Update app.py

app.py

@@ -416,24 +416,98 @@ def generate_video(
         tiling_config = TilingConfig.default()
         video_chunks_number = get_video_chunks_number(num_frames, tiling_config)
 
-        # >>> SET the LoRA descriptor strength at runtime
+        # >>> RUNTIME LoRA application (robust, multi-fallback)
+        # We cannot rely on mutating the original descriptor (some implementations are immutable),
+        # so create a fresh runtime descriptor and try multiple ways to install it.
+        runtime_strength = float(lora_strength)
+        replaced = False
+
+        # 1) Try simple approach: build a new LoraPathStrengthAndSDOps
+        runtime_lora = LoraPathStrengthAndSDOps(lora_path, runtime_strength, LTXV_LORA_COMFY_RENAMING_MAP)
+        print(f"[LoRA] attempting to apply runtime LoRA (strength={runtime_strength})")
+
+        # Try a few likely places to replace the descriptor used by the pipeline/ledger.
         try:
-            # lora_descriptor was created at module scope during init
-            lora_descriptor.strength = float(lora_strength)
-            print(f"[LoRA] set strength = {lora_descriptor.strength}")
-        except Exception as e:
-            # Defensive: if the descriptor isn't present, continue quietly
-            print(f"[LoRA] could not set strength: {e}")
-
-        # >>> FORCE transformer rebuild so LoRA strength updates
+            # common attribute on pipeline
+            if hasattr(pipeline, "loras"):
+                try:
+                    pipeline.loras = [runtime_lora]
+                    replaced = True
+                    print("[LoRA] replaced pipeline.loras")
+                except Exception as e:
+                    print(f"[LoRA] pipeline.loras assignment failed: {e}")
+        except Exception:
+            pass
+
         try:
-            # Clear cached transformer so new LoRA strength is applied
-            if hasattr(pipeline.model_ledger, "_transformer"):
-                pipeline.model_ledger._transformer = None
-            print("[LoRA] transformer cache cleared")
-        except Exception as e:
-            print(f"[LoRA] transformer reset failed: {e}")
-        
+            # common attribute on the model ledger
+            if hasattr(pipeline, "model_ledger") and hasattr(pipeline.model_ledger, "loras"):
+                try:
+                    pipeline.model_ledger.loras = [runtime_lora]
+                    replaced = True
+                    print("[LoRA] replaced pipeline.model_ledger.loras")
+                except Exception as e:
+                    print(f"[LoRA] pipeline.model_ledger.loras assignment failed: {e}")
+        except Exception:
+            pass
+
+        try:
+            # some internals use a private _loras list
+            if hasattr(pipeline, "model_ledger") and hasattr(pipeline.model_ledger, "_loras"):
+                try:
+                    pipeline.model_ledger._loras = [runtime_lora]
+                    replaced = True
+                    print("[LoRA] replaced pipeline.model_ledger._loras")
+                except Exception as e:
+                    print(f"[LoRA] pipeline.model_ledger._loras assignment failed: {e}")
+        except Exception:
+            pass
+
+        # 2) If we succeeded replacing the descriptor in-place, clear transformer cache so it will rebuild
+        if replaced:
+            try:
+                if hasattr(pipeline.model_ledger, "_transformer"):
+                    pipeline.model_ledger._transformer = None
+                # also clear potential caches named similar to 'transformer_cache' if present
+                if hasattr(pipeline.model_ledger, "transformer_cache"):
+                    try:
+                        pipeline.model_ledger.transformer_cache = {}
+                    except Exception:
+                        pass
+                print("[LoRA] in-place descriptor replacement done; transformer cache cleared")
+            except Exception as e:
+                print(f"[LoRA] replacement succeeded but cache clearing failed: {e}")
+
+        # 3) FINAL FALLBACK - if none of the in-place replacements worked, rebuild the pipeline
+        if not replaced:
+            print("[LoRA] in-place replacement FAILED; rebuilding pipeline with runtime LoRA (this is slow)")
+            try:
+                # Rebuild pipeline object with the new LoRA descriptor
+                # NOTE: this replaces the global `pipeline`. We must declare global to reassign it.
+                global pipeline
+                pipeline = LTX23DistilledA2VPipeline(
+                    distilled_checkpoint_path=checkpoint_path,
+                    spatial_upsampler_path=spatial_upsampler_path,
+                    gemma_root=gemma_root,
+                    loras=[runtime_lora],
+                    quantization=QuantizationPolicy.fp8_cast(),
+                )
+
+                # After rebuilding, we *do not* re-run the original module-level preloads here,
+                # because re-pinning may be complex; the rebuilt pipeline will construct its
+                # own ledger as part of the first call. This is slower but reliable.
+                # Clear any transformer caches if they exist on the new ledger as well.
+                try:
+                    if hasattr(pipeline.model_ledger, "_transformer"):
+                        pipeline.model_ledger._transformer = None
+                except Exception:
+                    pass
+
+                print("[LoRA] pipeline rebuilt with runtime LoRA")
+            except Exception as e:
+                print(f"[LoRA] pipeline rebuild FAILED: {e}")
+
+        # Finally, log memory then proceed
         log_memory("before pipeline call")
 
         video, audio = pipeline(