW + inner * 1.5; + + ctx.globalAlpha = on ? 1 : 0.45; + + // weight field (measure first, right-aligned) + ctx.font = "11px Arial"; + const weightText = (this.weightW.value ?? "").toString(); + const weightBoxW = Math.min(100, Math.max(46, ctx.measureText(weightText).width + 14)); + const weightX = widgetWidth - margin - inner - weightBoxW; + + // lora name (fills remaining space) + const nameX = x; + const nameW = Math.max(20, weightX - inner - nameX); + const hasLora = this.comboW.value && this.comboW.value !== "None"; + ctx.fillStyle = hasLora ? LiteGraph.WIDGET_TEXT_COLOR : LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillText(fitText(ctx, hasLora ? this.comboW.value : "— pick lora —", nameW), nameX, midY); + this.hitAreas.lora = [nameX, nameW]; + + // weight box + ctx.beginPath(); + ctx.fillStyle = "#00000055"; + ctx.roundRect(weightX, posY + 4, weightBoxW, height - 8, [4]); + ctx.fill(); + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.textAlign = "center"; + ctx.fillText(fitText(ctx, weightText, weightBoxW - 10), weightX + weightBoxW / 2, midY); + this.hitAreas.weight = [weightX, weightBoxW]; + + ctx.restore(); + } + _within(pos, bounds) { + return pos[0] >= bounds[0] && pos[0] <= bounds[0] + bounds[1]; + } + mouse(event, pos, node) { + if (event.type !== "pointerdown") return false; + if (this._within(pos, this.hitAreas.toggle)) { + this.onW.value = this.onW.value === false ? true : false; + node.setDirtyCanvas(true, true); + return true; + } + if (this._within(pos, this.hitAreas.lora)) { + let values = this.comboW.options?.values ?? []; + if (typeof values === "function") values = values(this.comboW, node) ?? []; + const canvas = app.canvas; + // Plain string array + single top-level callback is what makes + // LiteGraph.ContextMenu's built-in search/filter box appear (same + // pattern rgthree's showLoraChooser and KJNodes use) — passing + // {content, callback} objects per item silently skips that filter. + new LiteGraph.ContextMenu([...values], { + event, + scale: Math.max(1, canvas.ds?.scale ?? 1), + className: "dark", + title: "Choose a lora", + callback: (value) => { + if (typeof value !== "string") return; + this.comboW.value = value; + if (typeof this.comboW.callback === "function") { + this.comboW.callback(value, canvas, node, pos, event); + } + node.setDirtyCanvas(true, true); + }, + }); + return true; + } + if (this._within(pos, this.hitAreas.weight)) { + app.canvas.prompt( + "Weight (base, high, low)", + this.weightW.value ?? "", + (v) => { + if (v !== null) { + this.weightW.value = v; + node.setDirtyCanvas(true, true); + } + }, + event, + ); + return true; + } + return false; + } +} + +class DolphinTextFieldWidget { + constructor(label, targetW, multiline = false) { + this.type = "custom"; + this.name = "dolphin_field_" + targetW.name; + this.serialize = false; // SERIALIZE_FALSE 참고 + this.label = label; + this.targetW = targetW; + this.multiline = multiline; + this.hitAreas = { field: [0, 0] }; + } + computeSize(width) { + return [width, ROW_HEIGHT]; + } + serializeValue() { + return undefined; // real value lives on targetW + } + draw(ctx, node, widgetWidth, posY, height) { + const margin = 15; + const inner = 8; + ctx.save(); + ctx.beginPath(); + ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR; + ctx.roundRect(margin, posY + 1, widgetWidth - margin * 2, height - 2, [height * 0.25]); + ctx.fill(); + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.stroke(); + + const midY = posY + height * 0.5; + ctx.textAlign = "left"; + ctx.textBaseline = "middle"; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.fillText(this.label, margin + inner, midY); + const labelW = ctx.measureText(this.label).width; + + const valueX = margin + inner + labelW + inner; + const valueW = Math.max(20, widgetWidth - margin - inner - valueX); + const text = (this.targetW.value ?? "").toString().replace(/\n/g, " ⏎ "); + ctx.fillStyle = text ? LiteGraph.WIDGET_TEXT_COLOR : LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.fillText(fitText(ctx, text || "(empty, click to edit)", valueW), valueX, midY); + this.hitAreas.field = [margin, widgetWidth - margin * 2]; + ctx.restore(); + } + _within(pos, bounds) { + return pos[0] >= bounds[0] && pos[0] <= bounds[0] + bounds[1]; + } + mouse(event, pos, node) { + if (event.type !== "pointerdown") return false; + if (this._within(pos, this.hitAreas.field)) { + app.canvas.prompt( + this.label, + this.targetW.value ?? "", + (v) => { + if (v !== null) { + this.targetW.value = v; + node.setDirtyCanvas(true, true); + } + }, + event, + this.multiline, + ); + return true; + } + return false; + } +} + +// 숨겨진 스톡 위젯은 .value만 바꿔도 직렬화(widgets_values)에는 제대로 반영되지만, +// 이 빌드는 combo/string/boolean을 실제 DOM 엘리먼트로도 들고 있어서 그쪽 값이 남아 +// 있으면 나중에 ComfyUI의 per-frame DOM 싱크가 옛 값을 되돌려 쓸 수 있다. 그래서 +// .value를 바꿀 때 엘리먼트도 같이 맞춰준다. +function syncWidgetElement(widget) { + const el = widget?.element; + if (!el) return; + try { + if (el.type === "checkbox") el.checked = widget.value !== false; + else if ("value" in el) el.value = widget.value ?? ""; + } catch (e) { + console.error("[Dolphin.LoraRows] element sync failed:", e); + } +} + +// 6개 슬롯을 Python 쪽 기본값(lora="None", on=True, weights="0.0, 0.0, 0.0")으로 +// 되돌리는 버튼. 되돌릴 수 없는 조작이라 ContextMenu로 한 번 확인을 받는다 - +// 파일 상단의 lora chooser와 같은 방식이라 별도 UI 의존성이 없다. +class DolphinResetButtonWidget { + constructor(label, slots, extraW) { + this.type = "custom"; + this.name = "dolphin_reset_button"; + this.serialize = false; // SERIALIZE_FALSE 참고 + this.label = label; + this.slots = slots; // [{comboW, onW, weightW}, ...] + this.extraW = extraW; // manual_triggers (없을 수도 있음) + this.hitAreas = { button: [0, 0] }; + } + computeSize(width) { + return [width, ROW_HEIGHT]; + } + serializeValue() { + return undefined; // 값이 없는 순수 액션 위젯 + } + draw(ctx, node, widgetWidth, posY, height) { + const margin = 15; + ctx.save(); + ctx.beginPath(); + ctx.fillStyle = "#00000055"; + ctx.roundRect(margin, posY + 1, widgetWidth - margin * 2, height - 2, [height * 0.25]); + ctx.fill(); + ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR; + ctx.stroke(); + + ctx.font = "11px Arial"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + ctx.fillText(this.label, widgetWidth * 0.5, posY + height * 0.5); + this.hitAreas.button = [margin, widgetWidth - margin * 2]; + ctx.restore(); + } + _within(pos, bounds) { + return pos[0] >= bounds[0] && pos[0] <= bounds[0] + bounds[1]; + } + _reset(node) { + for (const { comboW, onW, weightW } of this.slots) { + comboW.value = "None"; + onW.value = true; + weightW.value = "0.0, 0.0, 0.0"; + syncWidgetElement(comboW); + syncWidgetElement(onW); + syncWidgetElement(weightW); + } + if (this.extraW) { + this.extraW.value = ""; + syncWidgetElement(this.extraW); + } + node.setDirtyCanvas(true, true); + } + mouse(event, pos, node) { + if (event.type !== "pointerdown") return false; + if (!this._within(pos, this.hitAreas.button)) return false; + const CONFIRM = `Reset all ${this.slots.length} slots`; + new LiteGraph.ContextMenu([CONFIRM, "Cancel"], { + event, + scale: Math.max(1, app.canvas.ds?.scale ?? 1), + className: "dark", + title: "Clear every lora, weight and trigger?", + callback: (value) => { + if (value === CONFIRM) this._reset(node); + }, + }); + return true; + } +} + +// buildRows(node) does the type-specific widget lookup + addCustomWidget calls +// and returns the array of stock widgets that got compacted into rows (so they +// can be hidden). Everything past this point (hiding, retry timers, size snap) +// is identical for every node type this gets installed on. +function installLoraRows(nodeType, buildRows) { + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const ret = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + const node = this; + + // Everything below is purely cosmetic (compact lora rows). If anything + // in here throws, it must NEVER propagate — an uncaught error here + // during graph.configure() can abort loading the rest of the workflow, + // leaving a blank canvas. Fail loud in the console, fail safe for the user. + try { + if (!node.__dolphinLoraRowsBuilt) { + node.__dolphinLoraRowsBuilt = true; + node.__dolphinHiddenWidgets = buildRows(node) || []; + } + + // widget.element (the real DOM ) may not exist yet at + // construction time, and ComfyUI's own per-frame DOM-sync can re-show + // it later — so (re-)apply hiding now and on a couple of deferred + // passes to make sure it sticks. This does NOT touch node.size, so it + // never fights a manual resize the user makes afterwards. + const applyHiding = () => { + try { + for (const w of node.__dolphinHiddenWidgets || []) hideWidget(w); + node.setDirtyCanvas(true, true); + } catch (e) { + console.error("[Dolphin.LoraRows] applyHiding failed:", e); + } + }; + applyHiding(); + setTimeout(applyHiding, 50); + setTimeout(applyHiding, 250); + + // Snap node.size to the freshly computed minimum ONCE on (re)build. + // Collapsing many stock widgets into compact rows frees up a lot of + // height; a stale saved node.size (from before this UI existed) would + // otherwise leave that freed space around, and flexible multiline + // widgets auto-grow to fill it. + setTimeout(() => { + try { + const computed = node.computeSize(); + node.size = node.size || [0, 0]; + node.size[1] = computed[1]; + node.setDirtyCanvas(true, true); + } catch (e) { + console.error("[Dolphin.LoraRows] size snap failed:", e); + } + }, 300); + } catch (e) { + console.error("[Dolphin.LoraRows] onNodeCreated hook failed:", e); + } + return ret; + }; +} + +function buildVisionDirectorRows(node) { + const hidden = []; + for (let c = 1; c <= NUM_CLIPS; c++) { + node.addCustomWidget(new DolphinSectionHeaderWidget(`── Clip ${c} Loras ──`)); + for (let n = 1; n <= NUM_LORAS; n++) { + const comboW = node.widgets.find((w) => w.name === `c${c}_lora_${n}`); + const onW = node.widgets.find((w) => w.name === `c${c}_lora_${n}_on`); + const weightW = node.widgets.find((w) => w.name === `c${c}_w_${n}`); + if (!comboW || !onW || !weightW) continue; + hidden.push(comboW, onW, weightW); + node.addCustomWidget(new DolphinLoraRowWidget(`c${c}_lora_${n}_row`, comboW, onW, weightW)); + } + } + const manualTriggersW = node.widgets.find((w) => w.name === "manual_triggers"); + if (manualTriggersW) { + hidden.push(manualTriggersW); + node.addCustomWidget(new DolphinTextFieldWidget("manual_triggers:", manualTriggersW, true)); + } + return hidden; +} + +// DolphinArchitectV16 (🐬 Dolphin Architect V16): one flat bank of lora_1..6 / +// lora_1_on..6_on / weights_1..6 (each already base+high+low as one comma +// string, e.g. "0.4, 0.7, 0") — no per-clip grouping, so no section headers. +// 파일명에 HIGH/LOW가 들어있으면 Wan2.2 분리 로라로 보고 그쪽 모델 슬롯만 켠다 +// (base,high,low). 둘 다 없으면 Wan2.1 등 범용 로라로 보고 셋 다 켠다. "high_noise"/ +// "low_noise" 같은 변형도 그냥 substring 매치라 자동으로 걸린다. +// HN/LN 약어(-HN-, _HN_, ...-HN.safetensors 식)는 앞뒤가 영숫자가 아닐 때만 매치하는 +// 토큰 경계 정규식을 쓴다 - 그냥 substring이면 "krea2DepthControlnet"(contro-L-N-et) +// 같은 무관한 파일명이 "ln"을 우연히 포함해서 Low Noise로 오인식된다. +const HN_TOKEN = /(?:^|[^a-z0-9])hn(?:[^a-z0-9]|$)/; +const LN_TOKEN = /(?:^|[^a-z0-9])ln(?:[^a-z0-9]|$)/; +// 한 글자 H/L 토큰(-H-, _H_, -L-, _L_, 그리고 확장자/이름 끝의 _H·-L 등). +// hn/ln보다 한 글자 짧아서 오탐 위험이 훨씬 크기 때문에 두 가지로 좁혔다: +// (1) 구분자를 '-'와 '_'로만 한정한다. hn/ln이 쓰는 일반 토큰 경계([^a-z0-9])까지 +// 허용하면 "flaccid-v1.0"의 점·공백·괄호 같은 문자도 경계가 되어 범위가 급격히 넓어진다. +// (2) 대문자만 매치한다(그래서 소문자화한 n이 아니라 원본 raw로 검사). HIGH/LOW 약어는 +// 관례적으로 대문자이고, 소문자까지 받으면 "..._v2_h_final" 같은 무관한 이름이 걸린다. +const H_TOKEN = /[-_]H(?=[-_]|\.[A-Za-z0-9]+$|$)/; +const L_TOKEN = /[-_]L(?=[-_]|\.[A-Za-z0-9]+$|$)/; +function guessLoraWeights(name) { + if (!name || name === "None") return null; + const raw = String(name); + const n = raw.toLowerCase(); + if (n.includes("high") || HN_TOKEN.test(n) || H_TOKEN.test(raw)) return "1, 1, 0"; + if (n.includes("low") || LN_TOKEN.test(n) || L_TOKEN.test(raw)) return "0, 0, 1"; + return "1, 1, 1"; +} + +// comboW.callback은 유저가 드롭다운/행-UI에서 실제로 선택했을 때만 불린다 - 워크플로우 +// 로드 시 configure()가 widgets_values를 복원하는 경로는 .value를 직접 대입할 뿐 이 +// callback을 거치지 않으므로, 저장된 수동 가중치를 자동으로 덮어쓸 위험이 없다. +function wireAutoWeight(comboW, weightW) { + const orig = comboW.callback; + comboW.callback = function (value, ...rest) { + const guess = guessLoraWeights(value); + if (guess !== null) weightW.value = guess; + return orig ? orig.apply(this, [value, ...rest]) : undefined; + }; +} + +function buildArchitectRows(node) { + const hidden = []; + const slots = []; + for (let n = 1; n <= NUM_ARCH_LORAS; n++) { + const comboW = node.widgets.find((w) => w.name === `lora_${n}`); + const onW = node.widgets.find((w) => w.name === `lora_${n}_on`); + const weightW = node.widgets.find((w) => w.name === `weights_${n}`); + if (!comboW || !onW || !weightW) continue; + wireAutoWeight(comboW, weightW); + hidden.push(comboW, onW, weightW); + slots.push({ comboW, onW, weightW }); + node.addCustomWidget(new DolphinLoraRowWidget(`lora_${n}_row`, comboW, onW, weightW)); + } + const manualTriggersW = node.widgets.find((w) => w.name === "manual_triggers"); + if (manualTriggersW) { + hidden.push(manualTriggersW); + node.addCustomWidget(new DolphinTextFieldWidget("manual_triggers:", manualTriggersW, true)); + } + if (slots.length) { + node.addCustomWidget(new DolphinResetButtonWidget("⟲ Reset all slots", slots, manualTriggersW)); + } + return hidden; +} + +app.registerExtension({ + name: "Dolphin.LoraRows", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === VISION_NODE_NAME) { + installLoraRows(nodeType, buildVisionDirectorRows); + } else if (nodeData?.name === ARCH_NODE_NAME) { + installLoraRows(nodeType, buildArchitectRows); + } + }, +}); diff --git a/custom_nodes/dolphin_nodes/web/dolphin_preset_sync.js b/custom_nodes/dolphin_nodes/web/dolphin_preset_sync.js new file mode 100644 index 0000000000000000000000000000000000000000..bedcb43b35550bf79985dcff21fec31da5f46b68 --- /dev/null +++ b/custom_nodes/dolphin_nodes/web/dolphin_preset_sync.js @@ -0,0 +1,102 @@ +import { app } from "../../scripts/app.js"; + +// When the user picks a llm_preset/vlm_preset dropdown value, mirror the +// resolved slug into the paired llm_model/vlm_model text widget so what's +// displayed matches what dolphin_vision_director.py's resolve_slug() will +// actually call. Picking the "custom" sentinel leaves the text field alone +// (that's the whole point of custom mode). +// +// Keep these two tables in sync with LLM_PRESETS / VLM_PRESETS in +// dolphin_vision_director.py — they're duplicated here only because the +// backend doesn't expose the label->slug mapping over the object_info API, +// just the label list. + +// 2026-08-28: 파이썬 쪽이 한글 -> 영어 라벨로 옮겨간 뒤 이 테이블을 안 맞춰서 +// table[value]가 항상 undefined가 되고(=키가 하나도 안 맞음) 프리셋을 바꿔도 +// llm_model/vlm_model 텍스트 칸이 전혀 갱신 안 되는 버그가 있었다. 실제 추론에 +// 쓰이는 슬러그는 백엔드 resolve_slug()가 콤보 선택값을 직접 보고 매핑하므로 +// 이 버그와 무관하게 정상이었을 가능성이 높지만, 사용자가 확인할 유일한 화면 +// 요소가 이 텍스트 칸이라 "모델이 안 바뀐다"는 리포트로 이어졌다. 라벨을 여기 +// 하드코딩하는 구조 자체가 파이썬 쪽 변경 때마다 또 어긋날 수 있으니, 프리셋 +// 목록을 고칠 땐 항상 두 파일(dolphin_vision_director.py의 LLM_PRESETS/ +// VLM_PRESETS, 그리고 dolphin_minimax_director.py의 동일 복제본)과 이 파일을 +// 세 군데 다 맞출 것. +const LLM_PRESETS = { + "🔞 DeepSeek V3.2 (uncensored · stable JSON · recommended)": "deepseek/deepseek-v3.2", + "🔞 Magnum v4 72B (natural phrasing)": "anthracite-org/magnum-v4-72b", + "🔞 Cydonia 24B v4.1 (lightweight · fast)": "thedrummer/cydonia-24b-v4.1", + // 2026-09-03: :free 슬러그 404 - 유료로 교체. + "🔞 Dolphin Mistral Venice (uncensored)": "cognitivecomputations/dolphin-mistral-24b-venice-edition", + // 2026-09-03 추가 (모델 페이지로만 확인, chat completions 스모크테스트 미완). + "🔞 Hermes 4 405B (steerable · strict format)": "nousresearch/hermes-4-405b", + // 2026-09-03: grok-4 / grok-4-fast deprecated(404) - grok-4.3으로 교체. + "🔞 Grok 4.3 (edgy prose · $1.25/$2.5)": "x-ai/grok-4.3", + "🔞 Euryale 70B L3.3 (sensory prose · loose format)": "sao10k/l3.3-euryale-70b", +}; +// Valkyrie 49B V1은 추가했다가 뺐다 (2026-08-28, 실제 API 404 "No endpoints +// found") - dolphin_vision_director.py의 LLM_PRESETS 옆 주석 참고. + +const VLM_PRESETS = { + "👁 Qwen2.5-VL 72B (special cases · recommended)": "qwen/qwen2.5-vl-72b-instruct", + "👁 Qwen2.5-VL 32B (balanced)": "qwen/qwen2.5-vl-32b-instruct", + "👁 Qwen2.5-VL 32B (free)": "qwen/qwen2.5-vl-32b-instruct:free", + "👁 Mistral Small 3.1 24B (Pixtral)": "mistralai/mistral-small-3.1-24b-instruct", + "👁 Gemma 3 27B (lightweight)": "google/gemma-3-27b-it", +}; + +const NODE_NAMES = [ + "DolphinVisionDirector", + "DolphinPromptDirector", + "DolphinPromptDirectorSingle", + "DolphinMinimaxDirector", +]; +const SYNC_PAIRS = [ + { presetName: "llm_preset", modelName: "llm_model", table: LLM_PRESETS }, + { presetName: "vlm_preset", modelName: "vlm_model", table: VLM_PRESETS }, +]; + +function wireSync(node, presetName, modelName, table) { + const presetW = node.widgets?.find((w) => w.name === presetName); + const modelW = node.widgets?.find((w) => w.name === modelName); + if (!presetW || !modelW || presetW.__dolphinPresetSynced) return; + presetW.__dolphinPresetSynced = true; + + const origCallback = presetW.callback; + presetW.callback = function (value, ...rest) { + const ret = origCallback ? origCallback.apply(this, [value, ...rest]) : undefined; + try { + const slug = table[value]; + if (slug) { + modelW.value = slug; + if (typeof modelW.callback === "function") modelW.callback(slug); + node.setDirtyCanvas(true, true); + } + } catch (e) { + console.error("[Dolphin.PresetSync] sync failed:", e); + } + return ret; + }; +} + +app.registerExtension({ + name: "Dolphin.PresetSync", + async beforeRegisterNodeDef(nodeType, nodeData) { + if (!NODE_NAMES.includes(nodeData?.name)) return; + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const ret = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + // Never let this cosmetic hook break graph loading (see dolphin_lora_rows.js + // for why: an uncaught error here during graph.configure() can abort + // loading the rest of the workflow). + try { + for (const { presetName, modelName, table } of SYNC_PAIRS) { + wireSync(this, presetName, modelName, table); + } + } catch (e) { + console.error("[Dolphin.PresetSync] onNodeCreated hook failed:", e); + } + return ret; + }; + }, +});