unsloth/scripts/sd_cpp_smoke.py
Daniel Han 2d09508951
Studio diffusion (Phase 6): img2img / inpaint / edit / LoRA / upscale on the native engine (#6680)
* Studio diffusion: cross-platform device policy, fp16 guard, lock split, validate-before-evict

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

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

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

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

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

73 prior + 35 new CPU tests pass.

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

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

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

112 CPU tests pass.

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

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

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

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

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

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

121 CPU tests pass.

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

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

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

127 CPU tests pass.

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

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

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

129 CPU tests pass.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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

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

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

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

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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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

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

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

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

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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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

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

* Studio diffusion (Phase 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

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

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

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

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

Codex review on the native engine arg builder:

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

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

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

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
2026-07-01 15:18:38 -03:00

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