test: l3 gpu smoke (t2i base/turbo + controlnet + upscale)

Add pytest.mark.gpu smoke tests for real-weight end-to-end validation.
Also add addopts=-m 'not gpu' to pyproject.toml so CI auto-deselects
these tests; run with `pytest -m gpu` to opt in on a GPU box.

pyproject.toml

@@ -13,6 +13,7 @@ quote-style = "double"
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 python_files = "test_*.py"
+addopts = "-m 'not gpu'"
 markers = [
     "gpu: requires a GPU (CUDA or MPS); skipped by default",
 ]

tests/test_smoke_gpu.py

@@ -0,0 +1,67 @@
+import pytest
+
+pytestmark = pytest.mark.gpu
+
+
+@pytest.fixture(scope="module")
+def real_backend():
+    """Build a real backend with real weights. ~30 GB download on first run."""
+    import backend
+    return backend.ZImageStudioBackend()
+
+
+def test_t2i_turbo_produces_image(real_backend):
+    from PIL import Image
+    image, meta = real_backend.generate(
+        mode="t2i",
+        params=dict(prompt="a red apple on a wooden table",
+                    negative_prompt="", model="Turbo",
+                    steps=8, cfg=1.0, width=384, height=384, seed=42,
+                    lora_path=None, lora_strength=0.0),
+    )
+    assert isinstance(image, Image.Image)
+    assert image.size == (384, 384)
+    assert meta["model"] == "Turbo"
+
+
+def test_t2i_base_produces_image(real_backend):
+    from PIL import Image
+    image, meta = real_backend.generate(
+        mode="t2i",
+        params=dict(prompt="a red apple on a wooden table",
+                    negative_prompt="blurry", model="Base",
+                    steps=15, cfg=4.0, width=384, height=384, seed=42,
+                    lora_path=None, lora_strength=0.0),
+    )
+    assert isinstance(image, Image.Image)
+
+
+def test_controlnet_produces_image(real_backend):
+    from PIL import Image
+    import numpy as np
+    arr = np.random.randint(0, 255, (384, 384, 3), dtype=np.uint8)
+    image, meta = real_backend.generate(
+        mode="controlnet",
+        params=dict(prompt="a portrait of a person, dramatic light",
+                    input_image=Image.fromarray(arr),
+                    preprocessor="Canny", controlnet_scale=1.0,
+                    steps=9, seed=42, lora_path=None, lora_strength=0.0),
+    )
+    assert isinstance(image, Image.Image)
+
+
+def test_upscale_produces_image(real_backend, tmp_path):
+    from PIL import Image
+    import numpy as np
+    from huggingface_hub import hf_hub_download
+    arr = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
+    image, meta = real_backend.generate(
+        mode="upscale",
+        params=dict(prompt="masterpiece, 8k",
+                    input_image=Image.fromarray(arr),
+                    refine_steps=5, refine_denoise=0.33, seed=42,
+                    lora_path=None, lora_strength=0.0,
+                    esrgan_model_path=hf_hub_download("xinntao/Real-ESRGAN",
+                                                     "RealESRGAN_x4plus.pth")),
+    )
+    assert image.size == (512, 512)