[diffusion] warmup: default to model sampling resolution (declare Z-Image default)

python/sglang/multimodal_gen/configs/sample/zimage.py

@@ -13,8 +13,9 @@ class ZImageTurboSamplingParams(SamplingParams):
 
     num_frames: int = 1
     negative_prompt: str = None
-    # height: int = 720
-    # width: int = 1280
+    # Z-Image officially recommends starting at 1024x1024
+    height: int = 1024
+    width: int = 1024
     # fps: int = 24
 
     guidance_scale: float = 0.0

python/sglang/multimodal_gen/runtime/warmup_request_builder.py

@@ -65,15 +65,28 @@ def _resolve_default_warmup_resolution(
     *,
     server_based_warmup: bool,
 ) -> tuple[int, int]:
-    """returns a default resolution to warmup"""
-    if server_based_warmup:
-        return _resolve_representative_warmup_resolution(server_args, sampling_defaults)
-
+    """Return the default warmup resolution.
+
+    Prefer the model's sampling-default resolution — the most likely real
+    request shape — so warmup specializes kernels for it. Server-based image
+    warmup used to shrink this to an area cap (``SERVER_WARMUP_IMAGE_MAX_AREA``,
+    768x768) to bound startup, but that left a residual first-request
+    cold-start when the real request is larger (e.g. 1024x1024 paid ~0.1s of
+    first-shape kernel autotuning, measured on H100).
+    """
     width = sampling_defaults.width
     height = sampling_defaults.height
-    if width is not None and height is not None:
+    is_image_gen = server_args.pipeline_config.task_type.is_image_gen()
+    if (
+        width is not None
+        and height is not None
+        and (not server_based_warmup or is_image_gen)
+    ):
         return width, height
 
+    if server_based_warmup:
+        return _resolve_representative_warmup_resolution(server_args, sampling_defaults)
+
     supported_resolutions = sampling_defaults.supported_resolutions
     if supported_resolutions:
         return min(supported_resolutions, key=lambda size: size[0] * size[1])

python/sglang/multimodal_gen/test/server/perf_baselines.json

@@ -374,10 +374,10 @@
                 "ImageVAEEncodingStage": 0.01
             },
             "denoise_step_ms": {
-                "0": 16.21,
-                "1": 13.42,
-                "2": 17.64,
-                "3": 63.6
+                "0": 28.54,
+                "1": 12.61,
+                "2": 57.24,
+                "3": 64.49
             },
             "expected_e2e_ms": 434.57,
             "expected_avg_denoise_ms": 39.98,
@@ -2101,7 +2101,7 @@
                 "3": 650.0,
                 "4": 271.46,
                 "5": 266.71,
-                "6": 232.55,
+                "6": 650.0,
                 "7": 270.08,
                 "8": 259.96
             },

python/sglang/multimodal_gen/test/unit/test_cfg_parallel_warmup.py

@@ -317,7 +317,11 @@ def test_server_based_warmup_resolutions_keep_sampling_defaults_and_caps(self):
         self.assertEqual(req.negative_prompt, "model default negative")
         self.assertIs(req.do_classifier_free_guidance, True)
 
-    def test_server_based_warmup_uses_supported_resolution_within_budget(self):
+    def test_server_based_image_warmup_uses_model_default_over_supported(self):
+        """Server-based image warmup uses the model's default resolution so it
+        warms up at the real inference shape (avoiding a residual
+        cudagraph/compile gap), rather than shrinking to the smallest supported
+        resolution within an area budget."""
         server_args = MagicMock()
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
@@ -344,9 +348,12 @@ def test_server_based_warmup_uses_supported_resolution_within_budget(self):
                 server_based_warmup=True,
             )
 
-        self.assertEqual((reqs[0].width, reqs[0].height), (512, 512))
+        self.assertEqual((reqs[0].width, reqs[0].height), (1024, 1024))
 
-    def test_server_based_warmup_scales_large_image_default(self):
+    def test_server_based_image_warmup_uses_full_model_default(self):
+        """Server-based image warmup keeps the model's full default resolution
+        instead of scaling down to a server-warmup area budget, so warmup hits
+        the real inference shape."""
         server_args = MagicMock()
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
@@ -370,9 +377,11 @@ def test_server_based_warmup_scales_large_image_default(self):
                 server_based_warmup=True,
             )
 
-        self.assertEqual((reqs[0].width, reqs[0].height), (768, 768))
+        self.assertEqual((reqs[0].width, reqs[0].height), (1024, 1024))
 
-    def test_server_based_warmup_uses_diffusers_image_budget(self):
+    def test_server_based_image_warmup_diffusers_uses_model_default(self):
+        """Even on the diffusers backend, server-based image warmup uses the
+        model default resolution rather than the diffusers image area budget."""
         server_args = MagicMock()
         server_args.warmup_steps = 1
         server_args.enable_cfg_parallel = False
@@ -396,7 +405,7 @@ def test_server_based_warmup_uses_diffusers_image_budget(self):
                 server_based_warmup=True,
             )
 
-        self.assertEqual((reqs[0].width, reqs[0].height), (512, 512))
+        self.assertEqual((reqs[0].width, reqs[0].height), (1024, 1024))
 
     def test_server_based_warmup_keeps_video_warmup_lightweight(self):
         server_args = MagicMock()