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* 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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). * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 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. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- 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>
563 lines
22 KiB
Python
563 lines
22 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Unit tests for the prebuilt sd-cli asset resolver (``install_sd_cpp_prebuilt``).
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Pure: the host -> release-asset matrix is exercised against a fixed asset list
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(a real stable-diffusion.cpp release), no network. The installer lives under
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``studio/`` (not ``studio/backend``), so the test puts that dir on the path.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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_STUDIO = Path(__file__).resolve().parents[2]
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if str(_STUDIO) not in sys.path:
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sys.path.insert(0, str(_STUDIO))
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import hashlib # noqa: E402
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import io # noqa: E402
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import json # noqa: E402
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import urllib.error # noqa: E402
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import zipfile # noqa: E402
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import pytest # noqa: E402
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import install_sd_cpp_prebuilt as sdmod # noqa: E402
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from install_sd_cpp_prebuilt import ( # noqa: E402
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DEFAULT_REPO,
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DEFAULT_TAG,
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_fetch_release,
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_pinned_tag,
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_repo,
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_safe_extractall,
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_verify_sha256,
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default_install_dir,
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install,
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resolve_release_asset,
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)
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# A real stable-diffusion.cpp latest-release asset list.
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_ASSETS = [
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"cudart-sd-bin-win-cu12-x64.zip",
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"sd-master-8caa3f9-bin-Darwin-macOS-15.7.7-arm64.zip",
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"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-rocm-7.13.0.zip",
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"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-rocm-7.2.1.zip",
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"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-vulkan.zip",
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"sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64.zip",
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"sd-master-8caa3f9-bin-win-avx-x64.zip",
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"sd-master-8caa3f9-bin-win-avx2-x64.zip",
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"sd-master-8caa3f9-bin-win-avx512-x64.zip",
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"sd-master-8caa3f9-bin-win-cuda12-x64.zip",
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"sd-master-8caa3f9-bin-win-noavx-x64.zip",
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"sd-master-8caa3f9-bin-win-rocm-7.13.0-x64.zip",
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"sd-master-8caa3f9-bin-win-vulkan-x64.zip",
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]
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def _resolve(
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system,
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machine,
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accelerator = "auto",
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):
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return resolve_release_asset(_ASSETS, system = system, machine = machine, accelerator = accelerator)
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# ── macOS (the key Apple-Silicon target) ────────────────────────────────────
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def test_macos_arm64_picks_darwin_arm64():
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assert _resolve("Darwin", "arm64") == "sd-master-8caa3f9-bin-Darwin-macOS-15.7.7-arm64.zip"
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# aarch64 spelling resolves the same
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assert _resolve("Darwin", "aarch64").startswith("sd-master") and "arm64" in _resolve(
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"Darwin", "aarch64"
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)
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def test_macos_intel_has_no_prebuilt():
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# only an arm64 Darwin asset exists -> Intel Macs must build from source
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assert _resolve("Darwin", "x86_64") is None
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# ── Linux (CPU is the default tier) ─────────────────────────────────────────
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def test_linux_x86_64_auto_picks_plain_cpu_build():
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# the plain x86_64 zip, NOT a rocm/vulkan one
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assert _resolve("Linux", "x86_64") == "sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64.zip"
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def test_linux_vulkan_and_rocm_select_accelerator_builds():
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assert (
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_resolve("Linux", "x86_64", "vulkan")
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== "sd-master-8caa3f9-bin-Linux-Ubuntu-24.04-x86_64-vulkan.zip"
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)
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assert "rocm" in _resolve("Linux", "x86_64", "rocm")
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def test_linux_arm64_has_no_prebuilt():
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assert _resolve("Linux", "aarch64") is None
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# ── Windows ─────────────────────────────────────────────────────────────────
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def test_windows_auto_picks_avx2():
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assert _resolve("Windows", "AMD64") == "sd-master-8caa3f9-bin-win-avx2-x64.zip"
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def test_windows_cuda_picks_cuda12():
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assert _resolve("Windows", "AMD64", "cuda") == "sd-master-8caa3f9-bin-win-cuda12-x64.zip"
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def test_windows_vulkan_picks_vulkan():
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assert _resolve("Windows", "AMD64", "vulkan") == "sd-master-8caa3f9-bin-win-vulkan-x64.zip"
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# ── cudart helper archive is never chosen as the engine ─────────────────────
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def test_cudart_runtime_archive_never_selected():
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for accel in ("auto", "cuda", "vulkan", "rocm"):
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chosen = _resolve("Windows", "AMD64", accel)
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assert chosen is None or not chosen.startswith("cudart")
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# ── install dir ─────────────────────────────────────────────────────────────
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def test_default_install_dir_is_sibling_of_llama(monkeypatch):
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monkeypatch.delenv("UNSLOTH_STUDIO_HOME", raising = False)
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monkeypatch.delenv("STUDIO_HOME", raising = False)
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d = default_install_dir()
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assert d.name == "stable-diffusion.cpp"
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assert d.parent.name == ".unsloth"
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# ── version pin + source repo (reproducibility) ─────────────────────────────
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def test_pinned_tag_default_and_override(monkeypatch):
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monkeypatch.delenv("UNSLOTH_SD_CPP_TAG", raising = False)
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assert _pinned_tag() == DEFAULT_TAG # pinned, not "latest"
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monkeypatch.setenv("UNSLOTH_SD_CPP_TAG", "master-999-deadbee")
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assert _pinned_tag() == "master-999-deadbee"
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monkeypatch.setenv("UNSLOTH_SD_CPP_TAG", "") # explicit empty -> track latest
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assert _pinned_tag() is None
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def test_repo_default_and_override(monkeypatch):
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monkeypatch.delenv("UNSLOTH_SD_CPP_REPO", raising = False)
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# Default is the Unsloth mirror; the env override can point back to leejet upstream.
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assert _repo() == DEFAULT_REPO == "unslothai/stable-diffusion.cpp"
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monkeypatch.setenv("UNSLOTH_SD_CPP_REPO", "leejet/stable-diffusion.cpp")
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assert _repo() == "leejet/stable-diffusion.cpp"
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# ── sha256 integrity check ──────────────────────────────────────────────────
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def test_verify_sha256_accepts_matching_digest(tmp_path):
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f = tmp_path / "asset.zip"
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f.write_bytes(b"hello sd-cli")
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digest = "sha256:" + hashlib.sha256(b"hello sd-cli").hexdigest()
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_verify_sha256(f, digest) # no raise
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def test_verify_sha256_rejects_mismatch(tmp_path):
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f = tmp_path / "asset.zip"
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f.write_bytes(b"tampered")
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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
|