docs/comic_model_research.md

# Open-weight image models ≤32B for the Weiner 4-koma pipeline

Research date: 2026-06-12. Method: deep-research workflow (5 search angles, 22 sources,
108 claims extracted, 18 adversarially verified 3-0). Claims marked *(unverified)* had
their verification cut off by a rate limit — they come from official/primary sources
but were not independently confirmed.

## Use-case requirements

Generate a 2×2-grid 4-panel comic in ONE image (claymation/3D-render style, recurring
sausage-sensei mascot). Speech-bubble text composited programmatically afterward (in-model
text = bonus only). Script comes from a separate LLM → prompt adherence for multi-panel
scene layout is the critical capability. Character consistency within the grid (free, by
construction) and across generations (LoRA and/or image-reference conditioning).
Must run on HF ZeroGPU and be license-clean for a public hackathon Space.

## Infrastructure correction (verified 3-0)

ZeroGPU is no longer H200/70GB: it now runs **RTX Pro 6000 Blackwell** — `large`
(default) = half GPU / **48GB VRAM** at 1× quota, `xlarge` = full GPU / 96GB at 2× quota.
Max `@spaces.GPU(duration=...)` remains effectively ~120s per call.
Source: https://huggingface.co/docs/hub/spaces-zerogpu

## Quality bar

- Nano Banana 2 (Gemini 3.1 Flash Image Preview): Elo 1258, #4 on Artificial Analysis arena (verified).
- **HiDream-O1-Image-1.5: Elo 1264, #3 — ABOVE Nano Banana 2** (verified). Open-weight
  availability NOT confirmed (HiDream-I1 was MIT). **Watch item: if weights are open, test it.**

## Candidates

### Qwen-Image-Edit-2509 / 2511 (~20B MMDiT + Qwen2.5-VL encoder)
- License: **Apache 2.0** (verified).
- **Multi-image reference editing** (person+person, person+product, person+scene) —
  condition generation on mascot reference images; character lock WITHOUT training (verified).
- 2509 explicitly improved identity/character preservation across pose/style changes (verified).
- 2511 (unverified, official Qwen blog): further character-consistency gains; bakes popular
  community LoRAs into the base model.
- Known best-in-class in-model text rendering (bonus: maybe legible bubbles for free).
- 20B bf16 is tight-but-fine on 48GB with standard offload/quantization; official ZeroGPU
  Spaces exist; diffusers support mature.

### FLUX.2-klein 4B (+ 9B)
- 4B: **Apache 2.0** (verified). 9B: FLUX Non-Commercial License (verified) → avoid for public Space.
- ~13GB bf16; distilled 4-step variant = seconds per image (verified).
- **LoRA in ~1hr on a single RTX 4090 (<24GB) via ostris/ai-toolkit**, diffusers-loadable (verified).
- Unified t2i + editing + multi-reference per BFL blog/card *(unverified — votes lost to rate limit)*.
- We already have klein infra (tiny-army sidecar Space + klein starter repo with LoRA tooling).

### Z-Image / Z-Image-Turbo (Tongyi, 6B)
- **Apache 2.0** (verified). 6B family (verified).
- Turbo: 8 DiT forward passes, sub-second on H800, fits 16GB consumer VRAM (verified).
- Already integrated in tiny-army's local image path.
- No verified reference/editing variant or comic-layout evidence; distilled few-step models
  typically trade away complex-prompt adherence → risk for 4-panel layouts.

### FLUX.2-dev (~32B + Mistral Small 3.1 24B encoder)
- At/over the param cap; bf16 >80GB, ~62GB offloaded, ~20GB at 4-bit (verified).
- Native multi-reference up to 10 images (verified) — best built-in character conditioning.
- FLUX dev line is non-commercial-licensed → **ruled out** for a public hackathon Space.

### SD 3.5 / HiDream-I1 / CogView4
- Superseded on quality/adherence by the above within the same size envelope; no
  verified advantage for this use case. Not shortlisted.

## Ranking for this use case

1. **Qwen-Image-Edit-2511 (or 2509) — the comic engine.** Apache 2.0, the only verified
   ≤32B model with multi-image character-reference conditioning (mascot consistency with
   zero training: feed a Weiner reference sheet each call), strongest prompt adherence
   reputation for complex multi-panel scenes, and text rendering good enough that in-model
   bubbles might work. Cost: heaviest of the shortlist; needs the 48GB slice + offload and
   ~tens of seconds per comic (within 120s).
2. **FLUX.2-klein 4B — the on-theme Build Small pick.** Apache 2.0, trivially fast
   (4-step distilled), cheap LoRA pipeline already in-house → Weiner LoRA locks the mascot.
   Lower ceiling on per-panel scene complexity than 20B Qwen; the fine-tune narrows the
   style/character gap.
3. **Z-Image-Turbo 6B — speed fallback.** Apache 2.0, near-instant, already integrated;
   use if quota pressure dominates and layouts prove simple enough.

## Recommended architecture

- **A/B test first** (same 10 scripts through Qwen-Edit-2511-with-reference-sheet and
  klein-4B-with/without-LoRA; blank-bubble prompting + programmatic text composite).
- Production default: Qwen-Image-Edit-2511 + Weiner reference sheet (no training needed).
- Featured small-model tier: klein 4B + Weiner LoRA (trained on a reference corpus we can
  bootstrap with the bigger model) — the "fine-tuned the 4B open model on our mascot" story.
- Watch item: HiDream-O1-Image-1.5 weights — if open, benchmark immediately.