diff --git a/torchdrive/models/vista.py b/torchdrive/models/vista.py
index 716d76a..cd99c04 100644
--- a/torchdrive/models/vista.py
+++ b/torchdrive/models/vista.py
@@ -55,9 +55,20 @@ def __init__(
         start = time.perf_counter()
 
         config = OmegaConf.load(config_path)
+        config["model"]["params"]["num_frames"] = num_frames
+        config["model"]["params"]["denoiser_config"]["params"][
+            "num_frames"
+        ] = num_frames
         model = load_model_from_config(config, ckpt_path)
         self.model = model.bfloat16().to(device).eval()
 
+        def compile(f):
+            return torch.compile(f, fullgraph=True, mode="reduce-overhead")
+
+        self.model.encode_first_stage = compile(self.model.encode_first_stage)
+        self.model.denoiser = compile(self.model.denoiser)
+        self.model.decode_first_stage = compile(self.model.decode_first_stage)
+
         print(f"loaded vista in {time.perf_counter() - start:.2f}s")
 
         guider = "VanillaCFG"
@@ -153,6 +164,8 @@ def generate(
 
 
 if __name__ == "__main__":
+    torch.manual_seed(10)
+
     dataset = NuscenesDataset(
         data_dir="~/nuscenes",
         version="v1.0-mini",
@@ -174,11 +187,38 @@ def generate(
 
     print(trajectory)
 
-    sampler = VistaSampler(device=device)
-    out = sampler.generate(cond_img, trajectory)
-    print(out.shape)
-    assert out.shape == (10, 3, 480, 640)
+    steps = 25
+    num_frames = 6
+
+    sampler = VistaSampler(device=device, num_frames=num_frames, steps=steps)
+    """
+    Encoding 10 frames: 0.053
+    Decoding 10 frames: 0.242
+
+
+    * 50 steps, 10 frames = 8.33s
+    * 50 steps, 5 frames = 5.24s
+    * 25 steps, 10 frames = 4.53s
+    * 30 steps, 10 frames = 5.31s
+
+    * 50 steps, 10 frames, compile = 5.22s
+    * 50 steps, 10 frames, compile reduce-overhead = 4.51s
+    * 25 steps, 10 frames, compile reduce-overhead = 2.43s
+    * 25 steps, 6 frames, compile reduce-overhead = 1.72s
+    """
+    # prewarm
+    for i in range(5):
+        torch.cuda.synchronize(device)
+        start = time.perf_counter()
+
+        out = sampler.generate(cond_img, trajectory)
+
+        torch.cuda.synchronize(device)
+        print(f"generate {time.perf_counter() - start:.2f}s")
+
+        print(out.shape)
+        assert out.shape == (num_frames, 3, 480, 640)
 
-    for i, img in enumerate(out):
-        img = to_pil_image(normalize_img(img))
-        img.save(f"vista_{i}.png")
+        for i, img in enumerate(out):
+            img = to_pil_image(normalize_img(img))
+            img.save(f"vista_{steps}_{i}.png")
diff --git a/torchdrive/tasks/diff_traj.py b/torchdrive/tasks/diff_traj.py
index f8c2795..b722d7c 100644
--- a/torchdrive/tasks/diff_traj.py
+++ b/torchdrive/tasks/diff_traj.py
@@ -751,8 +751,15 @@ def __init__(
 
         self.model = ConvNextPathPred()
 
+        # dream parameters
+        self.dream_steps = 1
+        self.vista_fps = 10
+        self.steps_per_second = 2
         if not test:
-            self.vista = VistaSampler()
+            vista_frames = (
+                1 + self.vista_fps * self.dream_steps // self.steps_per_second
+            )
+            self.vista = VistaSampler(steps=25, num_frames=vista_frames)
 
         self.batch_transform = Compose(
             NormalizeCarPosition(start_frame=0),
@@ -904,7 +911,7 @@ def forward(
             positions[:, :pred_traj_len],
             mask[:, :pred_traj_len],
             pred_traj[:, :pred_traj_len],
-            step=2,
+            step=self.dream_steps,
         )
 
         dream_losses, dream_traj, all_dream_traj = self.model(