unsloth/studio/install_sd_cpp_prebuilt.py
Daniel Han 96940d87b7
Studio diffusion (Phase 4): native stable-diffusion.cpp engine for CPU/Mac (#6679)
* 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 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.

---------

Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
2026-07-01 15:03:53 -03:00

278 lines
10 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Install a prebuilt ``sd-cli`` (stable-diffusion.cpp) for the native diffusion
engine.
The chat backend ships a prebuilt llama-server; this is the diffusion analogue,
kept deliberately small. stable-diffusion.cpp publishes per-platform release
zips (macOS-arm64/Metal, Linux x86_64 CPU, plus Vulkan / ROCm / Windows
variants), so on the Phase-4 targets (Apple Silicon and CPU) there is nothing to
compile: resolve the right asset, download, extract into
``~/.unsloth/stable-diffusion.cpp``, and the engine's finder picks it up.
``resolve_release_asset`` -- the host -> asset choice -- is a pure function so the
matching matrix is unit-tested without any network. CUDA / ROCm / XPU hosts stay
on diffusers and never need this; it exists for the engines diffusers serves
poorly.
Usage:
python studio/install_sd_cpp_prebuilt.py # auto-detect host
python studio/install_sd_cpp_prebuilt.py --accelerator vulkan
python studio/install_sd_cpp_prebuilt.py --print-asset # resolve only
"""
from __future__ import annotations
import argparse
import json
import os
import platform
import stat
import sys
import urllib.request
import zipfile
from pathlib import Path
from typing import Optional, Sequence
REPO = "leejet/stable-diffusion.cpp"
RELEASES_API = f"https://api.github.com/repos/{REPO}/releases/latest"
# accelerator -> the token that must appear in a Linux/Windows asset name.
_LINUX_ACCEL_TOKEN = {"rocm": "rocm", "vulkan": "vulkan"}
_WINDOWS_ACCEL_TOKEN = {
"cuda": "cuda12",
"vulkan": "vulkan",
"rocm": "rocm",
"cpu": "avx2",
"auto": "avx2",
}
# Tokens that mark an accelerator-specific Linux build; "auto"/"cpu" want none of them.
_LINUX_ACCEL_MARKERS = ("rocm", "vulkan", "cuda", "sycl", "musa")
_ARCH_TOKENS = {
"x86_64": ("x86_64", "x64", "amd64"),
"amd64": ("x86_64", "x64", "amd64"),
"arm64": ("arm64", "aarch64"),
"aarch64": ("arm64", "aarch64"),
}
def _arch_tokens(machine: str) -> tuple[str, ...]:
return _ARCH_TOKENS.get(machine.lower(), (machine.lower(),))
def resolve_release_asset(
asset_names: Sequence[str],
*,
system: str,
machine: str,
accelerator: str = "auto",
) -> Optional[str]:
"""Pick the best release asset for a host, or None if none matches.
``system`` / ``machine`` are ``platform.system()`` / ``platform.machine()``
values; ``accelerator`` is ``auto`` (CPU/Metal default), ``vulkan``,
``rocm``, or ``cuda`` (Windows only). Pure -- the caller passes the release's
asset name list.
"""
system = system.lower()
accel = accelerator.lower()
arch = _arch_tokens(machine)
zips = [
a for a in asset_names if a.lower().endswith(".zip") and not a.lower().startswith("cudart")
]
if system == "darwin":
pool = [
a
for a in zips
if ("darwin" in a.lower() or "macos" in a.lower()) and any(t in a.lower() for t in arch)
]
return pool[0] if pool else None
if system == "windows":
pool = [a for a in zips if "bin-win" in a.lower()]
token = _WINDOWS_ACCEL_TOKEN.get(accel, accel)
sel = [a for a in pool if token in a.lower()]
if not sel: # fall back to a plain avx2 CPU build
sel = [a for a in pool if "avx2" in a.lower()]
return sel[0] if sel else (pool[0] if pool else None)
# linux (and anything else unix-like)
pool = [a for a in zips if "linux" in a.lower() and any(t in a.lower() for t in arch)]
if accel in _LINUX_ACCEL_TOKEN:
sel = [a for a in pool if _LINUX_ACCEL_TOKEN[accel] in a.lower()]
else: # auto / cpu -> the plain build with no accelerator marker
sel = [a for a in pool if not any(m in a.lower() for m in _LINUX_ACCEL_MARKERS)]
return sel[0] if sel else None
def _fetch_latest_release(*, token: Optional[str] = None, timeout: float = 30.0) -> dict:
"""GET the latest-release JSON from GitHub (token optional, lifts rate limit)."""
req = urllib.request.Request(RELEASES_API, headers = {"Accept": "application/vnd.github+json"})
token = token or os.environ.get("GH_TOKEN") or os.environ.get("GITHUB_TOKEN")
if token:
req.add_header("Authorization", f"Bearer {token}")
with urllib.request.urlopen(req, timeout = timeout) as resp: # noqa: S310 (fixed https host)
return json.loads(resp.read().decode("utf-8"))
def default_install_dir() -> Path:
"""``~/.unsloth/stable-diffusion.cpp`` (or under ``UNSLOTH_STUDIO_HOME`` /
``STUDIO_HOME`` if set), the sibling of the llama.cpp install the finder
probes."""
home = os.environ.get("UNSLOTH_STUDIO_HOME") or os.environ.get("STUDIO_HOME")
base = Path(home).parent if home else Path.home() / ".unsloth"
return base / "stable-diffusion.cpp"
def _make_executable(path: Path) -> None:
mode = path.stat().st_mode
path.chmod(mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
def _locate_sd_cli(root: Path) -> Optional[Path]:
name = "sd-cli.exe" if sys.platform == "win32" else "sd-cli"
for p in root.rglob(name):
if p.is_file():
return p
return None
def _download(
url: str,
dest: Path,
*,
timeout: float = 300.0,
) -> None:
"""Stream ``url`` to ``dest`` with an explicit timeout. ``urlretrieve`` takes no
timeout and can hang forever on a stalled socket."""
import shutil
req = urllib.request.Request(url, headers = {"User-Agent": "unsloth-sd-cpp-installer"})
with urllib.request.urlopen(req, timeout = timeout) as resp, open(dest, "wb") as f: # noqa: S310
shutil.copyfileobj(resp, f)
def _safe_extractall(zf: zipfile.ZipFile, target: Path) -> None:
"""``extractall`` with a per-member containment check, so an archive carrying an
absolute path or a ``..`` entry can't write outside ``target`` (Zip-Slip)."""
base = target.resolve()
for member in zf.infolist():
dest = (base / member.filename).resolve()
if dest != base and base not in dest.parents:
raise RuntimeError(f"unsafe path in archive: {member.filename!r}")
zf.extractall(target)
def _maybe_fetch_windows_cudart(release: dict, chosen: str, target: Path) -> None:
"""On Windows + a CUDA build, also fetch the separate CUDA-runtime DLL archive.
Upstream ships the runtime as ``cudart-sd-...-win-cu12-...zip`` (which
``resolve_release_asset`` filters out); without those DLLs ``sd-cli.exe`` cannot start
on a machine that does not already have the CUDA runtime installed."""
if platform.system().lower() != "windows" or "cuda" not in chosen.lower():
return
cudart = next(
(
a
for a in release.get("assets", [])
if a["name"].lower().startswith("cudart") and "win" in a["name"].lower()
),
None,
)
if cudart is None:
return
dest = target / cudart["name"]
print(f"downloading CUDA runtime {cudart['name']} ...", flush = True)
try:
_download(cudart["browser_download_url"], dest)
with zipfile.ZipFile(dest) as zf:
_safe_extractall(zf, target)
finally:
dest.unlink(missing_ok = True)
def install(
*,
install_dir: Optional[Path] = None,
accelerator: str = "auto",
token: Optional[str] = None,
) -> Path:
"""Download + extract the prebuilt for this host. Returns the sd-cli path.
Raises ``RuntimeError`` if no asset matches the host (the caller should then
build from source) or the archive has no ``sd-cli``.
"""
target = install_dir or default_install_dir()
release = _fetch_latest_release(token = token)
names = [a["name"] for a in release.get("assets", [])]
chosen = resolve_release_asset(
names,
system = platform.system(),
machine = platform.machine(),
accelerator = accelerator,
)
if not chosen:
raise RuntimeError(
f"No prebuilt sd-cli for {platform.system()}/{platform.machine()} "
f"(accelerator={accelerator}). Build from source: "
f"https://github.com/{REPO}"
)
url = next(a["browser_download_url"] for a in release["assets"] if a["name"] == chosen)
target.mkdir(parents = True, exist_ok = True)
archive = target / chosen
print(f"downloading {chosen} -> {archive}", flush = True)
_download(url, archive)
print("extracting ...", flush = True)
with zipfile.ZipFile(archive) as zf:
_safe_extractall(zf, target)
archive.unlink(missing_ok = True)
# Windows CUDA builds need the separately-published cudart runtime DLLs.
_maybe_fetch_windows_cudart(release, chosen, target)
sd_cli = _locate_sd_cli(target)
if not sd_cli:
raise RuntimeError(f"archive {chosen} contained no sd-cli binary")
if sys.platform != "win32":
_make_executable(sd_cli)
print(f"installed sd-cli -> {sd_cli}", flush = True)
return sd_cli
def main(argv: Optional[list[str]] = None) -> int:
p = argparse.ArgumentParser(description = "Install a prebuilt sd-cli (stable-diffusion.cpp).")
p.add_argument(
"--accelerator", default = "auto", choices = ["auto", "cpu", "vulkan", "rocm", "cuda"]
)
p.add_argument("--install-dir", default = None)
p.add_argument(
"--print-asset", action = "store_true", help = "resolve + print the asset, don't download"
)
args = p.parse_args(argv)
if args.print_asset:
release = _fetch_latest_release()
names = [a["name"] for a in release.get("assets", [])]
chosen = resolve_release_asset(
names,
system = platform.system(),
machine = platform.machine(),
accelerator = args.accelerator,
)
print(chosen or "(no matching prebuilt; build from source)")
return 0 if chosen else 2
try:
install(
install_dir = Path(args.install_dir).expanduser() if args.install_dir else None,
accelerator = args.accelerator,
)
except RuntimeError as exc:
print(f"error: {exc}", file = sys.stderr)
return 1
return 0
if __name__ == "__main__":
raise SystemExit(main())