Index of everything on this site: https://ai.hiina.space/index.md
Pick a 6-page silent-heavy short with two or three characters and about 30 panels, in B&W line art with flat grays. Keep dialogue sparse, so failures show up in staging and transitions rather than being papered over by captions. The recommended stack has four layers. Claude Code as author, storyboarder and editor runs on existing agent compute, so orchestration has no marginal cost. Panels render locally on a single 24 GB consumer GPU through ComfyUI, using Qwen-Image-Edit-2511 (Apache-2.0) plus the InstantX ControlNet Union for pose and depth. A 20B model will need an FP8 or GGUF quant to fit in 24 GB, which I haven't verified, and this research found no timing for it on that class of card, so measure on day one. For a sense of scale, FLUX.2 dev Q8 takes 15-17 s per 1024px image on an RTX 4090 (RunAIHome). Add one API image model as a second arm: Gemini 3.1 Flash Image or GPT Image 2. Compositing is Python generating SVG, rendered by Inkscape CLI or Chromium, with resvg for fast previews and a Blambot indie-licensed font. Kumiko and Magi run as layout and reading-order checkers. The cold reader is Gemini or GPT, with exact crops supplied by the compositor.
For the human readers, recruit 6-10 people. Each ranks the 4 variants of the story and answers the author's comprehension questions, at about 20 minutes per reader. Compute Spearman between the judge's ranking and the human ranking. CANVAS's 0.66-0.71 is a reasonable bar.
Calibrate the reader before trusting it, with a shuffled-panel reorder probe. Shuffle each page's panels, ask the reader to restore the order, and score Kendall tau. Run the same probe on a human-made comic of similar length. Public-domain Golden Age comics from the Digital Comic Museum, the source CoMix draws on, are a reasonable choice (CoMix). Without that control, a low score can't separate "the comic is unclear" from "the reader is weak", since panel sorting is exactly where models trail humans by nearly 40 points (ComicVQA). Report the probe relative to the control, not as an absolute number. Pair it with a TeachQuiz-style functional test, borrowed from Code2Video's metric in which an "unlearned" model answers questions after watching (Code2Video PMLR). The reader retells the story and answers the author's questions without seeing the script.
| Ablation | Arms | What it shows |
|---|---|---|
| Whole page vs studio | GPT Image 2 or Nano Banana Pro given script plus sheets, one page per call, vs full pipeline | Whether the studio is worth it at today's model quality; extends MangaFlow's baseline to 2026 models and multiple pages |
| Sketch conditioning | Text-only panel spec vs spec plus rasterized SVG thumbnail vs spec plus pose/depth map from the SVG | The untested core claim about LLM sketches; measure first-try acceptance rate and spec-diff errors |
| Critic family | Claude reads Claude's pages vs Gemini/GPT reads them, same rubric, both validated against the human ranking | Whether the cross-family observation holds for visual critique; no published study covers this |
| Critic on/off | 0 vs 2 reader-driven revision rounds | Whether the loop adds value beyond the planner; Code2Video saw about 6.5-21 points from its critic against about 41 from its planner |
Rough budget, my own arithmetic, at prices as of late October 2026. Re-check the live pricing pages before spending. Character sheets come to about 18 images. Each studio arm is 30 panels x 3 candidates plus about 20% redraws, so about 108 images, and the two sketch arms double that. The whole-page baseline adds about 24 page generations and the critic arms about 36 redraws, so about 300 images in total. At Google's listed $0.067 per 1K image for Gemini 3.1 Flash Image (Gemini pricing), that is about $20. Segmind's third-party measurement of GPT Image 2 is $0.066 for medium and $0.264 for high quality at 1024px, which gives about $20 or $79 (Segmind). Segmind also measured Nano Banana 2 above Google's own figure, so test-call before budgeting. VLM reading is cheap. Claude bills images at about (w/28)x(h/28) tokens, capped at 4,784 for a high-res page (Claude vision). A page read with five crops comes to about 13K input tokens. At roughly 2/10 per million, Sonnet 5.5's listed rate (Claude pricing), and assuming a similar tier for the cross-family reader, that is about 5 cents per read. The 4 arms x 6 pages x 3 rounds x 2 critic families x 3 seeds come to about 430 reads, or about $20, plus a few dollars of panel QA.
| Configuration | Images | VLM reads | Estimated total |
|---|---|---|---|
| Panels local on a 24 GB consumer GPU, API only for the reader and the whole-page baseline | ~$2-6 | ~$20-25 | ~$25-30 |
| Mid-priced API images (Gemini Flash Image 1K, GPT Image 2 medium) | ~$20 | ~$20-25 | ~$40-50 |
| GPT Image 2 high for all panels | ~$80 | ~$20-25 | ~$100-130 |
Orchestration inside Claude Code on existing agent compute adds nothing at the margin. Running the agents through the API instead adds a few dollars: PosterAgent spent about 101K tokens per poster (PosterAgent). Rent a GPU only for FLUX.2 dev at full precision or for parallel arms. Runpod lists an RTX 4090 at $0.89/h and an H100 SXM at $3.99/h (Runpod). The scarcest resource is human attention: the three checkpoints plus the reader study.
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space