quantile schedule: optional alpha override (quantile@A) for evaluation

mf_stream.py

@@ -373,7 +373,7 @@ def stream_t2i(
 
     ``schedule``: "random" = mf's grid (sorted random draws from the training
     timestep distribution); "quantile" = same distribution at evenly spaced
-    quantiles (deterministic).
+    quantiles (deterministic); "quantile@A" uses shift alpha A instead.
 
     Yields ``{"type": "t2i", "step", "steps", "t", "image": Tensor[1,3,H,W] in [0,1]}``
     and finally ``{"type": "final", "image": Tensor}``.
@@ -404,10 +404,12 @@ def stream_t2i(
         device=device,
         noise_scale=sampler.vision_noise_scale,
     )
-    if schedule == "quantile":
+    if schedule.startswith("quantile"):
+        # "quantile" uses the checkpoint's training alpha; "quantile@A" overrides it
+        alpha = float(schedule.split("@", 1)[1]) if "@" in schedule else sampler.image_alpha
         grid = quantile_clean_time_grid(
             num_inference_steps=sampler.num_inference_steps,
-            alpha=sampler.image_alpha,
+            alpha=alpha,
             t_lognorm_mu=sampler.t_lognorm_mu,
             t_lognorm_sigma=sampler.t_lognorm_sigma,
             device=device,