unsloth/studio/backend/tests/test_sd_cpp_install.py
Daniel Han a0607e41a4
Studio: use the Unsloth stable-diffusion.cpp prebuilt mirror for native diffusion (#6787)
* Studio diffusion: cross-platform device policy, fp16 guard, lock split, validate-before-evict

Phase 1 of porting the richer diffusion stack onto the image-generation backend.

- Add a compartmentalized device/dtype policy module (diffusion_device.py)
  resolving CUDA/ROCm/XPU/MPS/CPU with capability flags. Keeps the NVIDIA
  capability-based bf16 choice; ROCm and XPU are isolated; MPS uses bf16 or
  fp32, never a silent fp16 that renders a black image.
- Add a per-family fp16_incompatible flag (Z-Image) and promote a resolved
  float16 to float32 for those families so they do not produce black images.
- Split the backend locks: a generation holds only _generate_lock, so status,
  unload, and a new load are never blocked by a long denoise. Add per-generation
  cancellation via callback_on_step_end so an eviction or a superseding load
  preempts a running generation; a replacement load waits for it to stop before
  allocating, so two pipelines never sit in VRAM at once.
- Validate a load request before the GPU handoff so an unloadable pick never
  evicts a working chat model, and reject missing local paths up front.
- Add CPU-only tests for the device policy, dtype guard, lock split and
  cancellation, and validate-before-evict, plus a GPU benchmark/regression
  script (scripts/diffusion_bench.py) measuring latency, peak VRAM, and PSNR
  against a saved reference.

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* Studio diffusion (Phase 2A): measured-budget memory planner + offload/VAE policy

Add a lean, backend-agnostic memory policy that picks a CPU-offload policy and
VAE tiling/slicing from measured free device memory vs the model's estimated
resident footprint, then applies it to the built pipeline. auto stays resident
when the model fits (byte-identical to the prior resident path), and falls to
whole-module offload when tight; fast/balanced/low_vram are explicit overrides.
Sequential submodule offload is unreliable for GGUF transformers on diffusers
0.38, so it falls back to whole-module offload and status reports the policy
actually engaged.

Verified on Z-Image-Turbo Q4_K_M (B200): auto reproduces the resident image with
no VRAM/latency regression (PSNR inf); balanced/low_vram cut generation peak VRAM
47.9% (15951 -> 8318 MB) with byte-identical output, at the expected latency cost.

73 prior + 35 new CPU tests pass.

* Studio diffusion (Phase 2D): streamed block-level offload + functional VAE tiling

Add a streamed 'group' offload tier (diffusers apply_group_offloading, block_level,
use_stream) that keeps the transformer flowing through the GPU a few blocks at a
time while the text encoder / VAE stay resident, and fix VAE tiling to drive the
VAE submodule (pipelines like Z-Image expose enable_tiling on pipe.vae, not the
pipeline). apply_memory_plan now returns the (policy, tiling) actually engaged so
status never overstates either, and group falls back to whole-module offload when
the transformer can't be streamed.

Measured on Z-Image (B200), all lossless (PSNR inf vs resident): balanced/group
cuts generation peak VRAM 32% (15951 -> 10840 MB) at near-resident speed (2.07 ->
2.99s); low_vram/model cuts it 48% (-> 8318 MB) but is slower (7.99s). Mode names
now match that tradeoff: balanced = stream the transformer, low_vram = offload
every component. auto picks group when the companions fit resident, else model.

112 CPU tests pass.

* Studio diffusion (Phase 5): image quality-vs-quant accuracy harness

Add scripts/diffusion_quality.py, the accuracy analogue of the KLD workflow: hold
prompt + seed fixed, render a grid with a reference quant (default BF16), then render
each candidate quant and measure drift from the reference. Records mean PSNR + SSIM
(pure-numpy, no skimage/scipy) and optional CLIP text-alignment + image-similarity
(transformers, --clip), plus file size, latency, and peak VRAM, then prints a
quality-vs-cost table and recommends the smallest quant within a quality budget.
--selftest validates the metrics on synthetic images with no GPU or model.

Verified on Z-Image (B200): the table degrades monotonically with quant size
(Q8 -> Q4 -> Q2: PSNR 21.7 -> 15.5, SSIM 0.82 -> 0.61), while CLIP-text stays flat
(~0.34) -- quantization erodes fine detail far more than prompt adherence.

* Studio diffusion (Phase 3): opt-in speed layer (channels_last / compile / TF32)

Add a speed_mode knob (off by default, so the render path stays bit-identical):
default applies channels_last VAE + regional torch.compile of the denoiser's
repeated block where eligible; max also enables TF32 matmul and fused QKV. Regional
compile is gated off for the GGUF transformer (dequantises per-op) and for families
flagged not compile-friendly (a new supports_torch_compile flag, False for Z-Image),
so it activates automatically only once a non-GGUF bf16 transformer is loaded. Speed
optims run before placement/offload, per the diffusers composition order. status now
reports speed_mode + the optims actually engaged.

Verified on Z-Image (B200): default -> ['channels_last'], max -> ['channels_last',
'tf32'], compile correctly skipped for GGUF; generation works in every mode.

121 CPU tests pass.

* Studio diffusion (Phase 2B): opt-in fp8 text-encoder layerwise casting

Add a text_encoder_fp8 knob that casts the companion text encoder(s) to fp8 (e4m3)
storage via diffusers apply_layerwise_casting, upcasting per layer to the bf16
compute dtype while normalisations and embeddings stay full precision. Applied
before placement, gated to CUDA + bf16, best-effort (a failure leaves the encoder
dense). status reports which encoders were cast.

Verified on Z-Image (B200, balanced/group mode where the encoder stays resident):
generation peak VRAM dropped 37% (10840 -> 6791 MB, below the lowest-VRAM offload)
at near-resident speed. It is a memory-vs-quality tradeoff, not free -- ~20 dB PSNR
vs the bf16 encoder, a larger shift than one transformer quant step -- so it is off
by default and documented as such, with the Phase 5 harness to size the cost.

127 CPU tests pass.

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* Studio diffusion (Phase 2C): NVFP4 text-encoder quant (+ generalise fp8 knob)

Generalise the text-encoder precision knob from a fp8 bool to text_encoder_quant
(fp8 | nvfp4). nvfp4 quantises the companion text encoder to 4-bit via torchao
NVFP4 weight-only (two-level microscaling) on Blackwell's FP4 tensor cores; fp8
stays the broader-hardware path (cc>=8.9). Both are gated, best-effort, and run
before placement; status reports the mode actually engaged. This is the lean
realisation of GGUF-native text-encoder quant: 4-bit on the encoder without the
3045-line port.

Verified on Z-Image (B200, balanced/group where the encoder stays resident), vs the
bf16 encoder: nvfp4 cut generation peak VRAM 48% (10840 -> 5593 MB, the lowest TE
option, below whole-model offload) at near-fp8 quality (16.4 vs 17.1 dB PSNR), and
both quants ran faster than bf16. A memory-vs-quality tradeoff (off by default);
size it per model with the Phase 5 quality harness. diffusion_bench gains
--text-encoder-quant.

129 CPU tests pass.

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* Studio diffusion (Phase 4): native stable-diffusion.cpp engine for CPU/Mac

Adds the CPU / Apple-Silicon tier of the two-engine strategy, mirroring the
chat backend's llama.cpp shell-out. Diffusers stays the default on CUDA / ROCm
/ XPU; this covers the hardware diffusers serves poorly, consuming the same
split GGUF assets Studio already curates.

- sd_cpp_args.py: pure sd-cli command builder. Maps the family to its
  text-encoder flag (Z-Image Qwen3 to --llm, Qwen-Image to --qwen2vl, FLUX.1
  CLIP-L + T5), and the diffusers memory policy (none/group/model/sequential)
  to sd.cpp's offload flags (--offload-to-cpu / --clip-on-cpu / --vae-on-cpu /
  --vae-tiling / --diffusion-fa), so one user knob drives both engines.
- sd_cpp_engine.py: SdCppEngine over a located sd-cli. find_sd_cpp_binary()
  with the same precedence as the llama finder (env override, then the Studio
  install root, then in-tree, then PATH), an is_available/version probe, and a
  one-shot subprocess generate that streams progress and returns the PNG.
  runtime_env() prepends the binary's directory to the platform library path
  so a prebuilt's bundled libstable-diffusion.so resolves.
  select_diffusion_engine() is the pure routing decision (GPU backends to
  diffusers, CPU/MPS to native when present).
- install_sd_cpp_prebuilt.py: resolve + download the per-host prebuilt
  (macOS-arm64/Metal, Linux x86_64 CPU, Vulkan/ROCm/Windows variants) into the
  Studio install root. resolve_release_asset() is a pure, unit-tested
  host-to-asset matrix.
- scripts/sd_cpp_smoke.py: end-to-end native generation harness.

Tests (CPU-only, subprocess/filesystem stubbed): 49 new across args, engine,
routing, runtime env, and the installer resolver. Full diffusion suite 166
passing.

Verified on a B200 box: built sd-cli (CUDA) and the prebuilt (CPU) both
generate Z-Image-Turbo Q4_K end to end through SdCppEngine: balanced (group
offload, 5.0s gen), low_vram (full CPU offload + VAE tiling, 13.4s), and the
dynamically-linked CPU prebuilt (50.4s on CPU), all producing coherent images.

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* Studio diffusion (Phase 6): img2img / inpaint / edit / LoRA / upscale on the native engine

Builds on Phase 4's native stable-diffusion.cpp engine, extending it from
text-to-image to the wider feature surface, since sd.cpp supports all of these
through the binary already. Pure command-builder additions plus one engine
method, so the txt2img path is unchanged.

- sd_cpp_args.py: SdCppGenParams gains image-conditioning fields. init_img +
  strength make a run img2img, adding mask makes it inpaint, ref_images drives
  FLUX-Kontext / Qwen-Image-Edit style editing (repeated --ref-image), and
  lora_dir + the <lora:name:weight> prompt syntax select LoRAs. New
  SdCppUpscaleParams + build_sd_cpp_upscale_command for the ESRGAN upscale run
  mode (input image + esrgan model, no prompt / text encoders).
- sd_cpp_engine.py: the subprocess runner is factored into a shared _run() so
  generate() (now carrying the conditioning flags) and a new upscale() reuse
  the same streaming / error / output-check path.
- scripts/sd_cpp_smoke.py: --task {txt2img,img2img,upscale} with --init-img /
  --strength / --upscale-model / --upscale-repeats.

Tests: 10 new across the img2img / inpaint / edit / LoRA flag construction, the
upscale builder and its validation, and the engine's img2img + upscale paths.
Full diffusion suite 176 passing.

Verified on a B200 box through SdCppEngine: img2img (Z-Image-Turbo Q4_K, the
init image conditioned at strength 0.6, 4.8s) and ESRGAN upscale
(512x512 -> 2048x2048 via RealESRGAN_x4plus_anime_6B, 2.7s), both producing
coherent images. Video and the diffusers-path feature wiring are deferred.

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* Studio diffusion (Phase 7): accuracy-preserving speed pass

Re-review of the diffusion stack (#6675/#6679/#6680) surfaced one real accuracy
bug and a dead-on-arrival speed path; this fixes both and adds the lossless /
near-lossless wins, all measured on a B200.

Correctness:
- TF32 global-state leak (fix). speed_mode=max flipped torch.backends.*.allow_tf32
  process-wide and never restored them, so a later `off` load silently inherited
  TF32 and was no longer bit-identical. Added snapshot_backend_flags /
  restore_backend_flags (TF32 + cudnn.benchmark), captured before the speed layer
  runs and restored on unload. Verified: load max -> unload -> load off is now
  byte-identical (PSNR inf) to a fresh off.
- sd-cli timeout could hang forever. _run() blocked in `for line in stdout` and
  only checked the timeout after EOF, so a child stuck in model load / GPU init
  with no output ignored the timeout. Drained stdout on a reader thread with a
  wall-clock deadline. Added a silent-hang regression test.

Speed (diffusers path), near-lossless, opt-in tiers:
- Regional torch.compile now runs on the GGUF transformer. The is_gguf gate (and
  Z-Image's supports_torch_compile=False) were stale: compile_repeated_blocks
  compiles and runs ~2.2x faster on the GGUF Z-Image transformer on
  torch 2.9.1 / diffusers 0.38 (the per-op dequant stays eager, the rest of the
  block compiles). Measured: off 1.80s -> default 0.82s/gen (+54.7%), PSNR 37.7 dB
  vs eager -- far above the Q4 quant noise floor (~21 dB), so it does not move
  output quality. Gate relaxed; default tier delivers it.
- cudnn.benchmark added to the default tier (autotunes the fixed-shape VAE convs).
- torch.inference_mode() around the pipeline call (lossless, strictly faster than
  the no_grad diffusers uses internally).

Memory path:
- VAE tiling (not bit-identical >1MP) restricted to the model/sequential/CPU tiers;
  the balanced (group) tier keeps exact slicing only, so it is now bit-identical to
  the resident image (verified PSNR inf) and slightly faster.
- Group offload adds non_blocking + record_stream on the CUDA stream path to
  overlap each block's H2D copy with compute (lossless; gated on the installed
  diffusers signature so older versions still work).

Native (sd.cpp) path:
- native_speed_flags: a first-class speed knob (default -> --diffusion-fa, a
  near-lossless CUDA win that was previously only added on offload tiers; max also
  -> --diffusion-conv-direct). conv-direct stays opt-in: measured +45% on CUDA, so
  it is never auto-on. Engine generate() merges it, de-duped against offload flags.

Default profile: a GGUF model with no explicit speed_mode now resolves to the
`default` profile (resolve_speed_mode), since compile's perturbation sits below the
quantisation noise floor and so does not reduce quality versus the dense reference;
out of the box a GGUF Z-Image generation drops from 1.80s to 0.81s. Dense models
stay `off` / bit-identical, and an explicit speed_mode -- including "off" -- is
always honored, so the byte-identical path remains one flag away and is the
regression reference.

Tooling: scripts/compile_probe.py (eager vs compiled GGUF probe), scripts/
perf_verify.py (the B200 verification above), and diffusion_bench.py gains
--speed-mode so the speed tiers are benchmarkable.

Tests: 183 passing (was 166); new coverage for the backend-flag snapshot/restore,
GGUF compile eligibility, the balanced tiling/slicing split, native_speed_flags +
the engine de-dup, and the sd-cli silent-hang timeout.

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* Studio diffusion (Phase 7): max tier uses max-autotune-no-cudagraphs + engine/lever benchmarks

The opt-in `max` speed tier now compiles the repeated block with
mode=max-autotune-no-cudagraphs (dynamic=False) instead of the default mode:
Triton autotuning for GEMM/conv-heavier models, gated to the tier where a longer
cold compile is acceptable. CUDA-graph modes (reduce-overhead / max-autotune) are
deliberately avoided -- both crash on the regionally-compiled block (its static
output buffer is overwritten across denoise steps), measured.

Adds two reproducible benchmarks used to validate the optimization research:
- scripts/compare_engines.py: PyTorch (diffusers GGUF) vs native sd.cpp head-to-head.
- scripts/leverage_probe.py: coordinate_descent_tuning + FirstBlockCache probes.

Measured on B200 (Z-Image Q4_K_M, 1024px, 8 steps): default compile 0.80s/gen;
coordinate_descent_tuning 0.79s (within noise, already covered by max-autotune);
FirstBlockCache does not run on Z-Image (diffusers 0.38 block-detection / Dynamo).

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* Studio diffusion (Phase 8): opt-in fast transformer (torchao int8/fp8/fp4 on a dense source)

Add an opt-in transformer_quant mode that loads the dense bf16 transformer and
torchao-quantises it onto the low-precision tensor cores, instead of the GGUF
transformer (which dequantises to bf16 per matmul and so runs at bf16 rate). On a
B200 (Z-Image-Turbo, 1024px/8 steps): auto picks fp8 at 0.614s vs GGUF+compile's
0.823s (1.34x), int8 0.626s (1.32x), both at lower LPIPS than GGUF's own 4-bit floor.

GGUF+compile stays the low-memory default and the fallback. The mode is gated on
CUDA + bf16 + resident VRAM headroom (the dense load peaks ~21GB vs GGUF's 13GB);
any unsupported arch/scheme, OOM, or quant failure falls back to GGUF with a logged
reason. auto picks the best scheme per GPU via a real quantise+matmul smoke probe
(Blackwell nvfp4/fp8/mxfp8, Ada/Hopper fp8, Ampere int8); a min-features filter skips
the tiny projections that crash int8's torch._int_mm. New module mirrors
diffusion_precision.py; quant runs before compile before placement.

184 -> tests pass; new test_diffusion_transformer_quant.py plus backend/route
coverage. scripts/diffusion_bench.py gains --transformer-quant; scripts/quant_probe.py
is the standalone torchao lever probe.

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* Studio diffusion (Phase 8): consumer-GPU tuning - lock fp8 fast accumulate, prefer fp8 over mxfp8, reject 2:4 sparsity

Consumer Blackwell halves tensor-core throughput on FP32 accumulate (fp8 419 vs 838
TFLOPS with FP16 accumulate; bf16 209), so:
- fp8 config locks use_fast_accum=True (Float8MMConfig). torchao already defaults it on;
  pinning it guards consumer cards against a default change. On B200 it is identical
  speed and slightly better quality (LPIPS 0.050 vs 0.091).
- the Blackwell auto ladder prefers fp8 over mxfp8 (measured faster + more accurate).

2:4 semi-structured sparsity evaluated and rejected (scripts/sparse_accum_probe.py):
2:4 magnitude-prune + fp8 gives LPIPS 0.858 (broken image) with no fine-tune, the
cuSPARSELt kernel errors on torch 2.9, and it does not compose with torch.compile
(our main ~2x). Documented as a dead end, not shipped.

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* Studio diffusion (Phase 8): add fp8 fast-accum overflow verification probe

scripts/fp8_overflow_check.py hooks every quantised linear during a real Z-Image
generation and reports max-abs + non-finite counts for use_fast_accum True vs False.
Confirms fast accumulation is an accumulation-precision knob, not an overflow one:
across 276 linears, including Z-Image's ~1.0e6 activation peaks (which overflow FP16),
0 non-finite elements and identical max-abs for both modes.

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* Studio diffusion (Phase 8): detect consumer vs data-center GPU for fp8 accumulate, with user override

Consumer/workstation GPUs (GDDR) halve fp8 FP32-accumulate throughput, so they want
fast (FP16) accumulate; data-center HBM parts (B200/H100/A100/L40) are not nerfed and
prefer the higher-precision FP32 accumulate. Add _is_consumer_gpu() (token-exact match
on the device name per NVIDIA's GPU list, so workstation A4000 != data-center A40;
GeForce/TITAN and unknown default to consumer) and gate the fp8 use_fast_accum on it.

Measured: fast accumulate is ~2x on consumer Blackwell and ~8% on B200 (0.608 vs 0.665s),
no overflow, quality below the quant noise floor. So the default leans to accuracy on
data-center; a new request field transformer_quant_fast_accum (null=auto, true/false=force)
lets the operator override per load (scripts/diffusion_bench.py --fp8-fast-accum auto|on|off).

187 diffusion tests pass (+ consumer detection, _resolve_fast_accum, and the override
threading).

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* Studio diffusion (Phase 8): add NVFP4 probe documenting it is not yet a win on torch 2.9

scripts/nvfp4_probe.py measures NVFP4 via torchao on the real Z-Image transformer.
Finding (B200, 1024px/8 steps): NVFP4 is a torchao feature and DOES run with
use_triton_kernel=False (the default triton path needs the missing MSLK library), but
only at bf16-compile rate (0.667s vs fp8 0.592s) -- it dequantises FP4->bf16 rather than
using the FP4 tensor cores. The real FP4 speedup needs MSLK or torch>=2.11 + torchao's
CUTLASS FP4 GEMM. The smoke probe (default triton=True) already keeps NVFP4 out of auto
on this env, so auto correctly stays on fp8; NVFP4 activates automatically once fast.

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* Studio diffusion (Phase 8): prefer fp8 over nvfp4 in Blackwell auto ladder

Validated NVFP4 on torch 2.11 + torchao CUTLASS FP4 in an isolated env. The FP4
tensor-core GEMM is genuinely active there (a 16384^3 GEMM hits ~3826 TFLOPS,
2.52x bf16 and 1.37x fp8), but it only beats fp8 on very large GEMMs. At the
diffusion transformer's shapes (hidden ~3072, MLP ~12288, M~4096) NVFP4 is both
slower (0.81x fp8 end to end on Z-Image 1024px) and less accurate (LPIPS 0.166
vs fp8's 0.044). Reorder the Blackwell auto ladder to fp8 before nvfp4 so auto is
correct even on a future MSLK-equipped box; nvfp4 stays an explicit opt-in. Add
scripts/nvfp4_t211_probe.py (extension diagnostics + GEMM micro + end-to-end).

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* Studio diffusion (Phase 9): pre-quantized transformer loading

The Phase 8 fast transformer_quant path materialises the dense bf16 transformer on
the GPU and torchao-quantises it in place, so its load peak is ~2x GGUF's (~21 vs
13.4 GB) plus a ~12 GB download. Add a pre-quantized branch: quantise once offline
(scripts/build_prequant_checkpoint.py) and at runtime build the transformer skeleton
on the meta device (accelerate.init_empty_weights) and load_state_dict(assign=True)
the quantized weights, so the dense bf16 never touches the GPU.

Measured (B200, Z-Image fp8): full-pipeline GPU load peak 21.2 -> 14.6 GB (matching
GGUF's 13.4), on-disk 12 -> 6.28 GB, output bit-identical (LPIPS 0.0). It is the same
torchao config + min_features filter the runtime path uses, applied ahead of time.

New core/inference/diffusion_prequant.py (resolve_prequant_source +
load_prequantized_transformer, best-effort, lazy imports). diffusion.py
_load_dense_quant_pipeline tries the pre-quant source first and falls back to the
dense materialise+quantise path, then to GGUF, so the default is unchanged.
DiffusionLoadRequest gains transformer_prequant_path; DiffusionFamily gains an empty
prequant_repos map for hosted checkpoints (hosting deferred). Hermetic CPU tests for
the resolver, the meta-init+assign loader, and the backend branch selection +
fallbacks; GPU verification via scripts/verify_prequant_backend.py.

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* Studio diffusion (Phase 10): attention-backend selection

Add a selectable attention kernel via the diffusers set_attention_backend
dispatcher. Attention is memory-bandwidth bound, so a better kernel is an
end-to-end win orthogonal to the linear-weight quantisation (it speeds the QK/PV
matmuls torchao never touches) and composes with torch.compile.

auto picks the best exact backend for the device: cuDNN fused attention
(_native_cudnn) on NVIDIA when a speed profile is active, measured ~1.18x
end-to-end on a B200 (Z-Image 1024px/8 steps) with LPIPS ~0.004 vs the default
(below the compile/quant noise floor); native SDPA elsewhere and when speed=off
(so off stays bit-identical). Explicit native/cudnn/flash/flash3/flash4/sage/
xformers/aiter are honored, and an unavailable kernel falls back to the default
rather than failing the load.

New core/inference/diffusion_attention.py (normalize + per-device select + apply,
best-effort, lazy imports). Set on pipe.transformer BEFORE compile in load_pipeline;
attention_backend threads through begin_load / load_pipeline / status like the other
load knobs. New request field attention_backend + status field. Hermetic CPU tests
for normalize / select policy / apply fallback, plus route threading + 422. Measured
via scripts/perf_levers_probe.py.

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* Studio diffusion (Phase 11): prefer int8 on consumer GPUs in the auto ladder

Consumer / workstation GPUs halve fp8 (and fp16/bf16) FP32-accumulate tensor-core
throughput, while int8 runs at full rate (int32 accumulate is not nerfed). Public
benchmarks (SDNQ across RTX 3090/4090/5090, AMD, Intel) confirm int8 via torch._int_mm
is as fast or faster than fp8 on every consumer part, and the only path on pre-Ada
consumer cards without fp8 tensor cores. So when transformer_quant=auto, reorder the
arch tier to put int8 first on a consumer/workstation GPU (detected by the existing
_is_consumer_gpu name heuristic), while data-center HBM parts keep fp8 first.

Pure ladder reorder via _prefer_consumer_scheme; no new flags. Verified non-regression
on a B200 (still picks fp8). Hermetic tests for consumer Blackwell/Ada/workstation
(-> int8) and data-center Ada/Hopper/Blackwell (-> fp8).

* Studio diffusion (Phase 12): First-Block-Cache step caching for many-step DiT

Add opt-in step caching (First-Block-Cache) for the diffusion transformer. Across
denoise steps a DiT's output settles, so once the first block's residual barely
changes the remaining blocks are skipped and their cached output reused. diffusers
ships it natively (FirstBlockCacheConfig + transformer.enable_cache, with the
standalone apply_first_block_cache hook as a fallback).

Measured on Flux.1-dev (28 steps, 1024px): ~1.4x on top of torch.compile (2.83 ->
2.03s) at LPIPS ~0.08 vs the no-cache output, well inside the quality bar.

OFF by default and a per-load opt-in: the win scales with step count, so it is for
many-step models (Flux / Qwen-Image) and pointless for few-step distilled models
(e.g. Z-Image-Turbo at ~8 steps), where a single skipped step is a large fraction
of the trajectory. It composes with regional compile only with fullgraph=False (the
cache's per-step decision is a torch.compiler.disable graph break), which the speed
layer now switches to automatically when a cache is engaged. Best-effort: a model
whose block signature the hook does not recognise is caught and the load proceeds
uncached.

- new core/inference/diffusion_cache.py: normalize_transformer_cache + apply_step_cache
  (enable_cache / apply_first_block_cache fallback; threshold auto-raised for a
  quantised transformer per ParaAttention's fp8 guidance; lazy diffusers import).
- diffusion_speed.py: apply_speed_optims takes cache_active; compile drops fullgraph
  when a cache is engaged.
- diffusion.py: apply_step_cache before compile; thread transformer_cache /
  transformer_cache_threshold through begin_load -> load_pipeline and report the
  engaged mode in status().
- models/inference.py + routes/inference.py: transformer_cache (off | fbcache) and
  transformer_cache_threshold request fields, engaged mode in the status response.
- hermetic tests for normalisation, the enable_cache / hook-fallback paths, threshold
  selection, and best-effort failure handling, plus route threading + validation.
- scripts/fbcache_flux_probe.py: the Flux validation probe (latency / speedup / VRAM /
  LPIPS vs the compiled no-cache baseline).

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* Studio diffusion (Phase 14): fix int8 dense quant on Flux / Qwen (skip M=1 modulation linears)

The opt-in dense int8 transformer path crashed on Flux.1 and Qwen-Image with
'torch._int_mm: self.size(0) needs to be greater than 16, but got 1'. int8 dynamic quant
goes through torch._int_mm, which requires the activation row count M > 16. A DiT's AdaLN
modulation projections (Flux norm1.linear 3072->18432, Qwen img_mod.1 / txt_mod.1, Flux.2
*_modulation.linear) and its timestep / guidance / pooled-text conditioning embedders are
computed once from the [batch, dim] conditioning vector (M = batch = 1), not per token, so
they hit _int_mm at M=1 and crash. Their feature dims are large, so the existing
min_features filter did not exclude them.

Fix: the int8 filter now also skips any Linear whose fully-qualified name matches a
modulation / conditioning-embedder token (norm, _mod, modulation, timestep_embed,
guidance_embed, time_text_embed, pooled). These layers run at M=1 once per block and are a
negligible share of the FLOPs, so int8 keeps the full speedup on the attention / FFN layers
(M = sequence length). fp8 / nvfp4 / mxfp8 use scaled_mm, which has no M>16 limit and
quantises these layers fine, so the exclusion is int8-only. Sequence embedders
(context_embedder / x_embedder / txt_in, M = seq) are deliberately not excluded -- note
'context_embedder' contains the substring 'text_embed', which is why the token is the
specific 'time_text_embed', not 'text_embed'.

Measured on a B200 (1024px, transformer_quant=int8 + speed=default), int8 now runs on every
supported model and is the fastest dense path on Flux/Qwen (int8 runs full-rate vs fp8's
FP32-accumulate): FLUX.1-dev 9.62s eager -> 1.98s (4.86x, vs fp8 2.15s), Qwen-Image -> 1.87s
(5.57x, vs fp8 2.09s), FLUX.1-schnell -> 0.41s (3.59x). Z-Image and Flux.2-klein (already
working) are unchanged.

- diffusion_transformer_quant.py: add _INT8_EXCLUDE_NAME_TOKENS; make_filter_fn takes
  exclude_name_tokens; quantize_transformer passes it for int8 only.
- hermetic test that the int8 filter excludes the modulation / embedder linears (and keeps
  attention / FFN / sequence-embedder linears), while fp8 keeps them.
- scripts/int8_linear_probe.py: the meta-device probe used to enumerate each transformer's
  Linear layers and derive the exclusion list.

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* Studio diffusion (Phase 15): build int8 pre-quantized checkpoints (skip M=1 modulation linears)

The prequant-checkpoint builder applied the dense quant filter without the int8-only
M=1 modulation / conditioning-embedder exclusion the runtime path uses, so a built int8
checkpoint baked those projections as int8 and crashed (torch._int_mm needs M>16) at the
first denoise step on Flux / Qwen. Factor the scheme->exclusion decision into a shared
exclude_tokens_for_scheme() used by both the runtime quantise path and the offline builder
so they can never drift, and apply it in build_prequant_checkpoint.py. int8 prequant now
produces a working checkpoint on every supported model, giving int8 (the consumer-preferred
scheme) the same ~2x load-VRAM and download reduction fp8 already had.

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* Studio diffusion (Phase 16): route no-GPU loads to the native sd.cpp engine

When no CUDA/ROCm/XPU GPU is available, route diffusion load/generate to the
native stable-diffusion.cpp engine instead of diffusers, with diffusers as the
guaranteed fallback. On CPU sd.cpp is 1.4-2.8x faster and uses 1.5-2.2x less RAM.

- diffusion_engine_router: centralised engine selection (built on the existing
  select_diffusion_engine), env opt-outs, MPS gating, recorded fallback reason.
- sd_cpp_backend (SdCppDiffusionBackend): the diffusers backend method surface
  backed by sd-cli, with lazy binary install, registry-driven asset fetch,
  step-progress parsing, and cancellation.
- diffusion_families: per-family single-file VAE + text-encoder asset mapping.
- sd_cpp_engine: cancellation support (process-group kill + SdCppCancelled).
- routes/inference + gpu_arbiter: drive the active engine via the router; the
  API now reports the active engine and any fallback reason.
- tests for the backend, router, route selection, and cancellation.

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* Phase 16 review fixes: engine-switch unload, sd.cpp error mapping, per-image seeds, Qwen sampler

Address review feedback on #6724:
- engine router: unload the engine being deactivated on a switch, so the old
  model is not left resident-but-unreachable (the evictor only targets the active
  engine).
- generate route: sd.cpp execution errors (nonzero exit / timeout / missing
  output) now map to 500, not 409 (which only means not-loaded / cancelled).
- native batch: return per-image seeds and persist the actual seed for each image
  so every batch image is reproducible.
- Qwen-Image native path: apply --sampling-method euler --flow-shift 3 per the
  stable-diffusion.cpp docs; other families keep sd-cli defaults.
- honor speed_mode (native --diffusion-fa) and, off-CPU, memory_mode/cpu_offload
  offload flags on the native load instead of hardcoding them off.
- fail the load when the sd-cli binary is present but not runnable (version()
  now returns None on exec error / nonzero exit).
- size estimate: only treat the transformer asset as a possible local path.

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* Studio diffusion (Phase 9): gate request-supplied local prequant paths behind operator opt-in

load_prequantized_transformer ends in torch.load(weights_only=False), which executes
arbitrary code from the pickle. The transformer_prequant_path load-request field reached
that unpickle for any local file an authenticated caller named, so a request could trigger
remote code execution. Refuse the source.kind=='path' branch unless the operator sets
UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH=1; the first-party hosted-repo checkpoint stays trusted
and unaffected. Document the requirement on the API field and add gate tests.

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* Studio diffusion (Phase 10): reset the global attention backend on native, gate arch-specific kernels, accept sdpa

- apply_attention_backend now restores the native default when no backend is requested or a
  kernel fails. diffusers keeps a process-wide active attention backend that
  set_attention_backend updates, and a fresh transformer's processors follow it, so a load
  that wanted native could silently inherit a backend (e.g. cuDNN) an earlier speed-profile
  load pinned, breaking the bit-identical/off guarantee.
- select_attention_backend drops flash3/flash4 up front when the CUDA capability is below
  Hopper/Blackwell. diffusers only checks the kernels package at set time, so an explicit
  request on the wrong card set fine then crashed mid-generation; it now falls back to native.
- Add the sdpa alias to the attention_backend Literal so an API request with sdpa (already a
  valid alias of native) is accepted instead of 422-rejected by Pydantic.
- Drop the dead replace('-','_') normalization (no alias uses dashes/underscores).
- perf_levers_probe.py output dir is now relative to the script, not a hardcoded path.

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* Studio diffusion (Phase 12): only engage FBCache on context-aware transformers; quantized threshold for GGUF

- apply_step_cache now engages only via the transformer's native enable_cache (the diffusers
  CacheMixin path), which exists exactly when the pipeline wraps the transformer call in a
  cache_context. The standalone apply_first_block_cache fallback installed on non-CacheMixin
  transformers too (e.g. Z-Image), whose pipeline opens no cache_context, so the load reported
  transformer_cache=fbcache and then the first generation crashed inside the hook. Such a model
  now runs uncached per the best-effort contract.
- GGUF transformers are quantized (the default Studio load path), so they now use the higher
  quantized FBCache threshold when the caller leaves it unset, instead of the dense default
  that could keep the cache from triggering.
- fbcache_flux_probe.py: compile cached runs with fullgraph=False (FBCache is a graph break, so
  fullgraph=True failed warmup and silently measured an eager cached run); output dir is now
  relative to the script, not a hardcoded path.

* Studio diffusion (Phase 11): keep professional RTX cards on the fp8 ladder

_is_consumer_gpu treated professional parts (RTX PRO 6000 Blackwell, RTX 6000 Ada) as
consumer because their names carry no datacenter token, so the auto ladder moved int8 ahead
of fp8 and the fp8 path chose fast accumulate for them. The rest of the backend already
classifies these as datacenter/professional (llama_cpp.py _DATACENTER_GPU_RE), so detect the
same RTX PRO 6000 / RTX 6000 Ada markers here and keep fp8 first with precise accumulate.

Also fix the consumer-Blackwell test to use compute capability (10, 0) instead of (12, 0).

* Studio diffusion (Phase 8): tolerate missing torch.float8_e4m3fn in the mxfp8 config

Accessing torch.float8_e4m3fn raises AttributeError on a torch build without it (not just
TypeError on older torchao), which would break the mxfp8 config helper instead of falling
back to the default. Catch both so the fallback is robust.

quant_probe.py: same AttributeError fallback; run LPIPS on CPU so the scorer never holds
CUDA memory during the per-row VRAM probe; output dir relative to the script.

* Studio diffusion (Phase 7): robust backend-flag snapshot/restore and restore on failed speeded load

- snapshot_backend_flags reads each flag defensively (getattr + hasattr), so a build/platform
  missing one (no cuda.matmul on CPU/MPS) still captures the rest instead of skipping the
  whole snapshot. restore_backend_flags restores each flag independently so one failure can't
  leave the others leaked process-wide.
- load_pipeline restores the flags (and clears the GPU cache) when the build fails after
  apply_speed_optims mutated the process-wide flags but before _state captured them for unload
  to restore -- otherwise a failed default/max load left cudnn.benchmark/TF32 on and
  contaminated later off generations.

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* Studio diffusion (Phase 4): enforce the sd-cli timeout while reading output

Iterating proc.stdout directly blocks until the stream closes, so a sd-cli that hangs
without producing output (or without closing stdout) would never reach proc.wait and the
wall-clock timeout was silently bypassed. Drain stdout on a daemon thread and wait on the
PROCESS, so the main thread always enforces the timeout and kills a hung process (which
closes the pipe and ends the reader). Add a test that times out even when stdout blocks,
and make the no-binary test hermetic so a host-installed sd-cli can't leak in.

* Studio diffusion (Phase 14): guard the int8 exclusion filter against a None fqn

The filter callback can be invoked without a module name, so fqn.lower() would raise
AttributeError on None. Fall back to an empty name (nothing matches the exclusion tokens,
so the linear is kept) instead of crashing the quantise pass.

* Studio diffusion (Phase 16) review fixes: native engine robustness

- sd_cpp_backend: stop truncating explicit seeds to 53 bits (mask to int64);
  a large requested seed was silently collapsed (2**53 -> 0) and distinct seeds
  aliased to the same image. Random seeds stay 53-bit (JS-safe).
- sd_cpp_backend: sanitize empty/whitespace hf_token to None so HfApi/hf_hub
  fall back to anonymous instead of failing auth on a blank token.
- sd_cpp_backend: a superseding load now cancels the in-flight generation, so the
  old sd-cli can no longer return/persist an image from the previous model.
- diffusion_engine_router: run the previous engine's unload() OUTSIDE the lock so a
  slow 10+ GB free / CUDA sync does not block engine selection.
- diffusion_engine_router: probe sd-cli runnability (version()) before committing to
  native, so a present-but-unrunnable binary falls back to diffusers at selection.
- diffusion_device: resolve a torch-free CPU target when torch is unavailable, so a
  CPU-only install can still reach the native sd.cpp engine instead of failing load.
- tests updated for the runnability probe + a not-runnable fallback case.

* Studio diffusion (Phase 9) review fixes: prequant safety + validation

- SECURITY: a request-supplied local pre-quant path is now unpickled only when it
  resolves inside an operator-configured ALLOWLIST of directories
  (UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH = dir[:dir...]). The previous boolean opt-in,
  once enabled for one trusted checkpoint, allowed torch.load(weights_only=False) on
  any path a load request named (arbitrary code execution). realpath() blocks symlink
  escapes; a bare on/off toggle is no longer a wildcard.
- Validate the checkpoint's min_features against the runtime Linear filter, so a
  checkpoint that quantised a different layer set is rejected instead of silently
  loading a model that mismatches the dense path while reporting the same scheme.
- Tolerant base_model_id compare (exact or same final path/repo segment), so a local
  path or fork of the canonical base is accepted instead of falling back to dense.
- _has_meta_tensors uses any(chain(...)) (no intermediate lists).
- prequant verify/probe scripts use repo-relative paths (+ env overrides), not the
  author's absolute /mnt paths.
- tests: allowlist-dir opt-in, outside-allowlist refusal, min_features mismatch, fork tail.

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* Studio diffusion (Phase 7) review fixes: offload fallback + bench scripts

- diffusion_memory: when group offload is unavailable and the plan falls back to
  whole-module offload, enable VAE tiling (the group plan left it off, but the fallback
  is the low-VRAM path where the decode spike can OOM). Covers both the group and
  sequential fallback branches.
- perf_verify: include the balanced-vs-off PSNR in the pass/fail condition, so a
  balanced bit-identity regression actually fails the check instead of exiting 0.
- compare_engines: --vae/--llm default to None (were author-absolute /mnt paths), and
  the load-progress poll has a 30 min deadline instead of looping forever on a hang.
- test for the group->model fallback enabling VAE tiling.

* Studio diffusion (Phase 8) review fixes: quant compile + nvfp4 path

- diffusion: a torchao-quantized transformer is committed only compiled. A dense model
  resolves to speed_mode=off, which would run the quant eager (~30x slower than the GGUF
  it replaced), so when transformer_quant engaged and speed resolved to off, promote to
  default (regional compile); warn loudly if compile still does not engage.
- diffusion_transformer_quant: build the nvfp4 config with use_triton_kernel=False so the
  CUTLASS FP4 path is used (torchao defaults to the Triton kernel, which needs MSLK);
  otherwise the smoke probe fails on CUTLASS-only Blackwell and silently drops to GGUF.
- nvfp4_probe: repo-relative output dir + --out-dir (was an author-absolute /mnt path).
- test asserts the eager-quant -> default-compile promotion.

* Studio diffusion (Phase 10) review fixes: attention gating + probe isolation

- diffusion_attention: gate the auto cuDNN-attention upgrade on SM80+; on pre-Ampere
  NVIDIA (T4/V100) cuDNN fused SDPA is accepted at set time but fails at first generation,
  so auto now stays on native SDPA there.
- diffusion_attention: _active_attention_backend handles get_active_backend() returning an
  enum/None (not a tuple); the old  unpack always raised and was swallowed, so
  the native-restore short-circuit never fired.
- perf_levers_probe: free the resident pipe on a skipped (attn/fbcache) variant; run LPIPS
  on CPU so it isn't charged to every variant's peak VRAM; reset force_fuse_int_mm_with_mul
  so the inductor_flags variant doesn't leak into later compiled rows.
- tests for the SM80 cuDNN gate.

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* Studio diffusion (Phase 4) review fixes: sd.cpp installer + engine hardening

- install_sd_cpp_prebuilt: download the release archive with urlopen + an explicit
  timeout + copyfileobj (urlretrieve has no timeout and hangs on a stalled socket);
  extract through a per-member containment check (Zip-Slip guard); expanduser the
  --install-dir so a tilde path is not taken literally; and on Windows CUDA also fetch
  the separately-published cudart runtime DLL archive so sd-cli.exe can start.
- sd_cpp_engine: find_sd_cpp_binary honors UNSLOTH_STUDIO_HOME / STUDIO_HOME like the
  installer, so a custom-root install is discovered without UNSLOTH_SD_CPP_PATH; start
  sd-cli with the parent-death child_popen_kwargs so it is not orphaned on a backend
  crash; reap the SIGKILLed child (proc.wait) so a cancel/timeout does not leave a zombie.
- tests: Zip-Slip rejection, normal extraction, studio-home discovery.

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* Studio diffusion (Phase 4) review round 2: collect sd-cli batch outputs

Codex review: when batch_count > 1, stable-diffusion.cpp's save_results() writes
the numbered files <stem>_<idx><suffix> (base_0.png, base_1.png, ...) instead of
the literal --output path. SdCppEngine.generate checked only the literal path, so
a batch generation would exit 0 and then raise 'no image' (or return a stale
file). generate now returns the literal path when present and otherwise falls
back to the numbered siblings; single-image behavior is unchanged.

Test: a fake sd-cli that writes img_0.png/img_1.png (not img.png) is collected
without error.

* Studio diffusion (Phase 6) review round 2: img2img source dims + upscale repeats

Codex review on the native engine arg builder:

- build_sd_cpp_command emitted --width/--height unconditionally, so an
  img2img/inpaint/edit run that left dims unset forced a 1024x1024 resize/crop of
  the input. width/height are now Optional (None = unset): an image-conditioned
  run (init_img or ref_images) with unset dims omits the flags so sd.cpp derives
  the size from the input image (set_width_and_height_if_unset); a plain txt2img
  run with unset dims keeps the prior 1024x1024 default; explicit dims are always
  honored. width/height are read only by the builder, so the type change is local.

- build_sd_cpp_upscale_command used a truthiness guard (params.repeats and ...)
  that silently swallowed repeats=0 into sd-cli's default of one pass, turning an
  explicit no-op into a real upscale. It now rejects repeats < 1 with ValueError
  and emits the flag for any explicit value != 1.

Tests: img2img unset dims omit width/height (init_img and ref_images), explicit
dims emitted, txt2img keeps 1024; upscale rejects repeats=0 and omits the flag at
the default. (Two pre-existing binary-discovery tests fail only because a real
sd-cli is installed in this dev environment; unrelated to this change.)

* Studio diffusion (Phase 9) review round 2: correct prequant allowlist doc

Codex review: the transformer_prequant_path field description still told operators
to enable local checkpoints with UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH=1, but the
prior security fix made that variable a directory allowlist -- _allowed_prequant_roots
deliberately drops bare on/off toggle tokens (1/true/yes/...). An operator
following the documented =1 would have every transformer_prequant_path request
silently refused. The description now states it must name one or more allowlisted
directories and that a bare on/off value is not accepted.

Test: asserts the field help references UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH, does
not say =1, and describes an allowlist/directory (guards against doc drift).

* Studio diffusion (Phase 10) review round 2: cudnn/flash3 gating + registry reset

Codex review on attention-backend selection:

- Explicit attention_backend=cudnn skipped the SM80 gate that auto applies, so on
  pre-Ampere NVIDIA (T4 SM75 / V100 SM70) it set fine then crashed at the first
  generation with no fallback. select_attention_backend now applies
  _cudnn_attention_supported() to an explicit cuDNN request too.

- flash3 used a minimum-only capability gate (>= SM90), so an explicit flash3 on a
  Blackwell B200 (SM100) passed and then failed at generation -- FlashAttention 3
  is a Hopper-SM90 rewrite with no Blackwell kernel. The arch gate is now a
  (min, max-exclusive) range: flash3 is SM9x-only, flash4 stays SM100+.

- apply_attention_backend's success path left diffusers' process-wide active
  backend pinned to the kernel it set; a later component whose processors are
  unconfigured (backend None) would inherit it. It now resets the global registry
  to native after a successful per-transformer set (the transformer keeps its own
  backend), best-effort. Also fixed _active_attention_backend: get_active_backend()
  returns a (name, fn) tuple, so the prior code stringified the tuple and never
  matched a name, defeating the native-restore short-circuit.

Tests: explicit cudnn dropped below SM80; flash3 dropped on SM100 and allowed on
SM90; global registry reset after a successful set; _active_attention_backend
reads the tuple return.

* Studio diffusion (Phase 11) review round 2: keep GH200/B300 on the fp8 ladder

Codex review: _DATACENTER_GPU_TOKENS omitted GH200 (Grace-Hopper) and B300
(Blackwell Ultra), though it has the distinct GB200/GB300 superchip tokens. So
_is_consumer_gpu returned True for 'NVIDIA GH200 480GB' / 'NVIDIA B300', and the
auto ladder moved int8 ahead of fp8 on those data-center parts -- contradicting
llama_cpp.py's datacenter regex, which lists both. Added GH200 and B300 so they
are treated as data-center class and keep the intended fp8-first behavior.

Test: extends the datacenter parametrize with 'NVIDIA B300' and
'NVIDIA GH200 480GB' (now _is_consumer_gpu False).

* Studio diffusion (Phase 14) review round 2: apply int8 M=1 exclusion in the builder

Codex review: the M=1 modulation/embedder exclusion was wired only into the dense
runtime quantiser; the offline builder scripts/build_prequant_checkpoint.py called
make_filter_fn(min_features) with no exclusion. So an int8 prequant checkpoint
quantised the AdaLN modulation and conditioning-embedder linears, and loading it
via transformer_prequant_path (the load path only loads already-quantised tensors,
it can't re-skip them) reintroduced the torch._int_mm M=1 crash this phase fixes
for the runtime path.

Extracted int8_exclude_name_tokens(scheme) as the single source of truth (int8 ->
the M=1 exclusion, every other scheme -> none) and use it in both the runtime
quantiser and the builder, so a prequant artifact's quantised-layer set always
matches the runtime. fp8/fp4/mx artifacts are byte-identical (empty exclusion).

Test: int8_exclude_name_tokens returns the exclusion for int8 and () for
fp8/nvfp4/mxfp8.

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* Studio diffusion (Phase 16) review round 2: native CPU arbiter, status offload, load race

Codex review on the native-engine routing:

- The /images/load route took the GPU arbiter (acquire_for(DIFFUSION) -> evict chat)
  unconditionally after engine selection. A native sd.cpp load on a pure-CPU host
  never touches the GPU, so that needlessly tore down the resident chat model. The
  handoff is now gated: diffusers always takes it, a force-native sd.cpp load on a
  CUDA/XPU/MPS box still takes it, but a native sd.cpp load on a CPU host skips it.

- sd_cpp status() hardcoded offload_policy 'none' / cpu_offload False even when
  _run_load computed real offload flags (balanced/low_vram/cpu_offload off-CPU), so
  the setting was unverifiable. status now derives them from state.offload_flags
  (still 'none' on CPU, where the flags are empty).

- _run_load committed the new state without cancelling/waiting on a generation that
  started during the (slow) asset download, so a stale sd-cli run against the OLD
  model could finish afterward and persist an image from the previous model once the
  new load reported ready. The commit now signals the in-flight cancel and waits on
  _generate_lock before swapping _state (taken only at commit, so the download never
  serialises against generation), mirroring the diffusers load path.

Tests: CPU native load skips the arbiter while a GPU native load takes it; status
reports offload active when flags are set; _run_load cancels and waits for an
in-flight generation before committing.

* Studio diffusion (Phase 14) review round 2: align helper name with the stack

Rename the int8 exclusion helper to exclude_tokens_for_scheme, matching the
identical helper already present higher in the diffusion stack (Phase 16). The
helper definition, the runtime quantiser call, and the offline builder are now
byte-identical to that version, so the two branches no longer introduce a
divergent name for the same single-source-of-truth and the stack merges without
a conflict on this fix. No behavior change.

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* Studio diffusion: eager patches + torch.compile cache speed phase

Adds the opt-in speed path for the GGUF diffusion transformer behind a
selectable speed mode (default off, so output is unchanged until a profile
is chosen):

- diffusion_eager_patches.py: shared eager fast-paths (channels_last,
  attention/backend selection, fused norms and QKV) installed at load and
  rolled back on unload or failed load.
- diffusion_compile_cache.py / diffusion_gguf_compile.py: a persistent
  torch.compile cache and the GGUF-transformer compile wiring.
- diffusion_arch_patches.py: architecture-specific patches.
- diffusion_patch_backend.py: shared install/restore plumbing.
- diffusion_speed.py: speed-profile planning.

Tests for each module plus the benchmarking and probe scripts used to
measure speed, memory, and accuracy of the path.

* Studio diffusion: image workflows (safetensors, image-conditioned, editing) + Images UI

Backend:
- Load non-GGUF safetensors models: full bnb-4bit pipelines and single-file
  fp8 transformers, gated to the unsloth org plus a curated allowlist.
- Image-conditioned workflows built with Pipeline.from_pipe so they reuse the
  loaded transformer/VAE/text-encoder with no extra VRAM: img2img, inpaint,
  outpaint, and a hires-fix upscale pass.
- Instruction editing as its own family kind (Qwen-Image-Edit-2511,
  FLUX.1-Kontext-dev) and FLUX.2-klein reference conditioning (single and
  multi-reference) plus klein inpaint.
- Auto-resize odd-sized inputs to a multiple of 16 (and resize the matched
  mask) so img2img/inpaint/edit no longer reject non-/16 uploads. Bound the
  decoded image size and cap upscale output to avoid OOM on large inputs.
- Fixes: from_pipe defaulting to a float32 recast that crashed torchao
  quantized transformers; image-conditioned calls forcing the slider size
  onto the input image. Native sd.cpp engine rejects image-conditioned and
  reference requests it cannot serve.

Frontend:
- Redesigned Images page with capability-gated workflow tabs (Create,
  Transform, Inpaint, Extend, Upscale, Reference, Edit), a brush mask editor,
  client-side outpaint, and a multi-reference picker.
- Advanced options moved to a right-docked panel mirroring Chat: closed by
  default, toggled by a single fixed top-bar button that stays in place.

sd.cpp installer: pin the release, verify each download's sha256, add a
download timeout, and make the source repo configurable for a future mirror.

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* Studio Images: correct the Advanced panel comment (closed by default, fixed toggle)

* Studio diffusion: LoRA adapters for the Images workflow

Add community LoRA support across both diffusion backends, the single
biggest step toward broad image-workflow coverage.

Backend
- New shared module core/inference/diffusion_lora.py: adapter discovery
  (local scan + curated catalog + owner/name[:file] Hub refs), download
  via hf_hub_download_with_xet_fallback, alias sanitization, native
  managed-dir materialization with collision-broken aliases, prompt-tag
  injection (deduped against user-typed tags), and a supports_lora gate.
- Native sd-cli: resolve + materialize selected LoRAs into a per-run
  managed dir, inject <lora:ALIAS:w> tags, pass --lora-model-dir with
  --lora-apply-mode auto. The arg builder already emitted these flags.
- Diffusers: non-fused load_lora_weights + set_adapters manager, tracked
  on the pipe so an unchanged selection is a no-op and a model swap
  resets; cleared on unload. Never fuses (breaks quantized transformers
  and blocks live weight tweaks).
- Gated off where unsupported: torchao fp8/int8 dense, GGUF-via-diffusers,
  and native Qwen-Image (no LoRA name-conversion branch upstream).
- Request contract: optional loras on DiffusionGenerateRequest; empty or
  omitted is identical to today. supports_lora surfaced in status; chosen
  LoRAs persisted in gallery recipe metadata.
- New GET /api/models/diffusion-loras for the picker (family-filtered).

Frontend
- Repeatable multi-LoRA picker (adapter select + weight slider 0..2 +
  remove), gated by the loaded model's supports_lora and family, max 8.

Tests
- New test_diffusion_lora.py (14): helpers, request validation, native
  tag/dir wiring, diffusers set_adapters manager, supports_lora matrix.

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* Studio diffusion: ControlNet for the Images workflow (diffusers)

Add ControlNet conditioning, the #2 most-used diffusion workflow after
LoRA, on the diffusers backend for the families with ControlNet pipelines
(FLUX.1 and Qwen-Image), with Union models as the default picks.

Backend
- New core/inference/diffusion_controlnet.py: family-gated discovery
  (curated Union models + local dirs + bare owner/name repos), resolution
  to a loadable repo/dir, control-image preprocessing (passthrough +
  a dependency-free canny edge map), and a supports_controlnet gate.
- diffusion.py: a ControlNet manager parallel to the LoRA one. Loads the
  (small) ControlNet model once via from_pretrained (cached by id) and
  builds the family's ControlNet pipeline via Pipeline.from_pipe(base,
  controlnet=model), reusing the resident base modules at their loaded
  dtype (no reload, no recast). Passes the control image + conditioning
  scale + guidance start/end at generate time; cleared on unload.
- Families: FLUX.1 -> FluxControlNetPipeline/Model, Qwen-Image ->
  QwenImageControlNetPipeline/Model. Others declare none (gated off).
- Gated off for the native engine, GGUF-via-diffusers, and torchao
  fp8/int8 dense (same rule as LoRA). v1 conditions txt2img only.
- Request contract: optional controlnet on DiffusionGenerateRequest;
  supports_controlnet in status; the choice persisted in gallery meta.
- New GET /api/models/diffusion-controlnets for the picker.

Frontend
- A ControlNet control in the Images rail (model select + control-image
  upload + control-type select + strength slider), gated by the loaded
  model's supports_controlnet + family, shown for text-to-image.

Tests
- New test_diffusion_controlnet.py (10): discovery/resolve/preprocess/gate
  helpers, request validation, family wiring, and the diffusers pipe
  manager (loads once, caches, from_pipe with controlnet, rejects
  unsupported families).

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* Studio ControlNet: show the picker on the Create tab (workflow id is 'create', not 'txt2img')

The ControlNet control gated on workflow === 'txt2img', but the Images workflow tab ids are create/transform/inpaint/extend/upscale/reference/edit -- there is no 'txt2img'. So the picker never rendered even with a ControlNet-capable model loaded. Gate on 'create' (the text-to-image tab) for both the picker and the request wiring. Found via a live Playwright capture of the running Studio.

* Studio: do not force diffusers pipelines cross-tagged gguf into the GGUF variant expander

Some diffusers image repos (e.g. unsloth/Qwen-Image-2512-unsloth-bnb-4bit) carry a
stray "gguf" tag on the Hub but ship no .gguf files. The model search classified
them as GGUF from the bare tag, so the picker rendered the GGUF variant expander,
which then dead-ended at "No GGUF variants found." Trust the bare gguf tag only when
the repo is not a diffusers pipeline; the -GGUF name suffix and real gguf metadata
(populated via expand=gguf) remain authoritative, so genuine GGUF repos are unaffected.

* Studio Images: load non-curated unsloth/on-device diffusers repos instead of no-op

handleModelSelect only loaded curated safetensors ids and GGUF variant picks; any other
non-GGUF pick (an on-device diffusers folder, or a future unsloth diffusers image repo
surfaced by search) silently did nothing. Treat such a pick as a full diffusers pipeline
load when the id is unsloth-hosted or on-device (the backend infers the family + base repo
and gates loads to unsloth/* or local paths), and show a clear message otherwise instead
of silently ignoring the click. Curated and GGUF paths are unchanged.

* Studio Images: keep curated safetensors models in Recommended after download

The curated bnb-4bit / fp8 diffusion rows were filtered out of the Images picker's
Recommended list once cached (curatedSafetensorsRows dropped anything in downloadedSet),
so they vanished from the picker after the first load and could only be found by typing an
exact search. The row already renders a downloaded badge, matching how GGUF Recommended
rows stay visible when cached. Drop the exclusion so the curated safetensors always list.

* Studio diffusion LoRA: sanitize dots out of adapter aliases

The LoRA alias is used as the diffusers PEFT adapter name, and PEFT rejects names
containing "." (module name can't contain "."). sanitize_alias kept dots, so a LoRA whose
filename carries a version tag (e.g. Qwen-Image-2512-Lightning-8steps-V1.0-bf16) failed to
apply with a 400. Replace dots too; the alias stays a valid native <lora:NAME:w> filename
stem. Adds regression coverage for internal dots.

* Studio Images: clarify the GGUF transformer-quant Advanced control

Renamed the confusing "Transformer quant / GGUF default" control to "GGUF speed mode"
with an "Off (run the GGUF)" default, and reworded the hint to state plainly that FP8/INT8/
FP4 load the FULL base model (larger download + more VRAM) rather than re-packing the GGUF,
falling back to the GGUF if it can't fit. Behavior unchanged; labels/hint only.

* Studio Images: list on-device unsloth diffusion models in the picker

The Images picker's On Device tab hid every non-GGUF cached repo whenever a
task filter was active, so downloaded unsloth diffusion pipelines (bnb-4bit
and FP8 safetensors) never showed up there. List cached repos that pass the
task gate, limited under a filter to unsloth-hosted ones so base repos (which
fail the diffusion load trust gate) don't appear only to dead-end on click.
Chat behavior is unchanged: the task gate still drops image repos there.

* Studio: hide single-file image checkpoints from the chat model picker

The chat picker treats a cached repo as an image model, and hides it, only
when it ships a diffusers model_index.json. Single-file, ComfyUI, and
ControlNet image checkpoints (an FP8 Qwen-Image, a z-image safetensors, a
Qwen-Image ControlNet) carry none, so they surfaced as loadable chat models.
Fall back to resolving the repo id against the known diffusion families, the
same resolver the Images backend loads from, so these checkpoints are tagged
text-to-image and stay in the Images picker only.

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* Studio Images: add the FLUX.2-dev model family

Loading unsloth/FLUX.2-dev-GGUF failed because detect_family knew only the
Qwen3-based FLUX.2-klein, so FLUX.2-dev (the full, Mistral-based Flux2Pipeline)
resolved to nothing and the load errored. Add a flux.2-dev family: Flux2Pipeline
+ Flux2Transformer2DModel over the black-forest-labs/FLUX.2-dev base repo (gated,
reachable with an HF token), with its FLUX.2 32-channel VAE and Mistral text
encoder wired for the sd-cli path from the open Comfy-Org/flux2-dev mirror.
text-to-image only: diffusers 0.38 ships no Flux2 img2img / inpaint pipeline for
dev. Frontend gets sensible dev defaults (28 steps, guidance 4), distinct from
klein's turbo defaults. Verified live: GGUF load resolves the family + gated base
repo and generates a real 1024x1024 image on GPU.

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* Studio Images: clearer error for an unsupported diffusion model

When a repo id resolves to no diffusion family the load raised 'Could not infer a
diffusion family... Pass family_override (z-image)', which points at an unrelated
family and doesn't say what is supported. Replace it with a message that lists the
supported families (from a new supported_family_names helper) and notes that video
models and image models whose diffusers transformer has no single-file loader are
not supported. Applies to both the diffusers and native sd.cpp load paths. Also
refreshes two stale family-registry comments that still called FLUX.2-dev omitted.

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* Studio: use the Unsloth stable-diffusion.cpp prebuilt mirror for native diffusion

Point the native sd-cli/sd-server installer at unslothai/stable-diffusion.cpp,
which builds and publishes our own CPU and Apple prebuilts (the same way
unslothai/llama.cpp ships prebuilts). GPU hosts run diffusers and never reach the
native installer, so the mirror only needs CPU (Linux/WSL/Windows) and Apple
(Metal) builds.

DEFAULT_REPO now points at unslothai/stable-diffusion.cpp and DEFAULT_TAG at
master-741-484baa4. A leejet upstream fallback is added: if the mirror cannot
serve a host (release missing, or a host we do not build) and the default repo is
in use, install resolves against leejet so native install still works. An explicit
UNSLOTH_SD_CPP_REPO override is respected as-is (no substitution).

Tests cover the mirror asset matrix for every CPU/Apple host, the default-repo
assertion, and the mirror-to-upstream fallback path.

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* Remove stray async task scratch outputs committed by mistake

* ControlNet: reject filesystem-like ids and do not cache a model past an unload race

Two review findings on the ControlNet path:
- resolve_controlnet's bare-repo fallback accepted any id with a slash, so a
  path-shaped id (/tmp/x, ../x) reached from_pretrained as a local directory.
  Restrict the fallback to a strict owner/name HF repo id shape.
- _controlnet_pipe now re-checks the cancel event after the blocking
  from_pretrained: an unload that raced the download had already cleared the
  caches, so caching the late module would pin it past the unload.

* sd-cli install: honor explicit accelerator/repo pins and route --print-asset through fallback

Five review findings on the mirror install path:
- resolve_release_asset returned a CPU build for an explicit --accelerator
  cuda/vulkan/rocm request when only a CPU asset existed (Windows and Linux),
  so the mirror CPU build was installed instead of the upstream accelerated
  build. An explicit GPU accelerator with no matching asset now returns None so
  the upstream fallback runs.
- The upstream fallback keyed on the repo string, so setting UNSLOTH_SD_CPP_REPO
  to the default value still fell back to leejet. Gate the fallback on the env
  var being unset, so an explicit pin gets exactly that repo.
- A pinned tag missing on the mirror but present upstream fell through to the
  mirror's unpinned latest. Try the exact pin on every repo before any repo's
  latest, so a pinned upstream build wins over an unpinned mirror-latest.
- --print-asset used a mirror-only fetch and reported no match for hosts the
  mirror does not build. Route it through the same primary/upstream resolution.
- Tidy the fetch-failed log line (own line, no trailing separator) and guard
  release.get(assets) against None.

Adds regression tests for each.

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* ControlNet: address review findings on the diffusers path

- resolve_controlnet enforces catalog family compatibility so a direct API call
  cannot load a ControlNet built for another family through the wrong pipeline.
- Unknown ControlNet ids now surface as a 400 (call site maps FileNotFoundError
  to ValueError) instead of a generic 500.
- strength 0 disables ControlNet entirely, so a no-op selection never pays the
  download / VRAM cost; the control image is decoded and validated BEFORE the
  ControlNet is resolved or built, so a malformed image fails fast for the same reason.
- ControlNet loads use the base compute dtype (state.dtype is a display string,
  not a torch.dtype, so it silently fell back to float32) and honor the base
  offload policy via group offloading instead of forcing the module resident.
- Empty/malformed HF token coerced to anonymous access.
- Flux Union ControlNet control_mode mapped from the selected control type.
- resolve_controlnet drops the unused hf_token/cancel_event params.
- ControlNetSpec validates guidance_start <= guidance_end (clean 422).
- Images UI ControlNet Select shows its placeholder when nothing is selected.

Adds regression tests for family enforcement and the union control-mode map.

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* Diffusion LoRA: harden resolution, native tag precedence, and diffusers teardown

Address review findings on the LoRA path:
- resolve_one: normalise a blank/whitespace hf_token to None (anonymous access)
  and reject a client-supplied weight file with traversal / absolute path.
- resolve_specs: convert FileNotFoundError from an unknown/stale id to ValueError
  so the route returns 400 instead of a generic 500.
- _scan_local: disambiguate local adapters that share a stem (foo.safetensors vs
  foo.gguf) so each is uniquely addressable.
- inject_prompt_tags: the backend-validated weight now wins over a user-typed
  <lora:ALIAS:...> for a selected adapter; unselected user tags are left alone.
- diffusers _apply_loras: reject a .gguf adapter with a clear error before touching
  the pipe (diffusers loads safetensors only).
- _unload_locked: drop the explicit unload_lora_weights() on teardown; the pipe is
  dropped wholesale (freeing adapters), so the previous call could race an in-flight
  denoise on the same pipe.
- Images page: use a stable LoRA key and clear the selection (not just the options)
  when the catalog refresh fails.

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* Diffusion: guard trust check against OSError and validate conditioning inputs

- _is_trusted_diffusion_repo: wrap Path.exists() so a repo id with invalid
  characters (or a bare owner/name id) can't raise OSError; treat any failure as
  not-a-local-path and fall through to the unsloth/ allowlist. validate_load_request
  still raises the clear FileNotFoundError for a genuinely missing local pick.
- generate(): reject mask_image / upscale / reference_images supplied without an
  input image, and reject reference_images on a family that does not support
  reference conditioning, instead of silently degrading to txt2img / img2img.

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* Address Codex review findings on the image-workflows PR

Keep diffusion.py importable without torch: the compile/arch patch modules
import torch at module level, so import them lazily at their load/unload
call sites instead of at module load. This restores the torchless contract
so get_diffusion_backend() works on a CPU/native sd.cpp install.

Match family reject keywords and aliases as whole path/name segments, not
raw substrings, so an unrelated word like edited, edition, or kontextual no
longer misroutes or hides a valid base image model, while supported edit
families (Qwen-Image-Edit, FLUX Kontext) still resolve. Mirror the same
segment matching in the picker task filter.

Route FLUX.2-dev native guidance through --guidance like the other FLUX
families rather than --cfg-scale. Reject native upscale requests that have
no input image. Read image header dimensions and reject over-limit inputs
before decoding pixels, so a crafted small-payload image cannot spike
memory. Reject an upscale that would shrink the source below its input
size. Validate the model_kind against the filename extension before the
GPU handoff. Estimate a local diffusers pipeline's size from its on-disk
weights so auto memory planning does not skip offload and OOM. Report
workflows: [txt2img] from the native backend status so the Create tab
stays enabled for a loaded native model. Clamp the outpaint canvas to the
backend's 4096px decode limit.

Adds regression tests for segment matching and kind/extension validation.

* Harden diffusion LoRA handling on the diffusers and native paths

Reject LoRA on a torch.compile'd diffusers transformer (Speed=default/max):
diffusers requires the adapter loaded before compilation, so applying one to
the already-compiled module fails with adapter-key mismatches. The status
gate now hides the picker and generate raises a clear message instead.

Convert a cancelled Hub LoRA download (RuntimeError Cancelled) to the
diffusion cancellation sentinel in resolve_specs, so an unload/superseding
load during resolution maps to a 409 instead of a generic server error.

Drop weight-0 LoRA rows before the native support gate so a request carrying
only disabled adapters stays a no-op on families where native LoRA is
unsupported, matching the diffusers path.

Reject duplicate LoRA ids in the request model: both apply paths suffix
colliding names, so a repeated id would stack the same adapter past its
per-adapter weight bound.

Strip all user-typed <lora:...> prompt tags on the native path (only the
selected adapters are materialized in the managed lora-model-dir, so an
unselected tag can never resolve), and restore saved LoRA selections from a
gallery recipe so restore reproduces a LoRA image.

* Harden ControlNet resolve, gallery metadata, and the control-type picker

Check cancellation immediately after a ControlNet from_pretrained and before
any device placement, so an unload/eviction that raced the download does not
allocate several GB onto the GPU after the load was already cleared.

Require a loadable weight or shard index (not just config.json) before a local
ControlNet folder is advertised, so an interrupted copy is hidden instead of
failing deep in from_pretrained as a generic 500.

Do not record a strength-0 ControlNet in the gallery recipe: it is treated as
disabled and skipped, so the image is unconditioned and the metadata must not
claim a ControlNet was applied.

Build the control-type picker from the selected ControlNet's advertised
control_types instead of a hardcoded passthrough/canny pair, so a union model
with a precomputed depth or pose map sends the correct control_mode.

* Route the sd.cpp repo-fallback diagnostic to stderr

--print-asset documents its stdout as the resolved asset name only, but the
upstream repo-fallback path printed 'falling back to ...' to stdout before the
asset line, so a script consuming the output as a single asset name parsed the
log instead. Send the diagnostic to stderr; the install note stays on stdout.

* Address further Codex findings on the image-workflows PR

- Persist the actual output image size in the gallery recipe instead of the
  request sliders: Transform/Inpaint/Edit derive the size from the uploaded
  image, Extend grows the canvas, and Upscale resizes it, so the sliders
  recorded (and later restored) the wrong dimensions for those workflows.
- Reject a remote '*-GGUF' repo loaded as a full pipeline (no single-file
  name) in validate_load_request, so the unloadable pick fails before chat is
  evicted rather than deep in from_pretrained.
- Only publish an image-conditioned from_pipe wrapper to the shared aux cache
  when the load is still current: from_pipe runs under the generate lock but
  not the state lock, so an unload racing its construction could otherwise
  cache a wrapper over torn-down modules that a later load would reuse.
- Verify the Windows CUDA runtime archive checksum before extracting it, like
  the main sd-cli archive, so a corrupt or tampered runtime is rejected rather
  than extracted next to the binary.

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
2026-07-03 16:02:51 -03:00

563 lines
22 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Unit tests for the prebuilt sd-cli asset resolver (``install_sd_cpp_prebuilt``).
Pure: the host -> release-asset matrix is exercised against a fixed asset list
(a real stable-diffusion.cpp release), no network. The installer lives under
``studio/`` (not ``studio/backend``), so the test puts that dir on the path.
"""
from __future__ import annotations
import sys
from pathlib import Path
_STUDIO = Path(__file__).resolve().parents[2]
if str(_STUDIO) not in sys.path:
sys.path.insert(0, str(_STUDIO))
import hashlib # noqa: E402
import io # noqa: E402
import json # noqa: E402
import urllib.error # noqa: E402
import zipfile # noqa: E402
import pytest # noqa: E402
import install_sd_cpp_prebuilt as sdmod # noqa: E402
from install_sd_cpp_prebuilt import ( # noqa: E402
DEFAULT_REPO,
DEFAULT_TAG,
_fetch_release,
_pinned_tag,
_repo,
_safe_extractall,
_verify_sha256,
default_install_dir,
install,
resolve_release_asset,
)
# A real stable-diffusion.cpp latest-release asset list.
_ASSETS = [
"cudart-sd-bin-win-cu12-x64.zip",
"sd-master-8caa3f9-bin-Darwin-macOS-15.7.7-arm64.zip",
"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-rocm-7.13.0.zip",
"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-rocm-7.2.1.zip",
"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-vulkan.zip",
"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64.zip",
"sd-master-8caa3f9-bin-win-avx-x64.zip",
"sd-master-8caa3f9-bin-win-avx2-x64.zip",
"sd-master-8caa3f9-bin-win-avx512-x64.zip",
"sd-master-8caa3f9-bin-win-cuda12-x64.zip",
"sd-master-8caa3f9-bin-win-noavx-x64.zip",
"sd-master-8caa3f9-bin-win-rocm-7.13.0-x64.zip",
"sd-master-8caa3f9-bin-win-vulkan-x64.zip",
]
def _resolve(
system,
machine,
accelerator = "auto",
):
return resolve_release_asset(_ASSETS, system = system, machine = machine, accelerator = accelerator)
# ── macOS (the key Apple-Silicon target) ────────────────────────────────────
def test_macos_arm64_picks_darwin_arm64():
assert _resolve("Darwin", "arm64") == "sd-master-8caa3f9-bin-Darwin-macOS-15.7.7-arm64.zip"
# aarch64 spelling resolves the same
assert _resolve("Darwin", "aarch64").startswith("sd-master") and "arm64" in _resolve(
"Darwin", "aarch64"
)
def test_macos_intel_has_no_prebuilt():
# only an arm64 Darwin asset exists -> Intel Macs must build from source
assert _resolve("Darwin", "x86_64") is None
# ── Linux (CPU is the default tier) ─────────────────────────────────────────
def test_linux_x86_64_auto_picks_plain_cpu_build():
# the plain x86_64 zip, NOT a rocm/vulkan one
assert _resolve("Linux", "x86_64") == "sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64.zip"
def test_linux_vulkan_and_rocm_select_accelerator_builds():
assert (
_resolve("Linux", "x86_64", "vulkan")
== "sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-vulkan.zip"
)
assert "rocm" in _resolve("Linux", "x86_64", "rocm")
def test_linux_arm64_has_no_prebuilt():
assert _resolve("Linux", "aarch64") is None
# ── Windows ─────────────────────────────────────────────────────────────────
def test_windows_auto_picks_avx2():
assert _resolve("Windows", "AMD64") == "sd-master-8caa3f9-bin-win-avx2-x64.zip"
def test_windows_cuda_picks_cuda12():
assert _resolve("Windows", "AMD64", "cuda") == "sd-master-8caa3f9-bin-win-cuda12-x64.zip"
def test_windows_vulkan_picks_vulkan():
assert _resolve("Windows", "AMD64", "vulkan") == "sd-master-8caa3f9-bin-win-vulkan-x64.zip"
# ── cudart helper archive is never chosen as the engine ─────────────────────
def test_cudart_runtime_archive_never_selected():
for accel in ("auto", "cuda", "vulkan", "rocm"):
chosen = _resolve("Windows", "AMD64", accel)
assert chosen is None or not chosen.startswith("cudart")
# ── install dir ─────────────────────────────────────────────────────────────
def test_default_install_dir_is_sibling_of_llama(monkeypatch):
monkeypatch.delenv("UNSLOTH_STUDIO_HOME", raising = False)
monkeypatch.delenv("STUDIO_HOME", raising = False)
d = default_install_dir()
assert d.name == "stable-diffusion.cpp"
assert d.parent.name == ".unsloth"
# ── version pin + source repo (reproducibility) ─────────────────────────────
def test_pinned_tag_default_and_override(monkeypatch):
monkeypatch.delenv("UNSLOTH_SD_CPP_TAG", raising = False)
assert _pinned_tag() == DEFAULT_TAG # pinned, not "latest"
monkeypatch.setenv("UNSLOTH_SD_CPP_TAG", "master-999-deadbee")
assert _pinned_tag() == "master-999-deadbee"
monkeypatch.setenv("UNSLOTH_SD_CPP_TAG", "") # explicit empty -> track latest
assert _pinned_tag() is None
def test_repo_default_and_override(monkeypatch):
monkeypatch.delenv("UNSLOTH_SD_CPP_REPO", raising = False)
# Default is the Unsloth mirror; the env override can point back to leejet upstream.
assert _repo() == DEFAULT_REPO == "unslothai/stable-diffusion.cpp"
monkeypatch.setenv("UNSLOTH_SD_CPP_REPO", "leejet/stable-diffusion.cpp")
assert _repo() == "leejet/stable-diffusion.cpp"
# ── sha256 integrity check ──────────────────────────────────────────────────
def test_verify_sha256_accepts_matching_digest(tmp_path):
f = tmp_path / "asset.zip"
f.write_bytes(b"hello sd-cli")
digest = "sha256:" + hashlib.sha256(b"hello sd-cli").hexdigest()
_verify_sha256(f, digest) # no raise
def test_verify_sha256_rejects_mismatch(tmp_path):
f = tmp_path / "asset.zip"
f.write_bytes(b"tampered")
bad = "sha256:" + hashlib.sha256(b"original").hexdigest()
with pytest.raises(RuntimeError, match = "sha256 mismatch"):
_verify_sha256(f, bad)
def test_verify_sha256_skips_when_absent_or_unknown(tmp_path):
f = tmp_path / "asset.zip"
f.write_bytes(b"x")
_verify_sha256(f, None) # no digest published -> warn + proceed (no raise)
_verify_sha256(f, "md5:abc") # unrecognised algo -> skip (no raise)
# ── _fetch_release: pinned-tag 404 -> latest fallback ───────────────────────
def test_fetch_release_falls_back_to_latest_on_404(monkeypatch):
calls: list[str] = []
class _Resp:
def __enter__(self):
return self
def __exit__(self, *a):
return False
def read(self):
return json.dumps({"tag_name": "latest-xyz", "assets": []}).encode()
def fake_urlopen(req, timeout = 30.0):
url = getattr(req, "full_url", req)
calls.append(url)
if "/tags/" in url:
raise urllib.error.HTTPError(url, 404, "not found", None, None)
return _Resp()
monkeypatch.setattr(sdmod.urllib.request, "urlopen", fake_urlopen)
rel = _fetch_release("gone-tag", repo = "leejet/stable-diffusion.cpp")
assert rel["tag_name"] == "latest-xyz"
assert any("/tags/gone-tag" in c for c in calls) and any(c.endswith("/latest") for c in calls)
def test_fetch_release_propagates_non_404(monkeypatch):
def fake_urlopen(req, timeout = 30.0):
url = getattr(req, "full_url", req)
raise urllib.error.HTTPError(url, 403, "rate limited", None, None)
monkeypatch.setattr(sdmod.urllib.request, "urlopen", fake_urlopen)
with pytest.raises(urllib.error.HTTPError):
_fetch_release("any-tag")
# ── install(): download -> verify -> extract -> locate (offline) ────────────
def _zip_with_sd_cli() -> bytes:
buf = io.BytesIO()
with zipfile.ZipFile(buf, "w") as zf:
zf.writestr("build/bin/sd-cli", b"#!/bin/sh\necho sd-cli\n")
return buf.getvalue()
def _stub_release(monkeypatch, *, zip_bytes: bytes, digest: str):
name = "sd-master-deadbee-bin-Linux-Ubuntu-24.04-x86_64.zip"
release = {
"tag_name": "master-1-deadbee",
"assets": [
{
"name": name,
"browser_download_url": f"https://example.invalid/{name}",
"digest": digest,
}
],
}
monkeypatch.setattr(sdmod, "_fetch_release", lambda *a, **k: release)
monkeypatch.setattr(sdmod, "_download", lambda url, dest, **k: dest.write_bytes(zip_bytes))
monkeypatch.setattr(sdmod.platform, "system", lambda: "Linux")
monkeypatch.setattr(sdmod.platform, "machine", lambda: "x86_64")
return name
def test_install_downloads_verifies_extracts(tmp_path, monkeypatch):
zb = _zip_with_sd_cli()
name = _stub_release(
monkeypatch, zip_bytes = zb, digest = "sha256:" + hashlib.sha256(zb).hexdigest()
)
sd_cli = install(install_dir = tmp_path)
assert sd_cli.name == "sd-cli" and sd_cli.is_file()
assert not (tmp_path / name).exists() # archive cleaned up after extract
def test_install_sha256_mismatch_raises_and_cleans_up(tmp_path, monkeypatch):
zb = _zip_with_sd_cli()
name = _stub_release(monkeypatch, zip_bytes = zb, digest = "sha256:" + "0" * 64)
with pytest.raises(RuntimeError, match = "sha256 mismatch"):
install(install_dir = tmp_path)
assert not (tmp_path / name).exists() # the finally: drops the bad archive
# ── safe extraction (Zip-Slip guard) ─────────────────────────────────────────
def test_safe_extractall_rejects_path_traversal(tmp_path):
target = tmp_path / "install"
target.mkdir()
archive = tmp_path / "evil.zip"
with zipfile.ZipFile(archive, "w") as zf:
zf.writestr("sd-cli", b"ok")
zf.writestr("../escape.txt", b"pwned") # escapes the install dir
with zipfile.ZipFile(archive) as zf:
with pytest.raises(RuntimeError, match = "unsafe path"):
_safe_extractall(zf, target)
assert not (tmp_path / "escape.txt").exists()
def test_safe_extractall_extracts_normal_members(tmp_path):
target = tmp_path / "install"
target.mkdir()
archive = tmp_path / "ok.zip"
with zipfile.ZipFile(archive, "w") as zf:
zf.writestr("build/bin/sd-cli", b"ok")
with zipfile.ZipFile(archive) as zf:
_safe_extractall(zf, target)
assert (target / "build" / "bin" / "sd-cli").read_bytes() == b"ok"
def test_find_sd_cpp_binary_honors_studio_home(tmp_path, monkeypatch):
# A binary installed under a custom Studio root must be discovered without also
# setting UNSLOTH_SD_CPP_PATH (matches default_install_dir's env handling).
from core.inference import sd_cpp_engine as eng
monkeypatch.delenv("SD_CLI_PATH", raising = False)
monkeypatch.delenv("UNSLOTH_SD_CPP_PATH", raising = False)
studio_home = tmp_path / "studio_root"
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
binary = tmp_path / "stable-diffusion.cpp" / "build" / "bin" / "sd-cli"
binary.parent.mkdir(parents = True)
binary.write_bytes(b"x")
assert eng.find_sd_cpp_binary() == str(binary)
# ── Unsloth mirror: default source + the CPU/Apple asset set it publishes ─────
_TAG = "master-741-484baa4"
# Exactly what unslothai/stable-diffusion.cpp's CI publishes (CPU + Apple only; GPU
# hosts run diffusers and never reach the native installer).
_MIRROR_ASSETS = [
f"sd-{_TAG}-bin-Darwin-macOS-arm64.zip",
f"sd-{_TAG}-bin-Darwin-macOS-x86_64.zip",
f"sd-{_TAG}-bin-Linux-Ubuntu-22.04-x86_64.zip",
f"sd-{_TAG}-bin-Linux-Ubuntu-24.04-aarch64.zip",
f"sd-{_TAG}-bin-win-cpu-x64.zip",
]
def _mresolve(
system,
machine,
accelerator = "auto",
):
return resolve_release_asset(
_MIRROR_ASSETS, system = system, machine = machine, accelerator = accelerator
)
def test_default_repo_is_the_unsloth_mirror():
assert sdmod.DEFAULT_REPO == "unslothai/stable-diffusion.cpp"
assert sdmod.UPSTREAM_FALLBACK_REPO == "leejet/stable-diffusion.cpp"
def test_mirror_matrix_resolves_every_cpu_apple_host():
assert _mresolve("Darwin", "arm64") == f"sd-{_TAG}-bin-Darwin-macOS-arm64.zip"
assert _mresolve("Darwin", "x86_64") == f"sd-{_TAG}-bin-Darwin-macOS-x86_64.zip"
# WSL reports as Linux x86_64, so this also covers WSL.
assert _mresolve("Linux", "x86_64") == f"sd-{_TAG}-bin-Linux-Ubuntu-22.04-x86_64.zip"
assert _mresolve("Linux", "aarch64") == f"sd-{_TAG}-bin-Linux-Ubuntu-24.04-aarch64.zip"
assert _mresolve("Windows", "AMD64") == f"sd-{_TAG}-bin-win-cpu-x64.zip"
assert _mresolve("Windows", "AMD64", "cpu") == f"sd-{_TAG}-bin-win-cpu-x64.zip"
# ── mirror -> upstream fallback in install() ─────────────────────────────────
def _stub_two_repos(monkeypatch, *, mirror_serves, upstream_serves, zip_bytes, digest):
"""Stub _fetch_release to serve (or 404) per repo, so install()'s mirror->upstream
fallback can be exercised without network. Host pinned to Linux x86_64."""
monkeypatch.delenv("UNSLOTH_SD_CPP_REPO", raising = False)
monkeypatch.delenv("UNSLOTH_SD_CPP_TAG", raising = False)
mirror_name = f"sd-{_TAG}-bin-Linux-Ubuntu-22.04-x86_64.zip"
upstream_name = "sd-master-741-484baa4-bin-Linux-Ubuntu-24.04-x86_64.zip"
def _rel(name):
return {
"tag_name": _TAG,
"assets": [
{
"name": name,
"browser_download_url": f"https://example.invalid/{name}",
"digest": digest,
}
],
}
def fake_fetch(
tag = None,
*,
repo = None,
token = None,
timeout = 30.0,
allow_latest = True,
):
r = repo or sdmod.DEFAULT_REPO
if r == sdmod.DEFAULT_REPO:
if mirror_serves:
return _rel(mirror_name)
raise urllib.error.HTTPError(f"https://api/{r}", 404, "not found", None, None)
if upstream_serves:
return _rel(upstream_name)
raise urllib.error.HTTPError(f"https://api/{r}", 404, "not found", None, None)
monkeypatch.setattr(sdmod, "_fetch_release", fake_fetch)
monkeypatch.setattr(sdmod, "_download", lambda url, dest, **k: dest.write_bytes(zip_bytes))
monkeypatch.setattr(sdmod.platform, "system", lambda: "Linux")
monkeypatch.setattr(sdmod.platform, "machine", lambda: "x86_64")
return mirror_name, upstream_name
def test_install_uses_mirror_when_available(tmp_path, monkeypatch, capsys):
zb = _zip_with_sd_cli()
_stub_two_repos(
monkeypatch,
mirror_serves = True,
upstream_serves = True,
zip_bytes = zb,
digest = "sha256:" + hashlib.sha256(zb).hexdigest(),
)
sd_cli = install(install_dir = tmp_path)
assert sd_cli.name == "sd-cli" and sd_cli.is_file()
assert "unslothai/stable-diffusion.cpp" in capsys.readouterr().out
def test_install_falls_back_to_upstream_when_mirror_missing(tmp_path, monkeypatch, capsys):
zb = _zip_with_sd_cli()
_stub_two_repos(
monkeypatch,
mirror_serves = False,
upstream_serves = True,
zip_bytes = zb,
digest = "sha256:" + hashlib.sha256(zb).hexdigest(),
)
sd_cli = install(install_dir = tmp_path)
assert sd_cli.name == "sd-cli" and sd_cli.is_file()
captured = capsys.readouterr()
# The repo-fallback diagnostic goes to stderr so --print-asset's stdout stays a single
# asset line; the "source ..." install note still prints to stdout.
assert "falling back to leejet/stable-diffusion.cpp" in captured.err
assert "source leejet/stable-diffusion.cpp" in captured.out
def test_install_errors_when_neither_source_serves(tmp_path, monkeypatch):
_stub_two_repos(
monkeypatch, mirror_serves = False, upstream_serves = False, zip_bytes = b"", digest = ""
)
with pytest.raises(RuntimeError, match = "No prebuilt sd-cli"):
install(install_dir = tmp_path)
# ── explicit GPU accelerator on a CPU-only mirror -> no CPU substitution ──────
def test_mirror_windows_gpu_accel_is_no_match_not_cpu():
# The mirror ships only a CPU win zip. An explicit --accelerator cuda/vulkan/rocm must
# NOT silently resolve to that CPU build; it returns None so install() falls back to
# upstream, which does build the accelerated asset.
for accel in ("cuda", "vulkan", "rocm"):
assert _mresolve("Windows", "AMD64", accel) is None
# auto / cpu still resolve to the CPU build.
assert _mresolve("Windows", "AMD64", "cpu") == f"sd-{_TAG}-bin-win-cpu-x64.zip"
def test_mirror_linux_gpu_accel_is_no_match_not_cpu():
for accel in ("cuda", "vulkan", "rocm"):
assert _mresolve("Linux", "x86_64", accel) is None
assert _mresolve("Linux", "x86_64", "cpu") == f"sd-{_TAG}-bin-Linux-Ubuntu-22.04-x86_64.zip"
def test_upstream_full_matrix_still_resolves_gpu_accel():
# Regression guard: the "explicit GPU accel -> None on miss" change must not break
# the upstream matrix, which does publish the accelerated builds.
assert _resolve("Windows", "AMD64", "cuda") == "sd-master-8caa3f9-bin-win-cuda12-x64.zip"
assert _resolve("Linux", "x86_64", "vulkan").endswith("x86_64-vulkan.zip")
assert "rocm" in _resolve("Linux", "x86_64", "rocm")
# ── explicit repo override suppresses the upstream fallback ───────────────────
def test_explicit_repo_override_equal_to_default_suppresses_fallback(tmp_path, monkeypatch):
# A user who pins UNSLOTH_SD_CPP_REPO (even to the default value) must get exactly that
# repo -- no surprise leejet substitution -- so a missing release errors instead.
_stub_two_repos(
monkeypatch, mirror_serves = False, upstream_serves = True, zip_bytes = b"", digest = ""
)
monkeypatch.setenv("UNSLOTH_SD_CPP_REPO", sdmod.DEFAULT_REPO)
with pytest.raises(RuntimeError, match = "No prebuilt sd-cli"):
install(install_dir = tmp_path)
# ── pinned tag missing on mirror -> pinned upstream before mirror latest ──────
def test_pinned_tag_prefers_upstream_pin_over_mirror_latest(tmp_path, monkeypatch, capsys):
# The mirror lacks the pinned tag but has a newer latest; upstream has the pin. The
# pinned upstream build must win over the unpinned mirror-latest (reproducibility).
monkeypatch.delenv("UNSLOTH_SD_CPP_REPO", raising = False)
monkeypatch.setenv("UNSLOTH_SD_CPP_TAG", "master-999-pinned")
zb = _zip_with_sd_cli()
digest = "sha256:" + hashlib.sha256(zb).hexdigest()
def _rel(name, tag):
return {
"tag_name": tag,
"assets": [
{
"name": name,
"browser_download_url": f"https://example.invalid/{name}",
"digest": digest,
}
],
}
mirror_latest = "sd-master-000-latest-bin-Linux-Ubuntu-22.04-x86_64.zip"
upstream_pinned = "sd-master-999-pinned-bin-Linux-Ubuntu-24.04-x86_64.zip"
def fake_fetch(
tag = None,
*,
repo = None,
token = None,
timeout = 30.0,
allow_latest = True,
):
r = repo or sdmod.DEFAULT_REPO
if r == sdmod.DEFAULT_REPO:
if tag == "master-999-pinned": # mirror lacks the pin
if not allow_latest:
return None
return _rel(mirror_latest, "master-000-latest")
return _rel(mirror_latest, "master-000-latest")
# upstream HAS the pin
if tag == "master-999-pinned":
return _rel(upstream_pinned, "master-999-pinned")
return _rel(upstream_pinned, "master-999-pinned")
monkeypatch.setattr(sdmod, "_fetch_release", fake_fetch)
monkeypatch.setattr(sdmod, "_download", lambda url, dest, **k: dest.write_bytes(zb))
monkeypatch.setattr(sdmod.platform, "system", lambda: "Linux")
monkeypatch.setattr(sdmod.platform, "machine", lambda: "x86_64")
install(install_dir = tmp_path)
out = capsys.readouterr().out
assert "source leejet/stable-diffusion.cpp release master-999-pinned" in out
# ── --print-asset routes through the primary/upstream fallback ────────────────
def test_print_asset_uses_upstream_fallback(monkeypatch, capsys):
# A host the mirror does not build (Linux Vulkan) must print the upstream asset that a
# real install would fetch, not "no matching prebuilt".
monkeypatch.delenv("UNSLOTH_SD_CPP_REPO", raising = False)
monkeypatch.delenv("UNSLOTH_SD_CPP_TAG", raising = False)
def fake_fetch(
tag = None,
*,
repo = None,
token = None,
timeout = 30.0,
allow_latest = True,
):
r = repo or sdmod.DEFAULT_REPO
if r == sdmod.DEFAULT_REPO:
return {"tag_name": _TAG, "assets": [{"name": n} for n in _MIRROR_ASSETS]}
return {"tag_name": "master-8caa3f9", "assets": [{"name": n} for n in _ASSETS]}
monkeypatch.setattr(sdmod, "_fetch_release", fake_fetch)
monkeypatch.setattr(sdmod.platform, "system", lambda: "Linux")
monkeypatch.setattr(sdmod.platform, "machine", lambda: "x86_64")
rc = sdmod.main(["--print-asset", "--accelerator", "vulkan"])
out = capsys.readouterr().out
assert rc == 0
assert "vulkan" in out and "no matching prebuilt" not in out