Round 8 reviewer surfaced event-loop stalls (blocking unload from
async routes), incomplete VRAM handoff coverage (is_active /
loading_models / is_export_active not checked), token leaks via
exception messages, /v1 exposure, and several fail-open paths.
Async / event-loop
* routes/inference.py /images/unload, GGUF chat-load handoff,
safetensors chat-load handoff: blocking DiffusionBackend.unload
pushed onto asyncio.to_thread. unload takes _load_lock +
_generate_lock and can block for the full duration of an
in-flight load / generation, which was freezing the FastAPI
worker, SSE stream, and hardware poller for minutes.
* routes/export.py + routes/training.py: same to_thread wrap on
diffusion unload during checkpoint / training start.
GPU-owner handoff completeness
* core/inference/diffusion.py _release_chat_backend_for_diffusion:
llama-server now also unloaded when is_active=True (mid-download
/ startup), not only when is_loaded; flushed in-flight
safetensors loads from loading_models too.
* core/inference/diffusion.py _release_other_gpu_owners_for_
diffusion: export shutdown now also fires when
is_export_active() returns True (checkpoint not yet assigned).
Security / scrubbing
* core/inference/diffusion.py: load failure paths now scrub
hf_token from both _last_error AND the raised RuntimeError
message (the previous scrub only cleared frame locals).
Falls back to a regex strip of hf_[A-Za-z0-9]{20,} to
catch tokens that came in via huggingface_hub default caching.
* routes/inference.py: image lifecycle endpoints moved from
router to studio_router so they no longer answer under
the /v1 OpenAI-compat prefix. Studio-only side effects
(download multi-GB GGUFs, unload chat, etc.) should not be
reachable via an OpenAI-compat client.
* models/inference.py: control-char validator now also rejects
tab. Some log sinks split fields on tab; allowing it left a
log-injection surface.
Fail-closed delete guards
* routes/models.py /delete-cached: llama.cpp and safetensors
branches now fail closed with 503 when their status check
raises (matches the diffusion-side guard added earlier).
* routes/export.py: split the try/except around the training
backend so import failure falls back to 'skip' (no
core.training in this build) while a runtime failure of
get_training_backend()/is_training_active() fails closed.
* routes/models.py /delete-finetuned: diffusion guard now also
compares against relative path candidates (Path.resolve() works
on relative input). Previously a load with a relative repo_id
bypassed the guard.
CUDA cleanup ordering
* core/inference/diffusion.py: split _release() (drops local +
gc.collect) from _drain_cuda_cache() (torch.cuda.empty_cache).
Callers now drain AFTER nulling every reference so the
allocator actually reclaims the freed slabs (previously
empty_cache ran while caller still held a local, which left
the cache pinned).
Generate response (P2 #16)
* routes/inference.py: response uses status()['active_repo_id']
instead of the UI-facing repo_id, so a queued /images/load
promoting a pending model cannot mislabel the just-rendered
image with the new model's identity.
Test wiring
* tests/test_diffusion_routes.py: mount inf.studio_router on the
test app so /images/* routes are reachable now that they live
on the Studio-only router.
281 lines
9.2 KiB
Python
281 lines
9.2 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
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"""Route-level tests for ``/api/inference/images/*``.
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Mounts the actual ``inference_router`` on a fresh FastAPI app with the
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auth dependency replaced by a stub so we exercise the same FastAPI
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handlers Studio ships in production. The diffusion backend is replaced
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with an in-memory stub so we don't need diffusers / GPUs to run these.
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To stay runnable in a minimal CPU-only env, ``routes/inference.py``
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is loaded directly via ``importlib`` so we do NOT trigger
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``routes/__init__.py`` -- that file eagerly imports training /
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datasets / data_recipe / export and would drag in heavy deps
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(matplotlib, etc.) that the diffusion tests do not need.
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"""
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from __future__ import annotations
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import importlib.util
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import sys
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from pathlib import Path
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import pytest
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from fastapi import FastAPI
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from fastapi.testclient import TestClient
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from PIL import Image
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_BACKEND_ROOT = Path(__file__).resolve().parents[1]
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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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def _import_inference_module():
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"""Load ``routes/inference.py`` without executing ``routes/__init__``.
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The package init imports training / datasets / data_recipe / export
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routers, which pull in matplotlib / pandas / training stack. The
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diffusion tests only need the inference module so we side-step the
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package import via importlib.spec_from_file_location.
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"""
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# If a previous test already imported routes the normal way, reuse
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# the cached module instead of re-loading.
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cached = sys.modules.get("routes.inference")
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if cached is not None:
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return cached
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target = _BACKEND_ROOT / "routes" / "inference.py"
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spec = importlib.util.spec_from_file_location(
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"routes.inference",
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target,
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# We do NOT set submodule_search_locations for routes itself
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# because that would re-trigger routes/__init__.py. The module
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# uses relative imports sparingly; absolute imports resolve via
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# sys.path[0] = backend root.
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)
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assert spec and spec.loader, "could not build spec for routes/inference.py"
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module = importlib.util.module_from_spec(spec)
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sys.modules["routes.inference"] = module
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spec.loader.exec_module(module)
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return module
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class _FakeBackend:
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def __init__(self) -> None:
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self._loaded = False
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self._repo: str | None = None
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self.calls: list[dict] = []
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@property
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def is_loaded(self) -> bool:
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return self._loaded
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def status(self) -> dict:
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return {
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"is_loaded": self._loaded,
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"is_loading": False,
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"repo_id": self._repo,
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"family": "flux.2-klein" if self._loaded else None,
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"pipeline_class": "Flux2KleinPipeline" if self._loaded else None,
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"base_repo": "black-forest-labs/FLUX.2-klein" if self._loaded else None,
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"gguf_filename": None,
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"device": "cpu",
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"dtype": "torch.bfloat16",
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"loaded_at": 0,
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"last_error": None,
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"supported_families": [],
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}
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def load_model(self, repo_id, **kw):
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self.calls.append({"op": "load", "repo_id": repo_id, **kw})
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self._loaded = True
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self._repo = repo_id
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return self.status()
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def unload_model(self) -> dict:
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self._loaded = False
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self._repo = None
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return {"is_loaded": False}
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def generate_image(self, **kw):
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self.calls.append({"op": "generate", **kw})
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return Image.new("RGB", (kw["width"], kw["height"]), color = (123, 45, 67))
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@pytest.fixture
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def app_with_stub(monkeypatch):
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"""Build a FastAPI app that mounts the real inference router with
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auth disabled and the diffusion backend swapped for a stub."""
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inf = _import_inference_module()
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import core.inference.diffusion as d
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stub = _FakeBackend()
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# Override the singleton accessor the route uses.
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monkeypatch.setattr(d, "get_diffusion_backend", lambda: stub)
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monkeypatch.setattr(inf, "_get_diffusion_backend", lambda: stub)
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app = FastAPI()
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# Diffusion image routes live on studio_router so they are NOT
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# exposed under /v1 (which would let OpenAI-compat clients
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# trigger Studio-only side effects).
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app.include_router(inf.router, prefix = "/api/inference")
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app.include_router(inf.studio_router, prefix = "/api/inference")
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# Bypass auth by overriding the dependency.
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from auth.authentication import get_current_subject
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app.dependency_overrides[get_current_subject] = lambda: "test-user"
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return app, stub
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def test_status_when_unloaded(app_with_stub):
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app, _ = app_with_stub
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c = TestClient(app)
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r = c.get("/api/inference/images/status")
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assert r.status_code == 200
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body = r.json()
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assert body["is_loaded"] is False
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assert body["repo_id"] is None
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def test_generate_without_load_returns_400(app_with_stub):
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app, _ = app_with_stub
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c = TestClient(app)
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r = c.post(
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"/api/inference/images/generate",
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json = {"prompt": "a red sphere"},
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)
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assert r.status_code == 400
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assert "No diffusion model" in r.json()["detail"]
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def test_load_then_generate_round_trip(app_with_stub):
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app, stub = app_with_stub
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c = TestClient(app)
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r = c.post(
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"/api/inference/images/load",
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json = {
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"repo_id": "unsloth/FLUX.2-klein-4B-GGUF",
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"gguf_filename": "flux-2-klein-4b-Q4_K_S.gguf",
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},
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)
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assert r.status_code == 200, r.text
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assert r.json()["is_loaded"] is True
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r = c.post(
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"/api/inference/images/generate",
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json = {
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"prompt": "a tiny synth-pop album cover",
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"width": 256,
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"height": 256,
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"num_inference_steps": 4,
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"seed": 7,
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},
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)
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assert r.status_code == 200, r.text
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body = r.json()
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assert body["image_b64"]
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assert body["image_mime"] == "image/png"
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assert body["width"] == 256
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assert body["height"] == 256
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assert body["seed"] == 7
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assert body["duration_ms"] >= 0
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# Round-trip the base64 -> PIL to confirm it is a real PNG of the
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# right size and not, say, an empty string.
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import base64
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import io
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raw = base64.b64decode(body["image_b64"])
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decoded = Image.open(io.BytesIO(raw))
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assert decoded.format == "PNG"
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assert decoded.size == (256, 256)
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# Backend stub should have recorded both calls.
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ops = [c["op"] for c in stub.calls]
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assert ops == ["load", "generate"]
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def test_generate_rejects_off_grid_size(app_with_stub):
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app, stub = app_with_stub
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c = TestClient(app)
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c.post(
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"/api/inference/images/load",
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json = {
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"repo_id": "unsloth/FLUX.2-klein-4B-GGUF",
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"gguf_filename": "x.gguf",
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},
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)
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r = c.post(
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"/api/inference/images/generate",
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json = {"prompt": "x", "width": 513, "height": 512},
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)
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# Pydantic v2 wraps validator errors in 422 by default.
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assert r.status_code in (400, 422), r.text
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def test_unload_clears_state(app_with_stub):
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app, _ = app_with_stub
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c = TestClient(app)
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c.post(
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"/api/inference/images/load",
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json = {"repo_id": "unsloth/FLUX.2-klein-4B-GGUF", "gguf_filename": "x.gguf"},
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)
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r = c.post("/api/inference/images/unload")
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assert r.status_code == 200
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assert r.json()["is_loaded"] is False
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r = c.get("/api/inference/images/status")
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assert r.json()["is_loaded"] is False
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def test_load_rejects_control_chars_in_repo_id(app_with_stub):
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"""Newline-laden repo ids must be rejected by Pydantic BEFORE the
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log line that echoes them. Catches log-injection from authenticated
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callers (issues a 422 instead of forging a fake log line)."""
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app, _ = app_with_stub
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c = TestClient(app)
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r = c.post(
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"/api/inference/images/load",
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json = {"repo_id": "owner/model\nFAKE_LOG_LINE"},
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)
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assert r.status_code == 422, r.text
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body = r.json()
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text = repr(body).lower()
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assert "control" in text or "repo_id" in text
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def test_generate_rejects_oversize_seed(app_with_stub):
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"""Huge seeds raise inside torch.Generator.manual_seed; Pydantic
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must clamp first with a 422 instead of a 500 traceback."""
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app, _ = app_with_stub
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c = TestClient(app)
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c.post(
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"/api/inference/images/load",
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json = {"repo_id": "unsloth/FLUX.2-klein-4B-GGUF", "gguf_filename": "x.gguf"},
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)
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r = c.post(
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"/api/inference/images/generate",
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json = {"prompt": "x", "seed": 2**100},
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)
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assert r.status_code == 422, r.text
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def test_generate_accepts_uint64_max_seed(app_with_stub):
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"""Boundary value: 2**64 - 1 (uint64 max) is the largest seed
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torch.Generator on CPU accepts; reject would frustrate users
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who paste large seeds from other tooling."""
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app, _ = app_with_stub
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c = TestClient(app)
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c.post(
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"/api/inference/images/load",
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json = {"repo_id": "unsloth/FLUX.2-klein-4B-GGUF", "gguf_filename": "x.gguf"},
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)
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r = c.post(
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"/api/inference/images/generate",
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json = {"prompt": "x", "seed": (2**64) - 1},
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)
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# The fake backend returns 200 on success; we only care that the
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# request did NOT 422 on seed bounds.
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assert r.status_code != 422, r.text
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