Update app.py (#2)

- Update app.py (bb27da4ee127a82638b19ed7e9f95475c0db8d04)

app.py

@@ -296,6 +296,7 @@ class LTX23UnifiedPipeline:
         ic_loras: list[LoraPathStrengthAndSDOps] | None = None,
         device: torch.device | None = None,
         quantization: QuantizationPolicy | None = None,
+        reference_downscale_factor: int | None = None,
     ):
         self.device = device or get_device()
         self.dtype = torch.bfloat16
@@ -334,17 +335,23 @@ class LTX23UnifiedPipeline:
             device=self.device,
         )
 
-        # Read reference downscale factor from IC-LoRA metadata
-        self.reference_downscale_factor = 1
-        for lora in ic_loras:
-            scale = _read_lora_reference_downscale_factor(lora.path)
-            if scale != 1:
-                if self.reference_downscale_factor not in (1, scale):
-                    raise ValueError(
-                        f"Conflicting reference_downscale_factor: "
-                        f"already {self.reference_downscale_factor}, got {scale}"
-                    )
-                self.reference_downscale_factor = scale
+        # Reference downscale factor: explicit value takes priority,
+        # otherwise read from IC-LoRA metadata, otherwise default to 1.
+        if reference_downscale_factor is not None:
+            self.reference_downscale_factor = reference_downscale_factor
+        else:
+            self.reference_downscale_factor = 1
+            for lora in ic_loras:
+                scale = _read_lora_reference_downscale_factor(lora.path)
+                if scale != 1:
+                    if self.reference_downscale_factor not in (1, scale):
+                        raise ValueError(
+                            f"Conflicting reference_downscale_factor: "
+                            f"already {self.reference_downscale_factor}, got {scale}"
+                        )
+                    self.reference_downscale_factor = scale
+
+        logging.info(f"[Pipeline] reference_downscale_factor={self.reference_downscale_factor}")
 
     # ── Video reference conditioning (from ICLoraPipeline) ───────────────
     def _create_ic_conditionings(
@@ -715,8 +722,11 @@ pipeline = LTX23UnifiedPipeline(
     distilled_checkpoint_path=checkpoint_path,
     spatial_upsampler_path=spatial_upsampler_path,
     gemma_root=gemma_root,
-    # ic_loras=ic_loras,
+    # ic_loras=ic_loras,  # LoRA already fused into checkpoint
     quantization=QuantizationPolicy.fp8_cast(),
+    # Union Control IC-LoRA was trained with reference videos at half resolution.
+    # Set explicitly so it works both with separate LoRA and fused checkpoints.
+    reference_downscale_factor=2,
 )
 
 # Preload all models for ZeroGPU tensor packing.