[diffusion] Postprocess: implement frame interpolation using RIFE (#19384)

python/sglang/multimodal_gen/test/server/testcase_configs.py

@@ -218,6 +218,10 @@ class DiffusionSamplingParams:
     # TeaCache acceleration
     enable_teacache: bool = False
 
+    # Frame interpolation
+    enable_frame_interpolation: bool = False
+    frame_interpolation_exp: int = 1  # 1 = 2×, 2 = 4×
+
 
 @dataclass(frozen=True)
 class DiffusionTestCase:
@@ -494,6 +498,22 @@ ONE_GPU_CASES_B: list[DiffusionTestCase] = [
             enable_teacache=True,
         ),
     ),
+    # Frame interpolation correctness (2× / exp=1)
+    # Uses the same 1.3B model already in the suite;
+    DiffusionTestCase(
+        "wan2_1_t2v_1.3b_frame_interp_2x",
+        DiffusionServerArgs(
+            model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+            modality="video",
+            custom_validator="video",
+        ),
+        DiffusionSamplingParams(
+            prompt=T2V_PROMPT,
+            num_frames=5,
+            enable_frame_interpolation=True,
+            frame_interpolation_exp=1,
+        ),
+    ),
     # LoRA test case for single transformer + merge/unmerge API test
     # Note: Uses dynamic_lora_path instead of lora_path to test LayerwiseOffload + set_lora interaction
     # Server starts WITHOUT LoRA, then set_lora is called after startup (Wan models auto-enable layerwise offload)