[ci] Add Wan2.2-TI2V-5B SSIM regression test

.github/scripts/plan_merge_ci.py

@@ -217,6 +217,7 @@ class FamilyCoverage:
             "test_causal_similarity.py",
             "test_wan_i2v_similarity.py",
             "test_wan_t2v_similarity.py",
+            "test_wan_ti2v_similarity.py",
         ),
     ),
     FamilyCoverage(

fastvideo/tests/contract/test_merge_ci_plan.py

@@ -34,7 +34,8 @@ def test_model_family_change_selects_focused_golden_and_ssim():
     assert plan.encoded_lanes() == ",golden-gate,ssim,"
     assert plan.encoded_golden_tests() == "test_wan_t2v.py"
     assert plan.encoded_ssim_tests() == (
-        "test_causal_similarity.py,test_wan_i2v_similarity.py,test_wan_t2v_similarity.py")
+        "test_causal_similarity.py,test_wan_i2v_similarity.py,test_wan_t2v_similarity.py,"
+        "test_wan_ti2v_similarity.py")
 
 
 def test_flux2_change_does_not_pull_unrelated_flux1_quality_tests():

fastvideo/tests/ssim/README.md

@@ -1,6 +1,7 @@
 The reference videos are used as part of e2e SSIM regression tests.
-Wan inference coverage now lives in `test_wan_t2v_similarity.py` and
-`test_wan_i2v_similarity.py` alongside the other model-specific SSIM files.
+Wan inference coverage now lives in `test_wan_t2v_similarity.py`,
+`test_wan_i2v_similarity.py` and `test_wan_ti2v_similarity.py` alongside the
+other model-specific SSIM files.
 These tests compare newly generated videos against references to detect quality
 regressions.
 

fastvideo/tests/ssim/test_wan_ti2v_similarity.py

@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+"""SSIM regression for Wan2.2-TI2V-5B.
+
+One checkpoint serves two workloads, so there are two cases: text-to-video,
+and image-to-video when an image is passed. For the image case the pipeline
+ignores ``height``/``width`` and derives the output size from the image's
+aspect ratio inside a hard-coded 480x832 pixel budget (see
+``InputValidationStage``), so the sizes below bind only the text case.
+"""
+import os
+
+import pytest
+
+from fastvideo.api.sampling_param import SamplingParam
+from fastvideo.logger import init_logger
+from fastvideo.tests.ssim.inference_similarity_utils import (
+    resolve_inference_device_reference_folder,
+    run_image_to_video_similarity_test,
+    run_text_to_video_similarity_test,
+)
+
+logger = init_logger(__name__)
+
+REQUIRED_GPUS = 1
+
+device_reference_folder = resolve_inference_device_reference_folder(logger)
+
+# Few steps and a short clip keep the CI run small; everything else (guidance,
+# flow shift, fps, negative prompt) comes from the model's registered preset.
+WAN_TI2V_PARAMS = {
+    "num_gpus": 1,
+    "model_path": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
+    "height": 480,
+    "width": 832,
+    "num_frames": 45,
+    "num_inference_steps": 4,
+    "seed": 1024,
+}
+_WAN_TI2V_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(WAN_TI2V_PARAMS["model_path"])
+WAN_TI2V_FULL_QUALITY_PARAMS = {
+    "num_gpus": WAN_TI2V_PARAMS["num_gpus"],
+    "model_path": WAN_TI2V_PARAMS["model_path"],
+    "height": _WAN_TI2V_FULL_QUALITY_DEFAULTS.height,
+    "width": _WAN_TI2V_FULL_QUALITY_DEFAULTS.width,
+    "num_frames": WAN_TI2V_PARAMS["num_frames"],  # preset default is 121; 45 keeps the run in budget
+    "num_inference_steps": _WAN_TI2V_FULL_QUALITY_DEFAULTS.num_inference_steps,
+    "seed": _WAN_TI2V_FULL_QUALITY_DEFAULTS.seed,
+}
+
+WAN_TI2V_MODEL_TO_PARAMS = {
+    "Wan2.2-TI2V-5B-Diffusers": WAN_TI2V_PARAMS,
+}
+FULL_QUALITY_WAN_TI2V_MODEL_TO_PARAMS = {
+    "Wan2.2-TI2V-5B-Diffusers": WAN_TI2V_FULL_QUALITY_PARAMS,
+}
+
+# Prompts and image are the ones in examples/inference/basic/basic_wan2_2_ti2v.py.
+WAN_TI2V_T2V_TEST_PROMPTS = [
+    ("A majestic lion strides across the golden savanna, its powerful frame glistening under the warm "
+     "afternoon sun. The tall grass ripples gently in the breeze, enhancing the lion's commanding presence. "
+     "The tone is vibrant, embodying the raw energy of the wild. Low angle, steady tracking shot, cinematic."),
+]
+
+WAN_TI2V_I2V_TEST_CASES = [
+    (
+        ("Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred "
+         "feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the "
+         "background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white "
+         "clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. "
+         "A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."),
+        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
+    ),
+]
+
+
+@pytest.mark.parametrize("prompt", WAN_TI2V_T2V_TEST_PROMPTS)
+@pytest.mark.parametrize("attention_backend_name", ["FLASH_ATTN", "TORCH_SDPA"])
+@pytest.mark.parametrize("model_id", list(WAN_TI2V_MODEL_TO_PARAMS.keys()))
+def test_wan_ti2v_t2v_inference_similarity(
+    prompt: str,
+    attention_backend_name: str,
+    model_id: str,
+) -> None:
+    run_text_to_video_similarity_test(
+        logger=logger,
+        script_dir=os.path.dirname(os.path.abspath(__file__)),
+        device_reference_folder=device_reference_folder,
+        prompt=prompt,
+        attention_backend_name=attention_backend_name,
+        model_id=model_id,
+        default_params_map=WAN_TI2V_MODEL_TO_PARAMS,
+        full_quality_params_map=FULL_QUALITY_WAN_TI2V_MODEL_TO_PARAMS,
+        min_acceptable_ssim=0.93,
+    )
+
+
+@pytest.mark.parametrize(("prompt", "image_path"), WAN_TI2V_I2V_TEST_CASES)
+@pytest.mark.parametrize("attention_backend_name", ["FLASH_ATTN"])
+@pytest.mark.parametrize("model_id", list(WAN_TI2V_MODEL_TO_PARAMS.keys()))
+def test_wan_ti2v_i2v_inference_similarity(
+    prompt: str,
+    image_path: str,
+    attention_backend_name: str,
+    model_id: str,
+) -> None:
+    run_image_to_video_similarity_test(
+        logger=logger,
+        script_dir=os.path.dirname(os.path.abspath(__file__)),
+        device_reference_folder=device_reference_folder,
+        prompt=prompt,
+        image_path=image_path,
+        attention_backend_name=attention_backend_name,
+        model_id=model_id,
+        default_params_map=WAN_TI2V_MODEL_TO_PARAMS,
+        full_quality_params_map=FULL_QUALITY_WAN_TI2V_MODEL_TO_PARAMS,
+        min_acceptable_ssim=0.97,
+    )