Wire te_prequant_pipe_kwargs into the three pipeline assembly sites:
the diffusion full-pipeline branch, the diffusion transformer-only and
GGUF branch (where the companion TE is the big remaining download), and
the shared video assembly path before the pipeline/component split.
Injection is gated exactly like the runtime cast (mode normalized to
fp8, device supported, family not denied), so it can never engage where
quantize_text_encoders would not; the later quantize_text_encoders call
re-applies the cast idempotently and keeps status reporting truthful.
With no hosted checkpoint configured the call returns {} and assembly
loads the dense encoder as before.
The runtime text_encoder_quant=fp8 path downloads the full bf16 text
encoder and layerwise-casts it in place on every fresh load. For the
heavyweight encoders (LTX's Gemma3-27B ~50 GB, FLUX.2-dev's Mistral-24B
~48 GB, Qwen-Image's Qwen2.5-VL ~16.6 GB) that download dominates load
time on a fresh machine.
diffusion_te_prequant.py loads a pre-cast fp8-storage state dict
instead: meta-init the encoder skeleton from the checkpoint's te_class,
load_state_dict(assign=True), rebuild on CPU if non-persistent buffers
stay on meta, then re-apply the same layerwise cast to install the
upcast hooks. The cast is a deterministic storage transform, so the
loaded encoder is bit-identical to dense-load-then-cast by construction.
v1 hosts the layerwise fp8 storage scheme only: its state dict is plain
tensors (torch.load(weights_only=True), no pickle execution). The
dynamic-compute schemes (fp8_dynamic, int8, nvfp4) build torchao
subclass wrappers at runtime and are deliberately not hosted.
Checkpoints validate format, scheme, component and base_model_id before
use and any problem falls back to the dense download and cast. Local
path overrides reuse the DiT prequant allowlist env var. Families opt in
via a new te_prequant_repos (scheme, component, repo_id) field on both
DiffusionFamily and VideoFamily; the field defaults empty so nothing
changes until a gate-validated artifact is wired.
The video loader always materialised the dense DiT(s) inside from_pretrained
and quantised them in place, so an int8/fp8 A14B load paid a ~57 GB dense
download and a dense-bf16 VRAM transient every time. Mirror the image loader's
hosted-prequant shortcut:
- diffusion_prequant grows an expert dimension: prequant_repo_filename /
prequant_filename / resolve_prequant_source take an expert attribute name, so
one repo carries a dual-DiT pair per scheme (<Model>-<SCHEME>.pt plus
<Model>-<SCHEME>-2.pt, legacy transformer_2_<scheme>.pt fallback), and
load_prequantized_transformer meta-inits from the expert's config subfolder.
A local path override never carries a pair, so an expert request under an
override resolves None and the whole load falls back to dense.
- VideoFamily gains prequant_repos (+ variant table for parity with the image
side); wired: Wan2.2-TI2V-5B and both A14B expert pairs at int8 + fp8, and
HunyuanVideo-1.5 480p/720p at int8 only per the measured deny list. LTX stays
unwired (no measured quant recipe).
- The pipeline build tries the shortcut first when the resolved plan is
resident and every expert's checkpoint resolves; loaded experts ride into
from_pretrained as component overrides and in-place quantise is skipped.
All-or-none per pair: a partial load frees and goes dense (mixed-precision
experts would corrupt the boundary handoff).
- An explicit wired scheme also lets the scoped pre-download skip the DiT
weight shards (configs kept for the meta-init); if the shortcut then falls
through, the build resolves from the hub id, gated on the same predicate so
ordinary pre-downloaded snapshots are untouched.
- build_prequant_checkpoint.py accepts --subfolder and resolves video families.
New tests: expert filename/resolution conventions, family wiring incl. the
LTX/720p split, shortcut engagement, partial-pair dense fallback, unwired
family bypass, and the pre-download skip predicate.
Gate-validated: all 28 per-case pairs pass per scheme (LPIPS suite means 0.291
int8 / 0.278 fp8, in the 50-step trajectory-divergence band; CLIP delta means
0.007-0.008), and the int8 checkpoint is verified bit-identical to on-the-fly
quantize across all 1615 state dict tensors (1073 quantized, max abs diff 0.0).
Uploaded to unsloth/HiDream-I1-Full-FP8.
Two live-test findings on the images load path:
- transformer_quant with baked LoRAs, when the dense quantized build is
declined for memory or fails: the load completed as a plain GGUF with the
adapters silently dropped (HTTP success, supports_lora=false after the
fact) -- wrong output with no signal. The load now fails with the recovery
options (drop the adapters, free VRAM, or pick a smaller model). Weight-0
adapters still count as no bake request, and the plain no-LoRA decline
keeps its silent GGUF fallback.
- A fresh GGUF load on a small GPU prefetched the base repo's full bf16
transformer shards (~47 GB on Qwen-Image) because the dense-quant prefetch
widening only checked scheme viability, not whether the device could ever
hold the candidate resident. Gate the widening on total device capacity
(reserve + 0.85 margin, the plan_fits_total_capacity bar) so a card that is
certain to decline the dense build never pays the download; capable devices
keep the prefetch.
Two live-test findings on the video progress endpoints:
- load-progress downloaded_bytes froze mid-download: the counter used
scan_cache_dir, which skips in-flight *.incomplete blobs, so it sat at the
last completed blob for the whole multi-GB shard pull while the disk kept
filling. Count the repo's cache directory directly (completed plus incomplete
blobs, snapshot symlinks skipped so nothing is double-counted).
- generate-progress reported total_steps=null / fraction=0 while step advanced:
the video API only carried the native total field while the image API exposes
total_steps and fraction, so one poller could not work against both. Derive
the image-compatible aliases in generate_progress and declare them on the
response model; the native total stays for back-compat.
Verified bit-identical to on-the-fly quantize: all 1264 state dict tensors
(456 quantized) dequantize equal between the loaded checkpoint and a fresh
quantize_ pass, so quality matches the runtime Dtype path exactly. Same-seed
LPIPS suite means (0.35 int8 / 0.28 fp8) blend trajectory divergence with this
family's own run-to-run nondeterminism (identical weights and seed reproduce a
17/255 mean pixel delta through the 50-step guider pipeline); per-case hard
checks pass and the drift is compositional, reviewed visually. Uploaded to
unsloth/HunyuanImage-2.1-FP8.
A 17B MoE DiT (16 double + 32 single layers, 4 routed experts) with four text
encoders, on HiDreamImagePipeline (diffusers 0.39). One family covers the open
Full / Dev / Fast repos (same arch); per-variant generation defaults follow the
upstream inference recipes (Full 50 steps at guidance 5, the distilled Dev 28
and Fast 16 guidance-free).
The repos name a Llama-3.1-8B text_encoder_4 in their model_index but do not
ship its weights; the official example passes the gated meta-llama repo in by
hand. The loader instead assembles the component from the open unsloth mirror
(byte-identical weights, already inside the non-GGUF trust gate), injected at
the three pipeline from_pretrained sites, with output_hidden_states matching
the official example. Memory planning counts the assembled TE4: 34.2 GB DiT +
28.8 GB encoders, ~63 GB bf16-resident.
The hunyuanvideo-community diffusers mirror carries the full stack in
standard layout: a 17B dual-stream DiT (32.5 GB bf16), a Qwen2.5-VL text
encoder, a ByT5 glyph encoder, the 32x HunyuanImage VAE, and
guider/ocr_guider components (AdaptiveProjectedMixGuidance) that diffusers
0.39 loads natively, so the generic from_pretrained pipeline path covers
everything with no per-component assembly.
Family notes:
- The call's guidance knob is distilled_guidance_scale (there is no
guidance_scale kwarg), so cfg_kwarg routes the UI value there; real CFG
runs inside the repo's guider at its baked scale. Defaults follow the
card recipe: 50 steps, 3.25.
- 2K-native: verified live at both 1024 and 2048.
- Coexists with the HunyuanImage-3.0 structured exclusion (3.0 has no
diffusers pipeline and stays excluded with its stated reason).
- int8/fp8 dense quantization verified live (LPIPS 0.186 both vs same-seed
bf16); a short prompt does not trip the int8 torch._int_mm minimum on
this arch, so no family exclude entry is needed.
- bf16 component table for the memory planner: (32.5, 16.3, 0.8) GB.
Gate-validated against same-seed bf16 renders (28/28 pairs per scheme, zero
failures): int8 LPIPS mean 0.146 / SSIM 0.937, fp8 LPIPS mean 0.116 /
SSIM 0.946. Uploaded to unsloth/Lumina-Image-2.0-FP8 following the existing
checkpoint repo conventions.
Wan-AI/Wan2.2-I2V-A14B-Diffusers is the image-to-video sibling of the already
supported T2V-A14B: the same dual-expert WanTransformer3DModel pair (boundary_ratio
0.9 in the pipeline config) behind WanImageToVideoPipeline, conditioning through the
VAE latent (no CLIP-vision image encoder in this 2.2 variant).
- New wan2.2-i2v-a14b family: image_conditioned flag, card recipe defaults (40 steps,
CFG 3.5, 81 frames at 16 fps), the T2V memory table (57.2 GB both experts bf16),
fp32-pinned VAE, and a wan2.2-i2v generation-defaults key ahead of the generic wan
50/5.0 entry.
- Source-image plumbing: /video/generate takes init_image (base64/data URL);
begin_generate 400s synchronously when an image-to-video family has no image or a
text-only family is given one, and generate() decodes and resizes the image to the
snapped output size before threading it as the pipeline's image kwarg. status()
reports image_input so the UI can gate the control.
- Trust the official -Diffusers repo for pipeline loads and transfer the measured wan
quant recipes: fp8 keeps condition_embedder in bf16 (zero padding-row scale), mxfp8
and nvfp4 stay denied, the UMT5 auto TE quant resolves dense, and the balanced
FBCache pin carries over. All tables share the T2V DiT profile.
- Tests: family detection/aliases/defaults, the dual-DiT image pipeline load, the
image gates on both begin_generate and generate, init_image route pass-through, and
the quant exclude/deny/auto entries.
GPU-verified on a B200: bf16 resident load (offload none, 72.8 GB peak) animating a
conditioning image at 832x480/33f in 28.7 s with first-frame MAE 5.7 vs the source,
and an int8 load with both experts quantized (43.1 GB peak, clean output).
Alpha-VLLM/Lumina-Image-2.0 is a 2.6B single-stream DiT with a Gemma2-2B
encoder and a standard 16-channel VAE, all transformers-4.x-compatible, so the
generic from_pretrained pipeline path loads it as a new lumina-2 family:
- Family entry (Lumina2Pipeline / Lumina2Transformer2DModel), aliased to
lumina-image-2.0 / lumina-image-2 / lumina2. No bare lumina alias: Lumina-Next
checkpoints are a different arch and must stay unknown rather than crash
mid-load. bf16-only upstream, so the fp16 fallback stays off like z-image.
- Trust the official repo for non-GGUF loads; bf16 component table entry
(ships fp32, ~5.2 GB transformer + 5.2 GB encoder bf16-resident).
- Generation defaults 50 steps / guidance 4.0 per the model card, and the
generate call passes the card's cfg_trunc_ratio=0.25 itself (family-gated,
signature-gated): the pipeline default (1.0) runs the CFG double-forward on
every step and oversaturates output.
- Catalog group with the single ungated bf16 pipeline artifact (11 GB resident)
plus routing assertions; images page defaults row.
- No GGUF artifact: none exists upstream (only finetune/LLM quants), so the
dense transformer_quant fast path (GGUF-kind-only) stays unreachable for now.
Offline probes of the future prequant campaign: int8 and fp8 both engage and
render cleanly (fp8 LPIPS 0.11 vs bf16, int8 0.33 from 50-step trajectory
drift with intact quality), so neither scheme is family-denied.
One family entry covers several published variants whose weights differ
(flux.1: schnell, dev, Krea-dev), but prequant resolution was keyed on
(family, scheme) alone, so only the default base could ever be served: the
loader's baked base_model_id validation correctly refused the schnell
checkpoint for dev and Krea-dev bases and every such load paid the dense
download plus on-the-fly quantise.
Add an optional prequant_variant_repos table on DiffusionFamily as
(base_repo, scheme, repo_id) triples and thread the resolved base repo
through resolve_prequant_source / usable_prequant_source and their three
call sites (load fast path, memory-plan probe, auto-policy candidate). A
base without its own entry keeps returning the family default, preserving
the existing refuse-then-dense behavior exactly.
Wire the flux.1 variants: the gate-validated unsloth/FLUX.1-dev-FP8
checkpoints (built in the earlier campaign but never reachable) and the
new unsloth/FLUX.1-Krea-dev-FP8.
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: 304 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 qwen-image / qwen-image-edit to the per-family int8 exclusions (txt_in,
add_q/k/v_proj, to_add_out, txt_mlp), the same recipe HunyuanVideo-1.5 already uses
for its trimmed text streams. The exclude list feeds 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). This is defense in depth on top of the
family excludes and deny list, which stay as-is: it changes the failure
mode of any future zero-row family from black frames to graceful
degradation. Same-seed renders with fused kernels present are unaffected,
and pre-quantized fp8 checkpoints stay valid since weight scales are
untouched.
- diffusion_attention: arch-gate FlashAttention 2 to Ampere (SM80)+ in both the
primary selector and the heterogeneous-replica guard (it crashed on pre-Ampere).
- diffusion_cfg_parallel: convert boolean attn masks to additive bias before the direct
cuDNN op so partial masks match F.scaled_dot_product_attention; make proxy disable_cache
transactional (clean both branches, mark broken, surface a reload-required error).
- diffusion_cache: fail closed when a magcache step-count resize or below-threshold
disable cannot remove the old cache; surface a failed enable+cleanup instead of a false
uncached None.
- video: roll back earlier experts when a later expert raises in the all-or-none step-cache
loop; fail the load when the primary-only cache cannot be re-engaged through the
CFG-parallel proxy; validate transformer_cache_quality and cfg_parallel before the worker.
- scripts: place the fp8 ablation pipeline on CUDA; fail closed on a failed magcache resize
in the speedmem bench; label OOM distinctly in the SDPA mask probe.
- tests: regressions for the FA2 arch gate, transactional proxy disable, all-or-none
exception rollback, magcache fail-closed transitions, and enable+cleanup failure.
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.
Under speed=max the video DiT compiles dynamic=False, so inductor
artifacts are per (width, height, frames). The save path never called
compile_cache.register_shape, so after a bundle hit ctx.saved stayed
true and later resolutions/frame counts never re-dirtied the bundle,
leaving those shapes to recompile on every restart. Register the actual
generation shape before saving, gated on the static tier, mirroring the
image backend.
- diffusion_attention: clear the HunyuanVideo-1.5 null-mask flag with an always_call
post-hook so it is scoped to one hooked forward and never latches across an
exception; add attention_backend_supported_on_device to arch-gate an
already-resolved backend on a specific (heterogeneous) CUDA device.
- video: make the explicit MagCache resize transactional via _step_cache_all_or_none
(refuse to stack a fresh cache over one that could not be disabled; roll a mixed
resize back and report the true state); raise on a failed all-or-none rollback
instead of falsely reporting an uncached pipeline.
- diffusion_cfg_parallel: re-validate the attention backend on the replica device
and pin native there when unsupported; mirror the primary's max tier on the
replica (max-autotune compile + direct QKV fusion) via a new speed_mode arg;
prefer a viable heterogeneous secondary GPU over an unusable identical one; clear
the const cache at each plan_generation.
- diffusion_vae_quant / diffusion_precision: detect a partial diffusers
layerwise-fp8 mutation (leftover casting hooks the torchao detector cannot see)
and fail the load closed, while a clean failure still falls back to dense.
- video_speedmem_bench: engage the dual-expert cache all-or-none like the loader.
- frontend video api: add text_encoder_quant / vae_quant and the auto/off literals
to VideoLoadRequest so typed callers match the backend contract.
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.
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.
- Wan2.2-A14B step cache: pin the balanced FBCache threshold to 0.08 even when
quant is active (per-family override in diffusion_cache.py). Auto-fp8 made the
generic quant promotion (0.12) the family's effective default at pairwise LPIPS
0.128, over the 0.08 quality gate the balanced preset is held to. Measured
operating point with fp8 actually engaged (1280x720/81f/50 steps, B200):
fb@0.08 = 1.08x at 0.129 vs the old fb@0.12 = 2.58x at 0.181; documented in
the preset table. Explicit thresholds and the fast preset are unaffected.
- MagCache curves: validated the shipped 33-frame calibrations at the production
121-frame default for hunyuanvideo-1.5-720p, hunyuanvideo-1.5 (480p) and
wan2.2-ti2v-5b. Fresh 121-frame calibrations differ by <= 0.024 max abs entry
and produce byte-identical frames at the auto presets (hv720 quality 1.69x at
LPIPS 0.042, hv480 quality 1.66x at 0.018, wan5b balanced 1.74x at 0.026, all
pairwise vs the same-load uncached stack), so the curves ship unchanged with
the frame-count transfer documented next to them.
- Dual-GPU CFG parallelism: the secondary-device pick now prefers a device whose
name and compute capability match the primary, and the gate declines a
mismatched pair in auto mode (eager kernel selection is arch-dependent, so the
advertised bit-identity cannot hold across different GPU models); an explicit
cfg_parallel=on proceeds but is downgraded to lossless=False with a warning.
- A14B expert step cache is now all-or-none, mirroring the transactional quant
loop: a mixed outcome (cache engaged on one expert but not the other) is
rolled back and reported uncached with the failure reason, on both the load
path and the generation-time auto toggle.
- Partial torchao quantization is no longer reported as dense: after an
in-place quantize_/caster failure, the DiT / text encoder / VAE is scanned
for leftover torchao tensor-subclass parameters and the load fails with a
clear error when any are found (a half-quantized module cannot run as dense,
and offload's Module.to() crashes on torchao tensors). Failures that swapped
nothing keep the best-effort dense fallback.
- Cleanup: apply_attention_backend / apply_speed_optims / the attention trim
are called once on the pipe (they already fan out over every DiT internally),
so the second A14B expert no longer passes through them twice; the stale
dual-DiT helper comment is rewritten to match the two helper shapes.
Tests: device-identity picker/gate/lossy-plan coverage, per-family threshold
pin scoping, all-or-none rollback in both failure directions, and partial-quant
detection for all three quant modules.
The prewarm registered its cancel event in _active_generate_cancel, but a
begin_generate arriving mid-warmup overwrote that slot with its own event
and then queued its worker behind the full warmup on _generate_lock. From
that point unload and cancel_generate signalled the wrong event, so the
warmup could no longer be aborted and the first real request waited out
the 9-54s the prewarm exists to hide.
Track the prewarm's event in a dedicated _prewarm_cancel slot (cleared
identity-checked alongside _active_generate_cancel) and signal it from
begin_generate before registering the real job's event, and from direct
generate() calls that skip begin_generate. The warmup then aborts at its
next step boundary and the real job takes the lock, while unload/cancel
keep working against whichever run is actually active.
vLLM and SGLang finish every compilation at server startup (dummy batches
through each compiled shape) so no request ever pays a compile mid-serving.
The video backend's compiled tier instead paid a first-generation extra after
every restart: ~54 s cold and ~11.3 s even with a warm Mega-cache bundle (the
residual is dynamo tracing plus cudnn.benchmark autotune, which the bundle
cannot carry).
After a compiled DEFAULT-tier resident load commits, a daemon thread now runs
one tiny throwaway generation (192x128 snapped, 4k+1-lattice 9 frames, 2
steps) under the generate lock. The default tier compiles dynamic=True, so
the small trace serves every later resolution. Measured through the real
backend (HunyuanVideo-1.5-480p, B200, 480x288/17f/30 steps):
warm bundle: first-generation extra 11.3 s -> 2.1 s (9.6 s background warmup)
cold start: the full compile moves off the user's first request entirely
(14.5 s background; first generation extra 2.1 s), and the
warmup persists the Mega-cache bundle itself
steady state: unchanged (2.4-2.5 s per 30-step clip in every phase)
Exactly lossless by construction: the warmup only changes when compilation
work happens. It resets its step-cache residuals, the real generation seeds
its own generator, and no process-wide flag is touched.
The warmup registers itself as the active cancellable job, so unload, a new
load, or cancel_generate abort it at a step boundary (verified: unload 2 s
into a running prewarm returns in 6.7 s with the warmup cancelled). It yields
untouched when a real request arrived first and is token-scoped against
superseded loads. Gated per family (supports_compile_prewarm), skipped for
speed=max (static per-shape graphs a warmup shape cannot serve), offload
(every warmup forward would stream the DiT over PCIe), and CFG parallel (its
planner owns compile-sensitive runs); the UNSLOTH_DIFFUSION_COMPILE_PREWARM
kill switch disables it. The decision and reason land in the resolved record.
Tests: +4 hermetic (decision gates, engaged-load spawn with snapped tiny
shape, skip-without-compile, yield to generations / stale tokens); related
backend set 551 passed; ruff clean.
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.
Every current release of the kernels package (0.13 through 0.16) builds its
dependency tables with huggingface_hub >= 1.0's strict-dataclass API, and with
an older hub the breakage is not contained to the requested backend: import
kernels raises at module scope, and diffusers imports kernels whenever it is
installed, so a single on-demand install (an explicit flash3/flash4 attention
request on a stack pinned to hub < 1.0) permanently breaks every later
diffusers pipeline import on the box until the package is removed. Reproduced
against hub 0.36.2 with kernels 0.13.0 and 0.16.0: the HunyuanVideo-1.5
pipeline import dies in hub's strict-dataclass validator both times.
_ensure_attention_backend_installed now checks the resident hub version before
installing kernels (_kernels_hub_compatible) and refuses on < 1.0, logging why
and falling back to the native default, which is the best-effort contract the
installer already promises for an uninstallable wheel. The refusal is a policy
decision, not a failed attempt, so it is not memoised and a later request on a
fixed environment can still install. An undeterminable hub version keeps the
previous permissive behaviour, and the gate applies only to the kernels
package: sage/flash-attn/xformers wheels do not import hub at module scope.
Tests: the refusal (nothing memoised), the hub >= 1.0 allow, the
package-scoping, and the version-parse fallback.
The pre-warmed torch.compile cache (diffusion_compile_cache.py: fingerprinted
bundles over torch.compiler.save/load_cache_artifacts plus a persistent per-key
TORCHINDUCTOR_CACHE_DIR) was wired into the image backend only, so every video
load re-paid the full first-generation compile after every process restart (the
stock inductor dir lives in /tmp).
video.py now mirrors the image backend exactly: compile_cache.begin runs after
the attention-backend set (the fingerprint keys on the engaged kernel) and
before apply_speed_optims on a compile-eligible default/max tier, keyed on the
same fullgraph decision as the compile itself (an engaged or still-toggleable
step cache and a planned offload both drop it); the context is committed to
_VideoLoadState and compile_cache.save persists the bundle after the first
successful generation (env-gated distributor / first-run-warm mode);
_teardown_state restores the inductor dir, and a token-scoped
_rollback_precommit_compile_cache covers loads that die before the state
commit, mirroring the globals and CFG-parallel rollbacks.
Measured on HunyuanVideo-1.5-480p through the real VideoBackend (B200,
480x288/17f/30 steps): the first-generation extra drops from 107.5 s cold to
13.8 s when the 12.8 MB bundle loads into a fresh inductor dir (0.10 s load)
and to 11.7 s from the persistent per-key dir alone; through the wired
production path a restart lands at 10.5-10.8 s (bundle-only included) vs
86.5 s cold. Steady state is unchanged (2.4-2.6 s), and the loaded artifacts
are the same bits a local compile would produce, so numerics are untouched.
Tests: begin/save/restore lifecycle with the fullgraph keying, the Speed=off
and compile-ineligible skips, and the token-scoped pre-commit rollback.
Speed=off is the reference contract: the loaders pin every auto speed
lever (transformer/TE/VAE quant tri-states) to off, but the cfg_parallel
auto path never consulted speed_active, so a resident two GPU
HunyuanVideo-1.5 load with Speed=off could still reserve a second GPU
and install the CFG-parallel proxy. Auto now returns off when
speed_active is false; an explicit cfg_parallel=on stays honored as a
deliberate override (the install-failure test now exercises exactly
that override path).
