- Both setup scripts now detect VRAM and determine which image gen
models the GPU can run (SD 1.5 at 4GB, SDXL at 8GB, Flux at 12-20GB)
- New setup-image-models.sh: interactive script that detects GPU,
shows available models with VRAM requirements, and installs into
InvokeAI and/or ComfyUI. Supports --auto for unattended install.
- Scales from 4GB cards through dual RTX 5000s to high-end 48GB cards
- README: added image gen VRAM tier table, expanded inpainting docs
with practical fix recipes (hands, fingers, eyes, backgrounds),
mask tips, and denoising strength guidance
- Setup end messages now show image gen capabilities and point to
setup-image-models.sh instead of manual model install instructions
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
Major expansion of the InvokeAI section:
- Add InvokeAI vs ComfyUI comparison table (when to use each)
- Step-by-step: install base model, import RunPod LoRA, text-to-image
with LoRA, img2img iteration with denoising strength guide
- Denoising strength table explaining what 0.2 vs 0.8 actually does
- Example prompts for age up/down, emotion, setting, art style changes
- Unified Canvas / inpainting instructions for selective editing
- 6GB GPU notes (SD 1.5 fits, SDXL is tight)
- Expanded troubleshooting for greyed-out buttons and VRAM issues
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
New script: comfyui-install-ipadapter.sh
- Installs cubiq/ComfyUI_IPAdapter_plus custom nodes
- Downloads CLIP Vision encoders and IP-Adapter models (SDXL/SD1.5)
- Optional --faceid flag for stronger face identity lock
- Skips already-downloaded files, pulls updates on re-run
- Prints wiring diagram and example prompts after install
README:
- Add "IP-Adapter: same face, different settings" section with
task table, install commands, workflow guide, weight tuning
- Clarify IP-Adapter = ComfyUI direct (not from OWUI chat)
- Add both new scripts to the file listing table
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
- README: Add "Combining multiple LoRAs" section with wiring diagram,
expand style management table with combo examples, clarify that
LoRAs are baked into workflows with no chat keyword activation
- Script: Add multi-LoRA tip and clarify no-keyword behavior
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
- New script: comfyui-import-lora.sh — copies .safetensors into
ComfyUI's Docker volume and prints step-by-step instructions for
wiring it into a workflow and exporting to Open WebUI
- README: Add "Using LoRAs with Open WebUI" section documenting the
workflow-per-style pattern, multi-LoRA management, and architecture
compatibility table
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
Setup script changes:
- All prompts now use whiptail dialogs with text fallback
- Q1b (SSH), Q2 (storage), Q3 (Kiwix), Q4b (firewall), Q5 (models),
Q6 (download), final confirm all converted
- Model tier selection uses radiolist with recommended tier pre-selected
- Custom model entry uses inputbox with current defaults pre-filled
- Fix speed_label: now shows actual VRAM needed (file size + 2GB overhead)
instead of misleading "fully in VRAM" for models that don't fit
- qwen3.5-35b-a3b MoE already in tier list (was there, now with accurate
VRAM estimate shown)
README changes:
- Add "Realistic expectations by model size" table
- 35B MoE highlighted as sweet spot for small GPUs
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
The stack already has a code-aware RAG server that auto-indexes repos
and retrieves relevant code chunks during chat - but this wasn't
documented clearly enough. Added:
- Architecture diagram showing RAG server data flow
- Three methods to index repos (manual, API, Gitea webhook)
- What gets indexed (file types, AST parsing, smart chunking)
- Comparison table: RAG server vs Knowledge Collections vs Memories
- Updated Local AI vs Claude Code comparison to reflect code awareness
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
- How to create knowledge collections from chat summaries (handoff workflow)
- RAG tuning settings for better retrieval quality
- Community functions for context management (summarization, clipping)
- How Open WebUI handles context overflow (truncation, not summarization)
- Honest comparison table: Local AI vs Claude Code tradeoffs
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
- Manual function/action installation without community signup
- Auto Memory setup with Ollama configuration
- Memory vs Knowledge collections comparison (context impact)
- Context window consumption analysis (memories are ~200 tokens fixed,
conversation history is the real context hog)
- Project-scoped memory workarounds (Knowledge collections recommended)
- System prompt fix for models outputting code instead of natural language
- VRAM reality check table (model file size != inference VRAM needed)
- Qwen 9B does NOT fit in 6GB VRAM despite setup script claiming so
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
- Add ComfyUI as optional service in setup wizard (alongside InvokeAI)
- Configure Open WebUI env vars for ComfyUI integration when selected
(ENABLE_IMAGE_GENERATION, IMAGE_GENERATION_ENGINE, COMFYUI_BASE_URL)
- Add ComfyUI docker service (ai-dock/comfyui, port 8188, GPU access)
- Add comprehensive image generation documentation to README:
- ComfyUI + Open WebUI setup steps (model install, workflow export, node mapping)
- AUTOMATIC1111 alternative setup
- Environment variables reference table
- VRAM considerations for simultaneous LLM + image gen
- Update all touchpoints: UFW rules, Caddyfile, start.sh URLs, volumes,
compose services, summary output, directory creation
- Note: InvokeAI does NOT integrate with Open WebUI natively (no compatible API)
ComfyUI is the recommended path for chat-integrated image generation
https://claude.ai/code/session_01PtYTPherSJaxDEVPgF6Nxu
Adds invokeai-import-lora.sh to copy LoRA .safetensors files directly
into InvokeAI's Docker model volume, bypassing the greyed-out UI upload
buttons. Also adds README documentation for LoRA usage and troubleshooting.
https://claude.ai/code/session_01RU7NQuTbA8S8NRoWojvhR5