Bring back the Seed control, leave Eta fixed

Seed is exposed again as a top-level input (not behind an Advanced panel): -1
draws a fresh seed each run, an explicit value reproduces a run exactly. Eta
stays internal at 0.0, so sampling is always on the deterministic ODE and the
demo does not surface an ODE/SDE knob.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app2.py

@@ -253,9 +253,9 @@ the abstract predictor, which is **consistency-distilled** and so is designed to
 very few steps — raising it buys little.
 
 **Randomness.** The {NUM_VIDEOS} rollouts share one context and one seed, but the
-solver draws independent initial noise per batch element, so the futures diverge. Each
-run draws a fresh seed, so generating again on the same clip gives you a new set of
-futures.
+solver draws independent initial noise per batch element, so the futures diverge.
+Leave *Seed* at -1 for a fresh draw each run, or set it explicitly to reproduce a run
+exactly.
 
 **Note.** The clip must be long enough for the L2 look-back window; very short clips
 are rejected with an explicit error. Frame and step counts are kept modest to fit the
@@ -348,7 +348,7 @@ def _stream_rollout(cmd: list[str], say) -> str:
 
 @spaces.GPU(duration=300)
 def run_rollout(video_path: str, num_gen_steps: int, l1_steps: int, l2_steps: int,
-                progress=gr.Progress()):
+                seed: int, progress=gr.Progress()):
     if video_path is None:
         raise gr.Error("Please upload a short mp4 clip first.")
 
@@ -356,12 +356,12 @@ def run_rollout(video_path: str, num_gen_steps: int, l1_steps: int, l2_steps: in
         progress(frac, desc=msg)
 
     try:
-        # Seed and eta are no longer exposed in the UI. Every run draws a fresh seed,
-        # so repeated runs on the same clip show different futures; the rollout script
-        # only seeds when seed > 0. eta stays at the deterministic-ODE default — within
-        # a batch each sample still draws its own noise, so the NUM_VIDEOS rollouts
-        # diverge regardless.
-        seed = random.randrange(1, 2**31 - 1)
+        # seed < 0 means "surprise me" — draw a fresh positive seed. The rollout script
+        # only seeds when seed > 0, hence the clamp. Within a batch each sample still
+        # draws its own noise, so one seed yields NUM_VIDEOS distinct rollouts; setting
+        # it explicitly lets a run be reproduced exactly.
+        seed = random.randrange(1, 2**31 - 1) if int(seed) < 0 else max(1, int(seed))
+        # eta is not exposed: 0.0 keeps sampling on the deterministic ODE.
         eta = DEFAULT_ETA
 
         job = Path(tempfile.gettempdir()) / f"orbis2_{uuid.uuid4().hex[:8]}"
@@ -447,6 +447,8 @@ def build_demo():
                 l2_steps = gr.Slider(4, 8, value=DEFAULT_L2_STEPS, step=1,
                                      label="L2 sampler steps",
                                      info="Abstract predictor — distilled, needs very few")
+                seed_in = gr.Number(value=-1, precision=0, label="Seed",
+                                    info="-1 draws a fresh seed each run; set a value to reproduce one")
 
                 btn = gr.Button("Generate rollouts", variant="primary")
 
@@ -456,7 +458,7 @@ def build_demo():
 
         btn.click(
             run_rollout,
-            inputs=[inp, n_steps_gen, l1_steps, l2_steps],
+            inputs=[inp, n_steps_gen, l1_steps, l2_steps, seed_in],
             outputs=outs,
             concurrency_limit=1,
         )