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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. 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.
  • 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.
  • 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.
  • 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, 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.
  • 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.

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.
  • 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).
  • 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 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, black-forest-labs/FLUX.1-schnell on Hugging Face (license: apache-2.0), FLUX.1-dev LICENSE.md, Stability AI — Introducing Stable Diffusion 3.5, stabilityai/stable-diffusion-3.5-large 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, city96/FLUX.1-dev-gguf model card.

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, diegosouzapw/OmniRoute repo description.

This means ComfyUI does not need an extra OpenAI-API wrapper project — the wrapper projects found during this research (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

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