| # Feasibility findings — Hindi text LoRA on SD1.5
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| Measured 2026-09-28, CPU-only, on this machine. Scripts in `src/diag/`.
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| ## 1. Shaping (`verify_shaping.py`) — SOLVED
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| Pillow's wheel ships **without Raqm/HarfBuzz**, so `ImageDraw.text()` renders
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| Devanagari unshaped (broken conjuncts, ि on the wrong side). Silent corruption:
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| it looks plausible unless you check.
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| Fix: `src/render/shaper.py` = uharfbuzz (shaping) + freetype-py (raster).
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| Verified: क्ष/त्र/ज्ञ collapse 3 cp -> 1 glyph; ि reorders before its consonant;
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| स्त्री -> 2 glyphs. Continuous shirorekha confirmed visually.
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| ## 2. VAE capacity (`vae_size_sweep.py`, `vae_visual.py`) — HARD FLOOR AT ~32px EM
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| SD1.5's 4-ch VAE round-trip, measured inside the ink box:
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| | font em size | result |
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| |---|---|
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| | 12-20px | destroyed; nuktas gone, conjuncts illegible |
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| | 24px | marginal; nukta merges into stroke |
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| | **32px** | **clean — minimum safe size** |
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| | 48-64px | near-perfect |
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| => Every training sample must render text at **font em size >= 32px**
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| (FreeType set_pixel_sizes). NOT ink-box height: the measured ink/em ratio
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| varies 0.67 (bhaarat, no tall matras) to 1.08 (hindii) depending on which
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| matras a word carries, so ink height is content-dependent and a bad assert
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| target. Em size controls stroke width and is what the sweep actually varied.
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| => At 512 canvas this caps usable text at roughly 2-4 words.
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| => SD1.5 can do headline/signage text. It cannot do small body text. Ever.
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| ## 3. Text conditioning (`tokenizer_test.py`) — THE REAL BOTTLENECK
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| CLIP ViT-L/14 tokenizer vs Devanagari:
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| - **0 of 49,408 vocab entries contain any Devanagari codepoint.**
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| - Falls back to raw UTF-8 byte BPE: ~1-2 tokens per character.
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| - Character identity IS preserved (decode round-trips), so the signal is not
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| information-theoretically lost — but CLIP never trained on these tokens, so
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| the *embeddings* are meaningless.
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| - 77-token cap: MEASURED ~1.1 tokens/char even for conjunct-heavy strings
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| (kshatriya 8ch->9tok; pradhanmantri karyalaya 21ch->23tok). BPE merges the
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| 3-byte UTF-8 sequences efficiently -- there is no conjunct blowup. With a
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| 12-token caption template that leaves ~65 tokens = ~55-60 chars.
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| => Must train the **text encoder** alongside the UNet. A UNet-only LoRA cannot
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| work: the conditioning vectors it would read are noise.
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| => The token cap is NOT the binding constraint. At em 32px (~17.6px/char
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| advance) only ~27 chars fit across a 512px line, so PIXELS bind first.
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| Enforce length in tokens anyway (exact, template included), but scope is
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| set by the VAE floor.
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| ## Verdict
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| Viable, scope bounded by the VAE floor: short, large Devanagari text
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| (~1-4 words, <=~27 chars per 512px line). Not viable: small or dense text.
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