Batched generation: /images/generate takes a prompts list (one image per
prompt, txt2img only) or a seeds list (one prompt, one image per seed);
the legacy batch_size path derives per-image seeds base..base+n-1 like
the native engine. Every image gets its own torch.Generator so any batch
member replays alone from its gallery recipe; the whole list runs as one
forward by default with OOM backoff that halves a failed chunk, and an
explicit batch_size caps images per forward. Validated 10-22x over
serial engines on 32-image suites with LPIPS deltas within 0.002.
Conditioning cache on the inference path: UNSLOTH_DIFFUSION_COND_CACHE_DIR
(the inference sibling of the trainers' cond_cache_dir, same persistent
store) wraps encode_prompt so repeated prompts skip the text-encoder
forward entirely; verified bit-identical outputs. Bypassed while LoRA
adapters are attached; tensor-argument calls pass through uncached.
Compile cache: GGUF loads fingerprint their own bundles (quant=gguf, a
different compiled graph than the dense family) and batched calls
register every distinct (w, h, batch) chunk shape they ran, so the heavy
GGUF batched warmups (~159 s at batch 32 on 12B-class, ~655 s on 20B
CFG-batched) are paid once ever.
GGUF loader: strip the sd.cpp model.diffusion_model. container prefix in
the single-file converter; diffusers' FLUX.2 converter KeyErrors on it
and the Qwen-Image identity mapping strands the model on meta.
