.env.example

@@ -48,10 +48,6 @@ LLAMA_PARALLEL=2
 # qwen-classifier service comment for the why and the VRAM/context math.
 LLAMA_CLASSIFIER_MODEL_FILE=Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf
 
-# --- Lazytainer ---
-# Seconds of inactivity before llama-server is stopped. 900 = 15 min.
-LAZYTAINER_INACTIVE_TIMEOUT=900
-
 # --- SearXNG web search (see docs/research/litellm-searxng-search.md) ---
 # Resolved automatically by ./scripts/update.sh from search.home on this
 # host — leave blank. Only set by hand if that resolution fails (e.g.
@@ -100,9 +96,9 @@ OMNIROUTE_WS_BRIDGE_SECRET=
 # Mint them by hand in the dashboard, add a KEY=value line here per workload
 # as you onboard one. See docs/proxy-key-onboarding.md.
 
-# --- ComfyUI (local image generation, see issue #38 wayfinder map) ---
+# --- GPU access (group_add GIDs) ---
-# Shared by every GPU-touching service (llama-server, llama-server-fast,
+# Shared by every GPU-touching service (llama-server, qwen-classifier)
-# comfyui) for group_add: — resolved to real host GIDs by ./scripts/update.sh
+# for group_add: — resolved to real host GIDs by ./scripts/update.sh
 # rather than left as plain group names in docker-compose.yml, because Docker
 # resolves a *named* group_add entry against the container's own /etc/group,
 # not the host's, and fails unpredictably when the image doesn't define one
@@ -110,30 +106,3 @@ OMNIROUTE_WS_BRIDGE_SECRET=
 # docs/research/rocm-gpu-pin-and-render-group.md and issue #5). Leave blank.
 HOST_VIDEO_GID=
 HOST_RENDER_GID=
-
-# --- ComfyUI diffusion model (Qwen-Image 2.1, uncensored GGUF — see
-# docs/research/image-generation-model-choice.md and issue #42) ---
-# Three files, all from abenzerps/Qwen-Image-2.1-Uncensored-GGUF on
-# HuggingFace: GGUF diffusion weights, safetensors text encoder, safetensors
-# VAE. Q4_K_M is the repo's own recommended size/quality balance (~4.6GB);
-# the int8 text encoder trades some quality for ~8GB less RAM than bf16.
-#
-# Requires the ComfyUI-GGUF custom node (github.com/city96/ComfyUI-GGUF) —
-# NOT confirmed bundled in yurisasc/comfyui-rocm7.1. If the comfyui
-# container doesn't show a "Unet Loader (GGUF)" node, `git clone` it into
-# the image's custom_nodes dir and reinstall its requirements.
-COMFYUI_DIFFUSION_MODEL_FILE=qwen-image-2.1-UC-Q4_K_M.gguf
-COMFYUI_TEXT_ENCODER_FILE=qwen3vl_8b_int8_convrot.safetensors
-COMFYUI_VAE_FILE=qwen_image_2.1_vae_bf16.safetensors
-
-# --- Open WebUI (chat + ComfyUI image-gen frontend) ---
-# Chat backend goes through omniroute like every other client — mint this
-# key by hand in the omniroute dashboard per docs/proxy-key-onboarding.md
-# (label it openwebui-chat), no scripted mint yet. Image generation talks
-# to comfyui directly, no key needed there.
-OPENWEBUI_OMNIROUTE_KEY=
-
-# --- RAG databases (qdrant + neo4j, see wayfinder notes) ---
-# No auth on qdrant (its default) — same trust boundary as llama-server:
-# ai-stack is not exposed off-box. Random, filled in automatically:
-NEO4J_PASSWORD=

docker-compose.yml

@@ -47,13 +47,6 @@ services:
       - "8080"
     restart: unless-stopped
     networks: [ai-stack]
-    labels:
-      # ponytail: idle-timeout tuning lives here, not in a separate lazytainer config file —
-      # one place to look. Raise LAZYTAINER_INACTIVE_TIMEOUT if 15 min proves too eager.
-      - "lazytainer.group.llamaserver.sleepMethod=stop"
-      - "lazytainer.group.llamaserver.ports=8080"
-      - "lazytainer.group.llamaserver.inactiveTimeout=${LAZYTAINER_INACTIVE_TIMEOUT:-900}"
-      - "lazytainer.group.llamaserver.minPacketThreshold=2"
 
   # Dedicated backend for qwen-code's tool-call harmfulness classifier
   # (fastModel in ~/.qwen/settings.json). Was aliased onto llama-server's own
@@ -168,105 +161,6 @@ services:
         curl -L --fail --create-dirs -o /models/${LLAMA_CLASSIFIER_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf}
         https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF/resolve/main/${LLAMA_CLASSIFIER_MODEL_FILE:-Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf}
 
-  # Fetches the three Qwen-Image 2.1 (uncensored GGUF) files ComfyUI needs
-  # (diffusion model, text encoder, VAE) — same test -f guard pattern as
-  # downloader above. See docs/research/image-generation-model-choice.md and
-  # issue #42.
-  #
-  # GGUF weights go in models/unet (ComfyUI-GGUF's own convention), not
-  # models/diffusion_models (that was the old FP8-safetensors layout) — see
-  # github.com/city96/ComfyUI-GGUF. Text encoder/VAE stay safetensors, loaded
-  # by ComfyUI's normal CLIPLoader/VAELoader, so their folders are unchanged.
-  #
-  # ponytail: target paths otherwise assume ComfyUI's standard models/ layout
-  # under BASE_STORAGE_PATH (/storage) — same "not independently confirmed
-  # against the image's Dockerfile" caveat flagged on the comfyui service
-  # below. If ComfyUI doesn't pick these up, check its actual models root
-  # first.
-  downloader-comfyui:
-    image: curlimages/curl:latest
-    profiles: ["tools"]
-    user: root
-    volumes:
-      - comfyui-data:/storage
-    entrypoint: ["sh", "-c"]
-    command:
-      - >
-        mkdir -p /storage/models/unet /storage/models/text_encoders /storage/models/vae &&
-        (test -f /storage/models/unet/${COMFYUI_DIFFUSION_MODEL_FILE:-qwen-image-2.1-UC-Q4_K_M.gguf} &&
-        echo "diffusion model already downloaded, skipping" ||
-        curl -L --fail --create-dirs -o /storage/models/unet/${COMFYUI_DIFFUSION_MODEL_FILE:-qwen-image-2.1-UC-Q4_K_M.gguf}
-        https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-GGUF/resolve/main/${COMFYUI_DIFFUSION_MODEL_FILE:-qwen-image-2.1-UC-Q4_K_M.gguf}) &&
-        (test -f /storage/models/text_encoders/${COMFYUI_TEXT_ENCODER_FILE:-qwen3vl_8b_int8_convrot.safetensors} &&
-        echo "text encoder already downloaded, skipping" ||
-        curl -L --fail --create-dirs -o /storage/models/text_encoders/${COMFYUI_TEXT_ENCODER_FILE:-qwen3vl_8b_int8_convrot.safetensors}
-        https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-GGUF/resolve/main/text_encoders/${COMFYUI_TEXT_ENCODER_FILE:-qwen3vl_8b_int8_convrot.safetensors}) &&
-        (test -f /storage/models/vae/${COMFYUI_VAE_FILE:-qwen_image_2.1_vae_bf16.safetensors} &&
-        echo "vae already downloaded, skipping" ||
-        curl -L --fail --create-dirs -o /storage/models/vae/${COMFYUI_VAE_FILE:-qwen_image_2.1_vae_bf16.safetensors}
-        https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-GGUF/resolve/main/vae/${COMFYUI_VAE_FILE:-qwen_image_2.1_vae_bf16.safetensors})
-
-  # Local image generation — see issue #38 (wayfinder map). yurisasc's image
-  # is gfx1201-tuned specifically (R9700's arch), unlike the official/AMD
-  # ComfyUI image which doesn't pin RDNA4 support — see
-  # docs/research/image-generation-options.md.
-  #
-  # Model is Qwen-Image 2.1 GGUF (uncensored variant, see .env.example) —
-  # needs the ComfyUI-GGUF custom node (github.com/city96/ComfyUI-GGUF) for
-  # its "Unet Loader (GGUF)" node. Not confirmed pre-installed in this image;
-  # if missing, exec in and `git clone` it into custom_nodes, then pip
-  # install its requirements.txt and restart the container.
-  comfyui:
-    image: yurisasc/comfyui-rocm7.1:latest
-    container_name: comfyui
-    devices:
-      - /dev/kfd
-      - /dev/dri
-    # Numeric GIDs, not names — see HOST_VIDEO_GID/HOST_RENDER_GID in
-    # .env.example and docs/research/rocm-gpu-pin-and-render-group.md.
-    group_add:
-      - "${HOST_VIDEO_GID:?run scripts/update.sh first to resolve this}"
-      - "${HOST_RENDER_GID:?run scripts/update.sh first to resolve this}"
-    security_opt:
-      - seccomp=unconfined
-    ipc: host
-    environment:
-      - HSA_OVERRIDE_GFX_VERSION=12.0.1
-      - PYTORCH_ROCM_ARCH=gfx1201
-    volumes:
-      - comfyui-data:/storage
-    # yurisasc/comfyui-rocm7.1 has no entrypoint/CMD of its own (just drops
-    # into bash) and doesn't read PUID/PGID/VIDEO_GID/RENDER_GID or
-    # BASE_STORAGE_PATH — confirmed by inspecting the image directly, so
-    # this command is required, not optional, or the container exits 0
-    # immediately and restart-loops. Runs as root (image's only user);
-    # group_add above is what actually grants /dev/kfd + /dev/dri access.
-    #
-    # --base-directory fully replaces ComfyUI's custom_nodes/input/output/
-    # user/temp dirs with ones under /storage (confirmed in folder_paths.py)
-    # rather than adding to the image's baked-in ones — so first boot must
-    # seed /storage/custom_nodes from the image's copy (ComfyUI-Manager
-    # lives there) or it's gone, and main.py hard-crashes if custom_nodes
-    # doesn't exist yet at all.
-    entrypoint: ["sh", "-c"]
-    command:
-      - >
-        mkdir -p /storage/custom_nodes /storage/input /storage/output /storage/user &&
-        [ -z "$(ls -A /storage/custom_nodes)" ] && cp -r /workspace/ComfyUI/custom_nodes/. /storage/custom_nodes/ || true;
-        exec python main.py --listen 0.0.0.0 --port 8188 --base-directory /storage
-    #
-    # Published host port (unlike llama-server's ai-stack-only pattern):
-    # ComfyUI's own UI is meant to be reachable directly too, for a planned
-    # external nginx reverse-proxy route to comfy.home — not just through
-    # OmniRoute. Still also reachable at http://comfyui:8188 internally on
-    # ai-stack, which is the URL to register as OmniRoute's comfyui
-    # provider (dashboard or POST /api/providers, per docs/proxy-key-onboarding.md
-    # — same undocumented-in-repo manual flow already used for llama-server).
-    ports:
-      - "8138:8188"
-    restart: unless-stopped
-    networks: [ai-stack]
-
   # Replaces litellm — see issue #31 (wayfinder map) for the full migration
   # rationale/findings. No static config.yaml equivalent: provider routing
   # (llama-server, searxng-search) is registered once through the dashboard
@@ -349,28 +243,8 @@ services:
       retries: 3
       start_period: 40s
 
-  lazytainer:
-    image: ghcr.io/vmorganp/lazytainer:master
-    container_name: lazytainer
-    # NOT network_mode: host — lazytainer identifies its own container by
-    # matching os.Hostname() against the Docker container-ID list
-    # (vmorganp/Lazytainer, configureFromLabels()); under host networking the
-    # container inherits the host's hostname instead of its own ID, so that
-    # match always fails and it panics with "Could not determine container ID
-    # of lazytainer" on every start. Host networking also can't see traffic
-    # to llama-server:8080 anyway — that port only exists on the ai-stack
-    # bridge network (no host port published, see issue #15 above). Joining
-    # ai-stack instead fixes both: hostname becomes the real container ID,
-    # and it's on the same network as the traffic it's watching.
-    networks: [ai-stack]
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock:ro
-    restart: unless-stopped
-    depends_on:
-      - llama-server
-
   # RAG vector store — see docs/agents/... (wayfinder). Dashboard UI published
-  # directly like comfyui above, not gatewayed through omniroute (it isn't an
+  # directly, not gatewayed through omniroute (it isn't an
   # LLM provider).
   qdrant:
     image: qdrant/qdrant:latest
@@ -382,67 +256,10 @@ services:
     restart: unless-stopped
     networks: [ai-stack]
 
-  # RAG graph store, native vector index too (can absorb qdrant's job later
-  # if the two-DB split proves unnecessary — see wayfinder notes).
-  neo4j:
-    image: neo4j:5-community
-    container_name: neo4j
-    environment:
-      - NEO4J_AUTH=neo4j/${NEO4J_PASSWORD:?run scripts/update.sh first to resolve this}
-    volumes:
-      - neo4j-data:/data
-    ports:
-      - "7474:7474" # browser UI
-      - "7687:7687" # bolt
-    restart: unless-stopped
-    networks: [ai-stack]
-
-  # Chat + image-generation frontend. Talks to ComfyUI directly for images —
-  # NOT through omniroute: OmniRoute's built-in `comfyui` provider hardcodes
-  # a CheckpointLoaderSimple (single-file-checkpoint) workflow in
-  # open-sse/handlers/imageGeneration/providers/comfyUI.ts with no override,
-  # so it can't drive Qwen-Image 2.1's split unet/text-encoder/vae GGUF
-  # loader chain. Open WebUI's ComfyUI backend is fully generic instead —
-  # you upload the exact workflow_api.json exported from ComfyUI's own
-  # graph editor and map arbitrary node IDs to parameters.
-  #
-  # Chat backend still goes through omniroute (OpenAI-compatible), same
-  # gateway every other client uses — mint OPENWEBUI_OMNIROUTE_KEY by hand
-  # per docs/proxy-key-onboarding.md, same as any other workload.
-  #
-  # ponytail: only wires the base ComfyUI URL + chat backend via env vars.
-  # COMFYUI_WORKFLOW/COMFYUI_WORKFLOW_NODES env vars exist but are reported
-  # flaky upstream — set the actual workflow JSON by hand in Settings >
-  # Images in the web UI instead, once you've exported it from ComfyUI.
-  open-webui:
-    image: ghcr.io/open-webui/open-webui:main
-    container_name: open-webui
-    depends_on:
-      comfyui:
-        condition: service_started
-      omniroute:
-        condition: service_started
-    environment:
-      - OPENAI_API_BASE_URL=http://omniroute:${OMNIROUTE_API_PORT:-20129}/v1
-      - OPENAI_API_KEY=${OPENWEBUI_OMNIROUTE_KEY:-}
-      - ENABLE_IMAGE_GENERATION=True
-      - IMAGE_GENERATION_ENGINE=comfyui
-      - COMFYUI_BASE_URL=http://comfyui:8188
-      - WEBUI_AUTH=True
-    volumes:
-      - open-webui-data:/app/backend/data
-    ports:
-      - "8008:8080"
-    restart: unless-stopped
-    networks: [ai-stack]
-
 networks:
   ai-stack:
 
 volumes:
   models:
   omniroute-data:
-  comfyui-data:
   qdrant-data:
-  neo4j-data:
-  open-webui-data:

docs/research/image-generation-model-choice.md

@@ -1,220 +0,0 @@
-# Research: which diffusion model to target with the full ~32GB R9700
-
-**Question:** With Qwen/llama-server fully stopped (per issue #39's premise — see
-map #38), ComfyUI has the whole ~32GB R9700 (ROCm/gfx1201) to itself instead
-of the ~6GB left over during concurrent operation
-(per [`image-generation-options.md`](image-generation-options.md)). Given that
-headroom, should the build stay on FLUX.1-schnell, or move up to
-FLUX.1-dev, SD3.5-large, Qwen-Image, HunyuanImage-3.0, or Krea-2?
-
-**Answer, short version:** move up to **Qwen-Image at FP8 precision**
-(`qwen_image_fp8_e4m3fn.safetensors` diffusion weights +
-`qwen_2.5_vl_7b_fp8_scaled.safetensors` text encoder, ~25GB combined). It's
-the only one of the five upgrade candidates with **direct, hardware-specific
-evidence of running on this exact GPU architecture** (gfx1201/R9700) rather
-than a generic "ComfyUI supports ROCm" inference, it carries the cleanest
-license of the group (Apache-2.0, no revenue threshold, no non-commercial
-clause), and its 20B MMDiT is a real capability step up from schnell's
-distilled 12B (notably for text rendering and prompt adherence), while still
-fitting with real margin inside 32GB.
-
-## Why not just re-derive the schnell/dev/SDXL/SD3.5 findings
-
-`docs/research/image-generation-options.md` already covers, with primary
-sources: ComfyUI's ROCm/gfx1201 story (official AMD docs + RDNA4 blog post +
-community gfx1201 Docker images), FLUX.1-schnell vs FLUX.1-dev vs SDXL vs
-SD3.5 licenses, and FLUX GGUF VRAM figures at the ~6GB-headroom scale. None
-of that is repeated here except where the ~32GB ceiling changes the
-conclusion. This doc adds: FLUX.1-dev/SD3.5 at the *larger* headroom, plus
-three models the prior doc didn't cover at all (Qwen-Image, HunyuanImage-3.0,
-Krea-2).
-
-## Candidate comparison
-
-| Model | License (primary source) | Params | Stated/typical VRAM | ROCm/gfx1201 evidence |
-|---|---|---|---|---|
-| FLUX.1-schnell (current) | Apache-2.0 | 12B | GGUF Q4_K_S ~7GB (per prior doc) | Confirmed on gfx1201 (prior doc) |
-| **Qwen-Image** | **Apache-2.0** | 20B (20.4B DiT + 8.3B Qwen2.5-VL text encoder) | fp8 ~16GB (diffusion) + ~9.4GB (fp8 text encoder) ≈ 25GB total; bf16 needs 24GB+ and "48GB+" per some quant write-ups | **Direct**: [kyuz0/amd-r9700-comfy](https://github.com/kyuz0/amd-r9700-comfy) ships a pre-validated "Qwen Image 2512 (FP8) & Lightning LoRA (4 steps)" ComfyUI workflow specifically for the R9700 AI Pro (gfx1201), on a ROCm 7 (TheRock nightlies) toolbox |
-| FLUX.1-dev | [FLUX.1-dev Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) — non-commercial weights, outputs usable commercially | 12B | bf16 ~24GB; GGUF Q8 ~12-13GB | Same *family* evidence as schnell (gfx1201 Docker images target FLUX generally), but no R9700-specific FLUX.1-dev report found |
-| SD3.5-large | [Stability Community License](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md) — free under $1M annual revenue | 8B | bf16 ~16GB; 4-bit NF4 fits small GPUs | AMD's own ComfyUI-ROCm doc lists an "SD3.5 Simple" template workflow (per prior doc) — vendor-blessed but not R9700-specific |
-| HunyuanImage-3.0 | [tencent-hunyuan-community license](https://github.com/Tencent-Hunyuan/HunyuanImage-3.0) | 80B total / 13B active (MoE, 64 experts) | Official repo: "≥ 3 × 80GB" VRAM for the base model, "≥ 8 × 80GB" for -Instruct; ~177GB at fp16 | **None, and actively contraindicated**: setup requires CUDA 12.8 + FlashAttention2/FlashInfer, no AMD/ROCm mention anywhere in the official repo |
-| Krea-2 (Turbo) | [Krea 2 Community License](https://www.krea.ai/krea-2-licensing) — free under $1M annual revenue, homelab/personal explicitly covered | 12-13B DiT | No official VRAM figure; community reports (RTX hardware only) cite fp8 ~16GB, GGUF ~12GB | **None found** — released [June 22, 2026 per the HF model card](https://huggingface.co/krea/Krea-2-Turbo); ComfyUI added native support per [blog.comfy.org](https://blog.comfy.org/p/krea-2-open-source-models-are-now), and GGUF quants exist ([molbal/krea2-gguf](https://huggingface.co/molbal/krea2-gguf)), but no R9700/gfx1201-specific report exists yet — too new for that evidence to have accumulated |
-
-## Per-model detail
-
-### Qwen-Image — recommended
-
-- **License**: Apache-2.0, stated directly on the model card, no revenue
-  threshold, no non-commercial clause, no attribution/naming requirement.
-  The cleanest license of every model considered in this doc or the prior
-  one. Source: [Qwen/Qwen-Image on Hugging Face](https://huggingface.co/Qwen/Qwen-Image).
-- **Architecture**: 20B-parameter MMDiT (Multimodal Diffusion Transformer)
-  combined with an 8.3B Qwen2.5-VL text encoder — notably larger and more
-  capable than FLUX.1-schnell's 12B distilled model, particularly for
-  multilingual text rendering and instruction-following, per the official
-  [QwenLM/Qwen-Image GitHub repo](https://github.com/QwenLM/Qwen-Image).
-- **ComfyUI support**: native, not a wrapper/custom-node integration —
-  landed August 2025 per [ComfyUI Wiki's native-support announcement](https://comfyui-wiki.com/en/news/2025-08-05-qwen-image),
-  with an official FP8 checkpoint (`qwen_image_fp8_e4m3fn.safetensors`) and
-  FP8-scaled text encoder (`qwen_2.5_vl_7b_fp8_scaled.safetensors`, 9.38GB)
-  published under [Comfy-Org/Qwen-Image_ComfyUI](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders).
-- **VRAM at FP8**: the official Comfy-Org FP8 diffusion checkpoint plus FP8
-  text encoder land around ~25GB combined — comfortably inside the 32GB
-  ceiling with headroom for ComfyUI runtime/VAE overhead, versus 24GB+ (some
-  sources say 48GB+) for the unquantized bf16 model. Sources: community
-  VRAM write-ups aggregated via [Comfy-Org/ComfyUI issue #10852 ("Qwen-image in 24GB VRAM and 32GB RAM")](https://github.com/Comfy-Org/ComfyUI/issues/10852)
-  and the [official ComfyUI Qwen-Image example](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/) —
-  treat exact GB figures as secondary/community-sourced, consistent with how
-  the prior doc flagged FLUX's GGUF VRAM table.
-- **Direct R9700/gfx1201 evidence (the deciding factor)**:
-  [kyuz0/amd-r9700-comfy](https://github.com/kyuz0/amd-r9700-comfy) is a
-  Fedora-toolbox ROCm 7 (TheRock nightlies) environment built specifically
-  for the "AMD Radeon 9700 AI PRO (32GB)" and ships a pre-validated
-  "Qwen Image 2512 (FP8) & Lightning LoRA (4 steps)" ComfyUI workflow
-  (plus a Qwen-Image-Edit 2511 FP8 workflow) as one of only four workflows
-  in the whole repo. This is the only model in this comparison with
-  card-architecture-specific (not just "ComfyUI supports ROCm generically")
-  validation — everything else relies on family-level or vendor-generic
-  ROCm claims. A related community discussion
-  ([pollockjj/ComfyUI-MultiGPU #133](https://github.com/pollockjj/ComfyUI-MultiGPU/discussions/133))
-  does flag that "Qwen image edit doesn't always work on AMD HIP/ROCm" in
-  some configurations — worth testing the specific workflow before assuming
-  zero friction, but this is a known-quantity, actively-discussed rough edge
-  rather than a documented hard blocker.
-- **4-step Lightning LoRA**: the R9700-validated workflow pairs Qwen-Image
-  with a "Lightning" LoRA for 4-step inference — the same fast-inference
-  pattern FLUX.1-schnell uses, so switching models doesn't have to mean
-  giving up the short GPU-resident-time-per-image property the prior doc
-  called out as valuable for time-sliced use alongside llama-server (even
-  though this ticket's premise is llama-server being stopped, that pattern
-  still helps if the two services are ever run in an overlapping window).
-
-### FLUX.1-dev — solid alternative, not the pick
-
-- License permits non-commercial use of the weights; BFL's own license page
-  states generated outputs are separately usable commercially (already
-  covered in the prior doc; unchanged here). Source:
-  [FLUX.1-dev LICENSE.md](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
-- At bf16 (~24GB) or GGUF Q8 (~12-13GB), it fits the 32GB ceiling with room
-  to spare — a legitimate move up from schnell's distilled quality.
-  Source: [city96/FLUX.1-dev-gguf](https://huggingface.co/city96/FLUX.1-dev-gguf)
-  and community Q8 VRAM reports.
-- Loses to Qwen-Image on two counts: license (non-commercial-weights clause
-  vs. Apache-2.0 — not a hard blocker for this homelab per the prior doc's
-  own reasoning, but strictly worse) and hardware evidence (FLUX's gfx1201
-  validation is at the *family* level — the prior doc's `yurisasc/comfyui-rocm-rdna4`
-  and `charlie12345/R9700AIProComfyUIPatch` sources are about running FLUX
-  models on this card generally, not a FLUX.1-dev-specific report the way
-  kyuz0's repo is Qwen-Image-specific).
-
-### SD3.5-large — no longer the best use of the freed headroom
-
-- License unchanged from the prior doc: Stability Community License, free
-  under $1M annual revenue (irrelevant threshold for this homelab). Source:
-  [stabilityai/stable-diffusion-3.5-large LICENSE.md](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md).
-- At 8B params (bf16 ~16GB), it's the smallest of the upgrade candidates —
-  which mattered when 6GB was the ceiling, but with 32GB available there's
-  no VRAM reason to pick the model the prior doc already flagged as "lower
-  fidelity than FLUX/SD3.5 by current standards" over Qwen-Image or
-  FLUX.1-dev. AMD's own ComfyUI-ROCm docs do list it as a first-party
-  example template ("SD3.5 Simple", per the prior doc), so it remains a fine
-  fallback if Qwen-Image's FP8 path hits the ROCm rough edge noted above.
-
-### HunyuanImage-3.0 — ruled out
-
-- Official repo states VRAM requirements of "≥ 3 × 80GB" for the base model
-  and "≥ 8 × 80GB" for HunyuanImage-3.0-Instruct — i.e. multi-GPU
-  datacenter-class NVIDIA clusters, not a single 32GB consumer/workstation
-  card at any precision. Source:
-  [Tencent-Hunyuan/HunyuanImage-3.0 GitHub repo](https://github.com/Tencent-Hunyuan/HunyuanImage-3.0).
-- Setup instructions require CUDA 12.8, PyTorch 2.8.0 built for CUDA, and
-  optionally FlashAttention2/FlashInfer for MoE routing speed — no AMD or
-  ROCm path is mentioned anywhere in the official repo. Even the
-  MoE-efficient "13B active" framing doesn't help here: the tooling itself
-  assumes an NVIDIA multi-GPU cluster, and the 80B total parameter set still
-  has to be resident somewhere.
-- 32GB of headroom on one AMD card doesn't move this model into reach at
-  any precision considered here; it's excluded regardless of how much VRAM
-  frees up on this specific box.
-
-### Krea-2 (Turbo) — promising, but unverified on this hardware
-
-- Verified directly against primary sources per the ticket's instruction
-  (this is a June 2026 release, past most training cutoffs): the
-  [Hugging Face model card](https://huggingface.co/krea/Krea-2-Turbo) states
-  a release date of **June 22, 2026**, a 12-billion-parameter single-stream
-  diffusion transformer, `torch.bfloat16` as the reference precision, and
-  the **Krea 2 Community License**.
-- License, per [krea.ai/krea-2-licensing](https://www.krea.ai/krea-2-licensing):
-  non-commercial (including explicitly personal/homelab) use is free;
-  commercial use is permitted royalty-free for entities under $1M
-  trailing-12-month revenue (same shape as SD3.5's and matching this
-  homelab's use case); content-filter and AI-disclosure obligations apply if
-  deployed publicly; derivative model names must start with "Krea".
-- ComfyUI added native support for both open-weight checkpoints (Krea 2 Raw
-  and Krea 2 Turbo) per [blog.comfy.org's announcement](https://blog.comfy.org/p/krea-2-open-source-models-are-now),
-  and community GGUF quants already exist
-  ([molbal/krea2-gguf](https://huggingface.co/molbal/krea2-gguf)), with
-  reports (RTX hardware only) of fp8 fitting 16GB and GGUF fitting 12GB.
-- **No AMD/ROCm mention anywhere** in the model card, and no gfx1201/R9700
-  community report was found — unsurprising given the model is roughly
-  2.5 months old at the time of this research. Its architecture (a
-  standard-shaped DiT that ComfyUI loads through its normal diffusion-model
-  nodes, per the ComfyUI blog post) gives reasonable expectation it will run
-  on the same ROCm/PyTorch backend already proven for FLUX and Qwen-Image on
-  this card, but that's an inference, not a verified fact the way
-  kyuz0's Qwen-Image workflow is.
-- **Not the pick today**, precisely because Qwen-Image already offers a
-  hardware-verified path at a comparable parameter count and VRAM budget.
-  Worth a follow-up research ticket once R9700/gfx1201-specific Krea-2
-  reports exist — the license and ComfyUI support are both already in
-  place, so the only open question is real-world ROCm behavior.
-
-## Recommendation
-
-**Qwen-Image, FP8 precision** (`qwen_image_fp8_e4m3fn.safetensors` +
-`qwen_2.5_vl_7b_fp8_scaled.safetensors`, ~25GB combined), optionally paired
-with the 4-step Lightning LoRA the R9700-specific validated workflow uses.
-It wins on all three axes the ticket asked about:
-
-1. **License**: Apache-2.0 — no restriction at all, strictly better than
-   every other candidate including the current FLUX.1-schnell pick.
-2. **ROCm/gfx1201 compatibility**: the only candidate with a workflow
-   pre-validated specifically on this GPU architecture
-   ([kyuz0/amd-r9700-comfy](https://github.com/kyuz0/amd-r9700-comfy)),
-   not just "ComfyUI supports ROCm in general."
-3. **VRAM at the ~32GB ceiling**: ~25GB at FP8 leaves real margin for
-   ComfyUI runtime/VAE overhead, without needing the multi-step,
-   non-distilled FLUX.1-dev's full 24GB bf16 footprint or accepting
-   SD3.5's lower fidelity ceiling — and it's a genuine capability upgrade
-   over schnell (20B vs. 12B, non-distilled-quality text rendering) rather
-   than just a bigger file.
-
-If the known Qwen-Image/ROCm edit-mode rough edge
-([pollockjj/ComfyUI-MultiGPU #133](https://github.com/pollockjj/ComfyUI-MultiGPU/discussions/133))
-turns out to affect plain text-to-image generation too, SD3.5-large (AMD's
-own first-party "SD3.5 Simple" ComfyUI-ROCm template workflow) is the
-fallback, with FLUX.1-dev as a second option. HunyuanImage-3.0 is excluded
-outright regardless of available VRAM (CUDA-only tooling, multi-GPU
-datacenter VRAM floor). Krea-2 is worth revisiting once R9700-specific
-field reports exist.
-
-## Sources consulted
-
-- [docs/research/image-generation-options.md](image-generation-options.md) (this repo — prior findings, not re-derived)
-- [Qwen/Qwen-Image (Hugging Face)](https://huggingface.co/Qwen/Qwen-Image)
-- [QwenLM/Qwen-Image (GitHub)](https://github.com/QwenLM/Qwen-Image)
-- [ComfyUI Wiki — Qwen-Image native support announcement](https://comfyui-wiki.com/en/news/2025-08-05-qwen-image)
-- [Comfy-Org/Qwen-Image_ComfyUI (Hugging Face, FP8 checkpoints)](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders)
-- [Comfy-Org/ComfyUI issue #10852 — Qwen-Image VRAM](https://github.com/Comfy-Org/ComfyUI/issues/10852)
-- [ComfyUI official Qwen-Image example](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
-- [kyuz0/amd-r9700-comfy (R9700-specific ROCm ComfyUI toolbox)](https://github.com/kyuz0/amd-r9700-comfy)
-- [pollockjj/ComfyUI-MultiGPU discussion #133 (Qwen-Image-Edit ROCm rough edge)](https://github.com/pollockjj/ComfyUI-MultiGPU/discussions/133)
-- [black-forest-labs/FLUX.1-dev (Hugging Face) + LICENSE.md](https://huggingface.co/black-forest-labs/FLUX.1-dev)
-- [city96/FLUX.1-dev-gguf](https://huggingface.co/city96/FLUX.1-dev-gguf)
-- [stabilityai/stable-diffusion-3.5-large (Hugging Face) + LICENSE.md](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)
-- [Tencent-Hunyuan/HunyuanImage-3.0 (GitHub)](https://github.com/Tencent-Hunyuan/HunyuanImage-3.0)
-- [krea/Krea-2-Turbo (Hugging Face)](https://huggingface.co/krea/Krea-2-Turbo)
-- [Krea 2 Community License Agreement (krea.ai)](https://www.krea.ai/krea-2-licensing)
-- [blog.comfy.org — Krea 2 open-source models in ComfyUI](https://blog.comfy.org/p/krea-2-open-source-models-are-now)
-- [molbal/krea2-gguf (Hugging Face)](https://huggingface.co/molbal/krea2-gguf)

docs/research/image-generation-options.md

@@ -1,249 +0,0 @@
-# Research: adding local image generation to the stack
-
-**Question:** What's the best way to add local image generation alongside
-the existing Qwen3.8-27B / llama.cpp text stack, given a single AMD Radeon
-R9700 (32GB VRAM, ROCm/gfx1201 — not CUDA), routed through the OmniRoute
-gateway on the `ai-stack` Docker network?
-
-**Answer, short version:** run **ComfyUI** (official AMD-blessed ROCm
-Docker path exists, and OmniRoute already has a first-class `comfyui`
-provider — no bespoke API wrapper needed) with **FLUX.1 [schnell]**
-(Apache-2.0, 4-step, GGUF-quantizable) as the default model, falling back to
-**SDXL** for anything schnell's distilled-step license/quality tradeoffs
-don't suit. VRAM headroom against the current llama-server footprint is too
-tight for both to be resident at once at any real image quality — plan for
-**time-sliced use** (llama-server's existing lazytainer stop-on-idle pattern,
-mirrored for the image-gen service, or a manual "stop one, start the other"
-toggle), not concurrent operation.
-
-## Current VRAM baseline (from this repo)
-
-Per `docker-compose.yml` and `.env.example`, llama-server runs
-`Qwen3.8-27B-UD-Q4_K_XL.gguf` (17.6 GB weights) at `--ctx-size 262144` with
-`--cache-type-k q8_0 --cache-type-v q8_0`, landing at **~25.6 GB** total
-(weights + q8_0 KV cache), leaving **~6 GB** free on the 32GB card — this
-matches the math already recorded in
-[`docs/research/qwen3.8-27b-quant.md`](qwen3.8-27b-quant.md). Per the auto-memory
-note on this repo, real measured VRAM use has run closer to ~75% (~24 GB) in
-practice versus the theoretical estimate, which doesn't change the
-conclusion below but means the ~6 GB figure is closer to a ceiling than a
-comfortable number.
-
-**Implication:** 6 GB is not enough for any current-generation image model at
-usable quality (see VRAM table below — even the smallest practical FLUX
-quant wants ~7 GB alone, before ComfyUI's own runtime/VAE overhead). Running
-image-gen *concurrently* with llama-server resident is not realistic on this
-card. The two need to time-share the GPU, not split it.
-
-## Backend evaluation (ROCm support, checked against primary sources)
-
-### ComfyUI — recommended
-
-- **Official AMD ROCm docs exist and are current.** AMD's own ROCm docs site
-  hosts a dedicated ComfyUI install guide with a prebuilt Docker image path
-  (recommended) or build-from-source, listing ROCm 7.2.0 and 7.1.0 as
-  supported versions, explicit `--device=/dev/kfd --device=/dev/dri
-  --group-add video` flags (same device-passthrough pattern this repo
-  already uses for llama-server), and template workflows including "SD3.5
-  Simple". Officially the guide only names AMD Instinct
-  MI355X/MI325X/MI300X (datacenter cards) as supported platforms.
-  Source: [ROCm docs — ComfyUI on ROCm installation](https://rocm.docs.amd.com/projects/comfyui/en/docs-26.04/install/comfyui-install.html).
-- **The upstream ComfyUI README itself documents AMD support directly**,
-  including consumer cards: stable ROCm install via
-  `pip install torch torchvision torchaudio --index-url
-  https://download.pytorch.org/whl/rocm7.2`, plus an experimental Windows
-  build explicitly naming **RDNA 3 (RX 7000), RDNA 3.5 (Strix Halo), and
-  RDNA 4 (RX 9000 series)** — i.e. the same RDNA4 generation as the R9700 —
-  and `HSA_OVERRIDE_GFX_VERSION` workarounds for older/unlisted cards.
-  Source: [comfyanonymous/ComfyUI README](https://github.com/comfyanonymous/ComfyUI).
-- **AMD has published a specific RDNA4/RX 9000 ComfyUI guide** (separate
-  from the Instinct-only install page above), confirming RDNA4 consumer
-  cards are an explicitly supported, first-party-documented target, not just
-  a community workaround.
-  Source: [ROCm blog — Getting Started with ComfyUI on AMD Radeon RX 9000 Series GPUs](https://rocm.blogs.amd.com/artificial-intelligence/comfyui-radeon-9000/README.html).
-- **gfx1201 (R9700's arch) specifically has active community Docker images**:
-  `yurisasc/comfyui-rocm-rdna4` targets ROCm 7.1 + PyTorch 2.9.1 with
-  `HSA_OVERRIDE_GFX_VERSION=12.0.1` / `PYTORCH_ROCM_ARCH=gfx1201` baked in,
-  and there's a published community patch specifically for R9700 AI Pro +
-  ComfyUI video-gen speedups, evidence the card is being run today, not just
-  theoretically compatible.
-  Sources: [yurisasc/comfyui-rocm-rdna4](https://github.com/yurisasc/comfyui-rocm-rdna4),
-  [charlie12345/R9700AIProComfyUIPatch](https://github.com/charlie12345/R9700AIProComfyUIPatch).
-- **Known gfx1201 caveat:** AMD's own TransformerEngine repo has an open
-  issue confirming gfx1201 is missing from the FP8 architecture table, so
-  FP8 kernels silently fall back to FP32 with ~50% throughput loss
-  (18-22 vs. 35-40 tok/s in the reporter's LLM benchmark) — not a
-  correctness blocker, but relevant if planning to use FP8-quantized image
-  models expecting native FP8 speed on this card; GGUF/Q-quants (see below)
-  avoid this path entirely since they dequantize to bf16/fp16, not fp8.
-  Source: [ROCm/TransformerEngine issue #520](https://github.com/ROCm/TransformerEngine/issues/520).
-- **Actively maintained community Docker packaging** beyond AMD's own image:
-  `YanWenKun/ComfyUI-Docker` ships parallel `rocm` (PyTorch-build-based,
-  faster releases) and `rocm7` (AMD-build-based, more comprehensive)
-  variants, both targeting ROCm 7, with ~1000 commits of ongoing history —
-  a viable alternative to the official AMD image if it lags behind ComfyUI
-  releases.
-  Source: [YanWenKun/ComfyUI-Docker](https://github.com/YanWenKun/ComfyUI-Docker).
-
-### AUTOMATIC1111 / Forge — usable but a step down for this hardware
-
-- ROCm support for A1111/Forge is real but community-patched, not
-  first-party. The upstream `lllyasviel/stable-diffusion-webui-forge` repo's
-  own discussion thread on AMD support points users to
-  `lshqqytiger/stable-diffusion-webui-amdgpu-forge`, a community fork
-  specifically maintained for AMD, "regarded as the go-to version" for
-  running FLUX-era models on AMD — i.e. the *mainline* Forge repo does not
-  claim ROCm support itself; you're expected to run a fork.
-  Source: [lllyasviel/stable-diffusion-webui-forge discussion #67](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/67).
-- No first-party AMD vendor documentation (unlike ComfyUI's AMD-authored
-  ROCm/RDNA4 blog posts above) was found for A1111/Forge specifically.
-  Given ComfyUI already has an AMD-blessed path plus a first-class OmniRoute
-  provider (below), there's no reason to take on a community fork's
-  maintenance risk instead.
-
-### InvokeAI — usable but weaker AMD story for a new-generation card
-
-- InvokeAI documents ROCm support but flags it as second-tier: "AMD GPUs
-  are only supported on Linux," and "support for newer AMD GPUs is spotty
-  ... you may experience garbled images, black images, or long startup
-  delays." Its own install docs reference ROCm 5.4.2-era wheels, notably
-  older than the ROCm 7.x this stack's llama-server image already runs on
-  gfx1201.
-  Source: [InvokeAI installation docs (mauwii mirror)](https://mauwii.github.io/InvokeAI/installation/030_INSTALL_CUDA_AND_ROCM/).
-- No OpenAI-compatible-images angle either — same drawback as A1111/Forge.
-  Not recommended as primary given ComfyUI's stronger, more current AMD
-  documentation trail.
-
-## Model choice: FLUX.1 [schnell] vs FLUX.1 [dev] vs SDXL vs SD3.5
-
-| Model | License | Params | Notes |
-|---|---|---|---|
-| **FLUX.1 [schnell]** | **Apache-2.0** — fully open, no commercial restriction | 12B | Distilled for 1-4 step inference (fast); Black Forest Labs' own model card states this license directly |
-| FLUX.1 [dev] | [FLUX.1-dev Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev) | 12B | Non-commercial for the *model/weights*; generated *outputs* are explicitly usable commercially per BFL's license page. Higher quality than schnell (more steps, non-distilled) but the weights themselves can't be redistributed/used commercially |
-| SDXL | CreativeML OpenRAIL++ (permissive, commercial-friendly) | ~3.5B | Older (2023), lower fidelity than FLUX/SD3.5 by current standards, but lowest VRAM footprint and best long-standing tooling maturity |
-| SD3.5 (Large/Medium) | Stability AI Community License — free commercial use under $1M annual revenue, else enterprise license required | 8B / 2.5B | Free for this repo's non-commercial homelab use regardless; template already listed in AMD's own ComfyUI-ROCm doc ("SD3.5 Simple") as a first-party example workflow |
-
-Sources: [black-forest-labs/flux model cards](https://github.com/black-forest-labs/flux/blob/main/model_cards/FLUX.1-dev.md),
-[black-forest-labs/FLUX.1-schnell on Hugging Face](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
-(license: apache-2.0), [FLUX.1-dev LICENSE.md](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md),
-[Stability AI — Introducing Stable Diffusion 3.5](https://stability.ai/news-updates/introducing-stable-diffusion-3-5),
-[stabilityai/stable-diffusion-3.5-large LICENSE.md](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md).
-
-**Recommendation: FLUX.1 [schnell].** For a private homelab, license
-enforcement isn't the deciding factor by itself, but schnell's Apache-2.0
-status removes any future ambiguity if outputs or the setup are ever shared
-or repurposed, and its whole design point — good quality in 1-4 sampling
-steps — directly addresses the VRAM/time-slicing constraint below (less
-GPU-resident time per image than a 20-50 step dev/SDXL/SD3.5 run).
-Quantized via `city96/ComfyUI-GGUF` (an actively-referenced, community-
-trusted quantization node — its GGUF Q-quants dequantize to bf16/fp16 at
-runtime, sidestepping the gfx1201 FP8 dequant bug above entirely), FLUX fits
-in a fraction of its fp16 footprint:
-
-| Precision | Approx. VRAM (model only) |
-|---|---|
-| fp16 (baseline) | ~24 GB |
-| fp8 | ~12 GB |
-| GGUF Q5_K_S | ~12-15 GB (practical quality floor) |
-| GGUF Q4_K_S | ~7 GB (quality starts degrading on hands/text below Q4) |
-
-Source: aggregated VRAM figures from GGUF-quantization write-ups referencing
-city96's FLUX GGUF conversions — treat as secondary/community sourced
-(no single BFL-published VRAM table was found), consistent across multiple
-independent sources.
-[city96/ComfyUI-GGUF README](https://github.com/city96/ComfyUI-GGUF/blob/main/README.md),
-[city96/FLUX.1-dev-gguf model card](https://huggingface.co/city96/FLUX.1-dev-gguf).
-
-**Fallback pick: SDXL.** If schnell's distilled quality ceiling proves too
-low for some use case, SDXL is the safer second choice over FLUX.1 [dev] or
-SD3.5 specifically *because* of this card's tight headroom: it's the
-smallest of the four by a wide margin, has the longest production track
-record on ROCm of any of these models, and its OpenRAIL++ license carries no
-revenue-threshold clause to track (unlike SD3.5's Community License) or
-non-commercial weight restriction (unlike FLUX.1 [dev]).
-
-## OpenAI-compatible API / OmniRoute integration
-
-This is the best news in this research: **OmniRoute already ships a
-first-class, built-in `comfyui` provider** — not a generic "point it at an
-OpenAI base URL and hope" integration. Its provider reference documents it
-explicitly: *"No API key required. Configure the local ComfyUI base URL
-(default: http://localhost:8188)."* OmniRoute's own image-routing feature
-set (`/v1/images/generations`, `/v1/images/edits`, `/v1/images/variations`,
-with automatic provider fallback) is designed for exactly this pattern:
-register ComfyUI as a backend, then any client already calling OmniRoute's
-OpenAI-compatible images endpoints reaches it with no extra shim.
-Source: [diegosouzapw/OmniRoute PROVIDER_REFERENCE.md](https://github.com/diegosouzapw/OmniRoute/blob/main/docs/reference/PROVIDER_REFERENCE.md),
-[diegosouzapw/OmniRoute repo description](https://github.com/diegosouzapw/OmniRoute).
-
-This means **ComfyUI does not need an extra OpenAI-API wrapper project** —
-the wrapper projects found during this research
-([`ComfyUI-OpenAI-Compatible-API`](https://github.com/yeeyou/ComfyUI-OpenAI-Compatible-API))
-turned out to be a ComfyUI *custom node* for calling *outbound* to LLM APIs
-from within a workflow (the reverse direction), not something this stack
-needs — OmniRoute's own native ComfyUI provider is the actual integration
-point, one layer up.
-
-**Confidence note:** the provider-reference detail above was fetched via an
-automated summarizer against the raw doc rather than manually re-verified
-line-by-line; re-check `PROVIDER_REFERENCE.md`'s `comfyui` entry directly
-before wiring this up, in case ComfyUI's own `/prompt` API (a
-workflow-graph-shaped API, not a simple text-prompt-in/image-out call) needs
-a specific default workflow JSON configured on the OmniRoute side to produce
-a plain text-to-image call.
-
-## Integration sketch (not a full compose — see caveats above)
-
-- New service in `docker-compose.yml`, e.g. `comfyui`, image
-  `rocm/comfyui-rocm` (or `yurisasc/comfyui-rocm-rdna4` for a gfx1201-tuned
-  build) or built from AMD's own ROCm ComfyUI Dockerfile, same
-  `/dev/kfd` + `/dev/dri` + `group_add: [video, render]` device-passthrough
-  block already used for `llama-server`, joined to the same `ai-stack`
-  network so `omniroute` can reach it as `http://comfyui:8188` — no host
-  port needed (matches the existing llama-server pattern of no published
-  port, gateway-only access).
-- Register it in OmniRoute's dashboard as a `comfyui` provider pointing at
-  that internal URL, same manual-registration pattern already used for
-  llama-server and searxng-search per `docs/proxy-key-onboarding.md`.
-- **VRAM contention is the real design problem, not networking.** Given the
-  ~6 GB headroom, the two services can't both sit GPU-resident.
-  Two workable patterns, in order of how well they fit what's already in
-  this repo:
-  1. **Mirror the existing lazytainer stop-on-idle pattern** already applied
-     to `llama-server` (`docker-compose.yml`'s `lazytainer.group.*` labels) —
-     add an equivalent idle-timeout group for `comfyui`, and rely on the two
-     services naturally not being hit at the same time for a single-user
-     homelab. This doesn't *guarantee* mutual exclusion (both could still be
-     woken concurrently and both try to fit in 6 GB free), so it's a
-     reasonable-effort fit, not a hard guarantee.
-  2. **Explicit mutual exclusion**: a small script/compose profile that
-     stops `llama-server` before starting `comfyui` (and vice versa) rather
-     than relying on lazytainer's independent idle timers — worth doing if
-     the reasonable-effort version above causes a visible OOM in practice.
-  Either way, this is a "pick one, then the other" story, not "run both."
-- Given FLUX.1 [schnell]'s 1-4 step design, a cold-start-and-generate cycle
-  (wake ComfyUI from lazytainer sleep, generate, let it idle back down) is
-  a reasonably good fit for occasional image requests through the same
-  gateway that already does this for llama-server.
-
-## Sources consulted
-
-- [ROCm docs — ComfyUI on ROCm installation](https://rocm.docs.amd.com/projects/comfyui/en/docs-26.04/install/comfyui-install.html)
-- [ROCm blog — ComfyUI on AMD Radeon RX 9000 Series (RDNA4)](https://rocm.blogs.amd.com/artificial-intelligence/comfyui-radeon-9000/README.html)
-- [comfyanonymous/ComfyUI README](https://github.com/comfyanonymous/ComfyUI)
-- [YanWenKun/ComfyUI-Docker](https://github.com/YanWenKun/ComfyUI-Docker)
-- [yurisasc/comfyui-rocm-rdna4](https://github.com/yurisasc/comfyui-rocm-rdna4)
-- [charlie12345/R9700AIProComfyUIPatch](https://github.com/charlie12345/R9700AIProComfyUIPatch)
-- [ROCm/TransformerEngine issue #520 (gfx1201 FP8 fallback)](https://github.com/ROCm/TransformerEngine/issues/520)
-- [lllyasviel/stable-diffusion-webui-forge discussion #67 (AMD support)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/67)
-- [InvokeAI CUDA/ROCm install docs](https://mauwii.github.io/InvokeAI/installation/030_INSTALL_CUDA_AND_ROCM/)
-- [black-forest-labs/flux GitHub repo + model cards](https://github.com/black-forest-labs/flux)
-- [black-forest-labs/FLUX.1-schnell (Hugging Face, Apache-2.0)](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
-- [black-forest-labs/FLUX.1-dev LICENSE.md](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
-- [Stability AI — Introducing Stable Diffusion 3.5](https://stability.ai/news-updates/introducing-stable-diffusion-3-5)
-- [stabilityai/stable-diffusion-3.5-large LICENSE.md](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md)
-- [city96/ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF)
-- [city96/FLUX.1-dev-gguf](https://huggingface.co/city96/FLUX.1-dev-gguf)
-- [diegosouzapw/OmniRoute](https://github.com/diegosouzapw/OmniRoute) and its `PROVIDER_REFERENCE.md`
-- [yeeyou/ComfyUI-OpenAI-Compatible-API](https://github.com/yeeyou/ComfyUI-OpenAI-Compatible-API) (checked and ruled out — wrong direction)
-- This repo: `docker-compose.yml`, `.env.example`, `docs/research/qwen3.8-27b-quant.md`

scripts/switch-model.sh

@@ -1,50 +0,0 @@
-#!/usr/bin/env bash
-# Swap GPU residency between llama-server (Qwen) and comfyui — they never
-# run concurrently, VRAM doesn't fit both (see issue #38's map). Manual
-# invocation only, no auto-switching.
-#
-# Bypasses lazytainer entirely and drives docker compose directly — its
-# idle-stop can't be used for this. Root cause (see
-# docs/research/lazytainer-omniroute-idle-stop.md, issue #40): lazytainer's
-# packet-threshold detector is source-blind and can't tell OmniRoute's
-# periodic health-check pings apart from real traffic on the same port, so
-# it never reliably sleeps a service on its own. A scripted swap always
-# knows which service should go up/down, so it doesn't need that heuristic.
-#
-# OmniRoute may show the just-stopped provider as errored/offline in its
-# dashboard for up to CREDENTIAL_HEALTH_CHECK_INTERVAL (default 5 min) after
-# a swap — cosmetic, not a functional problem (see the research doc above).
-set -euo pipefail
-cd "$(dirname "$0")/.."
-
-usage() {
-  echo "Usage: $0 {qwen|comfyui}" >&2
-  echo "  qwen    - stop comfyui, start llama-server" >&2
-  echo "  comfyui - stop llama-server, start comfyui" >&2
-  exit 1
-}
-
-[ $# -eq 1 ] || usage
-
-case "$1" in
-  qwen)
-    from=comfyui
-    to=llama-server
-    ;;
-  comfyui)
-    from=llama-server
-    to=comfyui
-    ;;
-  *)
-    usage
-    ;;
-esac
-
-echo "==> stopping $from"
-docker compose stop "$from"
-
-echo "==> starting $to"
-docker compose up -d "$to"
-
-echo "==> status"
-docker compose ps

scripts/update.sh

@@ -183,7 +183,6 @@ set_if_blank OMNIROUTE_STORAGE_ENCRYPTION_KEY "$(openssl rand -hex 32)"
 set_if_blank OMNIROUTE_MACHINE_ID_SALT "$(openssl rand -hex 16)"
 set_if_blank OMNIROUTE_CLI_SALT "$(openssl rand -hex 16)"
 set_if_blank OMNIROUTE_WS_BRIDGE_SECRET "$(openssl rand -hex 32)"
-set_if_blank NEO4J_PASSWORD "$(openssl rand -hex 16)"
 
 echo "==> resolving SEARXNG_LAN_IP"
 # search.home is a LAN mDNS/local-DNS name — resolvable from this host, just
@@ -227,7 +226,6 @@ docker compose build --pull
 echo "==> ensuring models are downloaded (skips already-present files)"
 docker compose --profile tools run --rm downloader
 docker compose --profile tools run --rm downloader-classifier
-docker compose --profile tools run --rm downloader-comfyui
 
 echo "==> bringing up omniroute"
 docker compose up -d --wait omniroute