Comment-only pass over the Python this PR touches: drop what the code already
says, collapse multi-line explanations that still read on one line, and keep
the reasoning that is not recoverable from the code. No code, docstring
semantics or behaviour changes; verified with an AST comparison against the
previous revision, and the backend suite is unchanged (same 37 environment
failures as before: the API integration tests that need a live keyed server,
the flash-attn install hooks, and the GPU memory fields).
Two correctness fixes:
- The cache keyed the checkpoint and its companion base by name only, so
a Hub repo advancing to a new commit, or a local directory updated in
place, kept returning embeddings from the previous text encoder. Pair
both with a revision marker: the locally resolved commit sha for a Hub
repo, config plus text-encoder file stats for a directory. Neither
loads the encoders, so a warm run still keeps them off the GPU.
- flow_shift only checked positivity, but JSON accepts 1e309, which
floats to inf, and inf <= 0 is False while NaN fails every comparison.
The sigma table then evaluates s * u / (1 + (s - 1) * u) as NaN, which
poisons every sampled sigma and saves a corrupted adapter while
progress looks normal. Require a finite value.
Four bugs in the batched inference path, all found by review:
- A mixed-prompt batch sent a scalar negative prompt against a prompt
list. Z-Image asserts on the length, and Qwen-Image, Krea 2 and FLUX
true-CFG encode a batch-1 negative against batch-N latents and fail in
the transformer's text/image concat. Broadcast it to match the batch.
- The FBCache step-cache reset sat above the chunk loop. diffusers only
resets that state at the end of a successful call, so a forward that
raised (the OOM the backoff is meant to recover) left its own residual
behind and the halved retry died on a shape mismatch. Reset before
every forward instead.
- The conditioning cache keyed on the checkpoint alone, but a GGUF or
single-file load takes its text encoders from the companion base, so
the same checkpoint against a different base reused the previous
base's embeddings. Key the base too.
- Gallery records stored the base seed and the requested batch size even
when a prompts/seeds list drove the run, so restoring the second image
of seeds=[5, 99] replayed seed 5. List-driven outputs now record as
single-image recipes on their own seed.
Also bound strength above 0: every img2img pipeline derives its step
count from it, so 0 leaves zero denoising steps and either raises or, on
SDXL, crashes on empty latents.
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.
