Index of everything on this site: https://ai.hiina.space/index.md
Character consistency now has two workable tiers. The low-friction tier is training-free multi-reference edit models. Qwen-Image-Edit-2509 takes several input images, though the card says it performs best "with 1 to 3 input images". It natively supports depth, edge and keypoint conditions, and it is Apache-2.0 (Qwen-Image-Edit-2509). The 2511 successor claims better character consistency and less drift (Qwen-Image-Edit-2511). FLUX.2 dev does multi-reference editing on an RTX 4090 via 4-bit quantization, under a non-commercial licence (FLUX.2 dev). Gemini's Nano Banana family, GPT Image and Seedream cover the API side. The older, persistent tier is a per-character LoRA. One SDXL-class example trained on 23 images in 34 minutes on an RTX 4090 (Civitai). That is still worth doing for a protagonist who appears in hundreds of panels. The failure modes are well documented. Multiple character references fuse in DiffSensei, especially for similar designs, and in stacked IP-Adapters (DiffSensei, lilting.ch). BFL warns that Kontext edits accumulate artifacts after roughly six sequential turns (Vercel/BFL). The working rules follow from that: re-anchor every panel to the canonical sheet instead of chaining from the previous panel, spend the reference budget on two characters plus one sketch, and do two-character panels as one-character passes plus regional inpainting. No source evaluates any of the modern edit models on black-and-white manga identity retention. That is untested, not known to fail.
Composition control is mature through ControlNets. InstantX's Qwen-Image ControlNet Union handles canny, soft edge, depth and pose in one Apache-2.0 model, with a recommended scale of 0.8-1.0 (InstantX). The question that matters most here has thin and dated evidence: does an LLM-authored sketch steer a modern model better than a text description? In Control-GPT (2023), GPT-4 drew TikZ sketches to condition a ControlNet. It improved spatial accuracy over SD (44% vs 19%), but position and size control stayed at 14% (Control-GPT). In Chat2SVG, Claude 3.5 Sonnet drew primitive SVG templates, which SDEdit plus a tile ControlNet then detailed. It won its user study, and the authors list "imprecise LLM layouts" as a limitation (Chat2SVG). LLM-grounded Diffusion reported roughly doubled accuracy on spatial and numeracy prompts from LLM-emitted boxes (LMD). None of this tests GPT Image, Nano Banana or Qwen-Image-Edit with a rasterized SVG thumbnail as a reference image. My reading is that a crude SVG works best as a blocking spec: panel frame, figure placeholders, horizon line, reserved balloon space. Rasterize it to lineart, or better, to a pose or depth map, and feed it at a loose conditioning scale. Do not expect an LLM drawing to carry anatomy.
Panel layout does not need a learned model. The classic CityU work (Cao, Chan and Lau, SIGGRAPH Asia 2012) optimizes layouts from per-panel importance, regions of interest and panel relations. Those are exactly the fields an LLM storyboarder can emit, but I found no public code (CityU). Layout is strongly author-specific. A classifier given only panel borders identifies which of 104 Manga109 titles a page came from at 84.3%, while genre from borders averages 20.8% (arXiv 2412.19141). So a "house style" is better carried as few-shot layout examples than as genre rules. The practical move is to have the storyboarder emit a recursive guillotine split tree or explicit polygons, then verify the result with existing extractors. Kumiko recovers panels with OpenCV contours and numbers them, with an RTL option (Kumiko). Magi detects panels, text and characters, links dialogue to speakers, and orders everything in reading order. Its models are licensed for academic research only (Magi).
Lettering should be deterministic code, and every serious system agrees. Practitioner rules are easy to encode. Tails point at the speaker's mouth and never cross each other or a face. Balloons don't cover key art. Reading order runs top-left downward for Western comics and top-right leftward for manga. And text is placed before final art so the space is reserved (Clip Studio Tips). That last rule matters for the pipeline: balloon placement belongs in the thumbnail stage as a constraint, not as an afterthought. Fonts are the real licensing trap. Blambot's indie-comics licence allows print and flattened web use even for projects that make money (Blambot). Anime Ace is free only for independent comic creation and non-profit use (1001fonts).
Agent-drivable tooling comes out clearly ranked. SVG
generated by code and rendered on the command line is the
lowest-friction path. resvg is a dependency-free Rust renderer, but its
README flags font loading as a rough edge (resvg). For text shaping,
rendering through Inkscape or headless Chromium is the safer bet; I
haven't verified which handles comic fonts best. Inkscape's CLI chains
--actions and can read from stdin and write to stdout. Many
actions still assume a GUI, though, and the wiki is unclear about
headless use, so plan on --batch-process or xvfb (Inkscape
wiki). Krita's Python API is rich: a vector layer accepts
addShapesFromSvg and returns toSvg (Krita
VectorLayer). But kritarunner has a confirmed, unfixed
bug where --script expects a module name and ignores
PYTHONPATH (KDE
391508). Headless Krita is undocumented. Use Krita as a human
handoff format at the end, not as a tool inside the loop. ComfyUI
exposes a clean queue API (POST /prompt,
GET /history/{id}, POST /upload/image,
WebSocket progress events), so an agent can drive a saved workflow as a
function call (ComfyUI
docs). Manga translation tools such as BallonsTranslator (GPL-3.0,
headless mode) and Koharu (MIT/Apache, Rust, PSD export) are most useful
as checkers and cleaners: they find stray glyphs the image model drew
anyway and inpaint them out (BallonsTranslator,
Koharu).
Screentone is the weakest link for true B&W manga. Sketch2Manga (arXiv 2403.08266) exists only as a research model. For a pilot, generate line art with flat grays and apply a deterministic halftone pass at compositing time. That is more consistent across panels than prompting each panel for tone.
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space