[CI] Revert "[Feature] gen-only FP8 Quantization Support for SenseNova-U1" (#4196)

tests/diffusion/quantization/test_quantization_fp8.py

@@ -6,13 +6,11 @@ End-to-end tests for the unified quantization framework (PR #1764).
 Validates FP8 quantization works correctly for all supported model types:
   - Single-stage diffusion models (FLUX.1-dev, Qwen-Image, Z-Image-Turbo)
   - Multi-stage models (BAGEL: LLM + Diffusion)
-  - MoT gen-only models (SenseNova-U1: gen-path FP8, und-path BF16)
 
 Tests verify:
   1. FP8 quantization produces valid images
   2. Memory usage is lower than BF16 baseline
   3. Multi-stage models only quantize the diffusion stage (not the LLM stage)
-  4. MoT models only quantize gen-path layers (mot_gen)
 
 Usage:
     # Run all FP8 quantization tests
@@ -438,92 +436,3 @@ def test_single_stage_quantization_config_key():
         quantization_config="fp8",
     )
     assert len(images) >= 1
-
-
-# ─── SenseNova-U1 gen-only FP8 tests ─────────────────────────────────────────
-
-_SENSENOVA_MODEL = "SenseNova/SenseNova-U1-8B-MoT"
-
-
-def _generate_sensenova_u1_image(
-    quantization_config: str | None = None,
-    num_inference_steps: int = 20,
-) -> tuple[Any, float]:
-    """Generate an image with SenseNova-U1 (single-stage MoT, gen-only FP8).
-
-    Returns (generated_image, peak_memory_gib).
-    """
-    omni_kwargs: dict[str, Any] = {}
-    if quantization_config:
-        omni_kwargs["quantization_config"] = quantization_config
-
-    with OmniRunner(_SENSENOVA_MODEL, **omni_kwargs) as runner:
-        torch.accelerator.reset_peak_memory_stats()
-
-        sampling_params = OmniDiffusionSamplingParams(
-            height=1024,
-            width=1024,
-            seed=42,
-            num_inference_steps=num_inference_steps,
-            extra_args={
-                "cfg_scale": 4.0,
-                "cfg_norm": "none",
-                "timestep_shift": 3.0,
-            },
-        )
-        omni_outputs = list(
-            runner.omni.generate(
-                prompts={"prompt": "A cat sitting on a windowsill", "modalities": ["image"]},
-                sampling_params_list=sampling_params,
-            )
-        )
-
-        peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)
-
-        generated_image = None
-        for req_output in omni_outputs:
-            if images := getattr(req_output, "images", None):
-                generated_image = images[0]
-                break
-
-        assert generated_image is not None, "No images generated from SenseNova-U1"
-        assert generated_image.size == (1024, 1024), f"Expected 1024x1024, got {generated_image.size}"
-
-        return generated_image, peak_mem
-
-
-@hardware_test(res={"cuda": "H100"})
-def test_sensenova_u1_fp8_generates_image():
-    """SenseNova-U1 with gen-only FP8 generates a valid image.
-
-    FP8 is applied only to gen-path (mot_gen) layers; understanding-path
-    layers stay in BF16.
-    """
-    image, _ = _generate_sensenova_u1_image(quantization_config="fp8")
-    image.save("test_sensenova_u1_fp8.png")
-
-
-@hardware_test(res={"cuda": "H100"})
-def test_sensenova_u1_bf16_generates_image():
-    """SenseNova-U1 without quantization generates a valid image (baseline)."""
-    image, _ = _generate_sensenova_u1_image(quantization_config=None)
-    image.save("test_sensenova_u1_bf16.png")
-
-
-@hardware_test(res={"cuda": "H100"})
-def test_sensenova_u1_fp8_uses_less_memory():
-    """Gen-only FP8 should use less peak memory than BF16 for SenseNova-U1."""
-    _, mem_bf16 = _generate_sensenova_u1_image(
-        quantization_config=None,
-        num_inference_steps=10,
-    )
-    torch.accelerator.empty_cache()
-
-    _, mem_fp8 = _generate_sensenova_u1_image(
-        quantization_config="fp8",
-        num_inference_steps=10,
-    )
-
-    print(f"SenseNova-U1 BF16 peak memory: {mem_bf16:.2f} GiB")
-    print(f"SenseNova-U1 FP8  peak memory: {mem_fp8:.2f} GiB")
-    assert mem_fp8 < mem_bf16, f"FP8 ({mem_fp8:.2f} GiB) should use less memory than BF16 ({mem_bf16:.2f} GiB)"