{
"session_id": "20260523_181441_fde1d8",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-23T17:58:17.228542",
"last_updated": "2026-05-23T18:29:15.461011",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,191/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; use `github_pat_`/`ghp_` cell; never quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [98% — 1,348/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts preference: free/local semi-automatic Retention Studio; motion-led mechanism-reveal Shorts with strong open loops. Avoid static AI slideshows, generic avatars, reused-content clones, bland “Was wäre wenn” KI-Müll, and paid video credits. Use ChatGPT Pro/NVIDIA LLMs for text and host RTX A5000 16GB for local video.\n\nConversation started: Saturday, May 23, 2026 06:14 PM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_doc_read",
"description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
"parameters": {
"type": "object",
"properties": {
"doc_token": {
"type": "string",
"description": "The document token (from the document URL or comment context)."
}
},
"required": [
"doc_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_add_comment",
"description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"content": {
"type": "string",
"description": "The comment text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comment_replies",
"description": "List all replies in a comment thread on a Feishu document.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to list replies for."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"page_size": {
"type": "integer",
"description": "Number of replies per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token",
"comment_id"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comments",
"description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"is_whole": {
"type": "boolean",
"description": "If true, only return whole-document comments.",
"default": false
},
"page_size": {
"type": "integer",
"description": "Number of comments per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_reply_comment",
"description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to reply to."
},
"content": {
"type": "string",
"description": "The reply text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"comment_id",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown  and the gateway will deliver it.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"aspect_ratio": {
"type": "string",
"enum": [
"landscape",
"square",
"portrait"
],
"description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
"default": "landscape"
}
},
"required": [
"prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
"default": "replace"
},
"path": {
"type": "string",
"description": "REQUIRED when mode='replace'. File path to edit."
},
"old_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit",
"close"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "send_message",
"description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"send",
"list"
],
"description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
},
"target": {
"type": "string",
"description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)"
},
"message": {
"type": "string",
"description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "session_search",
"description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
},
"role_filter": {
"type": "string",
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
},
"limit": {
"type": "integer",
"description": "Max sessions to summarize (default: 3, max: 5).",
"default": 3
}
}
}
}
},
{
"type": "function",
"function": {
"name": "skill_manage",
"description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"create",
"patch",
"edit",
"delete",
"write_file",
"remove_file"
],
"description": "The action to perform."
},
"name": {
"type": "string",
"description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
},
"content": {
"type": "string",
"description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
},
"category": {
"type": "string",
"description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
},
"file_path": {
"type": "string",
"description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
},
"file_content": {
"type": "string",
"description": "Content for the file. Required for 'write_file'."
},
"absorbed_into": {
"type": "string",
"description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."
}
},
"required": [
"action",
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skill_view",
"description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')."
},
"file_path": {
"type": "string",
"description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
}
},
"required": [
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skills_list",
"description": "List available skills (name + description). Use skill_view(name) to load full content.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional category filter to narrow results"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "The command to execute on the VM"
},
"background": {
"type": "boolean",
"description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"pty": {
"type": "boolean",
"description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
"default": false
},
"notify_on_complete": {
"type": "boolean",
"description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
"default": false
},
"watch_patterns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "video_analyze",
"description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
"parameters": {
"type": "object",
"properties": {
"video_url": {
"type": "string",
"description": "Video URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
}
},
"required": [
"video_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https), local file path, or data: URL to load."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
}
},
"required": [
"image_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "write_file",
"description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
},
"content": {
"type": "string",
"description": "Complete content to write to the file"
}
},
"required": [
"path",
"content"
]
}
}
}
],
"message_count": 177,
"messages": [
{
"role": "user",
"content": "[M G] Ok, nächster schritt\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 125 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti."
},
{
"role": "user",
"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"ok nächster schritt\"\n\n## Goal\nDer User möchte AutoShortsBot/Hermes von einer einfachen KI-Video-/Card-Rendering-Pipeline zu einem halbautomatischen **Retention-Studio für Shorts** weiterentwickeln. Ziel ist ein System, das datenbasierte, animierte Mechanismus-Erklärvideos produziert: starke Hooks in den ersten 1–2 Sekunden, visuelle Veränderung alle 1–2 Sekunden, keine statischen Slideshow-Videos, sondern „mechanism reveal / visual explanation / mini-investigation“-Content mit Fokus auf Hidden Money, AI Systems, Auto-/Finanzmechaniken und unsichtbare Systemeffekte.\n\n## Constraints & Preferences\n- Sprache/Kommunikation: Deutsch; User wird teilweise mit „Sir“ angesprochen.\n- Der User lehnt finale statische Textkarten-/KI-Bild-Slideshows ab; sie sind nur Preview/Storyboard, nicht Endprodukt.\n- Finale Videos sollen bewegte Sequenzen, AI-video Assets, Screen-Recording-Simulationen, Mechanismus-Animationen, kinetic captions, Sound/SFX und schnelle visuelle Wechsel enthalten.\n- Der aktuelle Card-/Preview-Renderer ist nur Timing-/Storyboard-Werkzeug.\n- `static_card_limit = 0` als Qualitätsprinzip für finale Inhalte.\n- Content-Richtung: kein generischer KI-Avatar-News-Kanal, kein „Was wäre wenn…“-KI-Müll, kein Reused-Content-Klon.\n- Avatare höchstens als Nebenfigur/Branding-Element, nicht als Hauptformat.\n- Bevorzugte Nische: **Hidden Money & AI Systems**.\n- Zielserie: **Unsichtbare Systeme in 30 Sekunden**.\n- Claim: „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“\n- Erfolgsmechanik: Hook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung.\n- Inspirationsmechaniken: Zack-D-Films-artige Mechanism Reveals, MrBeast/Jenny-Hoyos sofortiges Versprechen, TikTok Creative Code: Hook/Body/Close, dynamisches Editing, Bewegung, Text-Overlays, Sound/Voice/SFX.\n- User erlaubt, die untracked Telegram-Review-Dateien zu ignorieren, weil sie versehentlich durch einen falschen Telegram-Kanal entstanden sind.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen oder Artefakten bewahren; ggf. mit `[REDACTED]` ersetzen.\n\n## Completed Actions\n1. ANALYSED Preview-Renderer Typography Issue — festgestellt, dass Textgrößen im Preview uneinheitlich wirkten: Haupttext groß, Retention-/Erklärungstext unten deutlich kleiner; erklärt, dass diese Zwei-Ebenen-Logik technisch beabsichtigt, aber für Shorts visuell schwach ist [tool: conversation].\n2. UPDATED Preview-Renderer-Konzept — Renderer um adaptive Font-Fitting, konsistentere Text-Hierarchie, Mindestgrößen, maximale Zeilenanzahl und Typography-Metadaten erweitert; Ziel war weniger zu kleine/zu stark schwankende Textgrößen im Storyboard-Preview [tool: code/terminal claimed].\n3. ADDED Typography Test Rules für `540x960` Preview — Regeln dokumentiert/geprüft: `main_font_size >= 34`, `max(main_font_size) - min(main_font_size) <= 8`, `retention_font_size >= 22`, `main_line_count <= 4`, `retention_line_count <= 3` [tool: tests claimed].\n4. RENDERED Top-3 Previews neu — erzeugt unter `data/previews/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.mp4`, `ai-005-the-4-box-prompt-for-mental-overload.mp4`, `attention-002-the-algorithm-does-not-need-you-happy.mp4` [tool: terminal claimed].\n5. VERIFIED Preview `ai-004` — technische Werte: `width=540`, `height=960`, `avg_frame_rate=24/1`, `duration=43.041667`; Typography: `main_font_size: 34–42`, `retention_font_size: 25–26` [tool: ffprobe/tests claimed].\n6. TEST full suite nach Typography-Änderung — Ergebnis: `25 passed in 0.96s` [tool: pytest claimed].\n7. COMMITTED Typography-Fix — Commit `3cc201b fix: improve preview renderer text sizing` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n8. ANSWERED User-Frage zum Endergebnis — klargestellt: aktueller Preview-Renderer ist nicht finales Content-Format; Endprodukt soll Videosequenzen, Bewegung, AI-video Assets, Screen-Recordings, kinetic captions, Voiceover und Sounddesign nutzen, nicht statische Cards [tool: conversation].\n9. DEFINED bessere Zielrichtung für `ai-004` — vorgeschlagen: ca. 40% Screen Recording/UI Simulation, 30% cinematic human-at-desk/decision moments, 20% kinetic captions, 10% Cards nur für Prompt-Fragmente [tool: conversation].\n10. CREATED `autoshorts/asset_plan.py` — neue Asset-Plan-v2-Komponente für motion-led Asset Pipeline; entscheidet pro Szene zwischen `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, `kinetic_text_overlay` [tool: code claimed].\n11. CREATED `scripts/generate_asset_plans.py` — CLI zum Generieren von Asset-Plänen aus vorhandenen Render-/Storyboard-Daten [tool: code claimed].\n12. CREATED `tests/test_asset_plan.py` — Tests für Asset Plan Builder, u.a. Provider-Prompting und keine finalen statischen Cards [tool: code claimed].\n13. GENERATED Asset-Pläne Top-3 — Dateien erzeugt unter `data/asset_plans/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.json`, `ai-005-the-4-box-prompt-for-mental-overload.json`, `attention-002-the-algorithm-does-not-need-you-happy.json` [tool: terminal claimed].\n14. SET Asset Pipeline `static_card_limit = 0` — finale statische Textkarten explizit ausgeschlossen; Preview bleibt Storyboard/Timing-Werkzeug [tool: code claimed].\n15. BUILT Provider-ready Prompting für cinematic Szenen — Prompt-Felder mit photorealistischer vertikaler 9:16-Clip-Beschreibung, cinematic lighting, realistic motion, negative space for captions und „do not include readable text“ ergänzt [tool: code claimed].\n16. BUILT Negative Prompts für AI-video Provider — u.a. `no readable text`, `no subtitles`, `no logos`, `no watermarks`, `no fake UI text`, `no slideshow`, `no static image`, `no uncanny faces` [tool: code claimed].\n17. TEST full suite nach Asset Plan v2 — Ergebnis: `32 passed in 0.96s` [tool: pytest claimed].\n18. COMMITTED Motion-led Asset Planning — Commit `b60981b feat: add motion-led asset planning` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n19. IDENTIFIED untracked Telegram Review Package — nicht angefasst: `autoshorts/review_package.py`, `data/telegram_reviews/`, `scripts/generate_telegram_review_packages.py`, `tests/test_review_package.py` [tool: git status claimed].\n20. CREATED `autoshorts/retention_studio.py` — neue Strategiekomponente, die AutoShortsBot konzeptionell als `semi_automatic_retention_studio` statt KI-Video-Fabrik verankert [tool: code claimed].\n21. CREATED `scripts/generate_retention_studio.py` — CLI zum Generieren von Retention-Studio-Strategieartefakten [tool: code claimed].\n22. CREATED `tests/test_retention_studio.py` — Tests für Strategie, Dramaturgie, visuelle Bewegung, Sound Cues und Follow Trigger [tool: code claimed].\n23. GENERATED Retention-Studio-Artefakte — erzeugt unter `data/retention_studio/`: `strategy.json`, `pilot_ideas.json`, `mechanism_explainers.json` [tool: terminal claimed].\n24. SET Positionierung in Strategie — `Hidden Money & AI Systems`; Serie `Unsichtbare Systeme in 30 Sekunden`; Claim „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“ [tool: code claimed].\n25. EXCLUDED unerwünschte Content-Formate in Strategie — `ki_avatar_news`, `static_ai_slideshow`, `reused_content_clone`; Quality Gate `static_card_limit = 0` [tool: code claimed].\n26. IMPLEMENTED Mechanism-Explainer-Formel — Timing/Beats: `0.0–1.5s Pattern Interrupt`, `1.5–5s Problem`, `5–18s Mechanism Reveal`, `18–28s Twist`, `28–35s Follow Trigger` [tool: code claimed].\n27. ADDED Tests für Retention-Beats — Pattern Interrupt endet ≤ 1.5 Sekunden; jeder Beat braucht visuelle Bewegung und Sound Cue; kein generisches „Follow for more“; Follow Trigger als Serienversprechen [tool: pytest claimed].\n28. ADDED Format-Taxonomie — `mechanism_reveal`, `mistake_breakdown`, `hidden_cost`, `before_after`, `myth_vs_reality`, `mini_simulation`, `nobody_notices_this` [tool: code claimed].\n29. ADDED Content-Pillars — `hidden_money_systems`, `auto_trade_mechanics`, `ai_agents_explained`, `swiss_systems_explained`, `dangerous_thinking_errors`, plus `hidden_attention_systems` [tool: code claimed].\n30. GENERATED 10 Pilot-Ideen mit Scores — Top 3: Score 90 `hidden_attention_systems`/„Warum Shorts dich nicht wegen dem Inhalt halten“, Score 88 `hidden_money_systems`/„Warum ein billiges Auto teuer sein kann“, Score 86 `hidden_money_systems`/„Warum 0%-Finanzierung nicht kostenlos ist“ [tool: terminal claimed].\n31. SET Produktionsentscheidung — Empfehlung `produce_count = 5 von 10` Pilot-Ideen [tool: code claimed].\n32. BUILT Beispiel-Mechanism-Explainer für „Warum ein billiges Auto teuer sein kann“ — Beat-Struktur mit Occasion-Käufer, 3D-Car-X-Ray, Preis/Reparaturrisiko/Standtage/Händler-Marge/Liquidität, Twist „teuerster Fehler ist nicht Kaufpreis, sondern Zeitpunkt“ [tool: code claimed].\n33. TEST full suite nach Retention Studio — Ergebnis: `36 passed in 0.99s` [tool: pytest claimed].\n34. COMMITTED Retention-Studio-Strategie — Commit `a9fe897 feat: add retention studio strategy` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n35. UPDATED Skill-Notiz — Social-Shorts-/Retention-Studio-Skill um Retention Studio Strategy, Mechanism Explainer Shorts, Hidden Money & AI Systems und `static_card_limit = 0` erweitert [tool: file edit claimed].\n36. CREATED `autoshorts/mechanism_pipeline.py` — Adapter von Retention-Studio Mechanism Explainer zu renderer-nahem Mechanism Shot Plan [tool: code claimed].\n37. CREATED `scripts/generate_mechanism_shot_plans.py` — CLI zum Generieren von Mechanism Shot Plans aus Mechanism Explainern [tool: code claimed].\n38. CREATED `tests/test_mechanism_pipeline.py` — Tests für Adapter/Shot-Plan-Erzeugung, Timing, Asset Types, Bewegungs-/Sound-Metadaten und `static_card_limit = 0` [tool: code claimed].\n39. GENERATED Pilot-Shot-Plan — Datei `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` erzeugt [tool: terminal claimed].\n40. MAPPED Mechanism Beats zu Scenes — `pattern_interrupt`, `problem`, `mechanism`, `twist`, `follow_trigger` wurden in Szenen mit Timing, Asset Type, On-screen Text, Voiceover, Visual Direction, Visual Motion, Sound Cue, Retention Goal, Caption Beats und Final Render Intent übersetzt [tool: code claimed].\n41. CREATED Shot Plan für „Warum ein billiges Auto teuer sein kann“ — Scene 1 `0.0s–1.4s cinematic_ai_video`, Scene 2 `1.4s–5.0s cinematic_ai_video`, Scene 3 `5.0s–18.0s mechanism_animation`, Scene 4 `18.0s–28.0s mechanism_animation`, Scene 5 `28.0s–34.0s kinetic_text_overlay` [tool: terminal claimed].\n42. SET Shot-Plan-Constraints — `static_card_limit = 0`, `visual_change_every_seconds = 2`, `primary_style = mechanism_reveal_motion_explainer` [tool: code claimed].\n43. TEST full suite nach Mechanism Pipeline — Ergebnis: `40 passed in 0.97s` [tool: pytest claimed].\n44. COMMITTED Mechanism Explainer Adapter — Commit `5644c8b feat: adapt mechanism explainers to shot plans` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n45. UPDATED Retention-Studio-Skill-Notiz — Pipeline festgehalten: `strategy → scored ideas → mechanism explainer → mechanism shot plan → render manifest → asset plan → final renderer` [tool: file edit claimed].\n46. CONFIRMED Telegram Review Files ignored — User erklärte, dass die separaten Working-Tree-Dateien versehentlich durch falschen Telegram-Kanal entstanden sind; sie bleiben unberührt und uncommitted [tool: conversation].\n\n## Active State\n- Repository/Projekt: `/home/agent/jarvis_runtime/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Remote-Status zuletzt berichtet: `local == remote`.\n- Letzter bekannter Commit: `5644c8b feat: adapt mechanism explainers to shot plans`.\n- Vorherige relevante Commits:\n - `3cc201b fix: improve preview renderer text sizing`\n - `b60981b feat: add motion-led asset planning`\n - `a9fe897 feat: add retention studio strategy`\n- Letzter Teststatus: `40 passed in 0.97s`.\n- Aktuelles Pipeline-Level erreicht:\n - Retention Studio Strategie vorhanden.\n - Pilot-Ideen gescored.\n - Mechanism Explainer vorhanden.\n - Mechanism Shot Plan für Pilot „Warum ein billiges Auto teuer sein kann“ vorhanden.\n- Nächster logischer Schritt noch nicht erledigt:\n - `Mechanism Shot Plan → Render Manifest → Asset Plan` für den neuen Pilot, nicht mehr nur für alte AI-Life-Top-3.\n- Untracked/zu ignorieren:\n - `autoshorts/review_package.py`\n - `data/telegram_reviews/`\n - `scripts/generate_telegram_review_packages.py`\n - `tests/test_review_package.py`\n- Keine laufenden Server oder Prozesse erwähnt.\n- Keine Secrets/Credentials bewahren; falls vorhanden, als `[REDACTED]`.\n\n## In Progress\n- Der User hat „ok nächster schritt“ gesagt. Gemeinter nächster Arbeitsschritt laut vorheriger Antwort:\n - Mechanism Shot Plan für „Warum ein billiges Auto teuer sein kann“\n - → Render Manifest\n - → Asset Plan\n - Ziel: neue Retention-Studio-/Mechanism-Explainer-Pipeline an die bestehende Render-/Asset-Plan-Infrastruktur anbinden.\n- Noch nicht umgesetzt:\n - Adapter/CLI/Tests für `Mechanism Shot Plan → Render Manifest`.\n - Generierung eines Render Manifests für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.\n - Asset Plan für diesen neuen Pilot auf Basis des neuen Render Manifests.\n - Provider Job Specs für cinematic Szenen des Pilots.\n\n## Blocked\nKeine bekannten Blocker. \nKeine Fehlermeldungen offen. \nEinziger Hinweis: frühere 125 Messages konnten nicht zusammengefasst werden; daraus können ältere Details fehlen. Die aktuelle Richtung und letzten Artefakte sind aber in den neuen Turns beschrieben.\n\n## Key Decisions\n1. Der Preview-Renderer ist nicht das finale Produkt, sondern nur Storyboard-/Timing-Werkzeug. Grund: Statische Cards wirken langweilig und konkurrieren nicht mit modernen Shorts/Kling/Hailuo/Runway-Qualität.\n2. Finale Videos müssen motion-led sein: AI-video Clips, Screen-Recording-Simulationen, mechanism animations, kinetic captions, Voiceover, SFX und schnelle Schnitte.\n3. `static_card_limit = 0` ist ein strategisches Qualitätsgate für finale Videos. Grund: User lehnt statische KI-Bilder + Text/Voiceover als KI-Müll-Massenware ab.\n4. AutoShortsBot/Hermes wird als halbautomatisches Retention-Studio positioniert, nicht als KI-Video-Fabrik. Grund: Erfolg hängt von Hook, offenen Loops, visueller Stimulation und Payoff ab, nicht vom KI-Look.\n5. Gewählte Content-Nische: Hidden Money & AI Systems. Grund: verbindet Hermes, Autotrading, Finanzen, KI-Agenten, Politik/Systemverständnis und hat spätere Monetarisierungsmöglichkeiten über Newsletter, Tools, Beratung, Affiliate, Software oder Kurse.\n6. Kernformat: Mechanism Reveal / Visual Explanation / Mini-Investigation. Grund: zeigt unsichtbare Mechanismen visuell und bietet echten Erkenntniswert statt generischem Voiceover.\n7. Avatare nicht als Hauptformat. Grund: viele KI-Avatare wirken generisch, halten Retention schlecht, und es gibt Disclosure-/Trust-Risiken bei realistischen synthetischen Menschen.\n8. Re-Kuratierung erfolgreicher Videos ist strategisch erlaubt, aber nicht als Kopie/Neuvertontung. Grund: YouTube Reused Content vermeiden; Mechaniken adaptieren, nicht Inhalte klonen.\n9. Pilot-Fokus: „Warum ein billiges Auto teuer sein kann“. Grund: passt zu Money/Auto, klares visuelles Objekt, starke 3D-/X-Ray-/Layer-Animation möglich, hohe Monetarisierbarkeit.\n10. Untracked Telegram-Review-Dateien werden ignoriert. Grund: User schrieb versehentlich in falschen Telegram-Kanal; Änderungen sollen nicht mit dieser Pipeline vermischt werden.\n\n## Resolved Questions\n1. User fragte, warum Text oben groß und unten klein ist. Antwort: Der Preview-Renderer nutzt Haupttext plus kleineren Retention-/Erklärungstext; technisch beabsichtigt, aber visuell für Shorts ungeeignet. Finale Videos sollen weniger Text pro Frame und große rhythmische Captions nutzen.\n2. User fragte, ob Endergebnis Videosequenzen oder wieder statische Bilder sein sollen. Antwort: Nicht statische Bilder. Endziel sind Videosequenzen mit AI-video Assets, Screen Recordings, kinetic captions, Voiceover, Musik/SFX und fast cuts.\n3. User fragte, warum diese Videos besser sein sollen als bisheriger Content. Antwort: Nur wenn Scripting, visuelle Umsetzung und Retention-Struktur besser werden: starke Hooks, motion-led visuals, alle 1–2 Sekunden visueller Reiz, echter Mechanismus-Payoff.\n4. User fragte implizit, ob statische Cards reichen. Antwort: Nein, statische Cards sind nur Preview/Storyboard und als finales Format strategisch zu schwach.\n5. User brachte „Chloe vs History“, Kling, Hailuo, photorealistische KI-Videos als Qualitätsreferenz ein. Antwort: Diese Richtung wurde aufgenommen; finale Pipeline soll provider-ready prompts für cinematic AI-video Szenen erzeugen, aber nicht in generischem KI-Look enden.\n6. User schlug Retention-Studio statt KI-Video-Content vor. Antwort: Konzept wurde als `semi_automatic_retention_studio` umgesetzt und in Strategieartefakten verankert.\n7. User schlug Zack-D-Films-artige Mechanism Reveals vor. Antwort: Mechanism-Explainer-Formel, Beat-Struktur und Pilot „Warum ein billiges Auto teuer sein kann“ wurden umgesetzt.\n8. User erklärte, der separate Working Tree könne ignoriert werden. Antwort: Telegram-Review-Dateien wurden nicht angefasst und nicht committet.\n\n## Pending User Asks\n- „ok nächster schritt“ ist noch offen. Gemeint ist nach aktuellem Stand der nächste Build-Schritt:\n - Mechanism Shot Plan → Render Manifest → Asset Plan für den Pilot „Warum ein billiges Auto teuer sein kann“.\n\n## Relevant Files\n- `autoshorts/asset_plan.py` — Asset-Plan-v2-Komponente für motion-led finale Asset-Auswahl pro Szene.\n- `scripts/generate_asset_plans.py` — CLI zur Asset-Plan-Generierung.\n- `tests/test_asset_plan.py` — Tests für Asset-Plan-Logik.\n- `data/asset_plans/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `autoshorts/retention_studio.py` — Strategie-/Pilot-/Mechanism-Explainer-Komponente für Retention Studio.\n- `scripts/generate_retention_studio.py` — CLI zur Generierung von Retention-Studio-Artefakten.\n- `tests/test_retention_studio.py` — Tests für Strategie, Beats, visuelle Bewegung, Sound Cues, Follow Trigger.\n- `data/retention_studio/strategy.json` — Retention-Studio-Strategie.\n- `data/retention_studio/pilot_ideas.json` — gescorte Pilot-Ideen.\n- `data/retention_studio/mechanism_explainers.json` — Mechanism-Explainer-Daten.\n- `autoshorts/mechanism_pipeline.py` — Adapter von Mechanism Explainer zu Mechanism Shot Plan.\n- `scripts/generate_mechanism_shot_plans.py` — CLI zur Shot-Plan-Generierung.\n- `tests/test_mechanism_pipeline.py` — Tests für Mechanism Shot Plan Adapter.\n- `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` — generierter Pilot-Shot-Plan.\n- `data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4` — neu gerenderter Preview.\n- `autoshorts/review_package.py` — untracked/zu ignorieren.\n- `data/telegram_reviews/` — untracked/zu ignorieren.\n- `scripts/generate_telegram_review_packages.py` — untracked/zu ignorieren.\n- `tests/test_review_package.py` — untracked/zu ignorieren.\n\n## Remaining Work\n- Neuen Adapter bauen: `Mechanism Shot Plan → Render Manifest`.\n- Render Manifest für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` erzeugen.\n- Asset Plan v2 auf diesen neuen Mechanism-Render-Manifest anwenden.\n- Provider Job Specs für cinematic AI-video Szenen exportieren, z.B. Kling/Hailuo/Runway-kompatible Prompts.\n- Mechanism Animation Specs definieren für 3D-Car-X-Ray, Preis-/Risiko-/Marge-/Timing-Layer.\n- Final Renderer erweitern, damit er echte Clip-Dateien, Screen-Recording-Simulationen, mechanism animations und kinetic captions zusammensetzt.\n- Optional: Preview-Storyboard weiter nutzen, aber klar als nicht-final markieren.\n- Später: Analysepipeline für Retention nach 1s/3s, durchschnittliche Wiedergabedauer, Replays, Kommentare pro 1’000 Views, Follows pro 1’000 Views.\n\n## Critical Context\n- Frühere Kontextzusammenfassung war nicht verfügbar: „Summary generation was unavailable. 125 message(s) were removed…“. Frühere Details vor den neuen Turns können fehlen.\n- Branch: `feat/initial-growth-engine`.\n- PR-Link bleibt: `https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine`.\n- Letzter bekannter Teststatus: `40 passed in 0.97s`.\n- Wichtigster aktueller Pilot: `Warum ein billiges Auto teuer sein kann`.\n- Pilot-Shot-Plan-Szenen:\n - Scene 1 — `pattern_interrupt`, `0.0s–1.4s`, `cinematic_ai_video`, Visual: 3D car x-ray with price tag layers and risk meters freezes mid-action, Motion: hard zoom/freeze-frame/red signal pulse, Sound: bass hit + camera snap.\n - Scene 2 — `problem`, `1.4s–5.0s`, `cinematic_ai_video`, Visual: surface layer peels away to reveal hidden system, Motion: layer peel + parallax push.\n - Scene 3 — `mechanism_reveal`, `5.0s–18.0s`, `mechanism_animation`, Visual: 3D car x-ray with price tag layers and risk meters, Motion: sequential layer reveal every 1–2 seconds.\n - Scene 4 — `twist`, `18.0s–28.0s`, `mechanism_animation`, Visual: wrong cause fades out, real cause appears, Motion: snap zoom + wrong answer collapses.\n - Scene 5 — `follow_trigger`, `28.0s–34.0s`, `kinetic_text_overlay`, Visual: animated system map, Motion: map draws itself, final node glows.\n- Strategy constraints to preserve:\n - `static_card_limit = 0`\n - `visual_change_every_seconds = 2`\n - `primary_style = mechanism_reveal_motion_explainer`\n- Provider negative prompt concepts:\n - no readable text\n - no subtitles\n - no logos\n - no watermarks\n - no fake UI text\n - no slideshow\n - no static image\n - no uncanny faces\n- Do not include or preserve any credentials/secrets; replace with `[REDACTED]` if encountered.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 207 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---",
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"content": "Ja — aber mit einer wichtigen Trennung, Sir:\n\n## Was ich direkt generieren kann\n\nIch kann hier vollständig selbst erzeugen:\n\n- Shot Plans\n- Prompts\n- Captions\n- Voiceover-Text\n- lokale Animatics / Placeholder-Videos\n- Mechanism-Animationen\n- Kinetic Captions\n- finale FFmpeg-Montage\n- Review-Pakete\n- Tests / Code / Render-Pipeline\n\nAlso alles, was **lokal** läuft.\n\n## Was ich nicht sauber allein generieren kann\n\nDie **realistischen KI-Videoclips** aus PixVerse/Pika/CapCut kann ich nicht zuverlässig komplett autonom erzeugen, weil:\n\n- Login im Browser nötig ist\n- evtl. CAPTCHA / E-Mail / Account-Session kommt\n- Free Credits accountgebunden sind\n- ich keine Käufe/Top-ups machen darf\n- externe Webseiten häufig Export/Download manuell absichern\n\nIch kann es theoretisch im Browser versuchen, aber praktisch ist der stabile Workflow:\n\n```text\nIch generiere Prompt + genaue Anleitung\ndu / wir erzeugen Clips in PixVerse oder Pika\ndu lädst sie herunter\nich importiere sie in den Renderer\nich baue daraus das finale Video\n```\n\n## Konkreter Ablauf ab jetzt\n\n## Schritt 1 — Ich liefere dir 2 Clip-Aufträge\n\nFür den Pilot brauchen wir zuerst nur:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nDiese zwei Clips sind realistische B-Roll / Hook-Szenen.\n\n## Schritt 2 — Du erzeugst sie gratis\n\nEmpfohlen:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur falls Free Credits vorhanden\n```\n\nKeine paid Credits. Keine Subscription. Keine Top-ups. Natürlich. Wir sind nicht hier, um Kapitalismus zu füttern, bevor das Video überhaupt Views hat.\n\n## Schritt 3 — Clips ablegen\n\nNach Download müssen die Dateien hier landen:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Schritt 4 — Ich rendere final hybrid\n\nDann mache ich:\n\n```text\nimportierte realistische Clips\n+ lokale Mechanism-Layer\n+ Captions\n+ SFX\n+ Timing\n+ FFmpeg Assembly\n→ finales MP4\n```\n\n## Soll ich die Videos generieren?\n\nPräzise Antwort:\n\n```text\nDie lokalen Teile: ja, vollständig.\nDie Free-Tier KI-Clips: ich bereite sie vor; generieren/Download braucht wahrscheinlich dich im Browser/Login.\n```\n\nFalls du willst, kann ich beim nächsten Schritt **sofort** ein Telegram-taugliches Clip-Generation-Paket liefern:\n\n- PixVerse-Link\n- Pika-Link\n- Prompt für scene_01\n- Prompt für scene_02\n- Negative Prompt\n- Export-Einstellungen\n- Dateinamen\n- Qualitätscheckliste\n\nDann erzeugst du die zwei Clips, lädst sie herunter, und ich baue daraus das finale Video.",
"reasoning": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!",
"reasoning_content": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!",
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"text": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!"
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"text": "Ja — aber mit einer wichtigen Trennung, Sir:\n\n## Was ich direkt generieren kann\n\nIch kann hier vollständig selbst erzeugen:\n\n- Shot Plans\n- Prompts\n- Captions\n- Voiceover-Text\n- lokale Animatics / Placeholder-Videos\n- Mechanism-Animationen\n- Kinetic Captions\n- finale FFmpeg-Montage\n- Review-Pakete\n- Tests / Code / Render-Pipeline\n\nAlso alles, was **lokal** läuft.\n\n## Was ich nicht sauber allein generieren kann\n\nDie **realistischen KI-Videoclips** aus PixVerse/Pika/CapCut kann ich nicht zuverlässig komplett autonom erzeugen, weil:\n\n- Login im Browser nötig ist\n- evtl. CAPTCHA / E-Mail / Account-Session kommt\n- Free Credits accountgebunden sind\n- ich keine Käufe/Top-ups machen darf\n- externe Webseiten häufig Export/Download manuell absichern\n\nIch kann es theoretisch im Browser versuchen, aber praktisch ist der stabile Workflow:\n\n```text\nIch generiere Prompt + genaue Anleitung\ndu / wir erzeugen Clips in PixVerse oder Pika\ndu lädst sie herunter\nich importiere sie in den Renderer\nich baue daraus das finale Video\n```\n\n## Konkreter Ablauf ab jetzt\n\n## Schritt 1 — Ich liefere dir 2 Clip-Aufträge\n\nFür den Pilot brauchen wir zuerst nur:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nDiese zwei Clips sind realistische B-Roll / Hook-Szenen.\n\n## Schritt 2 — Du erzeugst sie gratis\n\nEmpfohlen:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur falls Free Credits vorhanden\n```\n\nKeine paid Credits. Keine Subscription. Keine Top-ups. Natürlich. Wir sind nicht hier, um Kapitalismus zu füttern, bevor das Video überhaupt Views hat.\n\n## Schritt 3 — Clips ablegen\n\nNach Download müssen die Dateien hier landen:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Schritt 4 — Ich rendere final hybrid\n\nDann mache ich:\n\n```text\nimportierte realistische Clips\n+ lokale Mechanism-Layer\n+ Captions\n+ SFX\n+ Timing\n+ FFmpeg Assembly\n→ finales MP4\n```\n\n## Soll ich die Videos generieren?\n\nPräzise Antwort:\n\n```text\nDie lokalen Teile: ja, vollständig.\nDie Free-Tier KI-Clips: ich bereite sie vor; generieren/Download braucht wahrscheinlich dich im Browser/Login.\n```\n\nFalls du willst, kann ich beim nächsten Schritt **sofort** ein Telegram-taugliches Clip-Generation-Paket liefern:\n\n- PixVerse-Link\n- Pika-Link\n- Prompt für scene_01\n- Prompt für scene_02\n- Negative Prompt\n- Export-Einstellungen\n- Dateinamen\n- Qualitätscheckliste\n\nDann erzeugst du die zwei Clips, lädst sie herunter, und ich baue daraus das finale Video."
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"content": "[M G] ok mach das. recherchiere zudem wo welche solche tools du per API ansteuern kannst, oder per gratis weboberfläche bedienen? irgendwelche opensource lösungen?"
},
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"content": "Ja, Sir. Vorgehen ist jetzt klar:\n\n## 1. Generiere ich die Videos?\n\n**Teilweise ja.**\n\nIch kann direkt generieren:\n\n- lokale Animatics\n- Mechanism-Animationen\n- Captions\n- Timing\n- SFX/Audio-Layout\n- finalen Schnitt mit FFmpeg\n- Hybrid-Renderer, der echte Clips einbaut\n\nDie **realistischen KI-B-Roll-Clips** selbst sind der schwierige Teil. Die kann ich nur dann autonom erzeugen, wenn:\n\n- ein API-fähiger Anbieter kostenlos nutzbar ist, oder\n- eine gratis Weboberfläche ohne Login/CAPTCHA stabil bedienbar ist, oder\n- wir lokal/Open-Source genug GPU haben.\n\nAktuell ist der robusteste Gratis-Weg:\n\n```text\nIch liefere Clip-Prompts + genaue Anleitung\ndu erzeugst 2 Clips in PixVerse/Pika/CapCut/HF Space\nich importiere sie\nich baue finalen Hybrid-Render\n```\n\nNicht glamourös. Aber kostenlos und kontrollierbar. Zwei Dinge, die selten im selben Raum gesehen werden.\n\n---\n\n## 2. Clip-Generation-Paket ist erstellt\n\nIch habe das konkrete Paket für den Pilot abgelegt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\nhidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann_CLIP_GENERATION_PACKET.md\n```\n\nCommit:\n\n```text\n494898b docs: add free clip generation packet\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDas Paket enthält:\n\n- PixVerse/Pika/CapCut/Hailuo Workflow\n- Prompt für `scene_01`\n- Prompt für `scene_02`\n- Negative Prompt\n- Qualitätscheckliste\n- Download-Dateinamen\n- Importpfade\n- API-/Open-Source-Recherche\n\n---\n\n## 3. Was du jetzt konkret tun sollst\n\nWir brauchen für den ersten Hybrid-Test nur zwei Dateien:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAblage später:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\n\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Scene 01 Prompt\n\nZiel: Hook / Occasion-Käufer denkt, er spart Geld.\n\nNutze zuerst diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Gesichter/Hände hässlich werden:\n\n```text\nVertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\n```\n\nSpeichern als:\n\n```text\nscene_01.mp4\n```\n\n---\n\n## Scene 02 Prompt\n\nZiel: harmloses Auto, aber versteckter Mechanismus/Risiko.\n\nNutze diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video inside a clean independent car garage. A used car is lifted slightly or parked under inspection lights while a mechanic points a flashlight under the car. The buyer watches as hidden problems are implied, not shown with text. Camera slowly pushes from the buyer's face to the underside of the car, subtle parallax, realistic motion, documentary explainer style, dramatic but believable lighting. No readable text, no subtitles, no logos, no watermarks, no fake UI, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Menschen schlecht aussehen:\n\n```text\nVertical 9:16 realistic cinematic video. A used car sits in a dark garage under a scanning light; the camera moves along the side of the car as faint glowing diagnostic reflections sweep across the body, suggesting hidden repair risks beneath the surface. Realistic car, cinematic lighting, smooth push-in, no fantasy hologram text, no readable labels, no logos, no watermarks. Leave negative space for local captions.\n```\n\nSpeichern als:\n\n```text\nscene_02.mp4\n```\n\n---\n\n## Global Negative Prompt\n\nFalls Tool ein Negative-Prompt-Feld hat:\n\n```text\nno readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\n```\n\n---\n\n## Empfohlene Reihenfolge der Tools\n\n## 1. PixVerse\n\n```text\nhttps://app.pixverse.ai/\n```\n\nWarum zuerst:\n\n- App zeigte öffentlich `90` Credits mit täglicher Aktualisierung\n- am besten für realistische kurze Clips geeignet\n- vermutlich höchster Gratis-Nutzen pro Tag\n\nRegel:\n\n```text\nNur Free Credits. Keine Top-ups.\n```\n\n## 2. Pika\n\n```text\nhttps://pika.art/\n```\n\nOffizielles Pricing zeigte:\n\n```text\nBasic $0\n80 monthly video credits\nPika 2.5\n480p only\nno-watermark downloads\ncommercial use\n```\n\nGut als Fallback, aber nur 480p. Für Shorts akzeptabel, weil wir lokal schneiden/captionen.\n\n## 3. CapCut\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nNützlich für:\n\n- schnelle Drafts\n- Editor/Export\n- vielleicht HD-Free-Export\n\nAber: Free-Quota weniger transparent, Pro-Assets vermeiden.\n\n## 4. Hailuo\n\n```text\nhttps://hailuoai.video/\n```\n\nNur falls bestehende Free-Credits da sind. Seite zeigte sichtbare `25 credits` pro 6s/768p Generation. Also:\n\n```text\nnicht kaufen\nnicht aufladen\nnicht abonnieren\n```\n\n---\n\n# Recherche: API / Web / Open Source\n\n## API-fähige Anbieter\n\n## Replicate\n\n```text\nhttps://replicate.com/collections/text-to-video\n```\n\nBietet API für viele starke Modelle:\n\n- Runway\n- Veo\n- Kling\n- Seedance\n- Hailuo\n- PixVerse\n- Wan\n\nAber: praktisch **paid API**. Gut für später, nicht für Gratis-Workflow.\n\n## fal.ai\n\n```text\nhttps://fal.ai/explore?categories=video\n```\n\nBietet API für:\n\n- Seedance\n- Kling\n- PixVerse\n- weitere Video-Modelle\n\nAber ebenfalls: normalerweise **paid per generation**. Nicht unser aktueller Pfad.\n\n## Hugging Face Spaces\n\n```text\nhttps://huggingface.co/spaces\n```\n\nEs gibt viele `Wan2.1` / `text-to-video` Spaces. Vorteil:\n\n- teils gratis im Browser nutzbar\n- manchmal via `gradio_client` automatisierbar\n- kein eigener GPU-Server nötig\n\nNachteil:\n\n- Warteschlangen\n- Spaces schlafen ein\n- Runtime Errors\n- Qualität/Verfügbarkeit stark schwankend\n\nFür uns: **experimenteller kostenloser API/Web-Fallback**.\n\n## NVIDIA build.nvidia.com\n\n```text\nhttps://build.nvidia.com/\n```\n\nDort gibt es free serverless APIs für Entwicklung, aber ich habe keinen klaren kostenlosen Text-to-Video-Generator gefunden, der unseren Clip-Bedarf deckt.\n\nFür uns:\n\n```text\nNVIDIA_API_KEY → Text-/Prompt-Refinement\nnicht Video-Generation\n```\n\n---\n\n# Open-Source / lokal\n\n## Wan2.1\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nSehr interessant. README sagt sinngemäß:\n\n```text\n5-second 480P video on RTX 4090 in about 4 minutes\n```\n\nDas ist der beste lokale Kandidat, falls wir Zugriff auf eine starke NVIDIA-GPU haben.\n\n## LTX-Video\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nGuter Open-Source-Kandidat, quantisierte/distilled Modelle, schneller als viele andere.\n\n## CogVideoX / CogVideoX1.5\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nSolide Open-Source-Text/Image-to-Video, aber GPU-heavy.\n\n## HunyuanVideo\n\n```text\nhttps://github.com/Tencent-Hunyuan/HunyuanVideo\n```\n\nStark, aber eher schwergewichtig. Ohne dicke GPU nicht ideal.\n\n## Mochi\n\n```text\nhttps://github.com/genmoai/mochi\n```\n\nOpen Source, aber README empfiehlt für deren Implementation mindestens 1 H100. Also eher nein, sofern wir nicht zufällig einen kleinen Supercomputer unter dem Sofa finden.\n\n---\n\n# Meine Empfehlung\n\n## Jetzt sofort\n\n```text\nPixVerse manuell testen\nwenn gut: scene_01 + scene_02 herunterladen\nwenn schlecht: Pika testen\nwenn beide schlecht: Hugging Face Wan2.1 Space testen\n```\n\nDanach:\n\n```text\nIch baue Hybrid Final Renderer:\nimportierte Clips\n+ lokale Mechanism-Animation\n+ Captions\n+ SFX\n+ FFmpeg\n```\n\n## Parallel technisch sinnvoll\n\nAls nächstes kann ich prüfen, ob diese Maschine eine brauchbare GPU hat:\n\n```text\nnvidia-smi\n```\n\nWenn ja:\n\n```text\nWan2.1 / LTX-Video lokale Installation evaluieren\n```\n\nWenn nein:\n\n```text\nOpen-Source lokal vergessen\nHF Spaces / Free Web bleiben der Gratisweg\n```\n\nKurz: **Ja, ich generiere alles, was lokal geht. Die realistischen KI-Clips holen wir über Free-Web/HF. Sobald du die zwei MP4s hast, mache ich daraus das finale Video.**",
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"text": "Ja, Sir. Vorgehen ist jetzt klar:\n\n## 1. Generiere ich die Videos?\n\n**Teilweise ja.**\n\nIch kann direkt generieren:\n\n- lokale Animatics\n- Mechanism-Animationen\n- Captions\n- Timing\n- SFX/Audio-Layout\n- finalen Schnitt mit FFmpeg\n- Hybrid-Renderer, der echte Clips einbaut\n\nDie **realistischen KI-B-Roll-Clips** selbst sind der schwierige Teil. Die kann ich nur dann autonom erzeugen, wenn:\n\n- ein API-fähiger Anbieter kostenlos nutzbar ist, oder\n- eine gratis Weboberfläche ohne Login/CAPTCHA stabil bedienbar ist, oder\n- wir lokal/Open-Source genug GPU haben.\n\nAktuell ist der robusteste Gratis-Weg:\n\n```text\nIch liefere Clip-Prompts + genaue Anleitung\ndu erzeugst 2 Clips in PixVerse/Pika/CapCut/HF Space\nich importiere sie\nich baue finalen Hybrid-Render\n```\n\nNicht glamourös. Aber kostenlos und kontrollierbar. Zwei Dinge, die selten im selben Raum gesehen werden.\n\n---\n\n## 2. Clip-Generation-Paket ist erstellt\n\nIch habe das konkrete Paket für den Pilot abgelegt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\nhidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann_CLIP_GENERATION_PACKET.md\n```\n\nCommit:\n\n```text\n494898b docs: add free clip generation packet\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDas Paket enthält:\n\n- PixVerse/Pika/CapCut/Hailuo Workflow\n- Prompt für `scene_01`\n- Prompt für `scene_02`\n- Negative Prompt\n- Qualitätscheckliste\n- Download-Dateinamen\n- Importpfade\n- API-/Open-Source-Recherche\n\n---\n\n## 3. Was du jetzt konkret tun sollst\n\nWir brauchen für den ersten Hybrid-Test nur zwei Dateien:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAblage später:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\n\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Scene 01 Prompt\n\nZiel: Hook / Occasion-Käufer denkt, er spart Geld.\n\nNutze zuerst diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Gesichter/Hände hässlich werden:\n\n```text\nVertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\n```\n\nSpeichern als:\n\n```text\nscene_01.mp4\n```\n\n---\n\n## Scene 02 Prompt\n\nZiel: harmloses Auto, aber versteckter Mechanismus/Risiko.\n\nNutze diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video inside a clean independent car garage. A used car is lifted slightly or parked under inspection lights while a mechanic points a flashlight under the car. The buyer watches as hidden problems are implied, not shown with text. Camera slowly pushes from the buyer's face to the underside of the car, subtle parallax, realistic motion, documentary explainer style, dramatic but believable lighting. No readable text, no subtitles, no logos, no watermarks, no fake UI, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Menschen schlecht aussehen:\n\n```text\nVertical 9:16 realistic cinematic video. A used car sits in a dark garage under a scanning light; the camera moves along the side of the car as faint glowing diagnostic reflections sweep across the body, suggesting hidden repair risks beneath the surface. Realistic car, cinematic lighting, smooth push-in, no fantasy hologram text, no readable labels, no logos, no watermarks. Leave negative space for local captions.\n```\n\nSpeichern als:\n\n```text\nscene_02.mp4\n```\n\n---\n\n## Global Negative Prompt\n\nFalls Tool ein Negative-Prompt-Feld hat:\n\n```text\nno readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\n```\n\n---\n\n## Empfohlene Reihenfolge der Tools\n\n## 1. PixVerse\n\n```text\nhttps://app.pixverse.ai/\n```\n\nWarum zuerst:\n\n- App zeigte öffentlich `90` Credits mit täglicher Aktualisierung\n- am besten für realistische kurze Clips geeignet\n- vermutlich höchster Gratis-Nutzen pro Tag\n\nRegel:\n\n```text\nNur Free Credits. Keine Top-ups.\n```\n\n## 2. Pika\n\n```text\nhttps://pika.art/\n```\n\nOffizielles Pricing zeigte:\n\n```text\nBasic $0\n80 monthly video credits\nPika 2.5\n480p only\nno-watermark downloads\ncommercial use\n```\n\nGut als Fallback, aber nur 480p. Für Shorts akzeptabel, weil wir lokal schneiden/captionen.\n\n## 3. CapCut\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nNützlich für:\n\n- schnelle Drafts\n- Editor/Export\n- vielleicht HD-Free-Export\n\nAber: Free-Quota weniger transparent, Pro-Assets vermeiden.\n\n## 4. Hailuo\n\n```text\nhttps://hailuoai.video/\n```\n\nNur falls bestehende Free-Credits da sind. Seite zeigte sichtbare `25 credits` pro 6s/768p Generation. Also:\n\n```text\nnicht kaufen\nnicht aufladen\nnicht abonnieren\n```\n\n---\n\n# Recherche: API / Web / Open Source\n\n## API-fähige Anbieter\n\n## Replicate\n\n```text\nhttps://replicate.com/collections/text-to-video\n```\n\nBietet API für viele starke Modelle:\n\n- Runway\n- Veo\n- Kling\n- Seedance\n- Hailuo\n- PixVerse\n- Wan\n\nAber: praktisch **paid API**. Gut für später, nicht für Gratis-Workflow.\n\n## fal.ai\n\n```text\nhttps://fal.ai/explore?categories=video\n```\n\nBietet API für:\n\n- Seedance\n- Kling\n- PixVerse\n- weitere Video-Modelle\n\nAber ebenfalls: normalerweise **paid per generation**. Nicht unser aktueller Pfad.\n\n## Hugging Face Spaces\n\n```text\nhttps://huggingface.co/spaces\n```\n\nEs gibt viele `Wan2.1` / `text-to-video` Spaces. Vorteil:\n\n- teils gratis im Browser nutzbar\n- manchmal via `gradio_client` automatisierbar\n- kein eigener GPU-Server nötig\n\nNachteil:\n\n- Warteschlangen\n- Spaces schlafen ein\n- Runtime Errors\n- Qualität/Verfügbarkeit stark schwankend\n\nFür uns: **experimenteller kostenloser API/Web-Fallback**.\n\n## NVIDIA build.nvidia.com\n\n```text\nhttps://build.nvidia.com/\n```\n\nDort gibt es free serverless APIs für Entwicklung, aber ich habe keinen klaren kostenlosen Text-to-Video-Generator gefunden, der unseren Clip-Bedarf deckt.\n\nFür uns:\n\n```text\nNVIDIA_API_KEY → Text-/Prompt-Refinement\nnicht Video-Generation\n```\n\n---\n\n# Open-Source / lokal\n\n## Wan2.1\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nSehr interessant. README sagt sinngemäß:\n\n```text\n5-second 480P video on RTX 4090 in about 4 minutes\n```\n\nDas ist der beste lokale Kandidat, falls wir Zugriff auf eine starke NVIDIA-GPU haben.\n\n## LTX-Video\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nGuter Open-Source-Kandidat, quantisierte/distilled Modelle, schneller als viele andere.\n\n## CogVideoX / CogVideoX1.5\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nSolide Open-Source-Text/Image-to-Video, aber GPU-heavy.\n\n## HunyuanVideo\n\n```text\nhttps://github.com/Tencent-Hunyuan/HunyuanVideo\n```\n\nStark, aber eher schwergewichtig. Ohne dicke GPU nicht ideal.\n\n## Mochi\n\n```text\nhttps://github.com/genmoai/mochi\n```\n\nOpen Source, aber README empfiehlt für deren Implementation mindestens 1 H100. Also eher nein, sofern wir nicht zufällig einen kleinen Supercomputer unter dem Sofa finden.\n\n---\n\n# Meine Empfehlung\n\n## Jetzt sofort\n\n```text\nPixVerse manuell testen\nwenn gut: scene_01 + scene_02 herunterladen\nwenn schlecht: Pika testen\nwenn beide schlecht: Hugging Face Wan2.1 Space testen\n```\n\nDanach:\n\n```text\nIch baue Hybrid Final Renderer:\nimportierte Clips\n+ lokale Mechanism-Animation\n+ Captions\n+ SFX\n+ FFmpeg\n```\n\n## Parallel technisch sinnvoll\n\nAls nächstes kann ich prüfen, ob diese Maschine eine brauchbare GPU hat:\n\n```text\nnvidia-smi\n```\n\nWenn ja:\n\n```text\nWan2.1 / LTX-Video lokale Installation evaluieren\n```\n\nWenn nein:\n\n```text\nOpen-Source lokal vergessen\nHF Spaces / Free Web bleiben der Gratisweg\n```\n\nKurz: **Ja, ich generiere alles, was lokal geht. Die realistischen KI-Clips holen wir über Free-Web/HF. Sobald du die zwei MP4s hast, mache ich daraus das finale Video.**"
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"text": "**Checking system requirements**\n\nI need to respond using a tool to check the current environment, maybe by hosting it somewhere. The user is asking if a GPU is necessary, which might involve skills related to MLOps inference or something like ComfyUI for video generation. It's also essential to verify the system state using the terminal, potentially checking something like \"nvidia-smi\" to get the GPU status. Let’s ensure we have everything needed for the task!"
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"arguments": "{\"todos\":[{\"content\":\"GPU/VRAM und CUDA-Zugriff auf dem Host prüfen\",\"id\":\"gpu-check\",\"status\":\"in_progress\"},{\"content\":\"Lokale Open-Source-Video-Optionen für 16GB VRAM bewerten\",\"id\":\"options\",\"status\":\"pending\"},{\"content\":\"Empfohlenen nächsten Implementierungsschritt für AutoShortsBot festlegen\",\"id\":\"plan\",\"status\":\"pending\"}]}"
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"content": "[todo] updated task list",
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"arguments": "{\"command\":\"python - <<'PY'\\ntry:\\n import torch\\n print('torch', torch.__version__)\\n print('cuda_available', torch.cuda.is_available())\\n if torch.cuda.is_available():\\n print('device', torch.cuda.get_device_name(0))\\n props=torch.cuda.get_device_properties(0)\\n print('total_vram_gb', round(props.total_memory/1024**3,2))\\nexcept Exception as e:\\n print('torch_check_error', type(e).__name__, str(e))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
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"content": "{\"output\": \"/usr/bin/bash: Zeile 3: nvidia-smi: Befehl nicht gefunden\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_m81UWyuXprLPFiOulhBvjfjw"
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{
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"content": "{\"output\": \"torch 2.11.0+cu130\\ncuda_available False\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_UCMxt6jcoisq1QAChk06Jp8t"
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{
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"content": "{\"output\": \"Dateisystem Größe Benutzt Verf. Verw% Eingehängt auf\\n/dev/vda3 78G 68G 5,8G 93% /\\n/dev/vda3 78G 68G 5,8G 93% /\", \"exit_code\": 0, \"error\": null}",
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"arguments": "{\"command\":\"command -v comfy || true; command -v nvcc || true; ls -l /dev/nvidia* 2>/dev/null || true\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
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"reasoning": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there.",
"reasoning_content": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there.",
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"text": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there."
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"content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}",
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{
"role": "tool",
"content": "[terminal] ran `du -sh /home/agent/jarvis_runtime/AutoShortsBot 2>/dev/null; git status --short` -> exit 0, 1 lines output",
"tool_call_id": "call_PcMIExdcjKrgee8h7rbBsYEY"
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"content": "Ja, Sir — **die RTX A5000 Laptop GPU mit 16 GB VRAM sollten wir nutzen.** Das ist für unseren Gratis-Workflow der beste Hebel.\n\nAber: In der aktuellen Hermes-Umgebung sehe ich die GPU **noch nicht**.\n\n## Live-Check hier\n\nIch habe geprüft:\n\n```text\nnvidia-smi → nicht gefunden\ntorch cuda_available → False\n/dev/nvidia* → nicht vorhanden\n```\n\nAußerdem ist der lokale Speicher hier knapp:\n\n```text\n/ → 78G total, 68G used, nur 5.8G frei\n```\n\nBedeutung:\n\n```text\nHost hat vermutlich GPU,\naber diese Hermes-Session / Container / VM hat keinen CUDA-Zugriff darauf.\n```\n\nUnd selbst wenn CUDA sichtbar wäre: Für Video-Modelle brauchen wir deutlich mehr freien Speicherplatz als 5.8 GB. Eher:\n\n```text\n50–150 GB frei\n```\n\nje nach Modell/Cache.\n\n---\n\n# Empfehlung\n\n## Ja, wir nutzen die A5000 — aber über lokalen Host/ComfyUI\n\nDer sinnvollste Setup wäre:\n\n```text\nHost mit RTX A5000\n→ ComfyUI lokal installieren/starten\n→ Wan2.1 / LTX-Video / CogVideoX Workflow\n→ Hermes steuert ComfyUI per API\n→ generierte Clips landen in AutoShortsBot Import-Ordner\n→ Hermes baut finalen Hybrid-Render\n```\n\nAlso:\n\n```text\nGPU auf Host = Videogenerator\nHermes = Orchestrator / Renderer / Qualitätskontrolle\n```\n\nDas ist sauberer als Browser-Free-Tier-Gefrickel.\n\n---\n\n# Welche Modelle passen zu 16 GB VRAM?\n\n## 1. Wan2.1 — bevorzugt\n\nRepo:\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nWarum gut:\n\n- Open Source\n- stark für realistische kurze Clips\n- README erwähnt 5s/480p auf RTX 4090 in ca. 4 Minuten\n- gute ComfyUI-Unterstützung in der Community\n- besserer Kandidat als viele ältere Open-Source-Video-Modelle\n\nFür 16 GB VRAM würde ich starten mit:\n\n```text\nWan2.1 T2V 1.3B\n```\n\nNicht direkt mit den riesigen 14B/27B Varianten. Die wären eher Schmerz mit Fortschrittsbalken.\n\n## 2. LTX-Video — sehr interessant\n\nRepo:\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nWarum gut:\n\n- stark auf Geschwindigkeit optimiert\n- quantisierte/distilled Modelle verfügbar\n- vermutlich praktischer auf 16 GB als schwerere Modelle\n- gut für schnelle Iteration\n\n## 3. CogVideoX / CogVideoX1.5 — möglich, aber vorsichtig\n\nRepo:\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nKann funktionieren, aber:\n\n- speicherhungriger\n- Offloading/Quantisierung nötig\n- eventuell langsamer/instabiler\n\n## Eher nicht für 16 GB\n\n```text\nHunyuanVideo\nMochi\ngroße Wan 14B/27B Varianten ohne Spezialsetup\n```\n\nDie sind eher für stärkere GPUs / Multi-GPU / Cloud.\n\n---\n\n# Neuer Ziel-Workflow\n\nIch würde den Workflow jetzt so ändern:\n\n```text\n1. Hermes erzeugt Scene Prompts\n2. Host-ComfyUI generiert scene_01.mp4 und scene_02.mp4 lokal mit RTX A5000\n3. Hermes importiert Clips\n4. Hermes rendert Mechanism-Layer, Captions, SFX\n5. FFmpeg baut finales Short\n```\n\nDas ersetzt PixVerse/Pika als Hauptpfad.\n\nPixVerse/Pika bleiben nur noch Fallback:\n\n```text\nwenn lokales Wan/LTX zu langsam oder qualitativ schlecht ist\n```\n\n---\n\n# Was wir technisch brauchen\n\n## Auf dem Host\n\nPrüfen:\n\n```bash\nnvidia-smi\n```\n\nDann:\n\n```bash\npython -c \"import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n```\n\nWenn das auf dem Host funktioniert, können wir loslegen.\n\n## ComfyUI lokal\n\nEmpfohlener Pfad:\n\n```bash\npipx install comfy-cli\ncomfy --skip-prompt tracking disable\ncomfy --skip-prompt install --nvidia --fast-deps\ncomfy launch -- --listen 0.0.0.0 --port 8188\n```\n\nDann kann Hermes per API sprechen mit:\n\n```text\nhttp://HOST_IP:8188\n```\n\nOder falls Hermes auf demselben Host läuft:\n\n```text\nhttp://127.0.0.1:8188\n```\n\n---\n\n# Wichtige Frage\n\nAktuell sieht Hermes die GPU nicht. Daher brauchen wir eine von zwei Varianten:\n\n## Variante A — GPU in Hermes verfügbar machen\n\nFalls Hermes in Docker/Container läuft:\n\n```bash\ndocker run --gpus all ...\n```\n\noder Compose:\n\n```yaml\ndeploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: all\n capabilities: [gpu]\n```\n\nDann müsste hier funktionieren:\n\n```bash\nnvidia-smi\n```\n\n## Variante B — ComfyUI direkt auf dem Host laufen lassen\n\nDas ist wahrscheinlich einfacher.\n\nDann muss Hermes nur die API erreichen:\n\n```text\nhttp://host-ip:8188\n```\n\nDas würde ich bevorzugen.\n\n---\n\n# Mein Vorschlag für den nächsten Schritt\n\nNicht sofort riesige Modelle installieren. Erst sauberer Spike:\n\n## GPU Spike v1\n\nZiel:\n\n```text\nKann Host-ComfyUI mit RTX A5000 einen 3–5s 9:16 Clip lokal generieren?\n```\n\nMinimaltest:\n\n1. Host-GPU prüfen\n2. ComfyUI installieren/starten\n3. kleines Wan2.1/LTX-Workflow-Setup\n4. Scene 01 Prompt testen\n5. Output nach AutoShortsBot importieren\n6. Hermes baut Hybrid-Render\n\nWenn der Test gut ist:\n\n```text\nlokaler GPU-Workflow wird Hauptpfad\n```\n\nWenn schlecht:\n\n```text\nPixVerse/Pika Free-Tier bleibt B-Roll-Quelle\n```\n\n---\n\n# Antwort auf deine Frage\n\nJa:\n\n```text\nWir wollen die RTX A5000 nutzen.\n```\n\nAber nicht in dieser aktuell sichtbaren Hermes-Umgebung, solange CUDA hier nicht verfügbar ist.\n\nDer beste nächste Schritt ist:\n\n```text\nComfyUI + Wan2.1/LTX-Video auf dem Host mit RTX A5000 starten\nHermes steuert es per API\n```\n\nDann kann ich die Clip-Generierung weitgehend automatisieren — ohne paid Credits, ohne Browser-Gefrickel, und mit lokal kontrollierbarem Output.",
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"text": "Ja, Sir — **die RTX A5000 Laptop GPU mit 16 GB VRAM sollten wir nutzen.** Das ist für unseren Gratis-Workflow der beste Hebel.\n\nAber: In der aktuellen Hermes-Umgebung sehe ich die GPU **noch nicht**.\n\n## Live-Check hier\n\nIch habe geprüft:\n\n```text\nnvidia-smi → nicht gefunden\ntorch cuda_available → False\n/dev/nvidia* → nicht vorhanden\n```\n\nAußerdem ist der lokale Speicher hier knapp:\n\n```text\n/ → 78G total, 68G used, nur 5.8G frei\n```\n\nBedeutung:\n\n```text\nHost hat vermutlich GPU,\naber diese Hermes-Session / Container / VM hat keinen CUDA-Zugriff darauf.\n```\n\nUnd selbst wenn CUDA sichtbar wäre: Für Video-Modelle brauchen wir deutlich mehr freien Speicherplatz als 5.8 GB. Eher:\n\n```text\n50–150 GB frei\n```\n\nje nach Modell/Cache.\n\n---\n\n# Empfehlung\n\n## Ja, wir nutzen die A5000 — aber über lokalen Host/ComfyUI\n\nDer sinnvollste Setup wäre:\n\n```text\nHost mit RTX A5000\n→ ComfyUI lokal installieren/starten\n→ Wan2.1 / LTX-Video / CogVideoX Workflow\n→ Hermes steuert ComfyUI per API\n→ generierte Clips landen in AutoShortsBot Import-Ordner\n→ Hermes baut finalen Hybrid-Render\n```\n\nAlso:\n\n```text\nGPU auf Host = Videogenerator\nHermes = Orchestrator / Renderer / Qualitätskontrolle\n```\n\nDas ist sauberer als Browser-Free-Tier-Gefrickel.\n\n---\n\n# Welche Modelle passen zu 16 GB VRAM?\n\n## 1. Wan2.1 — bevorzugt\n\nRepo:\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nWarum gut:\n\n- Open Source\n- stark für realistische kurze Clips\n- README erwähnt 5s/480p auf RTX 4090 in ca. 4 Minuten\n- gute ComfyUI-Unterstützung in der Community\n- besserer Kandidat als viele ältere Open-Source-Video-Modelle\n\nFür 16 GB VRAM würde ich starten mit:\n\n```text\nWan2.1 T2V 1.3B\n```\n\nNicht direkt mit den riesigen 14B/27B Varianten. Die wären eher Schmerz mit Fortschrittsbalken.\n\n## 2. LTX-Video — sehr interessant\n\nRepo:\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nWarum gut:\n\n- stark auf Geschwindigkeit optimiert\n- quantisierte/distilled Modelle verfügbar\n- vermutlich praktischer auf 16 GB als schwerere Modelle\n- gut für schnelle Iteration\n\n## 3. CogVideoX / CogVideoX1.5 — möglich, aber vorsichtig\n\nRepo:\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nKann funktionieren, aber:\n\n- speicherhungriger\n- Offloading/Quantisierung nötig\n- eventuell langsamer/instabiler\n\n## Eher nicht für 16 GB\n\n```text\nHunyuanVideo\nMochi\ngroße Wan 14B/27B Varianten ohne Spezialsetup\n```\n\nDie sind eher für stärkere GPUs / Multi-GPU / Cloud.\n\n---\n\n# Neuer Ziel-Workflow\n\nIch würde den Workflow jetzt so ändern:\n\n```text\n1. Hermes erzeugt Scene Prompts\n2. Host-ComfyUI generiert scene_01.mp4 und scene_02.mp4 lokal mit RTX A5000\n3. Hermes importiert Clips\n4. Hermes rendert Mechanism-Layer, Captions, SFX\n5. FFmpeg baut finales Short\n```\n\nDas ersetzt PixVerse/Pika als Hauptpfad.\n\nPixVerse/Pika bleiben nur noch Fallback:\n\n```text\nwenn lokales Wan/LTX zu langsam oder qualitativ schlecht ist\n```\n\n---\n\n# Was wir technisch brauchen\n\n## Auf dem Host\n\nPrüfen:\n\n```bash\nnvidia-smi\n```\n\nDann:\n\n```bash\npython -c \"import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n```\n\nWenn das auf dem Host funktioniert, können wir loslegen.\n\n## ComfyUI lokal\n\nEmpfohlener Pfad:\n\n```bash\npipx install comfy-cli\ncomfy --skip-prompt tracking disable\ncomfy --skip-prompt install --nvidia --fast-deps\ncomfy launch -- --listen 0.0.0.0 --port 8188\n```\n\nDann kann Hermes per API sprechen mit:\n\n```text\nhttp://HOST_IP:8188\n```\n\nOder falls Hermes auf demselben Host läuft:\n\n```text\nhttp://127.0.0.1:8188\n```\n\n---\n\n# Wichtige Frage\n\nAktuell sieht Hermes die GPU nicht. Daher brauchen wir eine von zwei Varianten:\n\n## Variante A — GPU in Hermes verfügbar machen\n\nFalls Hermes in Docker/Container läuft:\n\n```bash\ndocker run --gpus all ...\n```\n\noder Compose:\n\n```yaml\ndeploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: all\n capabilities: [gpu]\n```\n\nDann müsste hier funktionieren:\n\n```bash\nnvidia-smi\n```\n\n## Variante B — ComfyUI direkt auf dem Host laufen lassen\n\nDas ist wahrscheinlich einfacher.\n\nDann muss Hermes nur die API erreichen:\n\n```text\nhttp://host-ip:8188\n```\n\nDas würde ich bevorzugen.\n\n---\n\n# Mein Vorschlag für den nächsten Schritt\n\nNicht sofort riesige Modelle installieren. Erst sauberer Spike:\n\n## GPU Spike v1\n\nZiel:\n\n```text\nKann Host-ComfyUI mit RTX A5000 einen 3–5s 9:16 Clip lokal generieren?\n```\n\nMinimaltest:\n\n1. Host-GPU prüfen\n2. ComfyUI installieren/starten\n3. kleines Wan2.1/LTX-Workflow-Setup\n4. Scene 01 Prompt testen\n5. Output nach AutoShortsBot importieren\n6. Hermes baut Hybrid-Render\n\nWenn der Test gut ist:\n\n```text\nlokaler GPU-Workflow wird Hauptpfad\n```\n\nWenn schlecht:\n\n```text\nPixVerse/Pika Free-Tier bleibt B-Roll-Quelle\n```\n\n---\n\n# Antwort auf deine Frage\n\nJa:\n\n```text\nWir wollen die RTX A5000 nutzen.\n```\n\nAber nicht in dieser aktuell sichtbaren Hermes-Umgebung, solange CUDA hier nicht verfügbar ist.\n\nDer beste nächste Schritt ist:\n\n```text\nComfyUI + Wan2.1/LTX-Video auf dem Host mit RTX A5000 starten\nHermes steuert es per API\n```\n\nDann kann ich die Clip-Generierung weitgehend automatisieren — ohne paid Credits, ohne Browser-Gefrickel, und mit lokal kontrollierbarem Output."
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"content": "[M G] die GPU ist ausserhalb der VM; über Tailscale erreichbar. gib mir nun schritt für schritt die Befehle um das Modell zu isntallieren sodass du es dann erreichen und nutzen kannst."
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"content": "Ja, Sir. Wir nutzen die A5000. Für 16 GB VRAM würde ich **nicht** mit den riesigen Wan 14B/27B Modellen starten, sondern mit:\n\n```text\nComfyUI\n+ ComfyUI-WanVideoWrapper\n+ Wan2.1 T2V 1.3B FP8\n+ Wan2.1 VAE BF16\n+ UMT5 XXL FP8 Text Encoder\n```\n\nDas ist der pragmatische kostenlose Einstieg. Danach kann ich ComfyUI über Tailscale per API ansprechen.\n\nUnten sind die Befehle für den **GPU-Host**, nicht für die Hermes-VM.\n\nAnnahme: Host läuft Linux/Ubuntu mit NVIDIA-Treiber. Falls Windows: sag Bescheid, dann gebe ich die Windows-Variante.\n\n---\n\n## 0. Auf dem GPU-Host prüfen\n\n```bash\nnvidia-smi\n```\n\nErwartet ungefähr:\n\n```text\nRTX A5000 Laptop GPU\nMemory-Total: ~16384 MiB\n```\n\nDann:\n\n```bash\ndf -h ~\n```\n\nDu solltest für den Start mindestens haben:\n\n```text\n30 GB frei\n```\n\nBesser:\n\n```text\n50+ GB frei\n```\n\n---\n\n## 1. Systempakete installieren\n\n```bash\nsudo apt update\nsudo apt install -y \\\n git \\\n git-lfs \\\n python3 \\\n python3-venv \\\n python3-pip \\\n ffmpeg \\\n curl \\\n wget \\\n build-essential\n```\n\nGit LFS aktivieren:\n\n```bash\ngit lfs install\n```\n\n---\n\n## 2. Arbeitsverzeichnis anlegen\n\nIch würde alles unter `~/ai` installieren:\n\n```bash\nmkdir -p ~/ai\ncd ~/ai\n```\n\n---\n\n## 3. ComfyUI installieren\n\n```bash\ncd ~/ai\ngit clone https://github.com/comfyanonymous/ComfyUI.git\ncd ~/ai/ComfyUI\n```\n\nPython-Venv:\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\n```\n\nPip aktualisieren:\n\n```bash\npython -m pip install --upgrade pip wheel setuptools\n```\n\nPyTorch mit CUDA installieren:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n```\n\nFalls das scheitert, nimm CUDA 12.4:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n```\n\nComfyUI Requirements:\n\n```bash\npip install -r requirements.txt\n```\n\nCUDA-Test:\n\n```bash\npython - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda:\", torch.cuda.is_available())\nif torch.cuda.is_available():\n print(\"gpu:\", torch.cuda.get_device_name(0))\n print(\"vram_gb:\", round(torch.cuda.get_device_properties(0).total_memory / 1024**3, 2))\nPY\n```\n\nErwartet:\n\n```text\ncuda: True\ngpu: NVIDIA RTX A5000 Laptop GPU\nvram_gb: ~16\n```\n\n---\n\n## 4. ComfyUI-WanVideoWrapper installieren\n\n```bash\ncd ~/ai/ComfyUI/custom_nodes\ngit clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git\ncd ComfyUI-WanVideoWrapper\n../../.venv/bin/pip install -r requirements.txt\n```\n\nZurück nach ComfyUI:\n\n```bash\ncd ~/ai/ComfyUI\n```\n\n---\n\n## 5. HuggingFace CLI installieren\n\n```bash\nsource ~/ai/ComfyUI/.venv/bin/activate\npip install -U \"huggingface_hub[cli]\"\n```\n\nOptional anmelden, falls HuggingFace Download throttelt:\n\n```bash\nhuggingface-cli login\n```\n\nWenn kein Token vorhanden: erstmal ohne Login versuchen.\n\n---\n\n## 6. Modellordner anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 7. Wan2.1 1.3B FP8 Modell herunterladen\n\nDas ist der Haupt-Transformer:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 1.5 GB.\n\n---\n\n## 8. Wan2.1 VAE herunterladen\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n Wan2_1_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 250 MB.\n\n---\n\n## 9. Text Encoder herunterladen\n\nFür Wan brauchen wir den UMT5 Text Encoder. Nimm zuerst die FP8-Version, weil VRAM/RAM freundlicher:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 6.7 GB.\n\nFalls FP8 Probleme macht, später BF16:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders \\\n --local-dir-use-symlinks False\n```\n\nAber BF16 ist ca. 11.4 GB — erstmal vermeiden.\n\n---\n\n## 10. Optional: Clip Vision laden\n\nFür reines Text-to-Video brauchen wir es wahrscheinlich nicht. Für Image-to-Video später:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n open-clip-xlm-roberta-large-vit-huge-14_visual_fp16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/clip_vision \\\n --local-dir-use-symlinks False\n```\n\nDas kannst du vorerst überspringen.\n\n---\n\n## 11. Kontrolle der Modell-Dateien\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f | sort\n```\n\nDu solltest mindestens sehen:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n## 12. Tailscale-IP des Hosts ermitteln\n\n```bash\ntailscale ip -4\n```\n\nMerken. Beispiel:\n\n```text\n100.x.y.z\n```\n\nIch nenne sie unten:\n\n```text\nTAILSCALE_IP\n```\n\n---\n\n## 13. ComfyUI über Tailscale starten\n\nWichtig: Wir binden ComfyUI am besten direkt auf die Tailscale-IP, nicht öffentlich auf alle Interfaces.\n\nErsetze `TAILSCALE_IP` durch die Ausgabe von `tailscale ip -4`:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\npython main.py \\\n --listen TAILSCALE_IP \\\n --port 8188\n```\n\nBeispiel:\n\n```bash\npython main.py \\\n --listen 100.101.102.103 \\\n --port 8188\n```\n\nWenn das Probleme macht, vorübergehend:\n\n```bash\npython main.py \\\n --listen 0.0.0.0 \\\n --port 8188\n```\n\nDann aber bitte Firewall nur für Tailscale offen halten.\n\n---\n\n## 14. Firewall: Port nur über Tailscale erlauben\n\nFalls `ufw` aktiv ist:\n\n```bash\nsudo ufw status\n```\n\nTailscale-Interface heißt meist `tailscale0`.\n\nErlaube Port 8188 nur auf Tailscale:\n\n```bash\nsudo ufw allow in on tailscale0 to any port 8188 proto tcp\n```\n\nStatus prüfen:\n\n```bash\nsudo ufw status verbose\n```\n\n---\n\n## 15. Von einem anderen Gerät testen\n\nAuf einem Gerät im selben Tailscale-Netz:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nErwartet: JSON mit System/GPU-Infos.\n\nOder im Browser öffnen:\n\n```text\nhttp://TAILSCALE_IP:8188\n```\n\nWenn du die Oberfläche siehst: gut.\n\n---\n\n## 16. Dauerhaft als systemd-Service einrichten\n\nWenn der manuelle Start funktioniert, Service anlegen:\n\n```bash\nsudo tee /etc/systemd/system/comfyui.service >/dev/null <<'EOF'\n[Unit]\nDescription=ComfyUI Wan Video Server\nAfter=network-online.target tailscaled.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=REPLACE_WITH_YOUR_USERNAME\nWorkingDirectory=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI\nEnvironment=\"PYTHONUNBUFFERED=1\"\nExecStart=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI/.venv/bin/python main.py --listen REPLACE_WITH_TAILSCALE_IP --port 8188\nRestart=on-failure\nRestartSec=10\n\n[Install]\nWantedBy=multi-user.target\nEOF\n```\n\nDann Platzhalter ersetzen:\n\n```bash\nsudo sed -i \"s/REPLACE_WITH_YOUR_USERNAME/$USER/g\" /etc/systemd/system/comfyui.service\n```\n\nTailscale-IP einsetzen:\n\n```bash\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\nsudo sed -i \"s/REPLACE_WITH_TAILSCALE_IP/$TS_IP/g\" /etc/systemd/system/comfyui.service\n```\n\nService aktivieren:\n\n```bash\nsudo systemctl daemon-reload\nsudo systemctl enable --now comfyui\n```\n\nLogs prüfen:\n\n```bash\njournalctl -u comfyui -f\n```\n\nHealthcheck:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n---\n\n# Danach brauche ich von dir\n\nSobald es läuft, schick mir bitte:\n\n```text\nComfyUI URL:\nhttp://TAILSCALE_IP:8188\n```\n\nUnd idealerweise die Ausgabe von:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nDann kann ich von hier aus prüfen, ob ich den Server erreiche.\n\n---\n\n# Danach mache ich\n\nWenn ComfyUI erreichbar ist, baue ich hier im AutoShortsBot den nächsten Baustein:\n\n```text\nComfyUI Wan Client\n→ Prompt aus Scene Specs\n→ Job an http://TAILSCALE_IP:8188/api/prompt\n→ Output herunterladen\n→ speichern als scene_01.mp4 / scene_02.mp4\n→ Hybrid Renderer baut finales Short\n```\n\n---\n\n# Wichtiger Hinweis zu 16 GB VRAM\n\nFür den ersten Test bitte klein starten:\n\n```text\n480p\n3–5 Sekunden\nWan2.1 T2V 1.3B FP8\nwenige Steps\n```\n\nNicht direkt:\n\n```text\n720p\n10 Sekunden\n14B\n```\n\nDas wäre wie einen Bentley durch einen Gartenschlauch tanken. Technisch faszinierend, aber langsam und leicht absurd.\n\nSobald der erste Clip läuft, optimieren wir Qualität/Speed.",
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"text": "Ja, Sir. Wir nutzen die A5000. Für 16 GB VRAM würde ich **nicht** mit den riesigen Wan 14B/27B Modellen starten, sondern mit:\n\n```text\nComfyUI\n+ ComfyUI-WanVideoWrapper\n+ Wan2.1 T2V 1.3B FP8\n+ Wan2.1 VAE BF16\n+ UMT5 XXL FP8 Text Encoder\n```\n\nDas ist der pragmatische kostenlose Einstieg. Danach kann ich ComfyUI über Tailscale per API ansprechen.\n\nUnten sind die Befehle für den **GPU-Host**, nicht für die Hermes-VM.\n\nAnnahme: Host läuft Linux/Ubuntu mit NVIDIA-Treiber. Falls Windows: sag Bescheid, dann gebe ich die Windows-Variante.\n\n---\n\n## 0. Auf dem GPU-Host prüfen\n\n```bash\nnvidia-smi\n```\n\nErwartet ungefähr:\n\n```text\nRTX A5000 Laptop GPU\nMemory-Total: ~16384 MiB\n```\n\nDann:\n\n```bash\ndf -h ~\n```\n\nDu solltest für den Start mindestens haben:\n\n```text\n30 GB frei\n```\n\nBesser:\n\n```text\n50+ GB frei\n```\n\n---\n\n## 1. Systempakete installieren\n\n```bash\nsudo apt update\nsudo apt install -y \\\n git \\\n git-lfs \\\n python3 \\\n python3-venv \\\n python3-pip \\\n ffmpeg \\\n curl \\\n wget \\\n build-essential\n```\n\nGit LFS aktivieren:\n\n```bash\ngit lfs install\n```\n\n---\n\n## 2. Arbeitsverzeichnis anlegen\n\nIch würde alles unter `~/ai` installieren:\n\n```bash\nmkdir -p ~/ai\ncd ~/ai\n```\n\n---\n\n## 3. ComfyUI installieren\n\n```bash\ncd ~/ai\ngit clone https://github.com/comfyanonymous/ComfyUI.git\ncd ~/ai/ComfyUI\n```\n\nPython-Venv:\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\n```\n\nPip aktualisieren:\n\n```bash\npython -m pip install --upgrade pip wheel setuptools\n```\n\nPyTorch mit CUDA installieren:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n```\n\nFalls das scheitert, nimm CUDA 12.4:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n```\n\nComfyUI Requirements:\n\n```bash\npip install -r requirements.txt\n```\n\nCUDA-Test:\n\n```bash\npython - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda:\", torch.cuda.is_available())\nif torch.cuda.is_available():\n print(\"gpu:\", torch.cuda.get_device_name(0))\n print(\"vram_gb:\", round(torch.cuda.get_device_properties(0).total_memory / 1024**3, 2))\nPY\n```\n\nErwartet:\n\n```text\ncuda: True\ngpu: NVIDIA RTX A5000 Laptop GPU\nvram_gb: ~16\n```\n\n---\n\n## 4. ComfyUI-WanVideoWrapper installieren\n\n```bash\ncd ~/ai/ComfyUI/custom_nodes\ngit clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git\ncd ComfyUI-WanVideoWrapper\n../../.venv/bin/pip install -r requirements.txt\n```\n\nZurück nach ComfyUI:\n\n```bash\ncd ~/ai/ComfyUI\n```\n\n---\n\n## 5. HuggingFace CLI installieren\n\n```bash\nsource ~/ai/ComfyUI/.venv/bin/activate\npip install -U \"huggingface_hub[cli]\"\n```\n\nOptional anmelden, falls HuggingFace Download throttelt:\n\n```bash\nhuggingface-cli login\n```\n\nWenn kein Token vorhanden: erstmal ohne Login versuchen.\n\n---\n\n## 6. Modellordner anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 7. Wan2.1 1.3B FP8 Modell herunterladen\n\nDas ist der Haupt-Transformer:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 1.5 GB.\n\n---\n\n## 8. Wan2.1 VAE herunterladen\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n Wan2_1_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 250 MB.\n\n---\n\n## 9. Text Encoder herunterladen\n\nFür Wan brauchen wir den UMT5 Text Encoder. Nimm zuerst die FP8-Version, weil VRAM/RAM freundlicher:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders \\\n --local-dir-use-symlinks False\n```\n\nGröße: ca. 6.7 GB.\n\nFalls FP8 Probleme macht, später BF16:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders \\\n --local-dir-use-symlinks False\n```\n\nAber BF16 ist ca. 11.4 GB — erstmal vermeiden.\n\n---\n\n## 10. Optional: Clip Vision laden\n\nFür reines Text-to-Video brauchen wir es wahrscheinlich nicht. Für Image-to-Video später:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n open-clip-xlm-roberta-large-vit-huge-14_visual_fp16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/clip_vision \\\n --local-dir-use-symlinks False\n```\n\nDas kannst du vorerst überspringen.\n\n---\n\n## 11. Kontrolle der Modell-Dateien\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f | sort\n```\n\nDu solltest mindestens sehen:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n## 12. Tailscale-IP des Hosts ermitteln\n\n```bash\ntailscale ip -4\n```\n\nMerken. Beispiel:\n\n```text\n100.x.y.z\n```\n\nIch nenne sie unten:\n\n```text\nTAILSCALE_IP\n```\n\n---\n\n## 13. ComfyUI über Tailscale starten\n\nWichtig: Wir binden ComfyUI am besten direkt auf die Tailscale-IP, nicht öffentlich auf alle Interfaces.\n\nErsetze `TAILSCALE_IP` durch die Ausgabe von `tailscale ip -4`:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\npython main.py \\\n --listen TAILSCALE_IP \\\n --port 8188\n```\n\nBeispiel:\n\n```bash\npython main.py \\\n --listen 100.101.102.103 \\\n --port 8188\n```\n\nWenn das Probleme macht, vorübergehend:\n\n```bash\npython main.py \\\n --listen 0.0.0.0 \\\n --port 8188\n```\n\nDann aber bitte Firewall nur für Tailscale offen halten.\n\n---\n\n## 14. Firewall: Port nur über Tailscale erlauben\n\nFalls `ufw` aktiv ist:\n\n```bash\nsudo ufw status\n```\n\nTailscale-Interface heißt meist `tailscale0`.\n\nErlaube Port 8188 nur auf Tailscale:\n\n```bash\nsudo ufw allow in on tailscale0 to any port 8188 proto tcp\n```\n\nStatus prüfen:\n\n```bash\nsudo ufw status verbose\n```\n\n---\n\n## 15. Von einem anderen Gerät testen\n\nAuf einem Gerät im selben Tailscale-Netz:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nErwartet: JSON mit System/GPU-Infos.\n\nOder im Browser öffnen:\n\n```text\nhttp://TAILSCALE_IP:8188\n```\n\nWenn du die Oberfläche siehst: gut.\n\n---\n\n## 16. Dauerhaft als systemd-Service einrichten\n\nWenn der manuelle Start funktioniert, Service anlegen:\n\n```bash\nsudo tee /etc/systemd/system/comfyui.service >/dev/null <<'EOF'\n[Unit]\nDescription=ComfyUI Wan Video Server\nAfter=network-online.target tailscaled.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=REPLACE_WITH_YOUR_USERNAME\nWorkingDirectory=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI\nEnvironment=\"PYTHONUNBUFFERED=1\"\nExecStart=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI/.venv/bin/python main.py --listen REPLACE_WITH_TAILSCALE_IP --port 8188\nRestart=on-failure\nRestartSec=10\n\n[Install]\nWantedBy=multi-user.target\nEOF\n```\n\nDann Platzhalter ersetzen:\n\n```bash\nsudo sed -i \"s/REPLACE_WITH_YOUR_USERNAME/$USER/g\" /etc/systemd/system/comfyui.service\n```\n\nTailscale-IP einsetzen:\n\n```bash\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\nsudo sed -i \"s/REPLACE_WITH_TAILSCALE_IP/$TS_IP/g\" /etc/systemd/system/comfyui.service\n```\n\nService aktivieren:\n\n```bash\nsudo systemctl daemon-reload\nsudo systemctl enable --now comfyui\n```\n\nLogs prüfen:\n\n```bash\njournalctl -u comfyui -f\n```\n\nHealthcheck:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n---\n\n# Danach brauche ich von dir\n\nSobald es läuft, schick mir bitte:\n\n```text\nComfyUI URL:\nhttp://TAILSCALE_IP:8188\n```\n\nUnd idealerweise die Ausgabe von:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nDann kann ich von hier aus prüfen, ob ich den Server erreiche.\n\n---\n\n# Danach mache ich\n\nWenn ComfyUI erreichbar ist, baue ich hier im AutoShortsBot den nächsten Baustein:\n\n```text\nComfyUI Wan Client\n→ Prompt aus Scene Specs\n→ Job an http://TAILSCALE_IP:8188/api/prompt\n→ Output herunterladen\n→ speichern als scene_01.mp4 / scene_02.mp4\n→ Hybrid Renderer baut finales Short\n```\n\n---\n\n# Wichtiger Hinweis zu 16 GB VRAM\n\nFür den ersten Test bitte klein starten:\n\n```text\n480p\n3–5 Sekunden\nWan2.1 T2V 1.3B FP8\nwenige Steps\n```\n\nNicht direkt:\n\n```text\n720p\n10 Sekunden\n14B\n```\n\nDas wäre wie einen Bentley durch einen Gartenschlauch tanken. Technisch faszinierend, aber langsam und leicht absurd.\n\nSobald der erste Clip läuft, optimieren wir Qualität/Speed."
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"content": "[M G] bin bei punkt 8 hängengeblieben: (.venv) gam@PC-1:~/ai/ComfyUI$ huggingface-cli download Kijai/WanVideo_comfy \\\n Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n --local-dir-use-symlinks False\nWarning: huggingface-cli is deprecated and no longer works. Use hf instead.\n\nHint: hf is already installed! Use it directly.\n\nHint: Examples:\n hf auth login\n hf download unsloth/gemma-4-31B-it-GGUF\n hf upload my-cool-model . .\n hf models ls --search \"gemma\"\n hf repos ls --format json\n hf jobs run python:3.12 python -c 'print(\"Hello!\")'\n hf --help\n\n(.venv) gam@PC-1:~/ai/ComfyUI$\n\n[Your active task list was preserved across context compression]\n- [>] gpu-check. GPU/VRAM und CUDA-Zugriff auf dem Host prüfen (in_progress)\n- [ ] options. Lokale Open-Source-Video-Optionen für 16GB VRAM bewerten (pending)\n- [ ] plan. Empfohlenen nächsten Implementierungsschritt für AutoShortsBot festlegen (pending)"
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"reasoning": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status.",
"reasoning_content": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status.",
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"text": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status."
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"content": "Kein Problem, Sir. Hugging Face hat die CLI geändert. Statt:\n\n```bash\nhuggingface-cli download ...\n```\n\nverwenden wir jetzt:\n\n```bash\nhf download ...\n```\n\nDu bist im richtigen Venv. Führe einfach diese Befehle aus.\n\n## 1. Prüfen, ob `hf` funktioniert\n\n```bash\nhf --help | head\n```\n\nWenn das Output zeigt: gut.\n\nOptional Version:\n\n```bash\nhf version\n```\n\n---\n\n## 2. Modellordner sicher anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 3. Wan2.1 T2V 1.3B FP8 herunterladen\n\nDas ersetzt deinen hängengebliebenen Befehl:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nFalls `hf` wegen Symlinks meckert: ignorieren, solange die Datei im Zielordner liegt.\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/\n```\n\nDu solltest sehen:\n\n```text\nWan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.4–1.5 GB\n```\n\n---\n\n## 4. Wan2.1 VAE herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_1_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet:\n\n```text\nWan2_1_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n250 MB\n```\n\n---\n\n## 5. UMT5 Text Encoder FP8 herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nErwartet:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n6.3–6.8 GB\n```\n\nDas ist der dicke Brocken. Nicht erschrecken, er hat nur einen gesunden Appetit. Wie jedes moderne KI-Modell mit Standesdünkel.\n\n---\n\n## 6. Alles zusammen prüfen\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f -name \"*.safetensors\" -exec ls -lh {} \\;\n```\n\nErwartet mindestens:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\n---\n\n## 7. Falls Download Zugriff/Auth verlangt\n\nFalls `hf download` nach Login oder Token fragt:\n\n```bash\nhf auth login\n```\n\nDann HuggingFace Token einfügen.\n\nDanach den fehlgeschlagenen Download erneut ausführen.\n\n---\n\n## 8. Danach ComfyUI starten\n\nWenn die drei Dateien da sind:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\necho \"$TS_IP\"\n\npython main.py --listen \"$TS_IP\" --port 8188\n```\n\nWenn `--listen \"$TS_IP\"` Probleme macht:\n\n```bash\npython main.py --listen 0.0.0.0 --port 8188\n```\n\nDann bitte nur via Tailscale/Firewall offen lassen.\n\n---\n\n## 9. Healthcheck lokal auf dem Host\n\nIn einem zweiten Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: ComfyUI läuft.\n\n---\n\n## 10. Schick mir danach\n\nBitte poste dann:\n\n```text\n1. Ausgabe von:\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n\n2. Deine Tailscale-ComfyUI-URL:\nhttp://:8188\n```\n\nDann prüfe ich von hier aus die Verbindung und baue den Hermes/AutoShortsBot-Client dafür.",
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"text": "Kein Problem, Sir. Hugging Face hat die CLI geändert. Statt:\n\n```bash\nhuggingface-cli download ...\n```\n\nverwenden wir jetzt:\n\n```bash\nhf download ...\n```\n\nDu bist im richtigen Venv. Führe einfach diese Befehle aus.\n\n## 1. Prüfen, ob `hf` funktioniert\n\n```bash\nhf --help | head\n```\n\nWenn das Output zeigt: gut.\n\nOptional Version:\n\n```bash\nhf version\n```\n\n---\n\n## 2. Modellordner sicher anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 3. Wan2.1 T2V 1.3B FP8 herunterladen\n\nDas ersetzt deinen hängengebliebenen Befehl:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nFalls `hf` wegen Symlinks meckert: ignorieren, solange die Datei im Zielordner liegt.\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/\n```\n\nDu solltest sehen:\n\n```text\nWan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.4–1.5 GB\n```\n\n---\n\n## 4. Wan2.1 VAE herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_1_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet:\n\n```text\nWan2_1_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n250 MB\n```\n\n---\n\n## 5. UMT5 Text Encoder FP8 herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n --local-dir ~/ai/ComfyUI/models/text_encoders\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nErwartet:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n6.3–6.8 GB\n```\n\nDas ist der dicke Brocken. Nicht erschrecken, er hat nur einen gesunden Appetit. Wie jedes moderne KI-Modell mit Standesdünkel.\n\n---\n\n## 6. Alles zusammen prüfen\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f -name \"*.safetensors\" -exec ls -lh {} \\;\n```\n\nErwartet mindestens:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\n---\n\n## 7. Falls Download Zugriff/Auth verlangt\n\nFalls `hf download` nach Login oder Token fragt:\n\n```bash\nhf auth login\n```\n\nDann HuggingFace Token einfügen.\n\nDanach den fehlgeschlagenen Download erneut ausführen.\n\n---\n\n## 8. Danach ComfyUI starten\n\nWenn die drei Dateien da sind:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\necho \"$TS_IP\"\n\npython main.py --listen \"$TS_IP\" --port 8188\n```\n\nWenn `--listen \"$TS_IP\"` Probleme macht:\n\n```bash\npython main.py --listen 0.0.0.0 --port 8188\n```\n\nDann bitte nur via Tailscale/Firewall offen lassen.\n\n---\n\n## 9. Healthcheck lokal auf dem Host\n\nIn einem zweiten Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: ComfyUI läuft.\n\n---\n\n## 10. Schick mir danach\n\nBitte poste dann:\n\n```text\n1. Ausgabe von:\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n\n2. Deine Tailscale-ComfyUI-URL:\nhttp://:8188\n```\n\nDann prüfe ich von hier aus die Verbindung und baue den Hermes/AutoShortsBot-Client dafür."
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"content": "[M G] gam@PC-1:~/llama.cpp$ curl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n{\"system\": {\"os\": \"linux\", \"ram_total\": 33339789312, \"ram_free\": 5236817920, \"comfyui_version\": \"0.22.0\", \"required_frontend_version\": \"1.43.18\", \"installed_templates_version\": \"0.9.82\", \"required_templates_version\": \"0.9.82\", \"comfy_package_versions\": [{\"name\": \"comfyui-frontend-package\", \"installed\": \"1.43.18\", \"required\": \"1.43.18\"}, {\"name\": \"comfyui-workflow-templates\", \"installed\": \"0.9.82\", \"required\": \"0.9.82\"}, {\"name\": \"comfyui-embedded-docs\", \"installed\": \"0.5.0\", \"required\": \"0.5.0\"}, {\"name\": \"comfy-kitchen\", \"installed\": \"0.2.8\", \"required\": \"0.2.8\"}, {\"name\": \"comfy-aimdo\", \"installed\": \"0.4.3\", \"required\": \"0.4.3\"}], \"python_version\": \"3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]\", \"pytorch_version\": \"2.12.0+cu126\", \"embedded_python\": false, \"argv\": [\"main.py\", \"--listen\", \"100.101.173.25\", \"--port\", \"8188\"]}, \"devices\": [{\"name\": \"cuda:0 NVIDIA RTX A5000 Laptop GPU : cudaMallocAsync\", \"type\": \"cuda\", \"index\": 0, \"vram_total\": 16758472704, \"vram_free\": 16491151360, \"torch_vram_total\": 0, \"torch_vram_free\": 0}]}gam@PC-1:~/llama.cpp$ To see the GUI go to: http://100.101.173.25:8188"
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"content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed.\\n\\n4. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. 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Scaled modes only w\\n load_device : [['main_device', 'offload_device'], {'default': 'offload_device', 'tooltip': 'Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM'}]\\noptional ['attention_mode', 'compile_args', 'block_swap_args', 'lora', 'vram_management_args', 'extra_model', 'fantasytalking_model', 'multitalk_model', 'fantasyportrait_model', 'rms_norm_function']\\n\\n## WanVideoTextEncode\\ndisplay WanVideo TextEncode\\nrequired ['positive_prompt', 'negative_prompt']\\n positive_prompt : ['STRING', {'default': '', 'multiline': True}]\\n negative_prompt : ['STRING', {'default': '', 'multiline': True}]\\noptional ['t5', 'force_offload', 'model_to_offload', 'use_disk_cache', 'device']\\n\\n## WanVideoSampler\\ndisplay WanVideo Sampler\\nrequired ['model', 'image_embeds', 'steps', 'cfg', 'shift', 'seed', 'force_offload', 'scheduler', 'riflex_freq_index']\\n model : ['WANVIDEOMODEL']\\n image_embeds : ['WANVIDIMAGE_EMBEDS']\\n steps : ['INT', {'default': 30, 'min': 1}]\\n cfg : ['FLOAT', {'default': 6.0, 'min': 0.0, 'max': 30.0, 'step': 0.01}]\\n shift : ['FLOAT', {'default': 5.0, 'min': 0.0, 'max': 1000.0, 'step': 0.01}]\\n seed : ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}]\\n force_offload : ['BOOLEAN', {'default': True, 'tooltip': 'Moves the model to the offload device after sampling'}]\\n scheduler : [['unipc', 'unipc/beta', 'dpm++', 'dpm++/beta', 'dpm++_sde', 'dpm++_sde/beta', 'euler', 'euler/beta', 'longcat_distill_euler', 'deis', 'lcm', 'lcm/beta', 'res_multistep', 'er_sde', 'flowmatch_causvid', 'flowmatch_distill', 'flowmatch_pusa', 'multitalk', 'sa_ode_stable', 'rcm', 'vibt_unipc'], {'defau\\n riflex_freq_index : ['INT', {'default': 0, 'min': 0, 'max': 1000, 'step': 1, 'tooltip': 'Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping'}]\\noptional ['text_embeds', 'samples', 'denoise_strength', 'feta_args', 'context_options', 'cache_args', 'flowedit_args', 'batched_cfg', 'slg_args', 'rope_function', 'loop_args', 'experimental_args', 'sigmas', 'unianimate_poses', 'fantasytalking_embeds', 'uni3c_embeds', 'multitalk_embeds', 'freeinit_args', 'start_step', 'end_step']\\n\\n## WanVideoDecode\\ndisplay WanVideo Decode\\nrequired ['vae', 'samples', 'enable_vae_tiling', 'tile_x', 'tile_y', 'tile_stride_x', 'tile_stride_y']\\n vae : ['WANVAE']\\n samples : ['LATENT']\\n enable_vae_tiling : ['BOOLEAN', {'default': False, 'tooltip': 'Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams can be controlled by increasing the tile size. Seams become less obvious at 1.5x\\n tile_x : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n tile_y : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n tile_stride_x : ['INT', {'default': 144, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\n tile_stride_y : ['INT', {'default': 128, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\noptional ['normalization']\\n\\n## WanVideoVAELoader\\ndisplay WanVideo VAE Loader\\nrequired ['model_name']\\n model_name : [['Wan2_1_VAE_bf16.safetensors'], {'tooltip': \\\"These models are loaded from 'ComfyUI/models/vae'\\\"}]\\noptional ['precision', 'compile_args', 'use_cpu_cache', 'verbose']\\n\\n## LoadWanVideoT5TextEncoder\\ndisplay WanVideo T5 Text Encoder Loader\\nrequired ['model_name', 'precision']\\n model_name : [['umt5-xxl-enc-fp8_e4m3fn.safetensors'], {'tooltip': \\\"These models are loaded from 'ComfyUI/models/text_encoders'\\\"}]\\n precision : [['fp32', 'bf16'], {'default': 'bf16'}]\\noptional ['load_device', 'quantization']\\n\\n## WanTextToVideoApi\\ndisplay Wan Text to Video\\nrequired ['model', 'prompt']\\n model : ['COMBO', {'tooltip': 'Model to use.', 'default': 'wan2.6-t2v', 'multiselect': False, 'options': ['wan2.5-t2v-preview', 'wan2.6-t2v']}]\\n prompt : ['STRING', {'tooltip': 'Prompt describing the elements and visual features. Supports English and Chinese.', 'default': '', 'multiline': True}]\\noptional ['negative_prompt', 'size', 'duration', 'audio', 'seed', 'generate_audio', 'prompt_extend', 'watermark', 'shot_type']\\n\\n## Wan2TextToVideoApi\\ndisplay Wan 2.7 Text to Video\\nrequired ['model', 'seed', 'prompt_extend', 'watermark']\\n model : ['COMFY_DYNAMICCOMBO_V3', {'options': [{'key': 'wan2.7-t2v', 'inputs': {'required': {'prompt': ['STRING', {'tooltip': 'Prompt describing the elements and visual features. Supports English and Chinese.', 'default': '', 'multiline': True}], 'negative_prompt': ['STRING', {'tooltip': 'Negative prompt de\\n seed : ['INT', {'tooltip': 'Seed to use for generation.', 'default': 0, 'min': 0, 'max': 2147483647, 'step': 1, 'control_after_generate': True, 'display': 'number'}]\\n prompt_extend : ['BOOLEAN', {'tooltip': 'Whether to enhance the prompt with AI assistance.', 'advanced': True, 'default': True}]\\n watermark : ['BOOLEAN', {'tooltip': 'Whether to add an AI-generated watermark to the result.', 'advanced': True, 'default': False}]\\noptional ['audio']\\n\\n## SaveAnimatedWEBP\\ndisplay Save Animated WEBP\\nrequired ['images', 'filename_prefix', 'fps', 'lossless', 'quality', 'method']\\n images : ['IMAGE', {}]\\n filename_prefix : ['STRING', {'default': 'ComfyUI', 'multiline': False}]\\n fps : ['FLOAT', {'default': 6.0, 'min': 0.01, 'max': 1000.0, 'step': 0.01}]\\n lossless : ['BOOLEAN', {'default': True}]\\n quality : ['INT', {'default': 80, 'min': 0, 'max': 100}]\\n method : ['COMBO', {'multiselect': False, 'options': ['default', 'fastest', 'slowest']}]\\noptional []\\n\\n## SaveImage\\ndisplay Save Image\\nrequired ['images', 'filename_prefix']\\n images : ['IMAGE', {'tooltip': 'The images to save.'}]\\n filename_prefix : ['STRING', {'default': 'ComfyUI', 'tooltip': 'The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.'}]\\noptional []\", \"exit_code\": 0, \"error\": null}",
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"content": "{\"output\": \"## WanVideoEmptyEmbeds WanVideo Empty Embeds\\nrequired\\n width : ['INT', {'default': 832, 'min': 64, 'max': 8096, 'step': 8, 'tooltip': 'Width of the image to encode'}]\\n height : ['INT', {'default': 480, 'min': 64, 'max': 8096, 'step': 8, 'tooltip': 'Height of the image to encode'}]\\n num_frames : ['INT', {'default': 81, 'min': 1, 'max': 10000, 'step': 4, 'tooltip': 'Number of frames to encode'}]\\noptional\\n control_embeds : ['WANVIDIMAGE_EMBEDS', {'tooltip': 'control signal for the Fun -model'}]\\n extra_latents : ['LATENT', {'tooltip': 'First latent to use for the Pusa -model'}]\\n\\n## WanVideoTextEncode WanVideo TextEncode\\nrequired\\n positive_prompt : ['STRING', {'default': '', 'multiline': True}]\\n negative_prompt : ['STRING', {'default': '', 'multiline': True}]\\noptional\\n t5 : ['WANTEXTENCODER']\\n force_offload : ['BOOLEAN', {'default': True}]\\n model_to_offload : ['WANVIDEOMODEL', {'tooltip': 'Model to move to offload_device before encoding'}]\\n use_disk_cache : ['BOOLEAN', {'default': False, 'tooltip': 'Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory'}]\\n device : [['gpu', 'cpu'], {'default': 'gpu', 'tooltip': 'Device to run the text encoding on.'}]\\n\\n## WanVideoDecode WanVideo Decode\\nrequired\\n vae : ['WANVAE']\\n samples : ['LATENT']\\n enable_vae_tiling : ['BOOLEAN', {'default': False, 'tooltip': 'Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams \\n tile_x : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n tile_y : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n tile_stride_x : ['INT', {'default': 144, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\n tile_stride_y : ['INT', {'default': 128, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\noptional\\n normalization : [['default', 'minmax', 'none'], {'advanced': True}]\\n\\n## WanVideoSampler WanVideo Sampler\\nrequired\\n model : ['WANVIDEOMODEL']\\n image_embeds : ['WANVIDIMAGE_EMBEDS']\\n steps : ['INT', {'default': 30, 'min': 1}]\\n cfg : ['FLOAT', {'default': 6.0, 'min': 0.0, 'max': 30.0, 'step': 0.01}]\\n shift : ['FLOAT', {'default': 5.0, 'min': 0.0, 'max': 1000.0, 'step': 0.01}]\\n seed : ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}]\\n force_offload : ['BOOLEAN', {'default': True, 'tooltip': 'Moves the model to the offload device after sampling'}]\\n scheduler : [['unipc', 'unipc/beta', 'dpm++', 'dpm++/beta', 'dpm++_sde', 'dpm++_sde/beta', 'euler', 'euler/beta', 'longcat_distill_euler', 'deis', 'lcm', 'lcm/beta', 'res_multistep', 'er_sde', 'flowmatch_causvid', 'flowmatch_distill\\n riflex_freq_index : ['INT', {'default': 0, 'min': 0, 'max': 1000, 'step': 1, 'tooltip': 'Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping'}]\\noptional\\n text_embeds : ['WANVIDEOTEXTEMBEDS']\\n samples : ['LATENT', {'tooltip': 'init Latents to use for video2video process'}]\\n denoise_strength : ['FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}]\\n feta_args : ['FETAARGS']\\n context_options : ['WANVIDCONTEXT']\\n cache_args : ['CACHEARGS']\\n flowedit_args : ['FLOWEDITARGS', {'tooltip': 'FlowEdit support has been deprecated'}]\\n batched_cfg : ['BOOLEAN', {'default': False, 'tooltip': 'Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory'}]\\n slg_args : ['SLGARGS']\\n rope_function : [['default', 'comfy', 'comfy_chunked'], {'default': 'comfy', 'tooltip': \\\"Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked ver\\n loop_args : ['LOOPARGS']\\n experimental_args : ['EXPERIMENTALARGS']\\n sigmas : ['SIGMAS']\\n unianimate_poses : ['UNIANIMATE_POSE']\\n fantasytalking_embeds : ['FANTASYTALKING_EMBEDS']\\n uni3c_embeds : ['UNI3C_EMBEDS']\\n multitalk_embeds : ['MULTITALK_EMBEDS']\\n freeinit_args : ['FREEINITARGS']\\n start_step : ['INT', {'default': 0, 'min': 0, 'max': 10000, 'step': 1, 'tooltip': 'Start step for the sampling, 0 means full sampling, otherwise samples only from this step'}]\\n end_step : ['INT', {'default': -1, 'min': -1, 'max': 10000, 'step': 1, 'tooltip': 'End step for the sampling, -1 means full sampling, otherwise samples only until this step'}]\\n add_noise_to_samples : ['BOOLEAN', {'default': False, 'tooltip': 'Add noise to the samples before sampling, needed for video2video sampling when starting from clean video'}]\", \"exit_code\": 0, \"error\": null}",
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"content": "{\"output\": \"## SaveVideo Save Video\\nrequired ['video', 'filename_prefix', 'format', 'codec']\\n video ['VIDEO', {'tooltip': 'The video to save.'}]\\n filename_prefix ['STRING', {'tooltip': 'The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from node\\n format ['COMBO', {'tooltip': 'The format to save the video as.', 'default': 'auto', 'multiselect': False, 'options': ['auto', 'mp4']}]\\n codec ['COMBO', {'tooltip': 'The codec to use for the video.', 'default': 'auto', 'multiselect': False, 'options': ['auto', 'h264']}]\\noptional []\\n\\n## CreateVideo Create Video\\nrequired ['images', 'fps']\\n images ['IMAGE', {'tooltip': 'The images to create a video from.'}]\\n fps ['FLOAT', {'default': 30.0, 'min': 1.0, 'max': 120.0, 'step': 1.0}]\\noptional ['audio']\\n audio ['AUDIO', {'tooltip': 'The audio to add to the video.'}]\\n\\n## WanVideoCombineEmbeds WanVideo Combine Embeds\\nrequired ['embeds_1', 'embeds_2']\\n embeds_1 ['WANVIDIMAGE_EMBEDS']\\n embeds_2 ['WANVIDIMAGE_EMBEDS']\\noptional []\", \"exit_code\": 0, \"error\": null}",
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"reasoning": "**Explaining ComfyUI issues**\n\nI need to clarify why ComfyUI likely exited during the test. It seems my smoke test was too aggressive, especially with a T5 model running on a GPU with only 5GB of RAM, which may have caused it to crash. I should prompt the user to run commands in the terminal to access the logs and restart. The commands include navigating to the ComfyUI directory, activating the environment, and using additional arguments like `--lowvram` and `--reserve-vram`. Using the CPU instead might also help with memory issues.\n**Instructing to free RAM**\n\nI need to instruct to close any processes related to llama.cpp and others to ensure there is 12-16GB of free RAM. The current working directory is /llama.cpp, which could indicate that the llama server is running. I should prompt them to use `free -h` and `ps aux --sort=-%mem | head` to identify memory usage and then stop the llama process. Since the server crashed likely due to insufficient RAM, I need to be clear and concise. Should I update the to-do list as well?",
"reasoning_content": "**Explaining ComfyUI issues**\n\nI need to clarify why ComfyUI likely exited during the test. It seems my smoke test was too aggressive, especially with a T5 model running on a GPU with only 5GB of RAM, which may have caused it to crash. I should prompt the user to run commands in the terminal to access the logs and restart. The commands include navigating to the ComfyUI directory, activating the environment, and using additional arguments like `--lowvram` and `--reserve-vram`. Using the CPU instead might also help with memory issues.\n**Instructing to free RAM**\n\nI need to instruct to close any processes related to llama.cpp and others to ensure there is 12-16GB of free RAM. The current working directory is /llama.cpp, which could indicate that the llama server is running. I should prompt them to use `free -h` and `ps aux --sort=-%mem | head` to identify memory usage and then stop the llama process. Since the server crashed likely due to insufficient RAM, I need to be clear and concise. Should I update the to-do list as well?",
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"content": "Sehr gut, Sir — **ComfyUI war von hier aus erreichbar**, inklusive GPU.\n\nIch habe erfolgreich geprüft:\n\n```text\nhttp://100.101.173.25:8188/system_stats\n```\n\nErgebnis:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: 16.49 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\nCUDA: aktiv\n```\n\nAußerdem habe ich `/object_info` abgefragt:\n\n```text\nWan Nodes gefunden: 151\nWanVideoModelLoader: vorhanden\nWanVideoTextEncode: vorhanden\nWanVideoSampler: vorhanden\nWanVideoDecode: vorhanden\nWanVideoVAELoader: vorhanden\nLoadWanVideoT5TextEncoder: vorhanden\nSaveVideo/CreateVideo: vorhanden\n```\n\nHeißt: **ComfyUI + WanVideoWrapper + Modelle sind grundsätzlich korrekt installiert.**\n\n---\n\n## Aber: mein erster Smoke-Test hat ComfyUI beendet\n\nIch habe danach einen sehr kleinen Wan-Smoke-Test über die API queued:\n\n```text\n144x256\n13 Frames\n4 Steps\nWan2.1 T2V 1.3B FP8\n```\n\nDer Job wurde angenommen:\n\n```text\nprompt_id: 75ec9893-6631-4aa3-a8bd-c3d9822d1578\nnode_errors: {}\n```\n\nNach ca. 20 Sekunden war der Server aber nicht mehr erreichbar:\n\n```text\nConnection refused\n```\n\nDas bedeutet ziemlich sicher:\n\n```text\nComfyUI-Prozess ist gecrasht oder wurde vom OS gekillt.\n```\n\nWahrscheinlichster Grund: **RAM**, nicht VRAM.\n\nDein System meldete:\n\n```text\nRAM total: 33.3 GB\nRAM free: 5.2 GB\nVRAM free: 16.5 GB\n```\n\nDer UMT5 Text Encoder und Wan-Wrapper brauchen beim ersten Laden gern ordentlich System-RAM. Wenn nebenher `llama.cpp` oder ein anderes Modell läuft, wird es eng. Die GPU ist bereit; der Arbeitsspeicher spielt gerade Diva. Natürlich.\n\n---\n\n# Jetzt bitte auf dem GPU-Host ausführen\n\n## 1. Prüfen, ob ComfyUI noch läuft\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn `Connection refused`: Prozess ist weg.\n\n---\n\n## 2. RAM prüfen\n\n```bash\nfree -h\n```\n\nDann die größten RAM-Verbraucher:\n\n```bash\nps aux --sort=-%mem | head -20\n```\n\nWenn dort `llama.cpp`, `ollama`, `vllm`, Browser, IDE oder ein anderer Modellserver viel RAM zieht: bitte temporär stoppen.\n\nFalls du llama.cpp gerade laufen hast:\n\n```bash\npkill -f llama\n```\n\nFalls Ollama läuft:\n\n```bash\nollama stop --all 2>/dev/null || true\nsudo systemctl stop ollama 2>/dev/null || true\n```\n\nDanach nochmal:\n\n```bash\nfree -h\n```\n\nFür den nächsten Test würde ich gern mindestens sehen:\n\n```text\navailable RAM: 12–16 GB+\n```\n\n---\n\n## 3. ComfyUI neu starten — diesmal lowvram\n\nBitte im Host-Terminal:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\nFalls `--lowvram` unbekannt wäre, dann ohne:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188\n```\n\n---\n\n## 4. Nach Start prüfen\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: wieder bereit.\n\n---\n\n# Wichtig: Logs vom Crash\n\nFalls du das Terminal noch offen hast, in dem ComfyUI lief: bitte die letzten 30–80 Zeilen posten.\n\nOder falls du es als Service gestartet hast:\n\n```bash\njournalctl -u comfyui -n 120 --no-pager\n```\n\nWenn manuell gestartet, einfach Terminal-Ausgabe kopieren.\n\nIch suche konkret nach:\n\n```text\nCUDA out of memory\nKilled\nRuntimeError\nImportError\nMemoryError\n```\n\n---\n\n# Meine Anpassung für den nächsten Test\n\nIch werde den nächsten Smoke-Test konservativer fahren:\n\n```text\nText Encoder eher CPU/offload\nkleinstmögliche Framezahl\nlow VRAM settings\nggf. WanVideo TextEncode device=cpu\n```\n\nDer erste Test hat immerhin gezeigt:\n\n```text\nAPI erreichbar\nNodes vorhanden\nModelle werden von ComfyUI erkannt\nJob validiert ohne node_errors\n```\n\nDas ist gut. Jetzt müssen wir nur noch verhindern, dass der Prozess beim Laden vom System erschlagen wird. Ein sehr würdeloser Tod für ein Videomodell, aber immerhin diagnostizierbar.",
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"text": "Sehr gut, Sir — **ComfyUI war von hier aus erreichbar**, inklusive GPU.\n\nIch habe erfolgreich geprüft:\n\n```text\nhttp://100.101.173.25:8188/system_stats\n```\n\nErgebnis:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: 16.49 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\nCUDA: aktiv\n```\n\nAußerdem habe ich `/object_info` abgefragt:\n\n```text\nWan Nodes gefunden: 151\nWanVideoModelLoader: vorhanden\nWanVideoTextEncode: vorhanden\nWanVideoSampler: vorhanden\nWanVideoDecode: vorhanden\nWanVideoVAELoader: vorhanden\nLoadWanVideoT5TextEncoder: vorhanden\nSaveVideo/CreateVideo: vorhanden\n```\n\nHeißt: **ComfyUI + WanVideoWrapper + Modelle sind grundsätzlich korrekt installiert.**\n\n---\n\n## Aber: mein erster Smoke-Test hat ComfyUI beendet\n\nIch habe danach einen sehr kleinen Wan-Smoke-Test über die API queued:\n\n```text\n144x256\n13 Frames\n4 Steps\nWan2.1 T2V 1.3B FP8\n```\n\nDer Job wurde angenommen:\n\n```text\nprompt_id: 75ec9893-6631-4aa3-a8bd-c3d9822d1578\nnode_errors: {}\n```\n\nNach ca. 20 Sekunden war der Server aber nicht mehr erreichbar:\n\n```text\nConnection refused\n```\n\nDas bedeutet ziemlich sicher:\n\n```text\nComfyUI-Prozess ist gecrasht oder wurde vom OS gekillt.\n```\n\nWahrscheinlichster Grund: **RAM**, nicht VRAM.\n\nDein System meldete:\n\n```text\nRAM total: 33.3 GB\nRAM free: 5.2 GB\nVRAM free: 16.5 GB\n```\n\nDer UMT5 Text Encoder und Wan-Wrapper brauchen beim ersten Laden gern ordentlich System-RAM. Wenn nebenher `llama.cpp` oder ein anderes Modell läuft, wird es eng. Die GPU ist bereit; der Arbeitsspeicher spielt gerade Diva. Natürlich.\n\n---\n\n# Jetzt bitte auf dem GPU-Host ausführen\n\n## 1. Prüfen, ob ComfyUI noch läuft\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn `Connection refused`: Prozess ist weg.\n\n---\n\n## 2. RAM prüfen\n\n```bash\nfree -h\n```\n\nDann die größten RAM-Verbraucher:\n\n```bash\nps aux --sort=-%mem | head -20\n```\n\nWenn dort `llama.cpp`, `ollama`, `vllm`, Browser, IDE oder ein anderer Modellserver viel RAM zieht: bitte temporär stoppen.\n\nFalls du llama.cpp gerade laufen hast:\n\n```bash\npkill -f llama\n```\n\nFalls Ollama läuft:\n\n```bash\nollama stop --all 2>/dev/null || true\nsudo systemctl stop ollama 2>/dev/null || true\n```\n\nDanach nochmal:\n\n```bash\nfree -h\n```\n\nFür den nächsten Test würde ich gern mindestens sehen:\n\n```text\navailable RAM: 12–16 GB+\n```\n\n---\n\n## 3. ComfyUI neu starten — diesmal lowvram\n\nBitte im Host-Terminal:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\nFalls `--lowvram` unbekannt wäre, dann ohne:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188\n```\n\n---\n\n## 4. Nach Start prüfen\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: wieder bereit.\n\n---\n\n# Wichtig: Logs vom Crash\n\nFalls du das Terminal noch offen hast, in dem ComfyUI lief: bitte die letzten 30–80 Zeilen posten.\n\nOder falls du es als Service gestartet hast:\n\n```bash\njournalctl -u comfyui -n 120 --no-pager\n```\n\nWenn manuell gestartet, einfach Terminal-Ausgabe kopieren.\n\nIch suche konkret nach:\n\n```text\nCUDA out of memory\nKilled\nRuntimeError\nImportError\nMemoryError\n```\n\n---\n\n# Meine Anpassung für den nächsten Test\n\nIch werde den nächsten Smoke-Test konservativer fahren:\n\n```text\nText Encoder eher CPU/offload\nkleinstmögliche Framezahl\nlow VRAM settings\nggf. WanVideo TextEncode device=cpu\n```\n\nDer erste Test hat immerhin gezeigt:\n\n```text\nAPI erreichbar\nNodes vorhanden\nModelle werden von ComfyUI erkannt\nJob validiert ohne node_errors\n```\n\nDas ist gut. Jetzt müssen wir nur noch verhindern, dass der Prozess beim Laden vom System erschlagen wird. Ein sehr würdeloser Tod für ein Videomodell, aber immerhin diagnostizierbar."
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"content": "[M G] probier es jetzt nochmals. ich habe Comfy UI gestartet. jetzt noch verfügbar: free -h\n gesamt benutzt frei gemns. Puffer/Cache verfügbar\nSpeicher: 31Gi 13Gi 15Gi 1.1Gi 3.7Gi 17Gi\nAuslager: 1.9Gi 1.2Gi 680Mi zudem: gam@PC-1:~$ nvidia-smi\nSat May 23 17:57:50 2026 \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 |\n+-----------------------------------------+------------------------+----------------------+\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|=========================================+========================+======================|\n| 0 NVIDIA RTX A5000 Laptop GPU Off | 00000000:01:00.0 On | N/A |\n| N/A 67C P8 21W / 80W | 255MiB / 16384MiB | 5% Default |\n| | | N/A |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=========================================================================================|\n| 0 N/A N/A 1617 G /usr/lib/xorg/Xorg 69MiB |\n| 0 N/A N/A 269216 C python 160MiB |\n+---------------"
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"content": "Ja, Sir — **zweiter Versuch erfolgreich**.\n\n## Ergebnis\n\nIch konnte ComfyUI über Tailscale erreichen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nComfyUI läuft jetzt mit:\n\n```text\n--lowvram\n```\n\nund meldet:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: ca. 16.49 GB\nRAM free: ca. 18.8 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\n```\n\nDas ist jetzt stabil genug für erste lokale Wan-Tests.\n\n---\n\n## Smoke-Test 1: erfolgreich\n\nIch habe einen winzigen Wan-Test über die API queued:\n\n```text\n128x224\n9 Frames\n2 Steps\n8 fps\nWan2.1 T2V 1.3B FP8\nText Encoder auf CPU\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 128\nheight: 224\nfps: 8\nduration: 1.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n\nVisuell: stark artefaktig, aber das war nur ein Minimaltest. Wichtig war: **Pipeline läuft durch und crasht nicht mehr.**\n\n---\n\n## Smoke-Test 2: Scene-01 Probe erfolgreich\n\nDanach habe ich einen etwas größeren Test für unsere Auto-Szene gestartet:\n\n```text\n240x416\n17 Frames\n4 Steps\n8 fps\nPrompt: cheap used car in dim garage, cinematic push-in\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 240\nheight: 416\nfps: 8\nduration: 2.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n\nVisuelle Diagnose: \nMan erkennt dunkle, horizontale Formen und rote Lichtpunkte — **eher abstrakter Auto/Garage-Vibe**, aber noch kein brauchbarer realistischer Clip. Für 240x416, 4 Steps und 1.3B FP8 ist das erwartbar. Also: technisch erfolgreich, ästhetisch noch „Noir-Fiebertraum mit Rücklichtern“. Immerhin lokal und kostenlos.\n\n---\n\n## Wichtige Erkenntnisse\n\n## 1. Der Crash war RAM-bedingt\n\nVorher hattest du nur ca. 5 GB freien RAM. Jetzt mit ca. 17 GB verfügbar läuft es.\n\n## 2. `--lowvram` hilft\n\nBitte ComfyUI für diese Tests weiter so starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\n## 3. Text Encoder auf CPU ist stabiler\n\nIch habe im Workflow gesetzt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nDas hält VRAM sauberer und scheint auf deinem Setup stabil zu laufen.\n\n## 4. Modelle und Nodes sind korrekt installiert\n\nBestätigt:\n\n```text\nWanVideoModelLoader\nLoadWanVideoT5TextEncoder\nWanVideoTextEncode\nWanVideoSampler\nWanVideoDecode\nWanVideoVAELoader\nCreateVideo\nSaveVideo\n```\n\nAlle vorhanden und ausführbar.\n\n---\n\n# Nächster sinnvoller Schritt\n\nJetzt baue ich im AutoShortsBot den echten **ComfyUI Wan Client**:\n\n```text\nAutoShortsBot Scene Spec\n→ ComfyUI Workflow JSON\n→ /prompt API\n→ Poll Queue/History\n→ Download MP4 via /view\n→ speichern als imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisch iterieren:\n\n```text\nProbe 1: 240x416, 4 steps\nProbe 2: 360x640, 6–8 steps\nProbe 3: 432x768, 8–12 steps\n```\n\nund schauen, wo auf der A5000 der Sweet Spot liegt.\n\n## Für brauchbarere Qualität würde ich als nächstes testen\n\n```text\nwidth: 360\nheight: 640\nframes: 25\nfps: 8\nsteps: 8\nduration: ca. 3.1s\n```\n\nWenn das stabil läuft, gehen wir Richtung:\n\n```text\n432x768\n33 Frames\n8–12 Steps\n```\n\nFür finale Shorts müssen wir nicht direkt 1080x1920 aus Wan erzeugen. Besser:\n\n```text\nWan Clip kleiner generieren\n→ lokal upscalen / compositen\n→ Captions & Mechanism-Layer in 1080x1920\n```\n\nDas ist bei 16 GB VRAM realistischer.\n\n---\n\n## Fazit\n\n**Ja, die lokale GPU-Video-Pipeline funktioniert.**\n\nNoch nicht hübsch, aber technisch durchgängig:\n\n```text\nHermes VM\n→ Tailscale\n→ ComfyUI Host RTX A5000\n→ Wan2.1 T2V 1.3B FP8\n→ MP4 zurück in AutoShortsBot\n```\n\nDamit ist der Browser-Free-Tier-Weg nicht mehr Hauptpfad, sondern nur noch Fallback. Sehr gut. Kein Credits-Gebettel mehr.",
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"text": "Ja, Sir — **zweiter Versuch erfolgreich**.\n\n## Ergebnis\n\nIch konnte ComfyUI über Tailscale erreichen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nComfyUI läuft jetzt mit:\n\n```text\n--lowvram\n```\n\nund meldet:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: ca. 16.49 GB\nRAM free: ca. 18.8 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\n```\n\nDas ist jetzt stabil genug für erste lokale Wan-Tests.\n\n---\n\n## Smoke-Test 1: erfolgreich\n\nIch habe einen winzigen Wan-Test über die API queued:\n\n```text\n128x224\n9 Frames\n2 Steps\n8 fps\nWan2.1 T2V 1.3B FP8\nText Encoder auf CPU\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 128\nheight: 224\nfps: 8\nduration: 1.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n\nVisuell: stark artefaktig, aber das war nur ein Minimaltest. Wichtig war: **Pipeline läuft durch und crasht nicht mehr.**\n\n---\n\n## Smoke-Test 2: Scene-01 Probe erfolgreich\n\nDanach habe ich einen etwas größeren Test für unsere Auto-Szene gestartet:\n\n```text\n240x416\n17 Frames\n4 Steps\n8 fps\nPrompt: cheap used car in dim garage, cinematic push-in\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 240\nheight: 416\nfps: 8\nduration: 2.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n\nVisuelle Diagnose: \nMan erkennt dunkle, horizontale Formen und rote Lichtpunkte — **eher abstrakter Auto/Garage-Vibe**, aber noch kein brauchbarer realistischer Clip. Für 240x416, 4 Steps und 1.3B FP8 ist das erwartbar. Also: technisch erfolgreich, ästhetisch noch „Noir-Fiebertraum mit Rücklichtern“. Immerhin lokal und kostenlos.\n\n---\n\n## Wichtige Erkenntnisse\n\n## 1. Der Crash war RAM-bedingt\n\nVorher hattest du nur ca. 5 GB freien RAM. Jetzt mit ca. 17 GB verfügbar läuft es.\n\n## 2. `--lowvram` hilft\n\nBitte ComfyUI für diese Tests weiter so starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\n## 3. Text Encoder auf CPU ist stabiler\n\nIch habe im Workflow gesetzt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nDas hält VRAM sauberer und scheint auf deinem Setup stabil zu laufen.\n\n## 4. Modelle und Nodes sind korrekt installiert\n\nBestätigt:\n\n```text\nWanVideoModelLoader\nLoadWanVideoT5TextEncoder\nWanVideoTextEncode\nWanVideoSampler\nWanVideoDecode\nWanVideoVAELoader\nCreateVideo\nSaveVideo\n```\n\nAlle vorhanden und ausführbar.\n\n---\n\n# Nächster sinnvoller Schritt\n\nJetzt baue ich im AutoShortsBot den echten **ComfyUI Wan Client**:\n\n```text\nAutoShortsBot Scene Spec\n→ ComfyUI Workflow JSON\n→ /prompt API\n→ Poll Queue/History\n→ Download MP4 via /view\n→ speichern als imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisch iterieren:\n\n```text\nProbe 1: 240x416, 4 steps\nProbe 2: 360x640, 6–8 steps\nProbe 3: 432x768, 8–12 steps\n```\n\nund schauen, wo auf der A5000 der Sweet Spot liegt.\n\n## Für brauchbarere Qualität würde ich als nächstes testen\n\n```text\nwidth: 360\nheight: 640\nframes: 25\nfps: 8\nsteps: 8\nduration: ca. 3.1s\n```\n\nWenn das stabil läuft, gehen wir Richtung:\n\n```text\n432x768\n33 Frames\n8–12 Steps\n```\n\nFür finale Shorts müssen wir nicht direkt 1080x1920 aus Wan erzeugen. Besser:\n\n```text\nWan Clip kleiner generieren\n→ lokal upscalen / compositen\n→ Captions & Mechanism-Layer in 1080x1920\n```\n\nDas ist bei 16 GB VRAM realistischer.\n\n---\n\n## Fazit\n\n**Ja, die lokale GPU-Video-Pipeline funktioniert.**\n\nNoch nicht hübsch, aber technisch durchgängig:\n\n```text\nHermes VM\n→ Tailscale\n→ ComfyUI Host RTX A5000\n→ Wan2.1 T2V 1.3B FP8\n→ MP4 zurück in AutoShortsBot\n```\n\nDamit ist der Browser-Free-Tier-Weg nicht mehr Hauptpfad, sondern nur noch Fallback. Sehr gut. Kein Credits-Gebettel mehr."
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"content": "[M G] auf den videos ist nichts zu erkennen, nur blinkende farben. hier noch ein paar infos von der KI; bitte berücksichtigen. nenne mir den neuen Startbefehl bzw. die Anpassungen die wir machen müssen um effizient lokal videos zu generieren. es scheint so als würde die GPU nicht genutzt (Nvidia-SMI zeigt gerade mal 1gb benutzt an). hier die infos: 23.05.2026, 18:02 Schweizer Zeit\n\nJa — Wan 2.1 läuft bei dir, aber dein Log zeigt: nicht optimal effizient eingestellt.\n\nDer entscheidende Punkt:\n\nT5Encoder: 01:05\nT5Encoder: 00:46\nSampling: 2 steps in ~1s\nPrompt executed in 120.71 seconds\n\nDas heisst: Das eigentliche Video-Sampling ist schnell, aber der T5-Textencoder frisst fast die ganze Zeit. Wahrscheinlich läuft der Textencoder wegen --lowvram oder Workflow-Device-Settings auf CPU/offload. Für echte Effizienz solltest du zuerst versuchen, ohne `--lowvram` zu starten.\n\n## Was ist ComfyUI?\n\nComfyUI ist kein llama.cpp für Videos. Llama.cpp ist eher eine schlanke Runtime für LLMs/GGUF-Modelle. ComfyUI ist eher ein visueller Node-Workflow-Editor + Server für Bild-, Video- und Diffusion-Pipelines: Modelle laden, Text encoden, Sampler, VAE, Upscaler, Video-Combine usw. als verschaltbare Nodes. ComfyUI unterstützt Wan inzwischen auch nativ; Custom-Nodes wie Kijai WanVideoWrapper sind eher für Zusatzfeatures/Experimente sinnvoll. Der Wrapper-Autor schreibt selbst, dass man native Wan-Nodes nutzen sollte, ausser man braucht ein Feature, das nativ noch fehlt. ([GitHub][1])\n\n## Dein Startbefehl: besser so\n\nFür deinen Fall würde ich zuerst diesen Start testen:\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n\n--listen bindet ComfyUI an eine IP; Standard wäre nur 127.0.0.1, und der Web-Port ist typischerweise 8188. Dein Tailscale-Ansatz ist daher sinnvoll, solange du ComfyUI nicht öffentlich ins Internet hängst. ([ComfyUI Dokumentation][2])\n\n## Wichtig: --lowvram nur bei Out-of-Memory\n\nComfyUIs aktuelle CLI-Beschreibung sagt: --lowvram bringt bei aktiviertem Dynamic VRAM nichts; wenn Dynamic VRAM nicht aktiv ist, lässt es Textencoder auf CPU laufen. Genau das passt zu deinem Log: T5-Encoding dauert ewig, Sampling ist schnell. ([GitHub][3])\n\nDarum:\n\n# Normal / empfohlen\npython main.py --listen \"$TS_IP\" --port 8188 --reserve-vram 1.0\n\n# Nur testen, wenn du genug VRAM hast und wiederholt gleiche Modelle nutzt\npython main.py --listen \"$TS_IP\" --port 8188 --highvram --reserve-vram 1.0\n\n# Nur Notfall bei OOM\npython main.py --listen \"$TS_IP\" --port 8188 --lowvram\n\n--highvram hält Modelle eher im GPU-Speicher, was bei wiederholten Runs schneller sein kann, aber natürlich OOM riskieren kann. ([GitHub][3])\n\n## Welche Modellgrösse?\n\nFür dich aktuell:\n\n| Ziel | Empfehlung |\n| ------------------------------------------------------------------- | ------------------------------- |\n| Schnell testen, lokal stabil, wenig VRAM | Wan 2.1 T2V 1.3B |\n| Bessere finale Qualität, aber langsam/schwer | Wan 2.1 14B fp8/quantisiert |\n| Bestes Verhältnis 2026, falls du nicht strikt bei 2.1 bleiben musst | Wan 2.2 5B TI2V prüfen |\n\nWan 2.1 1.3B ist offiziell für Consumer-GPUs gedacht; die Wan-Entwickler nennen ca. 8.19 GB VRAM und etwa 4 Minuten für 5 Sekunden 480p auf einer RTX 4090 ohne zusätzliche Optimierungen. ([GitHub][4]) Die 14B-Variante ist sichtbar schwerer: ComfyUIs Wan2.1-Doku beschreibt 1.3B als lokale Lightweight-Variante und 14B als High-Performance-Modell mit 32GB+ Modellgrösse und höheren VRAM-Anforderungen. ([ComfyUI Dokumentation][5])\n\nMeine praktische Empfehlung: 1.3B für Iteration, 14B nur für finale Shots. Für Social-Media-Produktion zählt Durchsatz. Du willst viele Prompts, Shots und Varianten testen können.\n\n## Deine aktuellen\nTestwerte sind zu klein für echte Beurteilung\n\nDu generierst gerade:\n\n9 frames at 128x224\n2 steps\n\nDas ist nur ein Smoke-Test. Für echte Videos:\n\nPreview:\n480x832 oder 512x896\n33–49 Frames\n8–12 Steps mit Distill/LightX2V/CausVid\n20–30 Steps ohne Distill\n\nFinal:\n480x832 oder 720x1280\n49–81 Frames\n14B/fp8 nur für beste Clips\n\n2 Steps ohne spezielles Distill-Modell sind qualitativ praktisch zu wenig.\n\n## Workflow-Settings, die ich ändern würde\n\nIn ComfyUI selbst:\n\n1. Textencoder prüfen\n Stelle im Wan/T5/CLIP-Loader sicher, dass du umt5_xxl_fp8_e4m3fn_scaled.safetensors nutzt, nicht fp16, wenn VRAM knapp ist. ComfyUIs Wan-Doku nutzt genau diesen fp8 Textencoder in den Wan2.1/Wan2.2 Workflows. ([ComfyUI Dokumentation][5])\n\n2. T5 nicht auf CPU, wenn VRAM reicht\n Dein Bottleneck ist T5. Also Textencoder möglichst GPU/fp8 statt CPU/offload.\n\n3. VAE tiled nur bei OOM\n Tiled VAE spart Speicher, kann aber bremsen. Wenn keine OOMs kommen, nicht unnötig aggressiv tilen.\n\n4. Nicht jedes Mal Modell wechseln\n Gleiche Auflösung, gleiche Framezahl, gleiche Modelle halten. Torch/Comfy/Caches profitieren davon.\n\n5. Native Wan zuerst\n Für normale Wan2.1/2.2 Workflows zuerst native ComfyUI-Templates nutzen. Kijai WanVideoWrapper nur, wenn du TeaCache, Spezial-LoRAs, GGUF/fp8-Varianten oder experimentelle Features brauchst. ([GitHub][1])\n\n## Sofort-Test\n\nMach genau diesen Vergleich:\n\n# 1. Start ohne lowvram\npython main.py --listen \"$TS_IP\" --port 8188 --reserve-vram 1.0\n\nDann denselben Prompt nochmal laufen lassen. Wenn der T5Encoder nicht mehr 60+ Sekunden braucht, war `--lowvram`/CPU-Offload das Problem.\n\nZusätzlich:\n\nnvidia-smi\n\nWenn du mir die Ausgabe von nvidia-smi schickst, kann ich dir die passende Einstellung für deine echte VRAM-Grösse sagen. Compute Capability 8.6 sagt nur grob Ampere/CUDA-fähig, aber nicht, ob du 8, 10, 12, 16 oder 24 GB VRAM hast.\n\n[1]: https://github.com/kijai/ComfyUI-WanVideoWrapper \"GitHub - kijai/ComfyUI-WanVideoWrapper · GitHub\"\n[2]: https://docs.comfy.org/interface/settings/server-config \"Server Config - ComfyUI\"\n[3]: https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/cli_args.py \"ComfyUI/comfy/cli_args.py at master · Comfy-Org/ComfyUI · GitHub\"\n[4]: https://github.com/Wan-Video/Wan2.1 \"GitHub - Wan-Video/Wan2.1: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub\"\n[5]: https://docs.comfy.org/tutorials/video/wan/fun-control \"ComfyUI Wan2.1 Fun Control Video Examples - ComfyUI\""
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"content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed. For WanVideoWrapper,\\n `/object_info` is especially useful: inspect loader dropdowns to verify\\n files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n If it warns that it is deprecated, use `hf download --local-dir `\\n instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n12. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a micro smoke test. System RAM can be the first bottleneck even when VRAM is free; ask the user to free RAM before the first load. See `references/wan-video-tailscale-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. 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"content": "Ja, die KI-Analyse ist korrekt, Sir. **Unser aktuelles Setup ist stabil, aber absichtlich zu defensiv** — dadurch wird die GPU kaum genutzt.\n\nIch habe gerade von hier geprüft: ComfyUI läuft noch mit:\n\n```text\nargv: main.py --listen 100.101.173.25 --port 8188 --lowvram\nVRAM free: ca. 15.25 GB\nRAM free: ca. 13.13 GB\n```\n\nDas erklärt exakt dein Problem:\n\n```text\n--lowvram\n+ TextEncode device=cpu\n+ load_device=offload_device\n+ 2–4 Steps\n+ winzige Auflösung\n= kaum GPU-Auslastung, lange T5-Zeit, blinkender Pixelbrei\n```\n\nWir haben damit bewiesen: Pipeline läuft. \nJetzt schalten wir von **Überlebensmodus** auf **Produktionsmodus**. Endlich darf die GPU arbeiten. Schockierend moderne Idee.\n\n---\n\n# Neuer Startbefehl\n\nBitte ComfyUI stoppen:\n\n```bash\nCTRL+C\n```\n\nFalls es im Hintergrund hängt:\n\n```bash\npkill -f \"python main.py\"\n```\n\nDann neu starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nDas ist ab jetzt unser **Standard-Startbefehl**.\n\nNicht mehr:\n\n```bash\n--lowvram\n```\n\naußer bei OOM/Crash.\n\n---\n\n# Wenn das stabil läuft: High-VRAM-Test\n\nErst nach einem erfolgreichen normalen Test:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --highvram \\\n --reserve-vram 1.0\n```\n\n`--highvram` kann bei wiederholten Runs schneller sein, weil Modelle eher im VRAM bleiben. Aber: höheres OOM-Risiko.\n\nFür jetzt bitte zuerst:\n\n```bash\n--reserve-vram 1.0\n```\n\nohne `--highvram`.\n\n---\n\n# Workflow-Anpassungen\n\nIch ändere in den nächsten API-Workflows diese Punkte:\n\n## 1. T5/Textencoder auf GPU\n\nAlt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\n```\n\nAlt:\n\n```text\nLoadWanVideoT5TextEncoder load_device = offload_device\n```\n\nNeu:\n\n```text\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nGrund: T5 war dein Bottleneck. Nicht Sampling.\n\n---\n\n## 2. Model Loader weniger offloaden\n\nAlt:\n\n```text\nWanVideoModelLoader load_device = offload_device\n```\n\nNeu für 1.3B FP8:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nBei 1.3B FP8 sollte das auf 16 GB VRAM gehen.\n\nWenn OOM:\n\n```text\nzurück auf offload_device\n```\n\n---\n\n## 3. VAE Tiling nicht aggressiv erzwingen\n\nAlt:\n\n```text\nenable_vae_tiling = true\ntile_x = 128\ntile_y = 128\n```\n\nNeu für Preview:\n\n```text\nenable_vae_tiling = false\n```\n\nWenn OOM beim Decode:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nTiling spart Speicher, kann aber bremsen und Artefakte fördern.\n\n---\n\n## 4. Realistische Testparameter statt Smoke-Test-Müll\n\nBisher:\n\n```text\n128x224 / 240x416\n9–17 Frames\n2–4 Steps\n```\n\nDas war nur Funktionstest. Für visuelle Beurteilung nutzlos.\n\nNeuer Preview-Test:\n\n```text\nwidth: 480\nheight: 832\nframes: 33\nfps: 12\nsteps: 10\ncfg: 3.5\nscheduler: unipc\n```\n\nDauer:\n\n```text\n33 / 12 = 2.75 Sekunden\n```\n\nDas ist erstmals halbwegs beurteilbar.\n\nFalls OOM oder zu langsam:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\n---\n\n# Bitte jetzt ausführen\n\n## 1. ComfyUI stoppen\n\n```bash\npkill -f \"python main.py\"\n```\n\n## 2. Neu starten\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\n## 3. Healthcheck\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n## 4. GPU-Monitor parallel laufen lassen\n\nWährend ich dann den nächsten Job starte:\n\n```bash\nwatch -n 1 nvidia-smi\n```\n\nDu solltest beim T5-Encoding/Sampling deutlich mehr als 1 GB sehen, wenn GPU-Encoding greift.\n\n---\n\n# Falls der Start sofort crasht oder OOM kommt\n\nDann nehmen wir Zwischenmodus:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 2.0\n```\n\nFalls immer noch Problem:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 3.0\n```\n\nErst danach zurück zu:\n\n```bash\n--lowvram\n```\n\n`--lowvram` ist jetzt nur noch Notfallmodus, nicht Produktionsmodus.\n\n---\n\n# Warum `nvidia-smi` vorher kaum GPU zeigte\n\nDrei Gründe:\n\n1. **T5 lief auf CPU/offload** \n Deshalb 45–65 Sekunden Textencoder, aber kaum GPU.\n\n2. **Sampling war winzig und extrem kurz** \n 2–4 Steps bei Mini-Auflösung sind in Sekunden vorbei. `nvidia-smi` kann den Spike leicht verpassen.\n\n3. **Das Modell wurde absichtlich offloaded** \n Wir hatten alles auf Stabilität getrimmt, nicht auf Speed/Qualität.\n\n---\n\n# Native Wan vs Kijai Wrapper\n\nDie Info ist auch richtig: Für Standard-Wan sollten wir langfristig **native ComfyUI Wan-Workflows** testen.\n\nAber kurzfristig:\n\n```text\nKijai Wrapper läuft bereits\nModelle werden erkannt\nAPI-Workflow funktioniert\n```\n\nDeshalb machen wir jetzt zuerst:\n\n```text\nWrapper optimieren → brauchbaren Clip erzeugen\n```\n\nDanach:\n\n```text\nnative Wan Template vergleichen\n```\n\nNicht beides gleichzeitig. Sonst debuggen wir Workflow-Religion statt Videos. Und davon hat die Welt wahrlich genug.\n\n---\n\n# Sobald du neu gestartet hast\n\nSchreib einfach:\n\n```text\nläuft ohne lowvram\n```\n\nDann starte ich von hier den nächsten richtigen Test:\n\n```text\n480x832\n33 Frames\n10 Steps\nT5 GPU\nModel main_device\nVAE tiling off\n```\n\nWenn das durchläuft, haben wir den ersten echten lokalen Video-Kandidaten.",
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"text": "Ja, die KI-Analyse ist korrekt, Sir. **Unser aktuelles Setup ist stabil, aber absichtlich zu defensiv** — dadurch wird die GPU kaum genutzt.\n\nIch habe gerade von hier geprüft: ComfyUI läuft noch mit:\n\n```text\nargv: main.py --listen 100.101.173.25 --port 8188 --lowvram\nVRAM free: ca. 15.25 GB\nRAM free: ca. 13.13 GB\n```\n\nDas erklärt exakt dein Problem:\n\n```text\n--lowvram\n+ TextEncode device=cpu\n+ load_device=offload_device\n+ 2–4 Steps\n+ winzige Auflösung\n= kaum GPU-Auslastung, lange T5-Zeit, blinkender Pixelbrei\n```\n\nWir haben damit bewiesen: Pipeline läuft. \nJetzt schalten wir von **Überlebensmodus** auf **Produktionsmodus**. Endlich darf die GPU arbeiten. Schockierend moderne Idee.\n\n---\n\n# Neuer Startbefehl\n\nBitte ComfyUI stoppen:\n\n```bash\nCTRL+C\n```\n\nFalls es im Hintergrund hängt:\n\n```bash\npkill -f \"python main.py\"\n```\n\nDann neu starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nDas ist ab jetzt unser **Standard-Startbefehl**.\n\nNicht mehr:\n\n```bash\n--lowvram\n```\n\naußer bei OOM/Crash.\n\n---\n\n# Wenn das stabil läuft: High-VRAM-Test\n\nErst nach einem erfolgreichen normalen Test:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --highvram \\\n --reserve-vram 1.0\n```\n\n`--highvram` kann bei wiederholten Runs schneller sein, weil Modelle eher im VRAM bleiben. Aber: höheres OOM-Risiko.\n\nFür jetzt bitte zuerst:\n\n```bash\n--reserve-vram 1.0\n```\n\nohne `--highvram`.\n\n---\n\n# Workflow-Anpassungen\n\nIch ändere in den nächsten API-Workflows diese Punkte:\n\n## 1. T5/Textencoder auf GPU\n\nAlt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\n```\n\nAlt:\n\n```text\nLoadWanVideoT5TextEncoder load_device = offload_device\n```\n\nNeu:\n\n```text\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nGrund: T5 war dein Bottleneck. Nicht Sampling.\n\n---\n\n## 2. Model Loader weniger offloaden\n\nAlt:\n\n```text\nWanVideoModelLoader load_device = offload_device\n```\n\nNeu für 1.3B FP8:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nBei 1.3B FP8 sollte das auf 16 GB VRAM gehen.\n\nWenn OOM:\n\n```text\nzurück auf offload_device\n```\n\n---\n\n## 3. VAE Tiling nicht aggressiv erzwingen\n\nAlt:\n\n```text\nenable_vae_tiling = true\ntile_x = 128\ntile_y = 128\n```\n\nNeu für Preview:\n\n```text\nenable_vae_tiling = false\n```\n\nWenn OOM beim Decode:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nTiling spart Speicher, kann aber bremsen und Artefakte fördern.\n\n---\n\n## 4. Realistische Testparameter statt Smoke-Test-Müll\n\nBisher:\n\n```text\n128x224 / 240x416\n9–17 Frames\n2–4 Steps\n```\n\nDas war nur Funktionstest. Für visuelle Beurteilung nutzlos.\n\nNeuer Preview-Test:\n\n```text\nwidth: 480\nheight: 832\nframes: 33\nfps: 12\nsteps: 10\ncfg: 3.5\nscheduler: unipc\n```\n\nDauer:\n\n```text\n33 / 12 = 2.75 Sekunden\n```\n\nDas ist erstmals halbwegs beurteilbar.\n\nFalls OOM oder zu langsam:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\n---\n\n# Bitte jetzt ausführen\n\n## 1. ComfyUI stoppen\n\n```bash\npkill -f \"python main.py\"\n```\n\n## 2. Neu starten\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\n## 3. Healthcheck\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n## 4. GPU-Monitor parallel laufen lassen\n\nWährend ich dann den nächsten Job starte:\n\n```bash\nwatch -n 1 nvidia-smi\n```\n\nDu solltest beim T5-Encoding/Sampling deutlich mehr als 1 GB sehen, wenn GPU-Encoding greift.\n\n---\n\n# Falls der Start sofort crasht oder OOM kommt\n\nDann nehmen wir Zwischenmodus:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 2.0\n```\n\nFalls immer noch Problem:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 3.0\n```\n\nErst danach zurück zu:\n\n```bash\n--lowvram\n```\n\n`--lowvram` ist jetzt nur noch Notfallmodus, nicht Produktionsmodus.\n\n---\n\n# Warum `nvidia-smi` vorher kaum GPU zeigte\n\nDrei Gründe:\n\n1. **T5 lief auf CPU/offload** \n Deshalb 45–65 Sekunden Textencoder, aber kaum GPU.\n\n2. **Sampling war winzig und extrem kurz** \n 2–4 Steps bei Mini-Auflösung sind in Sekunden vorbei. `nvidia-smi` kann den Spike leicht verpassen.\n\n3. **Das Modell wurde absichtlich offloaded** \n Wir hatten alles auf Stabilität getrimmt, nicht auf Speed/Qualität.\n\n---\n\n# Native Wan vs Kijai Wrapper\n\nDie Info ist auch richtig: Für Standard-Wan sollten wir langfristig **native ComfyUI Wan-Workflows** testen.\n\nAber kurzfristig:\n\n```text\nKijai Wrapper läuft bereits\nModelle werden erkannt\nAPI-Workflow funktioniert\n```\n\nDeshalb machen wir jetzt zuerst:\n\n```text\nWrapper optimieren → brauchbaren Clip erzeugen\n```\n\nDanach:\n\n```text\nnative Wan Template vergleichen\n```\n\nNicht beides gleichzeitig. Sonst debuggen wir Workflow-Religion statt Videos. Und davon hat die Welt wahrlich genug.\n\n---\n\n# Sobald du neu gestartet hast\n\nSchreib einfach:\n\n```text\nläuft ohne lowvram\n```\n\nDann starte ich von hier den nächsten richtigen Test:\n\n```text\n480x832\n33 Frames\n10 Steps\nT5 GPU\nModel main_device\nVAE tiling off\n```\n\nWenn das durchläuft, haben wir den ersten echten lokalen Video-Kandidaten."
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"text": "**Considering output setup**\n\nI’m thinking about running the next output with a resolution of 480x832, 33 frames, and 10 steps since it worked quickly before. The user might've asked for a new start command, but we’ve already run it. I could aim for higher quality or test the same settings again, but manage the possibility of running out of memory. I should also verify that it remains recognizable, so I’ll proceed with caution. Let's go ahead and run it."
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"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beurteile knapp den Frame: Ist ein Auto/Garagenmotiv klar erkennbar? Ist der Clip als B-Roll/Hook-Grundlage brauchbar oder noch zu artefaktisch?\n[screenshot]",
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"content": "Perfekt, Sir. Ich habe es direkt getestet: **ohne `--lowvram` funktioniert es deutlich besser.**\n\n## Ergebnis\n\nComfyUI läuft jetzt korrekt mit:\n\n```text\nmain.py --listen 100.101.173.25 --port 8188 --reserve-vram 1.0\n```\n\nIch habe von hier aus zwei neue GPU-Workflows gestartet.\n\n---\n\n## Test 1 — 416×736, 25 Frames, 8 Steps\n\nSettings:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 47 Sekunden\n```\n\nGPU-Auslastung laut ComfyUI-Systemstats:\n\n```text\nVRAM used: ca. 8–10.5 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n\nVisuell: **endlich erkennbar** — rotes Auto in dunkler Garage. Noch nicht final, aber kein blinkender Pixelbrei mehr.\n\n---\n\n## Test 2 — 480×832, 33 Frames, 10 Steps\n\nSettings:\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\nDauer: 2.75 Sekunden\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 160 Sekunden\n```\n\nGPU-Auslastung:\n\n```text\nVRAM used: ca. 8.2–10.3 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n\nVisuelle Diagnose: \n**Auto/Garage ist klar erkennbar.** Dunkel, filmisch, rotes Auto, Close-up. Als Hook-B-Roll-Grundlage brauchbar. Noch nicht „finaler YouTube-Shorts-Killer“, aber jetzt sind wir in echtem Video-Territorium statt „blinkender GPU-Schamanismus“.\n\n---\n\n# Neuer Standard-Startbefehl\n\nBitte ab jetzt für lokale Video-Generierung:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\n## Nicht mehr standardmäßig verwenden\n\n```bash\n--lowvram\n```\n\n`--lowvram` nur noch bei Crash/OOM.\n\n---\n\n# Wenn du noch mehr Speed bei wiederholten Runs willst\n\nSpäter testen wir:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --highvram \\\n --reserve-vram 1.0\n```\n\nAber erst später. Aktuell ist der normale Modus stabil und nutzt die GPU.\n\n---\n\n# Workflow-Anpassungen, die jetzt gelten\n\n## 1. Textencoder auf GPU\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nNicht mehr:\n\n```text\ndevice = cpu\nload_device = offload_device\n```\n\nDas war der Grund, warum T5 ewig dauerte.\n\n---\n\n## 2. Wan Model auf GPU/main_device\n\nNeu:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nNicht mehr:\n\n```text\noffload_device\n```\n\nFür `Wan2.1 T2V 1.3B FP8` passt das auf deiner A5000.\n\n---\n\n## 3. VAE Tiling aus\n\nNeu:\n\n```text\nenable_vae_tiling = false\n```\n\nNur bei OOM:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\n---\n\n# Empfohlene Produktionsprofile\n\n## Fast Preview / Prompt-Iteration\n\nDas ist aktuell der Sweet Spot:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\n```\n\nDauer:\n\n```text\n~45–60 Sekunden\n```\n\nNutzen:\n\n```text\nschnell genug für Varianten\ngut genug, um Motiv/Komposition zu beurteilen\n```\n\n## Better Preview / Kandidat\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\n```\n\nDauer:\n\n```text\n~2.5–3 Minuten\n```\n\nNutzen:\n\n```text\nbrauchbare B-Roll-Basis\nbesserer Bildaufbau\nmehr Bewegung\n```\n\n## Noch nicht sofort\n\n```text\n720x1280\n49–81 Frames\n20+ Steps\n14B Modell\n```\n\nDas wäre jetzt unnötig schwer. Erst Prompt-/Shot-Struktur sauber bekommen.\n\n---\n\n# Warum `nvidia-smi` vorher nur ~1 GB zeigte\n\nWeil wir im Notfallmodus waren:\n\n```text\n--lowvram\nTextencoder auf CPU\nModel offload_device\nwinzige Auflösung\n2–4 Steps\n```\n\nJetzt sah ich während der Jobs:\n\n```text\n8–10.5 GB VRAM used\n```\n\nAlso: GPU wird jetzt genutzt.\n\n---\n\n# Nächster Schritt\n\nIch würde jetzt im Repo den **ComfyUI Wan Client** als feste Pipeline-Komponente bauen:\n\n```text\nScene Spec\n→ Workflow JSON\n→ ComfyUI /prompt\n→ Poll /queue + /history\n→ Download /view\n→ speichern als:\ndata/imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisiert Varianten erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nund die beste Variante in den Hybrid-Renderer ziehen.\n\nKurz: **Startbefehl ist jetzt `--reserve-vram 1.0`, nicht `--lowvram`. GPU-Pipeline funktioniert. Erste brauchbare Auto-B-Roll ist erzeugt.**",
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"text": "Perfekt, Sir. Ich habe es direkt getestet: **ohne `--lowvram` funktioniert es deutlich besser.**\n\n## Ergebnis\n\nComfyUI läuft jetzt korrekt mit:\n\n```text\nmain.py --listen 100.101.173.25 --port 8188 --reserve-vram 1.0\n```\n\nIch habe von hier aus zwei neue GPU-Workflows gestartet.\n\n---\n\n## Test 1 — 416×736, 25 Frames, 8 Steps\n\nSettings:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 47 Sekunden\n```\n\nGPU-Auslastung laut ComfyUI-Systemstats:\n\n```text\nVRAM used: ca. 8–10.5 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n\nVisuell: **endlich erkennbar** — rotes Auto in dunkler Garage. Noch nicht final, aber kein blinkender Pixelbrei mehr.\n\n---\n\n## Test 2 — 480×832, 33 Frames, 10 Steps\n\nSettings:\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\nDauer: 2.75 Sekunden\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 160 Sekunden\n```\n\nGPU-Auslastung:\n\n```text\nVRAM used: ca. 8.2–10.3 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n\nVisuelle Diagnose: \n**Auto/Garage ist klar erkennbar.** Dunkel, filmisch, rotes Auto, Close-up. Als Hook-B-Roll-Grundlage brauchbar. Noch nicht „finaler YouTube-Shorts-Killer“, aber jetzt sind wir in echtem Video-Territorium statt „blinkender GPU-Schamanismus“.\n\n---\n\n# Neuer Standard-Startbefehl\n\nBitte ab jetzt für lokale Video-Generierung:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\n## Nicht mehr standardmäßig verwenden\n\n```bash\n--lowvram\n```\n\n`--lowvram` nur noch bei Crash/OOM.\n\n---\n\n# Wenn du noch mehr Speed bei wiederholten Runs willst\n\nSpäter testen wir:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --highvram \\\n --reserve-vram 1.0\n```\n\nAber erst später. Aktuell ist der normale Modus stabil und nutzt die GPU.\n\n---\n\n# Workflow-Anpassungen, die jetzt gelten\n\n## 1. Textencoder auf GPU\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nNicht mehr:\n\n```text\ndevice = cpu\nload_device = offload_device\n```\n\nDas war der Grund, warum T5 ewig dauerte.\n\n---\n\n## 2. Wan Model auf GPU/main_device\n\nNeu:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nNicht mehr:\n\n```text\noffload_device\n```\n\nFür `Wan2.1 T2V 1.3B FP8` passt das auf deiner A5000.\n\n---\n\n## 3. VAE Tiling aus\n\nNeu:\n\n```text\nenable_vae_tiling = false\n```\n\nNur bei OOM:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\n---\n\n# Empfohlene Produktionsprofile\n\n## Fast Preview / Prompt-Iteration\n\nDas ist aktuell der Sweet Spot:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\n```\n\nDauer:\n\n```text\n~45–60 Sekunden\n```\n\nNutzen:\n\n```text\nschnell genug für Varianten\ngut genug, um Motiv/Komposition zu beurteilen\n```\n\n## Better Preview / Kandidat\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\n```\n\nDauer:\n\n```text\n~2.5–3 Minuten\n```\n\nNutzen:\n\n```text\nbrauchbare B-Roll-Basis\nbesserer Bildaufbau\nmehr Bewegung\n```\n\n## Noch nicht sofort\n\n```text\n720x1280\n49–81 Frames\n20+ Steps\n14B Modell\n```\n\nDas wäre jetzt unnötig schwer. Erst Prompt-/Shot-Struktur sauber bekommen.\n\n---\n\n# Warum `nvidia-smi` vorher nur ~1 GB zeigte\n\nWeil wir im Notfallmodus waren:\n\n```text\n--lowvram\nTextencoder auf CPU\nModel offload_device\nwinzige Auflösung\n2–4 Steps\n```\n\nJetzt sah ich während der Jobs:\n\n```text\n8–10.5 GB VRAM used\n```\n\nAlso: GPU wird jetzt genutzt.\n\n---\n\n# Nächster Schritt\n\nIch würde jetzt im Repo den **ComfyUI Wan Client** als feste Pipeline-Komponente bauen:\n\n```text\nScene Spec\n→ Workflow JSON\n→ ComfyUI /prompt\n→ Poll /queue + /history\n→ Download /view\n→ speichern als:\ndata/imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisiert Varianten erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nund die beste Variante in den Hybrid-Renderer ziehen.\n\nKurz: **Startbefehl ist jetzt `--reserve-vram 1.0`, nicht `--lowvram`. GPU-Pipeline funktioniert. Erste brauchbare Auto-B-Roll ist erzeugt.**"
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"content": "[M G] sollen wir Wan 2.2 TI2V 5B testen für bessere qualität? Wan 2.2 wurde mit deutlich mehr Daten trainiert: laut Modellkarte mit +65.6 % mehr Bildern und +83.2 % mehr Videos gegenüber Wan 2.1. Das soll besonders Bewegung, Semantik und Ästhetik verbessern. Ausserdem unterstützt das Wan2.2-TI2V-5B Text-to-Video und Image-to-Video in einem Modell und ist für 720p bei 24 FPS ausgelegt."
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"content": "{\"output\": \"## Kijai/WanVideo_comfy\\nFastWan/Wan2_2-TI2V-5B-FastWanFullAttn_bf16.safetensors 9.31 GB\\nFastWan/Wan2_2_5B_FastWanFullAttn_lora_rank_128_bf16.safetensors 0.62 GB\\nFlowRVS/wan21_flow_rvs_mask_vae_bf16.safetensors 0.24 GB\\nFun/VACE/Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors 5.68 GB\\nFun/VACE/Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors 5.68 GB\\nLoRAs/AniSora/Wan2_2_I2V_AniSora_3_2_HIGH_rank_64_fp16.safetensors 0.59 GB\\nLoRAs/Stable-Video-Infinity/v2.0/SVI_Wan2.2-I2V-A14B_lora_HIGH_v2.0_rank_128_fp16.safetensors 1.14 GB\\nLoRAs/Stable-Video-Infinity/v2.0/SVI_Wan2.2-I2V-A14B_lora_LOW_v2.0_rank_128_fp16.safetensors 1.14 GB\\nLoRAs/Stable-Video-Infinity/v2.0/SVI_v2_PRO_Wan2.2-I2V-A14B_HIGH_lora_rank_128_fp16.safetensors 1.14 GB\\nLoRAs/Stable-Video-Infinity/v2.0/SVI_v2_PRO_Wan2.2-I2V-A14B_LOW_lora_rank_128_fp16.safetensors 1.14 GB\\nLoRAs/Stand-In/Stand-In_wan2.2_T2V_A14B_HIGH_fp16.safetensors 0.29 GB\\nLoRAs/Stand-In/Stand-In_wan2.2_T2V_A14B_LOW_fp16.safetensors 0.29 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_I2V-A14B-4steps-lora_LOW_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_T2V-A14B-4steps-lora_HIGH_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_T2V-A14B-4steps-lora_LOW_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_T2V-v1.1-A14B-4steps-lora_HIGH_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Lightning/old/Wan2.2-Lightning_T2V-v1.1-A14B-4steps-lora_LOW_fp16.safetensors 0.57 GB\\nLoRAs/Wan22-Turbo/Wan22_TI2V_5B_Turbo_lora_rank_64_fp16.safetensors 0.31 GB\\nLoRAs/Wan22-Turbo/Wan22_TI2V_5B_Turbo_lora_rank_adaptive_quantile_0.15_fp16.safetensors 0.18 GB\\nLoRAs/Wan22_FunReward/Wan2.2-Fun-A14B-InP-HIGH-HPS2.1_resized_dynamic_avg_rank_14_bf16.safetensors 0.09 GB\\nLoRAs/Wan22_FunReward/Wan2.2-Fun-A14B-InP-HIGH-MPS_resized_dynamic_avg_rank_21_bf16.safetensors 0.13 GB\\nLoRAs/Wan22_FunReward/Wan2.2-Fun-A14B-InP-LOW-HPS2.1_resized_dynamic_avg_rank_15_bf16.safetensors 0.09 GB\\nLoRAs/Wan22_FunReward/Wan2.2-Fun-A14B-InP-LOW-MPS_resized_dynamic_avg_rank_22_bf16.safetensors 0.14 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_HIGH_lightx2v_4step_lora_260412_rank_256_fp16.safetensors 2.33 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_HIGH_lightx2v_4step_lora_260412_rank_64_fp16.safetensors 0.59 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_HIGH_lightx2v_4step_lora_v1030_rank_64_bf16.safetensors 0.59 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_HIGH_lightx2v_MoE_distill_lora_rank_64_bf16.safetensors 0.59 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_LOW_lightx2v_4step_lora_260412_rank_256_fp16.safetensors 2.33 GB\\nLoRAs/Wan22_Lightx2v/Wan_2_2_I2V_A14B_LOW_lightx2v_4step_lora_260412_rank_64_fp16.safetensors 0.59 GB\\nMTVCrafter/WanVideo_MTV_Crafter_4DMoT_VQVAE_fp32.safetensors 0.27 GB\\nOvi/Wan_2_2_Ovi_audio_model_bf16.safetensors 11.35 GB\\nOvi/Wan_2_2_Ovi_video_model_bf16.safetensors 10.37 GB\\nOvi/mmaudio_vae_16k_bf16.safetensors 0.32 GB\\nOvi/mmaudio_vae_16k_fp32.safetensors 0.64 GB\\nWan22-Turbo/Wan2_2-TI2V-5B-Turbo_fp16.safetensors 9.49 GB\\nWan2_1_VAE_bf16.safetensors 0.24 GB\\nWan2_1_VAE_fp32.safetensors 0.47 GB\\nWan2_2-I2V-A14B-HIGH_bf16.safetensors 26.62 GB\\nWan2_2-I2V-A14B-LOW_bf16.safetensors 26.62 GB\\nWan2_2_VAE_bf16.safetensors 1.31 GB\\ntaew2_2.safetensors 0.02 GB\\numt5-xxl-enc-bf16.safetensors 10.58 GB\\numt5-xxl-enc-fp8_e4m3fn.safetensors 6.27 GB\\n\\n## Kijai/WanVideo_comfy_fp8_scaled\\nTI2V 0.0 GB\\nTI2V/Ovi 0.0 GB\\nFun/Wan2_2-Fun-Control-A14B-HIGH_fp8_e4m3fn_scaled_KJ_fixed.safetensors 13.53 GB\\nFun/Wan2_2-Fun-Control-A14B-HIGH_fp8_e5m2_scaled_KJ_fixed.safetensors 13.53 GB\\nFun/Wan2_2-Fun-Control-A14B-LOW_fp8_e4m3fn_scaled_KJ_fixed.safetensors 13.53 GB\\nFun/Wan2_2-Fun-Control-A14B-LOW_fp8_e5m2_scaled_KJ_fixed.safetensors 13.53 GB\\nFun/Wan2_2-Fun-Control-Camera-A14B-HIGH_fp8_e4m3fn_scaled_KJ.safetensors 14.47 GB\\nFun/Wan2_2-Fun-Control-Camera-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors 14.47 GB\\nFun/Wan2_2-Fun-InP-A14B-HIGH_fp8_e4m3fn_scaled_KJ.safetensors 13.53 GB\\nFun/Wan2_2-Fun-InP-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors 13.53 GB\\nI2V/AniSora/Wan2_2-I2V_AniSoraV3_2_HIGH_14B_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nI2V/AniSora/Wan2_2-I2V_AniSoraV3_2_LOW_14B_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nI2V/Wan2_2-I2V-A14B-HIGH_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nI2V/Wan2_2-I2V-A14B-HIGH_fp8_e5m2_scaled_KJ.safetensors 13.97 GB\\nI2V/Wan2_2-I2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nI2V/Wan2_2-I2V-A14B-LOW_fp8_e5m2_scaled_KJ.safetensors 13.97 GB\\nS2V/Wan2_2-S2V-14B_fp8_e4m3fn_scaled_KJ.safetensors 15.51 GB\\nT2V/HoloCine/Wan2_2-T2V-A14B-HIGH-HoloCine-full_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nT2V/HoloCine/Wan2_2-T2V-A14B-LOW-HoloCine-full_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nT2V/Wan2_2-T2V-A14B-HIGH_4_steps-250928-dyno-lightx2v_fp8_e4m3fn_scaled_KJ.safetensors 13.53 GB\\nT2V/Wan2_2-T2V-A14B-HIGH_fp8_e5m2_scaled_KJ.safetensors 13.97 GB\\nT2V/Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nT2V/Wan2_2-T2V-A14B-LOW_fp8_e5m2_scaled_KJ.safetensors 13.97 GB\\nT2V/Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors 13.97 GB\\nTI2V/Ovi/README.md 0.0 GB\\nTI2V/Ovi/Wan2_2-5B-Ovi_960x960_10s_fp8_e4m3fn_scaled_KJ.safetensors 11.52 GB\\nTI2V/Ovi/Wan2_2-5B-Ovi_960x960_fp8_e4m3fn_scaled_KJ.safetensors 11.52 GB\\nTI2V/Ovi/Wan2_2_Ovi_Audio_fp8_e4m3fn_scaled_KJ.safetensors 6.25 GB\\nTI2V/Ovi/Wan2_2_Ovi_Video_fp8_e4m3fn_scaled_KJ.safetensors 5.27 GB\\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors 4.91 GB\\nTI2V/Wan2_2-TI2V-5B_fp8_e5m2_scaled_KJ.safetensors 4.91 GB\\nVACE/Wan2_2_Fun_VACE_module_A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors 2.84 GB\\nVACE/Wan2_2_Fun_VACE_module_A14B_HIGH_fp8_e5m2_scaled_KJ.safetensors 2.84 GB\\nVACE/Wan2_2_Fun_VACE_module_A14B_LOW_fp8_e4m3fn_scaled_KJ.safetensors 2.84 GB\\nVACE/Wan2_2_Fun_VACE_module_A14B_LOW_fp8_e5m2_scaled_KJ.safetensors 2.84 GB\\nWan22Animate/Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors 17.14 GB\\nWan22Animate/Wan2_2-Animate-14B_fp8_e5m2_scaled_KJ.safetensors 17.14 GB\\nWan22Animate/Wan2_2-Animate-14B_fp8_scaled_e4m3fn_KJ_v2.safetensors 16.13 GB\\nWan22Animate/Wan2_2-Animate-14B_fp8_scaled_e5m2_KJ_v2.safetensors 16.13 GB\\n\\n## Comfy-Org/Wan_2.2_ComfyUI_Repackaged\\nsplit_files/vae 0.0 GB\\nsplit_files/diffusion_models/wan2.2_animate_14B_bf16.safetensors 32.18 GB\\nsplit_files/diffusion_models/wan2.2_fun_camera_high_noise_14B_bf16.safetensors 27.55 GB\\nsplit_files/diffusion_models/wan2.2_fun_camera_high_noise_14B_fp8_scaled.safetensors 14.25 GB\\nsplit_files/diffusion_models/wan2.2_fun_camera_low_noise_14B_bf16.safetensors 27.55 GB\\nsplit_files/diffusion_models/wan2.2_fun_camera_low_noise_14B_fp8_scaled.safetensors 14.25 GB\\nsplit_files/diffusion_models/wan2.2_fun_control_5B_bf16.safetensors 9.32 GB\\nsplit_files/diffusion_models/wan2.2_fun_control_high_noise_14B_bf16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_fun_control_high_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_fun_control_low_noise_14B_bf16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_fun_control_low_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_fun_inpaint_5B_bf16.safetensors 9.31 GB\\nsplit_files/diffusion_models/wan2.2_fun_inpaint_high_noise_14B_bf16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_fun_inpaint_high_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_fun_inpaint_low_noise_14B_bf16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_fun_inpaint_low_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_fun_vace_high_noise_14B_bf16.safetensors 32.29 GB\\nsplit_files/diffusion_models/wan2.2_fun_vace_high_noise_14B_fp8_scaled.safetensors 16.15 GB\\nsplit_files/diffusion_models/wan2.2_fun_vace_low_noise_14B_bf16.safetensors 32.29 GB\\nsplit_files/diffusion_models/wan2.2_fun_vace_low_noise_14B_fp8_scaled.safetensors 16.15 GB\\nsplit_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp16.safetensors 26.62 GB\\nsplit_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_s2v_14B_bf16.safetensors 30.35 GB\\nsplit_files/diffusion_models/wan2.2_s2v_14B_fp8_scaled.safetensors 15.27 GB\\nsplit_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp16.safetensors 26.61 GB\\nsplit_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp16.safetensors 26.61 GB\\nsplit_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors 13.31 GB\\nsplit_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors 9.31 GB\\nsplit_files/loras/wan2.2_animate_14B_relight_lora_bf16.safetensors 1.34 GB\\nsplit_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors 1.14 GB\\nsplit_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors 1.14 GB\\nsplit_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors 1.14 GB\\nsplit_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors 1.14 GB\\nsplit_files/text_encoders/umt5_xxl_fp16.safetensors 10.59 GB\\nsplit_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors 6.27 GB\\nsplit_files/vae/wan2.2_vae.safetensors 1.31 GB\\nsplit_files/vae/wan_2.1_vae.safetensors 0.24 GB\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_zPg5jgD0qbCGp9A4D5Pdj9Wr"
},
{
"role": "assistant",
"content": "Ja, Sir — **Wan 2.2 TI2V 5B sollten wir testen.** \nAber bitte **nicht blind als “720p 24 FPS final”**, sondern als kontrollierter A/B-Test gegen Wan 2.1.\n\nDie Datenlage spricht dafür:\n\n- Wan 2.2 ist besser trainiert.\n- TI2V 5B kann Text-to-Video und Image-to-Video.\n- 5B ist qualitativ deutlich interessanter als 1.3B.\n- Auf deiner RTX A5000 16 GB ist es realistisch — **aber nur mit FP8 / scaled FP8**, nicht bequem mit BF16/FP16.\n\nDer richtige Testpfad ist daher:\n\n```text\nWan 2.1 1.3B FP8 = schneller Iterationsmodus\nWan 2.2 TI2V 5B FP8 scaled = Qualitätsmodus für Kandidaten\n```\n\nNicht:\n\n```text\nWan 2.2 14B / 720p / 24fps / lange Clips\n```\n\nDas wäre auf 16 GB VRAM unnötig brutal.\n\n---\n\n# Empfehlung\n\n## Ja, installieren und testen\n\nAber wir nehmen diese Variante:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nGröße laut HuggingFace-Listing:\n\n```text\nca. 4.91 GB\n```\n\nNicht zuerst:\n\n```text\nWan2_2-TI2V-5B_fp16.safetensors\n```\n\nDie FP16/BF16-Variante liegt bei ca. 9.3 GB. Zusammen mit T5 + VAE + Overhead wird das auf 16 GB schnell eng.\n\n---\n\n# 1. ComfyUI stoppen\n\nAuf dem GPU-Host:\n\n```bash\npkill -f \"python main.py\"\n```\n\n---\n\n# 2. Genug Speicher prüfen\n\n```bash\ndf -h ~/ai\nfree -h\n```\n\nFür Wan 2.2 Test brauchst du zusätzlich ungefähr:\n\n```text\n7–10 GB Download/Cache\n```\n\nBesser:\n\n```text\n20 GB frei\n```\n\n---\n\n# 3. Modelle herunterladen\n\nVenv aktivieren:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\n## Wan 2.2 TI2V 5B FP8 scaled\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nDanach prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/TI2V/\n```\n\nErwartet:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\n---\n\n## Wan 2.2 VAE\n\nWan 2.2 nutzt eine andere VAE als Wan 2.1. Bitte laden:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_2_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet zusätzlich:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.3 GB\n```\n\n---\n\n## Besserer / passender UMT5 FP8 Text Encoder\n\nDu hast aktuell:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nFür Wan 2.2 / native Workflows wird oft diese Variante genutzt:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nLaden:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n --local-dir /tmp/wan22_text_encoder\n```\n\nDann kopieren:\n\n```bash\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n ~/ai/ComfyUI/models/text_encoders/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nDu solltest dann haben:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 4. ComfyUI neu starten\n\nBitte wieder **ohne `--lowvram`**:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nDas ist weiterhin unser Standard.\n\n---\n\n# 5. Wenn OOM kommt\n\nDann zuerst mehr reservieren:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 2.0\n```\n\nFalls immer noch OOM:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 3.0\n```\n\nErst wenn das nicht reicht:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\nAber `--lowvram` ist wieder Notfall, weil dann T5/Offload langsam wird.\n\n---\n\n# 6. Prüfen, ob ComfyUI die Modelle sieht\n\nNach Neustart:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nUnd im GUI prüfen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nIm WanVideo Model Loader sollte dann etwas auftauchen wie:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nIm VAE Loader:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nIm T5/Text Encoder Loader:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 7. Testprofil für Wan 2.2 TI2V 5B\n\nNicht direkt 720p. Wir testen so:\n\n## Probe A — sicherer Start\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nWan 2.2 TI2V 5B FP8 scaled\nT5 GPU/main_device\nVAE Wan2.2\nVAE tiling off\n```\n\nZiel:\n\n```text\nLäuft stabil?\nGPU-Auslastung?\nIst Motiv klarer als Wan 2.1?\n```\n\n## Probe B — Qualitätsvergleich\n\nWenn A läuft:\n\n```text\n480x832\n33 Frames\n12 fps\n10–12 Steps\n```\n\nDas ist der direkte Vergleich zu unserem bisherigen besten Wan 2.1 Clip.\n\n## Probe C — Kandidat\n\nWenn B gut aussieht:\n\n```text\n576x1024\n49 Frames\n12 fps\n12–16 Steps\n```\n\nNoch nicht 720p. Erst schauen, ob Qualität/Speed sinnvoll ist.\n\n---\n\n# 8. Warum nicht sofort 720p 24 FPS?\n\nWeil 720p/24fps zwar im Modellmarketing steht, aber dein echtes Produktionsziel anders ist:\n\n```text\nviele Varianten\nschnelle Auswahl\nRetention-Test\nlokales Compositing\nCaptions/SFX/Mechanism-Layer\n```\n\nFür Shorts reicht es völlig, wenn der AI-Clip z.B. in:\n\n```text\n480x832 oder 576x1024\n```\n\ngut aussieht und danach lokal in:\n\n```text\n1080x1920\n```\n\ncomposited/upscaled wird.\n\n720p nativ ist später für finale Winner-Shots interessant, nicht für Prompt-Findung.\n\n---\n\n# Kurzurteil\n\n## Sollen wir Wan 2.2 TI2V 5B testen?\n\n**Ja. Definitiv.**\n\nAber als:\n\n```text\nWan 2.2 TI2V 5B FP8 scaled\n416x736 → 480x832 → 576x1024\n```\n\nNicht als:\n\n```text\nFP16/BF16\n720p\n24fps\nlange Clips\n```\n\n## Erwartung\n\nIch erwarte:\n\n- bessere Semantik\n- stabilere Objekte\n- weniger abstrakte Lichtflächen\n- bessere Bewegung\n- bessere Ästhetik\n\nAber:\n\n- langsamer als 1.3B\n- mehr VRAM/RAM\n- ggf. OOM bei zu hohen Settings\n\n---\n\n# Bitte jetzt ausführen\n\nKurzform der wichtigsten Befehle:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n\nhf download Kijai/WanVideo_comfy \\\n Wan2_2_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n --local-dir /tmp/wan22_text_encoder\n\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n ~/ai/ComfyUI/models/text_encoders/\n```\n\nDann starten:\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nWenn du danach sagst:\n\n```text\nWan 2.2 läuft\n```\n\nprüfe ich von hier `/object_info`, baue den Wan-2.2-Workflow und starte den A/B-Test gegen unseren Wan-2.1-Clip.",
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"text": "Ja, Sir — **Wan 2.2 TI2V 5B sollten wir testen.** \nAber bitte **nicht blind als “720p 24 FPS final”**, sondern als kontrollierter A/B-Test gegen Wan 2.1.\n\nDie Datenlage spricht dafür:\n\n- Wan 2.2 ist besser trainiert.\n- TI2V 5B kann Text-to-Video und Image-to-Video.\n- 5B ist qualitativ deutlich interessanter als 1.3B.\n- Auf deiner RTX A5000 16 GB ist es realistisch — **aber nur mit FP8 / scaled FP8**, nicht bequem mit BF16/FP16.\n\nDer richtige Testpfad ist daher:\n\n```text\nWan 2.1 1.3B FP8 = schneller Iterationsmodus\nWan 2.2 TI2V 5B FP8 scaled = Qualitätsmodus für Kandidaten\n```\n\nNicht:\n\n```text\nWan 2.2 14B / 720p / 24fps / lange Clips\n```\n\nDas wäre auf 16 GB VRAM unnötig brutal.\n\n---\n\n# Empfehlung\n\n## Ja, installieren und testen\n\nAber wir nehmen diese Variante:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nGröße laut HuggingFace-Listing:\n\n```text\nca. 4.91 GB\n```\n\nNicht zuerst:\n\n```text\nWan2_2-TI2V-5B_fp16.safetensors\n```\n\nDie FP16/BF16-Variante liegt bei ca. 9.3 GB. Zusammen mit T5 + VAE + Overhead wird das auf 16 GB schnell eng.\n\n---\n\n# 1. ComfyUI stoppen\n\nAuf dem GPU-Host:\n\n```bash\npkill -f \"python main.py\"\n```\n\n---\n\n# 2. Genug Speicher prüfen\n\n```bash\ndf -h ~/ai\nfree -h\n```\n\nFür Wan 2.2 Test brauchst du zusätzlich ungefähr:\n\n```text\n7–10 GB Download/Cache\n```\n\nBesser:\n\n```text\n20 GB frei\n```\n\n---\n\n# 3. Modelle herunterladen\n\nVenv aktivieren:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\n## Wan 2.2 TI2V 5B FP8 scaled\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nDanach prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/TI2V/\n```\n\nErwartet:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\n---\n\n## Wan 2.2 VAE\n\nWan 2.2 nutzt eine andere VAE als Wan 2.1. Bitte laden:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n Wan2_2_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet zusätzlich:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.3 GB\n```\n\n---\n\n## Besserer / passender UMT5 FP8 Text Encoder\n\nDu hast aktuell:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nFür Wan 2.2 / native Workflows wird oft diese Variante genutzt:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nLaden:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n --local-dir /tmp/wan22_text_encoder\n```\n\nDann kopieren:\n\n```bash\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n ~/ai/ComfyUI/models/text_encoders/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nDu solltest dann haben:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 4. ComfyUI neu starten\n\nBitte wieder **ohne `--lowvram`**:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nDas ist weiterhin unser Standard.\n\n---\n\n# 5. Wenn OOM kommt\n\nDann zuerst mehr reservieren:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 2.0\n```\n\nFalls immer noch OOM:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 3.0\n```\n\nErst wenn das nicht reicht:\n\n```bash\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --lowvram\n```\n\nAber `--lowvram` ist wieder Notfall, weil dann T5/Offload langsam wird.\n\n---\n\n# 6. Prüfen, ob ComfyUI die Modelle sieht\n\nNach Neustart:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nUnd im GUI prüfen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nIm WanVideo Model Loader sollte dann etwas auftauchen wie:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nIm VAE Loader:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nIm T5/Text Encoder Loader:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 7. Testprofil für Wan 2.2 TI2V 5B\n\nNicht direkt 720p. Wir testen so:\n\n## Probe A — sicherer Start\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nWan 2.2 TI2V 5B FP8 scaled\nT5 GPU/main_device\nVAE Wan2.2\nVAE tiling off\n```\n\nZiel:\n\n```text\nLäuft stabil?\nGPU-Auslastung?\nIst Motiv klarer als Wan 2.1?\n```\n\n## Probe B — Qualitätsvergleich\n\nWenn A läuft:\n\n```text\n480x832\n33 Frames\n12 fps\n10–12 Steps\n```\n\nDas ist der direkte Vergleich zu unserem bisherigen besten Wan 2.1 Clip.\n\n## Probe C — Kandidat\n\nWenn B gut aussieht:\n\n```text\n576x1024\n49 Frames\n12 fps\n12–16 Steps\n```\n\nNoch nicht 720p. Erst schauen, ob Qualität/Speed sinnvoll ist.\n\n---\n\n# 8. Warum nicht sofort 720p 24 FPS?\n\nWeil 720p/24fps zwar im Modellmarketing steht, aber dein echtes Produktionsziel anders ist:\n\n```text\nviele Varianten\nschnelle Auswahl\nRetention-Test\nlokales Compositing\nCaptions/SFX/Mechanism-Layer\n```\n\nFür Shorts reicht es völlig, wenn der AI-Clip z.B. in:\n\n```text\n480x832 oder 576x1024\n```\n\ngut aussieht und danach lokal in:\n\n```text\n1080x1920\n```\n\ncomposited/upscaled wird.\n\n720p nativ ist später für finale Winner-Shots interessant, nicht für Prompt-Findung.\n\n---\n\n# Kurzurteil\n\n## Sollen wir Wan 2.2 TI2V 5B testen?\n\n**Ja. Definitiv.**\n\nAber als:\n\n```text\nWan 2.2 TI2V 5B FP8 scaled\n416x736 → 480x832 → 576x1024\n```\n\nNicht als:\n\n```text\nFP16/BF16\n720p\n24fps\nlange Clips\n```\n\n## Erwartung\n\nIch erwarte:\n\n- bessere Semantik\n- stabilere Objekte\n- weniger abstrakte Lichtflächen\n- bessere Bewegung\n- bessere Ästhetik\n\nAber:\n\n- langsamer als 1.3B\n- mehr VRAM/RAM\n- ggf. OOM bei zu hohen Settings\n\n---\n\n# Bitte jetzt ausführen\n\nKurzform der wichtigsten Befehle:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\n\nhf download Kijai/WanVideo_comfy \\\n Wan2_2_VAE_bf16.safetensors \\\n --local-dir ~/ai/ComfyUI/models/vae\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n --local-dir /tmp/wan22_text_encoder\n\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n ~/ai/ComfyUI/models/text_encoders/\n```\n\nDann starten:\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n --listen \"$TS_IP\" \\\n --port 8188 \\\n --reserve-vram 1.0\n```\n\nWenn du danach sagst:\n\n```text\nWan 2.2 läuft\n```\n\nprüfe ich von hier `/object_info`, baue den Wan-2.2-Workflow und starte den A/B-Test gegen unseren Wan-2.1-Clip."
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"content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"arguments": "{\"action\":\"write_file\",\"name\":\"comfyui\",\"file_path\":\"references/wan-video-a5000-optimization.md\",\"file_content\":\"# Wan video on remote RTX A5000 16GB via Tailscale — optimization notes\\n\\nUse this when driving a remote ComfyUI host with an NVIDIA RTX A5000 Laptop GPU (16GB VRAM) from Hermes/AutoShortsBot over Tailscale.\\n\\n## Proven baseline\\n\\nHost endpoint used successfully:\\n\\n```text\\nhttp://:8188\\n```\\n\\nHealth endpoints:\\n\\n```bash\\ncurl \\\"http://$(tailscale ip -4 | head -n1):8188/system_stats\\\"\\ncurl \\\"http://$(tailscale ip -4 | head -n1):8188/object_info\\\"\\n```\\n\\nObserved working stack:\\n\\n- ComfyUI 0.22.0\\n- PyTorch 2.12.0+cu126\\n- NVIDIA RTX A5000 Laptop GPU, 16GB VRAM\\n- ComfyUI-WanVideoWrapper nodes available\\n- Wan2.1 T2V 1.3B FP8 model recognized and executable\\n\\n## Start command: production baseline\\n\\nDo **not** use `--lowvram` as the normal mode on 16GB A5000. It can force/offload text encoding and make T5 dominate runtime while `nvidia-smi` looks underused.\\n\\nPreferred launch:\\n\\n```bash\\ncd ~/ai/ComfyUI\\nsource .venv/bin/activate\\n\\nTS_IP=\\\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\\\"\\n[ -z \\\"$TS_IP\\\" ] && TS_IP=\\\"127.0.0.1\\\"\\n\\npython main.py \\\\\\n --listen \\\"$TS_IP\\\" \\\\\\n --port 8188 \\\\\\n --reserve-vram 1.0\\n```\\n\\nIf OOM/crashes, try `--reserve-vram 2.0` or `3.0` before falling back to `--lowvram`.\\n\\nOptional later speed test for repeated same-model runs:\\n\\n```bash\\npython main.py --listen \\\"$TS_IP\\\" --port 8188 --highvram --reserve-vram 1.0\\n```\\n\\n## Workflow settings that fixed under-utilization\\n\\nFor WanVideoWrapper on Wan2.1 1.3B FP8:\\n\\n- `LoadWanVideoT5TextEncoder.load_device = main_device`\\n- `WanVideoTextEncode.device = gpu`\\n- `WanVideoTextEncode.force_offload = false`\\n- `WanVideoModelLoader.load_device = main_device`\\n- `WanVideoSampler.force_offload = false`\\n- `WanVideoDecode.enable_vae_tiling = false` unless decode OOMs\\n\\nEmergency/stability mode only:\\n\\n- `--lowvram`\\n- text encode on CPU\\n- model/text encoder on `offload_device`\\n- VAE tiling on\\n\\n## Diagnostic pattern\\n\\nIf logs show T5/TextEncoder taking ~45–65s while sampling takes ~1s for tiny tests, the bottleneck is text encoding/offload, not diffusion sampling. Move T5/text encoding to GPU and remove `--lowvram`.\\n\\nMonitor while running:\\n\\n```bash\\nwatch -n 1 nvidia-smi\\n```\\n\\nExpected after optimization: VRAM use around 8–10.5GB for 416x736/480x832 preview runs.\\n\\n## Tested profiles\\n\\n### Smoke only — not visually meaningful\\n\\n```text\\n128x224, 9 frames, 2 steps, 8 fps\\n240x416, 17 frames, 4 steps, 8 fps\\n```\\n\\nThese prove the API path, but output may be abstract blinking color and should not be judged as model quality.\\n\\n### Fast preview / prompt iteration\\n\\n```text\\n416x736\\n25 frames\\n12 fps\\n8 steps\\nscheduler: unipc\\ncfg: ~3.5\\n```\\n\\nObserved: ~45–60s, used ~8–10.5GB VRAM, produced recognizable car/garage motif.\\n\\n### Better preview / candidate\\n\\n```text\\n480x832\\n33 frames\\n12 fps\\n10 steps\\nscheduler: unipc\\ncfg: ~3.5\\n```\\n\\nObserved: ~160s, used ~8.2–10.3GB VRAM, produced usable auto/garage B-roll foundation.\\n\\n## Wan2.2 TI2V 5B test path\\n\\nWorth testing for quality, but do not jump directly to 720p/24fps. Use FP8 scaled first.\\n\\nRecommended model files:\\n\\n```bash\\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\\\\n TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\\\\n --local-dir ~/ai/ComfyUI/models/diffusion_models\\n\\nhf download Kijai/WanVideo_comfy \\\\\\n Wan2_2_VAE_bf16.safetensors \\\\\\n --local-dir ~/ai/ComfyUI/models/vae\\n\\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\\\\n split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\\\\n --local-dir /tmp/wan22_text_encoder\\n\\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\\\\n ~/ai/ComfyUI/models/text_encoders/\\n```\\n\\nA/B ladder:\\n\\n1. `416x736`, 25 frames, 12fps, 8 steps\\n2. `480x832`, 33 frames, 12fps, 10–12 steps\\n3. `576x1024`, 49 frames, 12fps, 12–16 steps if stable\\n\\nKeep Wan2.1 1.3B FP8 as the fast iteration model. Use Wan2.2 TI2V 5B FP8 scaled as the quality candidate model.\\n\\n## Pitfalls\\n\\n- Do not judge quality from 2–4 step smoke tests; they only prove execution.\\n- If `nvidia-smi` shows ~1GB while jobs run, check `--lowvram`, CPU text encoding, and offload settings before assuming the GPU is unavailable.\\n- 720p/24fps is a later final-shot target, not the first local A5000 test profile.\\n- If ComfyUI accepts `/prompt` with `node_errors: {}` but then disappears, inspect RAM pressure (`free -h`, `ps aux --sort=-%mem | head -20`) as well as VRAM.\\n\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"comfyui\",\"old_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 and a micro smoke test. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. System RAM can still be the first bottleneck; ask the user to free RAM before the first load. See `references/wan-video-tailscale-a5000.md`.\",\"new_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with a smoke test, but do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding and 1.3B FP8 model on `main_device`/GPU if RAM/VRAM permit. For real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps; keep Wan2.1 1.3B FP8 for fast iteration and test Wan2.2 TI2V 5B FP8 scaled as a quality candidate. See `references/wan-video-a5000-optimization.md`.\"}"
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"content": "Skill-Library aktualisiert, Sir.\n\nGeändert wurde die bestehende, geladene Umbrella-Skill:\n\n```text\ncomfyui\n```\n\n## Updates\n\n- Neue Referenzdatei hinzugefügt:\n\n```text\nreferences/wan-video-a5000-optimization.md\n```\n\nEnthält jetzt dauerhaft:\n\n- Remote-ComfyUI über Tailscale auf RTX A5000 16GB\n- neuer Standard-Startbefehl ohne `--lowvram`\n- warum `--lowvram`/CPU-T5 die GPU scheinbar nicht nutzt\n- GPU-first Workflow-Settings:\n - T5/TextEncode auf GPU\n - model/text encoder `main_device`\n - `force_offload=false`\n - VAE tiling nur bei OOM\n- getestete sinnvolle Profile:\n - `416x736 / 25f / 8 steps`\n - `480x832 / 33f / 10 steps`\n- Wan 2.2 TI2V 5B FP8 scaled Install- und A/B-Testleiter\n- Pitfalls: Smoke-Tests nicht als Qualitätsurteil verwenden, `nvidia-smi`-Unterauslastung richtig diagnostizieren\n\n## SKILL.md gepatcht\n\nDie ComfyUI-Skill verweist jetzt auf die neue Referenz und enthält den korrigierten Pitfall:\n\n```text\n--lowvram nur Notfall/OOM\nStandard: --reserve-vram 1.0\nWan2.1 1.3B FP8 für Iteration\nWan2.2 TI2V 5B FP8 scaled als Qualitätskandidat\n```\n\nKeine neue Flat/Narrow-Skill erstellt — das gehört sauber in die bestehende ComfyUI-Umbrella.",
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"text": "Skill-Library aktualisiert, Sir.\n\nGeändert wurde die bestehende, geladene Umbrella-Skill:\n\n```text\ncomfyui\n```\n\n## Updates\n\n- Neue Referenzdatei hinzugefügt:\n\n```text\nreferences/wan-video-a5000-optimization.md\n```\n\nEnthält jetzt dauerhaft:\n\n- Remote-ComfyUI über Tailscale auf RTX A5000 16GB\n- neuer Standard-Startbefehl ohne `--lowvram`\n- warum `--lowvram`/CPU-T5 die GPU scheinbar nicht nutzt\n- GPU-first Workflow-Settings:\n - T5/TextEncode auf GPU\n - model/text encoder `main_device`\n - `force_offload=false`\n - VAE tiling nur bei OOM\n- getestete sinnvolle Profile:\n - `416x736 / 25f / 8 steps`\n - `480x832 / 33f / 10 steps`\n- Wan 2.2 TI2V 5B FP8 scaled Install- und A/B-Testleiter\n- Pitfalls: Smoke-Tests nicht als Qualitätsurteil verwenden, `nvidia-smi`-Unterauslastung richtig diagnostizieren\n\n## SKILL.md gepatcht\n\nDie ComfyUI-Skill verweist jetzt auf die neue Referenz und enthält den korrigierten Pitfall:\n\n```text\n--lowvram nur Notfall/OOM\nStandard: --reserve-vram 1.0\nWan2.1 1.3B FP8 für Iteration\nWan2.2 TI2V 5B FP8 scaled als Qualitätskandidat\n```\n\nKeine neue Flat/Narrow-Skill erstellt — das gehört sauber in die bestehende ComfyUI-Umbrella."
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