Krea's guidance-distilled FLUX.1-dev finetune keeps the exact dev layout, so it
runs under the existing flux.1 family unchanged. Wire it up end to end:
- Catalog group with the gated official bf16 pipeline and the open QuantStack
GGUF quants; the gated artifact is skipped on auto-routing when undownloaded.
- Trust the official repo for non-GGUF from_pretrained loads, next to the other
black-forest-labs bases.
- Generation defaults: 28 steps at guidance 4.5 per the model card. The generic
"krea" defaults key (Krea-2-Turbo's 8-step no-CFG recipe) used to swallow the
id, which would have produced garbage output; the new flux.1-krea key precedes
it on both the backend table and the images page table.
- The flux.1 prequant checkpoints are schnell-based; the loader's baked
base_model_id validation refuses them for the Krea-dev base, so int8/fp8
requests dense-quantize instead (covered by existing prequant tests).
Adapters are baked at load time: they attach to the dense transformer,
then quantize_ converts only the frozen base linears (the lora_ side
path is excluded by name), then the loader compiles. Post-quant PEFT
injection is not possible on a manually quantized module, so the
prequant shortcut is skipped for a baked load and the memory plan is
sized for the dense build (force_dense on the quant candidate).
At generation time the baked topology is frozen: weight tweaks and
disabling (scale 0 reproduces the quantized base exactly) go through
set_adapters, while adding or removing adapters returns a clean 400
telling the client to reload with the new selection.
supports_lora now returns True for int8/fp8 diffusers loads (checked
before the gguf-kind early return, since the quant fast path keeps the
picker kind); nvfp4/mxfp8 and GGUF-via-diffusers stay blocked. The
load request model takes an optional loras list, threaded through
begin_load on both engines (native ignores it and keeps applying LoRA
at generation).
Verified end to end on GPU: Z-Image GGUF picker + int8 + trained
adapter loads through the API, bake marker logged, weight 1.0 vs 0
renders differ visibly, weight 0.5 accepted live, unknown adapter
rejected as 400. Affected suites: 296 passed.
Register flux.2-klein and flux.2-dev in the DiT trainer following the
upstream DreamBooth references: latents train patchified and batch-norm
normalized from the VAE posterior mode, the packed forward reuses
step-invariant position ids, and the guidance vector (3.5) is gated on
the variant's guidance_embeds config. Conditioning stacks load per
variant (Mistral via Flux2Pipeline for dev, Qwen3 via Flux2KleinPipeline
for Klein) and are encoded and freed before the transformer lands on the
device. The fused single-stream to_qkv_mlp_proj joins the attention
projections in the LoRA targets; the single-stream out projection stays
dense because its to_out suffix would also match the double-stream
ModuleList container.
Wire both families through the training registry (family set, labels,
VRAM notes, rank 16 / lr 1e-4 defaults, bf16-only preflight), mark them
trainable with train base repos in the family registry, add FLUX.2-dev
to the gated-repo token check, and trust both official bases for
training downloads.
Verified on B200: 30-step klein int8 (19.6s) and nf4 (20.9s) and dev
int8 (52.0s) runs train with finite decreasing loss and the saved
adapters apply on the bf16 base pipeline (weight 0 reproduces the base
image exactly, weight 1 visibly restyles it).
A cold FLUX.2-dev int8 load on an idle 183 GB B200 planned offload=model
(companions exceed budget) and silently served the GGUF as-is; the identical
retry went resident and engaged the hosted prequant. The plan arithmetic was
byte-identical across both loads (required 90,228 MiB, resident needs free of
about 124 GB); the only divergent input was torch.cuda.mem_get_info, which is
device-wide and instantaneous: a transient foreign CUDA context briefly held
about 100 GB at the first snapshot, and the planner trusted that single read.
Three changes:
- settled_snapshot_device_memory: on cuda, synchronize + empty_cache
(best-effort) and take the MAX free over up to 3 spaced reads. A transient
can only shrink free, so the max rejects transient undercounts while a
persistent tenant still caps every read. _plan_memory now uses it.
- plan_fits_total_capacity + one replan retry: when the dense/prequant
candidate fits TOTAL device capacity under the standard reserve and the 0.85
resident margin, an offload verdict can only stem from the free reading, so
the loader re-snapshots and replans once before declining the fast path.
Explicit balanced/low_vram modes skip the retry (they offload by mode).
- diffusion.transformer_quant_declined log line with required/budget/free and
the plan reasons, so the next decline is diagnosable from the server log
(previously silent).
Verified: cold FLUX.2-dev int8 first load in a fresh server now engages the
hosted prequant resident (offload=none).
Qwen-Image's MMDiT runs every text-stream Linear at M = actual prompt tokens: the
Qwen2.5-VL embeds are not padded to a fixed length like FLUX's 512-token T5. A short
prompt (13 tokens) or the near-empty negative prompt drives torch._int_mm below its
M > 16 floor and the first denoise step raises 'self.size(0) needs to be greater than
16, but got 13' (measured on B200 through the Studio images tab).
Add per-family int8 exclusions (txt_in, add_q/k/v_proj, to_add_out, txt_mlp) for
qwen-image and qwen-image-edit, threaded through exclude_tokens_for_scheme(scheme,
family) and the prequant checkpoint validation, so a checkpoint baked under the old
token list is rejected and re-quantised instead of loaded crashing. The text stream
runs at M = tens vs the image stream's M ~ 4k, so the exclusion costs nothing; the
rebuilt hosted checkpoint gates 28/28 PASS with LPIPS mean 0.057 (was 0.069).
_assemble_pipe used Pipeline.from_pretrained for every family, but the krea repo
ships transformers-5.x configs and no top-level tokenizer files, so the tokenizer
dies with vocab_file=None. The pre-quantized checkpoint loaded fine and then the
assembly crashed, dropping the load to the GGUF build, which krea-2 cannot take
(Krea2Transformer2DModel has no from_single_file). Assemble per-component via
load_krea2_pipeline like the pipeline-kind and single-file paths already do.
Verified live: Krea-2-Turbo int8 and fp8 hosted prequant loads now assemble and
render through the Studio images tab.
Point prequant_repos for flux.1, flux.2-klein, flux.2-dev, qwen-image
(int8 only there; fp8 is family-denied), z-image and krea-2 at the
unsloth/<Model>-FP8 Hub repos carrying gate-validated int8 and fp8
transformer checkpoints, so the fast quant path loads the small
pre-quantized file instead of materialising the dense bf16 transformer
and quantising on device. Measured on FLUX.2-dev int8: build peak drops
from 60.7 GB (dense + quantize) to 30.7 GB (hosted prequant), identical
30.7 GB resident after either path since loading a checkpoint is
bit-identical to on-the-fly quantisation.
The hosted repos name files <Model>-<SCHEME>.pt, so resolve_prequant_source
now derives that model-name filename from the repo id (scheme suffix
stripped case-insensitively) and carries the legacy transformer_<scheme>.pt
as a fallback the resolver tries when the primary 404s, keeping older
repos loadable.
Wiring a repo also exposed a fallback hazard: with a prequant source
present, the dense-fit preflight used to be skipped entirely, so a failed
prequant download would fall through to the dense bf16 load the memory
plan never budgeted, OOMing after eviction. The preflight now always runs
and gates an allow_dense_fallback flag through _load_dense_quant_pipeline:
a dense misfit still skips the fast path when no prequant exists, but with
one it proceeds and a prequant failure raises to the GGUF build instead of
loading dense. The same flag is set when the auto-policy replans an
offloaded GGUF against a prequant-sized transient.
Tests updated to the new filename convention plus new coverage for the
derivation and the legacy-name fallback; the prequant-skips-refit test now
asserts the re-check runs and forbids the dense fallback. Verified end to
end on GPU: z-image int8 resolves the hosted repo, downloads the
model-name file and renders (6.8s load, 5.9 GB peak).
An all-zero activation token row makes the dynamic per-row fp8 scale 0,
which turns the quantized data to NaN and the render to black frames on
torchao's plain-torch kernel path. The fused fbgemm/mslk quantize kernels
clamp zero rows internally, so the bug only reproduces on machines without
them, which is most user environments. Zero rows are real inputs, not a
corner case: Wan 2.2 zero-pads its text conditioning, and Hunyuan-1.5 and
Qwen-Image regenerate zero rows inside their transformer blocks every step.
Pass activation_value_lb=1e-12 to Float8DynamicActivationFloat8WeightConfig
whenever the installed torchao supports the kwarg (Float8Tensor rework,
0.13+), checked via inspect.signature so older torchao keeps exactly the
current behaviour; the existing Float8MMConfig fallback chain is unchanged.
Verified on GPU: with the forced plain-torch kernel path a zero-row input
NaNs without the floor and stays finite with it, and end to end on
HunyuanVideo-1.5 fp8 goes from a solid black frame (LPIPS 1.00) to a normal
render (LPIPS 0.225); on Wan the floor matches the condition_embedder
exclusion (LPIPS 0.211 vs 0.206). Same-seed renders with fused kernels
present are unaffected, and pre-quantized fp8 checkpoints stay valid since
weight scales are untouched.
Images/Video: a background model load that fails AFTER starting (error/eviction during download) leaves the previous pipeline resident, but handleLoad had already overwritten lastLoad.current with the failed pick, so "Reapply to loaded model" reloaded the failed model. Carry the prior Reapply target into the poller and restore it on the async error/null paths, mirroring the quant rollback.
Training: an in-flight diffusion dataset upload passed _require_diffusion_dataset_mutable() at entry but could still commit files after a concurrent /diffusion/start reserved the training slot, mutating the dataset underneath the trainer. Re-check the interlock immediately before the commit phase; a 409 there leaves the staged temps for the finally to clean.
sd.cpp install: an interrupted extraction (disk full, killed process, a raising post-extract cudart fetch) left the target non-empty with no owner marker, so the next lazy install tripped the "not a Studio-managed directory" refusal and wedged native install. Write the ownership marker before the partial writes when the target is reclaimable, so a retry recognises the debris as ours and re-extracts.
Gallery clear/delete now scope to Studio-owned files: image_gallery and
video_gallery skip PNGs / MP4s without a readable recipe (a hand-dropped or
orphan file the listing already hides), so clear() and a guessed-id delete no
longer destroy files the gallery never surfaced.
Remote ControlNets now force use_safetensors: a bare owner/name reaches
from_pretrained without the base trust gate, and the Hub scan fails open when
unavailable, so requiring safetensors closes the pickle deserialization vector.
POSIX uninstall now stops resident sd-server / sd-cli under an owned sd.cpp root
before removing the tree (marker-gated), mirroring the Windows stop-before-delete
scan; a live native server no longer survives unlinking its binary.
Diffusion dataset containment: the training-start read path and the discovery
picker route bare names through the protected resolver, so a symlinked dataset
is rejected / not advertised like the caption/delete routes already do. Uploads
gain the inference decode guard (oversized real images 400 before OOMing the
trainer) and dataset upload/caption/delete/import are blocked with 409 while a
diffusion run is active.
JSONL readers (trainer + routes) tolerate non-object JSON and invalid UTF-8
instead of raising AttributeError / 500.
LoRA family compatibility is enforced in the shared resolver, not only the
picker, so a direct API client cannot apply a mismatched-family adapter.
GPU arbiter gains release_if so the image/video unload idle-check and release
are atomic against a concurrent same-owner load's registration. Native batch
recipes persist the base batch_seed and restore replays from it, so a native
batch_index>0 image no longer advances its seed twice.
FLUX.2-klein selects its sd.cpp text encoder by variant (4B -> Qwen3-4B,
9B -> Qwen3-8B) instead of the single family default.
- _scan_models_dir: admit a scan folder that is itself a diffusers pipeline
(root model_index.json, weights in transformer/ vae/ subdirs). _is_model_directory
rejects such a root, so the child scan would list the component subdirs as bogus
models and hide the real pipeline; treat the root as one model via _local_pipeline_index.
- _local_is_diffusers / _local_model_task: include the sole checkpoint filename in the
family-detection needles (_local_family_needles, resolved via resolve_local_single_file).
A generically named folder holding one loadable qwen-image-*.safetensors / ltx-*.safetensors
identifies its family only from the filename; the load route already resolves that file, so
tag it or the task-scoped picker (which rejects task=null) hides the on-device model.
- list_cached_models: mark a companion-only base snapshot partial. A GGUF image load prefetches
the base repo's VAE / text-encoder / model_index.json but skips the transformer (the GGUF
supplies it); the snapshot has a pipeline manifest yet is not a loadable BF16 pipeline, and
_cached_repo_partial misses it. _repo_pipeline_missing_denoiser flags a pipeline snapshot whose
transformer/ or unet/ component carries no weight, so the picker drops it instead of advertising
it as fully on-device.
Publish native sd.cpp generate progress (_gen) before LoRA resolution so a reload probe reads active during setup, matching the diffusers path.
Register the diffusion/video GPU load under the arbiter lock (acquire_for now takes a register callback) so a competing acquire cannot evict an owner before its load is marked in-flight and let two loaders allocate VRAM at once.
Admit local diffusers pipeline folders (root model_index.json, weights in component subdirs) in the local model scan so they reach task tagging and the On Device picker.
When the install target already exists, is non-empty and lacks the
.unsloth-studio-owned marker (a user's own stable-diffusion.cpp checkout,
or unrelated files beside a custom Studio root), install() previously still
extracted the release into it. Skipping the ownership marker only stopped the
uninstaller from deleting the directory; extraction still merged binaries into
the user's working tree and could overwrite same-named files.
Fail up front with a clear message pointing the user at a fresh/empty location
before any download or extraction, leaving their directory untouched. Update
the ownership test suite to assert the refusal.
install_sd_cpp_prebuilt: only write the .unsloth-studio-owned marker when the
install created the target directory or it was empty. Adopting a pre-existing,
unowned, non-empty directory (a user's own stable-diffusion.cpp checkout) made
it eligible for the uninstaller's recursive delete.
routes/training upload: make the multi-file promotion transactional. Back up
each displaced original and roll every destination back on any failure, so a
mid-loop rename error can no longer partially overwrite the live dataset.
routes/training _resolve_dataset_folder: reject a symlinked dataset directory
and prove the resolved folder stays under the datasets root, so image
read/caption/delete cannot escape the root through a link.
routes/training delete: escape glob metacharacters in the thumbnail filename so
deleting an image named like [ab].png removes only its own thumbnails.
image_gallery / video_gallery listing: filter records against the response
schema inside the pager via a valid callback, so offset/limit/has_more all count
over accepted records. A leading schema-invalid record no longer returns an
empty page with has_more=true and stalls infinite scroll at offset 0.
image_gallery / video_gallery save: publish via a temp file plus atomic rename
(the sidecar is the video pair's commit marker) and clean up on failure, so a
partial write never surfaces a truncated PNG or strands an orphan MP4.
diffusion_train_common discovery: treat an empty caption sidecar as a metadata
tombstone that still falls through to the dreambooth instance prompt, so
clearing every metadata caption no longer fails with no captioned images found.
diffusion backend unload: wait for an in-flight denoise to exit before tearing
down process-wide patches and state, mirroring the load path.
diffusion_engine_router: serialize the whole check/unload/publish transition so
a concurrent selection cannot return the engine being unloaded.
uninstall.ps1: gate the default sd.cpp process stop on the owner marker so a
user's own sd-server is not terminated for a directory we then keep.
generate() assigned self._gen only at the pipe() call, after deferred
compile, LoRA resolution/application, and ControlNet download/build had
run. Across that setup window generate_progress() reported inactive even
though _generate_lock was held, so a reloaded page's mount probe showed
idle and let a second generate queue behind the first.
Publish an active step-0 _GenState the moment the generation lock is
acquired, before the setup work, and clear it in the outer finally so a
setup-time error cannot leave the UI stuck active. Mirrors the video
backend's queued phase and the training start guard.
Route on-device single-checkpoint video folders through the single_file loader:
a bare local .safetensors directory (no model_index.json) is advertised as a
pipeline with no filename, so validation rejected it before it could load.
Reinterpret the pick as a single_file load of the sole checkpoint, mirroring the
image load route.
Treat a reserved-but-not-yet-spawned LLM training start as active in
is_training_active() so /images/load, /video/load, and /diffusion/start cannot
race the reserved run for VRAM during the pre-spawn free window. Mirrors the
diffusion training service reservation.
Resume an in-flight image generation on the Images page mount: probe
generate-progress, re-enter the poll loop, and refresh the gallery on completion
so a run started elsewhere is reflected and its saved image appears without a
manual refresh. Seed resident image defaults from the resolved base_repo rather
than a possibly path-shaped repo_id so the first resident generation uses the
right recipe.
The previous commit introduced the single-device budget but wired it
into only the downloaded-group and Hub search sites. The LM Studio,
custom-folder, local-dir, live-search and exported-GGUF expanders
reachable from the Images/Video pickers still measured against the
summed multi-GPU total, as did the size-based GGUF row badge, so those
paths could still recommend a quant that OOMs on a single device. All
GgufVariantExpander call sites in HubModelPicker now share
expanderGpuGb, and the row badge derives the same task-scoped budget.
The generate callback omitted loraCapable from its dependencies, so when
an auto-compile flips supports_lora off mid-session the memoized handler
still sent the previously selected adapters and the next generation
failed with the backend's LoRA-not-supported error instead of omitting
adapters the UI had already hidden.
HubModelPicker's GGUF variant expanders, format lists and Hub row fit
hints measured against the summed multi-GPU total. When the picker is
task-scoped (Images/Video) the loaders place the whole pipeline on one
device, so a variant could be recommended as fitting and then OOM at
load; those sites now share the single-device budget the group fit gate
already uses, while chat pickers keep the summed total since llama.cpp
splits layers across devices.
The dataset labeling grid's Remove button relied on group-hover with no
group parent, leaving it permanently invisible to mouse users; the image
wrapper now carries the group class.
Register the dims the forward actually compiled with: image-conditioned
workflows (img2img, inpaint, upscale, edit) run at the input image's size,
not the slider's, so recording the slider values marked never-compiled
shapes as covered and warm restarts kept paying compile for the real one.
Validate a request-supplied transformer_prequant_path (existence plus the
UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH allowlist) before treating prequant as
available at the resident-fit re-check: an unusable path skipped the dense
fit check up front and then fell back to materializing dense bf16 after
the previous pipeline was evicted, recreating the post-eviction OOM path.
Shared as usable_prequant_source, also used by the auto-policy planner.
A generation runs on a backend daemon thread and survives a page reload,
but the mount effect only probed the load progress, so reloading during a
generate showed an idle page that never picked up the finished clip until
a manual refresh. Hoist the generate poll loop out of handleGenerate and
re-enter it on mount when generate-progress reports an active job; merge a
terminal completed record into the gallery to cover the race with the
mount gallery fetch.
* Auto-detect completion masking markers with template table fallback
Studio's train_on_completions previously relied only on the hardcoded
MODEL_TO_TEMPLATE_MAPPER / TEMPLATE_TO_RESPONSES_MAPPER tables and
silently disabled masking when a model was not in the table, so unmapped
models (LFM2-8B-A1B, DeepSeek, and others) trained on full sequences
without telling the user. Several mapped templates (glm, mistral, llama,
starling, zephyr, qwen3-thinking) also carried markers that mask every
assistant token, which made every row drop in the post-masking filter.
Both training callsites (CUDA trainer.py and MLX worker.py) now share
utils.datasets.completion_masking.apply_completion_masking:
- Try unsloth_zoo chat template auto-detection first; it raises loudly
when the template cannot be parsed and never masks the EOS token.
- gpt-oss models keep their manual markers so non-final assistant
<|end|> tokens stay trained, matching current behavior.
- If auto-detection raises, fall back to the template table exactly as
before.
- If the table also misses, emit an explicit user-visible warning that
completion masking could not be applied and full-sequence training
will occur, instead of a quiet log line.
The >30 percent dropped-rows safety net in trainer.py now guards the
auto path as well. Table consumers for inference and chat templates are
unchanged. Validated against one representative tokenizer for every
template in TEMPLATE_TO_RESPONSES_MAPPER plus the unmapped models:
no template regresses; unit tests cover the four decision paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Restrict masking fallback to marker detection failures
The auto branch wrapped the whole train_on_responses_only call, so a real
failure while applying the masking (dataset map, tokenization) was treated
as a detection miss and training silently proceeded on full sequences.
Detect markers separately via get_chat_template_parts (test seam via
detect_fn), then apply them with errors propagating, matching the manual
path. Tokenizers with preset unsloth marker attrs skip detection and call
bare so zoo reuses the stored parts.
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* Fail the run when applying completion masking raises
The helper already falls back internally on detection failures and returns
applied=False on a double miss, so an exception reaching the callsites is a
real failure applying the masking. Remove the callsite catches that
downgraded it to full-sequence training; the run now fails visibly instead.
Also use the explicit re-export alias form in utils/datasets/__init__.py for
the two new names, satisfying the import-hoist source lint.
* Import completion masking from its submodule
The import-hoist source lint counts only real name loads, so package-level
re-exports of the two new names cannot satisfy it. Import
apply_completion_masking from utils.datasets.completion_masking directly at
both callsites and leave utils/datasets/__init__.py untouched.
* Completion masking: gpt-oss renames and MLX raw/alpaca parity
Renamed or private gpt-oss checkpoints are name-detected as gpt-oss but miss
the exact-name table; default them to the gpt-oss template markers instead of
falling through to full-sequence training.
Gate the MLX masking call on not raw_text_mode and format_type != alpaca,
mirroring the CUDA path: raw/CPT text has no chat turns to mask and
Alpaca-rendered text lacks the tokenizer's chat markers.
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* Define raw_text_mode outside the MLX feature-detect block
With an older zoo lacking the append_eos config field, the masking
gate referenced raw_text_mode before assignment. Hoist the assignment
above the feature detection so both consumers see it.
* Gate MLX masking on the formatter's resolved format
format_type auto can resolve to alpaca or raw text; the masking skip
checked only the requested value, so auto-detected Alpaca data got
chat-template markers applied to rendered prompt text. Track the
final_format returned by format_and_template_dataset and gate on it,
matching the CUDA path.
* Unwrap the mlx-lm TokenizerWrapper before marker checks
The wrapper delegates plain reads to the wrapped HF tokenizer but hides
underscore attrs, so preset unsloth markers were invisible and detection
relied on the loader's call patch. Unwrap to the real tokenizer first,
as the zoo MLX resolver does.
* Tighten masking comments
* gpt-oss: auto-detect markers first like every other template
The quantized and BF16 gpt-oss checkpoints ship a chat template without
the channel final header, so the pinned manual markers match nothing
there and masking trained zero tokens. Auto-detection derives markers
from whichever template the checkpoint ships and keeps the final
terminator trained; the manual gpt-oss markers remain the detection
failure fallback, including for renamed checkpoints.
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* Studio: route models by CONFIG_MAPPING_NAMES instead of hardcoded tables
A model whose model_type is absent from an overlay's transformers cannot load
there, so a new MoE arch not yet in the tier tables gets routed to default and
fails (e.g. lfm2_moe, deepseek_v4). Add a static resolver that parses each
overlay's CONFIG_MAPPING_NAMES straight from source (AST only, no import, no
network, no trust_remote_code) and picks the lowest tier that ships the
model_type. Runs after the existing checks and only ever upgrades default, so
no existing routing changes and new archs no longer need a table edit.
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* Studio router: harden the CONFIG_MAPPING_NAMES resolver
- Resolve the default tier map from the base install, skipping any .venv_t5_*
sidecar on sys.path, so an in-process 5.x activation cannot make a 5.x-only
model look loadable by 4.x.
- Do not cache an overlay whose sidecar dir is absent, so a later call re-reads
it once provisioned instead of serving a stale empty map.
- Also collect model types added via CONFIG_MAPPING_NAMES.update({...}) and
**{...} unpacking, not just the literal assignment (5.10 uses both).
- Wrap the AST walk in the try/except so a malformed source can never crash tier
resolution.
- Feed the mapping fallback from _load_config_json so a config served from the
hub cache during a transient outage still routes new architectures.
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LFM2-8B-A1B and any other lfm2_moe checkpoint were missing from the
transformers tier tables, so they fell through to the default 4.57.x
sidecar, which does not register lfm2_moe and errors with
"not supported yet in transformers==4.57.6". Only lfm2_vl was listed.
Add Lfm2MoeForCausalLM / lfm2_moe to the 5.3.0 tier (lfm2_moe is
registered in transformers 5.3.0). get_transformers_tier now returns
530 for LFM2-8B-A1B and the model loads and trains as expected.
diffusion_compile_cache: auto mode now saves the Mega-cache bundle after the
first compiled generation (UNSLOTH_DIFFUSION_COMPILE_CACHE_SAVE=0 opts out), so
users get warm restarts without the distributor env; a bundle hit starts clean
(no pointless rewrite of the just-loaded artifacts) and explicit mode 1/on keeps
the distributor-style re-save. New register_shape + manifest shape coverage: a
STATIC compile produces new artifacts per (width, height, batch), so the
generate path registers each generation's shape and an uncovered shape
re-dirties the context, growing the bundle to cover every shape the session
used. Measured (B200, real backend): Qwen-Image deferred gen-3 hitch 29.1 ->
22.2 s warm with bit-identical output (7.9 MB bundle, ~0.5 s save); SDXL gen-3
115.7 -> 24.7 s and a mid-session 768px recompile 65.8 -> 12.6 s (bundle 63.6 ->
98.7 MB after the 768 re-save).
diffusion_speed: U-Net denoisers (UNet2DConditionModel; no _repeated_blocks, so
the regional compile never reached them) now get a whole-module STATIC
torch.compile on the default tier, plus fused QKV projections and a compiled VAE
decode. Measured on SDXL (30 steps / 7.0 / 1024px, 4 prompts, LPIPS vs the
bit-exact reference): 6.16 -> 3.14 s end to end (1.96x) at LPIPS 0.035, steady
state 0.70-0.88 s/image through the real backend. Rejected on measurement:
dynamic=True whole-module (366 s compile for 39.3 ms/step vs static's 73 s for
26.9), regional BasicTransformerBlock only (45.0 ms/step; ResNet convs stay
eager), max-autotune + inductor flags (25.9 ms/step for a 445 s warmup),
channels-last UNet alone (neutral). DiT tiers unchanged: fused QKV measured
exactly neutral under the regional compile (Qwen-Image 6.53 vs 6.52 s), so it
stays max-only there, and the DiT VAE decode stays eager (a few % of a DiT
generation). compiled_shapes_are_static tells the cache layer which loads are
per-shape (max tier, U-Net whole-module).
diffusion: register each generation's shape with the compile cache before the
save, pass pipe.unet to the cache fingerprint when the pipe has no transformer,
and correct the transformer_quant resolved reason on dense loads (it claimed a
GGUF transformer was loaded on every non-quantized pipeline load).
Tests: 333 passing across the related suites (speed 42, compile_cache 27, cache
40, precision 20, backend, base_precision, transformer_quant, memory); ruff
clean. Full measurement record: outputs/image_optim_round2_audit.md.
Reject an image whose name differs from an existing one only by extension
case (cat.PNG vs cat.png): the stems are exactly equal, so on a
case-sensitive filesystem both files land and both resolve to one cat.txt
caption sidecar, silently sharing and corrupting the caption. Stem case
variants (Pic.png vs pic.png) stay exempt: they are one file on
case-insensitive filesystems and write separate sidecars on Linux.
Treat an empty precision_modes list on a DiT family as the backend's
deliberate cannot-train signal (a non-bf16 CUDA GPU fails the trainer's
preflight for every mode) instead of falling back to the full mode list:
the precision selector disables and the start button reads not supported,
so the form no longer offers a run that always 400s. An absent field still
means an older backend and keeps the fallback.
Hide the Images page Reapply button when no reload target is known: a
resident GGUF or single_file model discovered by refresh carries no
checkpoint filename in status, so clicking was a silent no-op. A resident
full pipeline keeps the button (it reloads by repo id alone).
A load request that is rejected up front (validation error, gated repo,
training guard) leaves the previously loaded model resident, but both the
Images and Video pages had already pointed lastLoad at the failed pick, so
Reapply and the resident-default seeding retried the wrong model. Snapshot
the prior target before the optimistic assignment and restore it (plus the
video page's canReapply flag) when the start POST rejects; successful loads
and failures after the background load starts behave as before.
