Round 5 reviewer findings, mostly symmetric-lifecycle and input
validation gaps the earlier rounds left open.
Backend lifecycle (P1)
* routes/training.py: training start now also unloads the GGUF
llama-server subprocess; was previously only unloading the
safetensors backend, so starting training while a GGUF chat
model was loaded kept the subprocess pinned to VRAM.
* routes/inference.py: new _raise_if_training_active helper. Both
GGUF and standard chat loads, plus /api/inference/images/load,
now refuse with HTTP 409 when training is active instead of
silently stopping training to free VRAM.
* core/inference/diffusion.py: _release_other_gpu_owners_for_
diffusion no longer stops active training. The route layer
refuses the request first, so reaching the helper with training
live would only happen from programmatic backend calls; better
to surface OOM than terminate a long training run.
* core/inference/diffusion.py: BF16 dtype is now gated on
torch.cuda.is_bf16_supported. Pascal/Turing GPUs report
is_available()=True but lack BF16 ALUs; FLUX kernels then fail
inside from_pretrained. Falls back to FP16 instead of refusing.
* core/inference/diffusion.py: GGUF transformer allocation and
pipeline allocation now run AFTER releasing chat/export GPU
owners; previously from_single_file ran first and could OOM
before the intended VRAM handoff happened.
* routes/models.py: /delete-cached now also blocks delete when
diffusion is_loading=True (not just is_loaded); concurrent
delete during hf_hub_download / from_single_file would have
raced the rmtree.
* routes/models.py: /delete-finetuned now also checks the
diffusion backend before unlinking a Studio outputs/exports
path. A user who exported a FLUX LoRA locally and loaded it via
/images/load could previously rmtree the directory the
diffusion backend was reading from.
Backend correctness / safety (P2)
* core/inference/diffusion.py: _FAMILY_EXCLUDE for qwen-image now
also covers qwen_image_edit / qwenimageedit underscore spellings
so '...qwen_image_edit-GGUF' no longer misdetects as Qwen-Image.
* core/inference/diffusion.py: detect_family now scans
_FULL_REPO_FAMILIES in addition to _FAMILIES, so SDXL repos
(stabilityai/stable-diffusion-xl-base-1.0) are auto-detected
instead of failing with 'Could not infer a diffusion family'.
* core/inference/diffusion.py: generate_image now uses a separate
_generate_lock for the pipeline forward instead of holding
_lock for the whole call. status() polls and concurrent unload
requests no longer block for the full minutes-long generation.
* routes/models.py: diffusion delete guard now uses exact repo-id
match instead of prefix match; previously loading 'org/model-v2'
would block deleting unrelated cached 'org/model'.
* models/inference.py: DiffusionLoadRequest now rejects ASCII
control characters in repo_id / gguf_filename / base_repo /
family via field_validator (closes log-injection surface from
authenticated callers). Also caps lengths at 256 chars.
* models/inference.py: DiffusionGenerateRequest seed is now
bounded to the int64/uint64 range; previously a huge seed
(e.g. 2**100) passed Pydantic then crashed inside
torch.Generator.manual_seed with 'Overflow when unpacking long
long'.
Frontend (P2)
* features/images/images-page.tsx: Custom HF repo panel now
exposes a Pipeline family override dropdown; previously the
backend supported it via DiffusionLoadRequest.family but the UI
had no way to send it, so custom repos whose names did not
contain a hard-coded substring failed to load.
* features/images/images-page.tsx: handleLoad now re-fetches
status on error. The backend clears its old pipeline before
allocating the replacement; a failed swap previously left the
UI showing 'Loaded:' with Generate enabled until manual
refresh.
Tests (10 new)
* underscore qwen-image-edit exclusion + SDXL full-repo detection
* BF16 fallback when is_bf16_supported() returns False
* status() does not block while generate_image holds _generate_lock
* route layer rejects control chars in repo_id
* route layer rejects 2**100 seeds (uint64-max boundary accepted)
* route layer happy-path with negative-prompt true_cfg_scale
forwarding (Qwen/Flux) and skip-when-no-neg (distilled CFG)
241 lines
7.4 KiB
Python
241 lines
7.4 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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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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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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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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from routes import inference as inf
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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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app.include_router(inf.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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