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Ywul30/dltwrkn_1920
dltwrkn_1920 is a text-to-image model from Ywul30. Use it when you need an image from a text prompt. It is set up for diffusers.
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From the Hugging Face model README
FLUX.1-dev LoRA. Trigger token: dltwrkn
| Base model | flux1-dev.safetensors (BFL checkpoint) |
| Dataset | 30 images, 6 repeats, 20 epochs |
| Steps | 3600 (180 per epoch) |
| Per-image exposure | 120 |
| Resolution | 1920 × 2880, enable_bucket = false |
| Network | dim 32 / alpha 16, networks.lora_flux |
| Learning rate | 5e-4, cosine, 1 cycle |
| Text encoder LR | 0 |
| Optimizer | adamw8bit |
| Batch size | 1 |
| Precision | bf16 (save fp16) |
| Noise offset | 0.05 |
| Timestep sampling | shift, model_prediction_type raw, guidance 1.0 |
| Attention | sdpa, gradient checkpointing on |
| Seed | 42 |
| Trainer | sd-scripts @ b8d1eb067eba32bb105984678b97f05b11452940 |
| Init | from scratch — no resumed weights |
Bucketing was disabled rather than configured: all 30 source files are exactly 1920 × 2880, so the bucketing mechanism had nothing to resolve. A mis-sized file therefore fails loudly instead of being silently rebucketed.
Checkpoints published: epochs 12, 15, 18, 20 (dltwrkn_1920.safetensors
is epoch 20).
dltwrkn is the first token of every caption. keep_tokens = 1,
shuffle_caption = false.Constants left deliberately unnamed: violet mass, orange contours, mesh, particles, black void.
Two phrases are verbatim in the corpus and must match at inference:
| Phrase | Instances | Expected binding |
|---|---|---|
seen through | 7 | supported |
bridges of light | 2 | partial |
Corpus tag count: 91 distinct tags across 30 captions.
Volume is carried by contour behaviour — weave compressing at a turning edge, ribbons riding curvature, foreshortening toward a silhouette — not by fill. A surface fails when the lattice runs straight across it and does not compress at the silhouette.
Three material classes, applied by rule but not consistently across the corpus:
Opaque vs. translucent has no corpus rule; the same figure can resolve both ways within one frame. A global caption cannot route this regionally — per-region control would require masking on the conditioning.
The previous LoRA (dltwrkn_1536) was trained on 2 caption tags total
and showed prompt-independent residue: unrequested hands, scrollwork,
tables, grid backgrounds. Its recorded per-image exposure was also double
what was intended (duplicate files in a .ipynb_checkpoints directory were
trained alongside the originals), placing it near the memorisation regime.
This run carries 91 tags, a clean file list, and a higher training resolution. Whether the caption scheme suppresses the residue is the open question.
Attribution caveat: corpus, caption scheme, and training resolution all changed together relative to the predecessor. A clean result is therefore not attributable to the caption scheme alone. The residue features (hand, scrollwork, table, grid) are the more diagnostic signal, since resolution has no plausible mechanism for suppressing them; material quality is confounded.
Recommended strength 1.00. A 1.00 / 1.12 / 1.50 sweep on the predecessor moved the weave finer and thinner rather than toward the target, and composition bleed appeared at 1.12 — strength is not the dial for material quality.