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