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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 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 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.) * [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>
180 lines
6.5 KiB
Python
180 lines
6.5 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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"""End-to-end smoke for the native stable-diffusion.cpp engine.
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Drives the real ``SdCppEngine`` over a built ``sd-cli`` and a set of split GGUF
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assets (the same the diffusers path consumes), running one txt2img generation
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and reporting wall time. This is the GPU/native analogue of
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``scripts/diffusion_bench.py``: it proves the engine wiring (finder -> command
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builder -> subprocess -> output PNG) works against real weights.
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Example (Z-Image-Turbo on one GPU):
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SD_CLI_PATH=.../sd-cli CUDA_VISIBLE_DEVICES=6 python scripts/sd_cpp_smoke.py \\
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--family z-image \\
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--diffusion-model .../z-image-turbo-Q4_K_M.gguf \\
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--vae .../ae.safetensors \\
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--llm .../Qwen3-4B-Instruct-2507-Q4_K_M.gguf \\
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--memory-mode balanced --steps 8 --cfg-scale 1.0 --width 512 --height 512
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"""
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from __future__ import annotations
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import argparse
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import sys
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import time
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from pathlib import Path
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_BACKEND_ROOT = Path(__file__).resolve().parent.parent / "studio" / "backend"
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if str(_BACKEND_ROOT) not in sys.path:
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sys.path.insert(0, str(_BACKEND_ROOT))
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from core.inference.diffusion_memory import ( # noqa: E402
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MEMORY_MODE_BALANCED,
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MEMORY_MODE_FAST,
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MEMORY_MODE_LOW_VRAM,
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OFFLOAD_GROUP,
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OFFLOAD_MODEL,
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OFFLOAD_NONE,
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)
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from core.inference.sd_cpp_args import ( # noqa: E402
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SdCppGenParams,
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SdCppModelFiles,
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SdCppUpscaleParams,
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offload_flags,
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)
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from core.inference.sd_cpp_engine import SdCppEngine, find_sd_cpp_binary # noqa: E402
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# memory-mode (user knob) -> sd.cpp offload policy, matching the diffusers planner.
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_MODE_TO_POLICY = {
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MEMORY_MODE_FAST: OFFLOAD_NONE,
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MEMORY_MODE_BALANCED: OFFLOAD_GROUP,
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MEMORY_MODE_LOW_VRAM: OFFLOAD_MODEL,
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}
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def main(argv: list[str] | None = None) -> int:
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p = argparse.ArgumentParser(description = "Native sd-cli engine smoke test.")
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p.add_argument("--task", default = "txt2img", choices = ["txt2img", "img2img", "upscale"])
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p.add_argument("--binary", default = None, help = "sd-cli path (else env / finder)")
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p.add_argument("--family", default = "z-image")
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p.add_argument("--diffusion-model", default = None)
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# img2img + upscale inputs
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p.add_argument("--init-img", default = None)
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p.add_argument("--strength", type = float, default = 0.6)
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p.add_argument("--upscale-model", default = None)
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p.add_argument("--upscale-repeats", type = int, default = 1)
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p.add_argument("--vae", default = None)
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p.add_argument("--clip_l", default = None)
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p.add_argument("--t5xxl", default = None)
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p.add_argument("--llm", default = None)
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p.add_argument("--qwen2vl", default = None)
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p.add_argument(
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"--prompt",
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default = "A cinematic photograph of a red fox in a snowy forest at dawn, highly detailed",
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)
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p.add_argument("--negative-prompt", default = None)
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p.add_argument("--width", type = int, default = 512)
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p.add_argument("--height", type = int, default = 512)
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p.add_argument("--steps", type = int, default = 8)
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p.add_argument("--cfg-scale", type = float, default = 1.0)
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p.add_argument("--seed", type = int, default = 42)
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p.add_argument("--memory-mode", default = "balanced", choices = list(_MODE_TO_POLICY))
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p.add_argument("--out-image", default = "outputs/sdcpp_verify/sdcpp_smoke.png")
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p.add_argument("--timeout", type = float, default = 1800.0)
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args = p.parse_args(argv)
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binary = args.binary or find_sd_cpp_binary()
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engine = SdCppEngine(binary = binary)
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print(f"binary: {engine.binary}", flush = True)
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print(f"available: {engine.is_available()}", flush = True)
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print(f"version: {engine.version()}", flush = True)
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if not engine.is_available():
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print(
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"ERROR: sd-cli not found (set --binary / SD_CLI_PATH / UNSLOTH_SD_CPP_PATH).",
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flush = True,
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)
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return 2
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out = Path(args.out_image)
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if args.task == "upscale":
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if not args.init_img or not args.upscale_model:
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print("ERROR: upscale needs --init-img and --upscale-model.", flush = True)
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return 2
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t0 = time.time()
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result = engine.upscale(
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SdCppUpscaleParams(
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input_image = args.init_img,
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upscale_model = args.upscale_model,
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repeats = args.upscale_repeats,
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),
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output_path = str(out),
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verbose = True,
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timeout = args.timeout,
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on_log = lambda ln: print(f" [sd] {ln}", flush = True),
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)
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dt = time.time() - t0
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print(
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f"\nOK: upscaled {result} ({result.stat().st_size/1024:.0f} KB) in {dt:.1f}s",
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flush = True,
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)
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print("SD-CPP-SMOKE-OK", flush = True)
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return 0
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if not args.diffusion_model:
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print("ERROR: --diffusion-model is required for txt2img / img2img.", flush = True)
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return 2
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files = SdCppModelFiles(
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diffusion_model = args.diffusion_model,
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vae = args.vae,
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clip_l = args.clip_l,
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t5xxl = args.t5xxl,
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llm = args.llm,
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qwen2vl = args.qwen2vl,
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)
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is_img2img = args.task == "img2img"
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params = SdCppGenParams(
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prompt = args.prompt,
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negative_prompt = args.negative_prompt,
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width = args.width,
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height = args.height,
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steps = args.steps,
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cfg_scale = args.cfg_scale,
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seed = args.seed,
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init_img = args.init_img if is_img2img else None,
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strength = args.strength if is_img2img else None,
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)
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if is_img2img and not args.init_img:
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print("ERROR: img2img needs --init-img.", flush = True)
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return 2
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policy = _MODE_TO_POLICY[args.memory_mode]
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off = offload_flags(policy)
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print(
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f"task: {args.task}"
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+ (f" (init={args.init_img}, strength={args.strength})" if is_img2img else ""),
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flush = True,
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)
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print(f"memory: {args.memory_mode} -> policy={policy} -> flags={off}", flush = True)
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t0 = time.time()
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result = engine.generate(
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files,
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params,
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output_path = str(out),
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offload = off,
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verbose = True,
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timeout = args.timeout,
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on_log = lambda ln: print(f" [sd] {ln}", flush = True),
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)
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dt = time.time() - t0
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size_kb = result.stat().st_size / 1024 if result.is_file() else 0
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print(f"\nOK: generated {result} ({size_kb:.0f} KB) in {dt:.1f}s", flush = True)
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print("SD-CPP-SMOKE-OK", flush = True)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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