[Perf][DreamZero]dreamzero supports CUDA graph, torch.compile, and DiT Caching. (#4213)

Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>

vllm_omni/diffusion/cache/stepcache/config.py

@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Configuration for step-level DiT velocity caching."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from vllm_omni.diffusion.data import DiffusionCacheConfig
+
+# Pipeline attributes set by StepCacheBackend.enable().
+STEP_CACHE_CONFIG_ATTR = "_stepcache_config"
+STEP_CACHE_STATE_ATTR = "_stepcache_state"
+
+
+@dataclass(frozen=True)
+class StepCacheConfig:
+    """Runtime config for velocity-based step skipping.
+
+    Skips entire DiT forwards when successive video velocity predictions are
+    highly aligned (cosine similarity above configured thresholds).
+
+    Reference: DreamZero paper DiT Caching / dreamzero.git ``should_run_model``.
+    """
+
+    enabled: bool = True
+    min_history_steps: int = 2
+    max_history: int = 2
+    sim_thresholds: tuple[float, ...] = (0.95, 0.93)
+    skip_countdowns: tuple[int, ...] = (4, 2)
+
+    @classmethod
+    def from_diffusion_cache_config(cls, config: DiffusionCacheConfig) -> StepCacheConfig:
+        enabled = config.step_cache_dit_enabled
+
+        thresholds = tuple(config.velocity_sim_thresholds)
+        countdowns = tuple(config.velocity_skip_countdowns)
+        if len(thresholds) != len(countdowns):
+            raise ValueError(
+                "velocity_sim_thresholds and velocity_skip_countdowns must have the same length; "
+                f"got {len(thresholds)} and {len(countdowns)}."
+            )
+
+        return cls(
+            enabled=enabled,
+            min_history_steps=config.step_cache_dit_min_history,
+            max_history=config.step_cache_dit_max_history,
+            sim_thresholds=thresholds,
+            skip_countdowns=countdowns,
+        )