Update app.py

app.py

@@ -267,6 +267,7 @@ class LTX23DistilledA2VPipeline(DistilledPipeline):
 # Model repos
 LTX_MODEL_REPO = "Lightricks/LTX-2.3"
 GEMMA_REPO ="Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"
+DISTILLED_LORA_FILE = "ltx-2.3-22b-distilled-lora-384.safetensors"
 EROS_REPO = "dagloop5/LoRA"
 EROS_FILE = "ltx2310eros_beta.safetensors"
 
@@ -328,6 +329,7 @@ checkpoint_path = EROS_FIXED
 
 spatial_upsampler_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.0.safetensors")
 gemma_root = snapshot_download(repo_id=GEMMA_REPO)
+distilled_lora_path = hf_hub_download(repo_id=LTX_MODEL_REPO, filename=DISTILLED_LORA_FILE)
 
 # ---- Insert block (LoRA downloads) between lines 268 and 269 ----
 # LoRA repo + download the requested LoRA adapters
@@ -360,6 +362,7 @@ print(f"Demopose LoRA: {demopose_lora_path}")
 print(f"Checkpoint: {checkpoint_path}")
 print(f"Spatial upsampler: {spatial_upsampler_path}")
 print(f"Gemma root: {gemma_root}")
+print(f"Distilled LoRA: {distilled_lora_path}")
 
 # Initialize pipeline WITH text encoder and optional audio support
 # ---- Replace block (pipeline init) lines 275-281 ----
@@ -367,7 +370,13 @@ pipeline = LTX23DistilledA2VPipeline(
     distilled_checkpoint_path=checkpoint_path,
     spatial_upsampler_path=spatial_upsampler_path,
     gemma_root=gemma_root,
-    loras=[],
+    loras=[
+        LoraPathStrengthAndSDOps(
+            distilled_lora_path,
+            1.0,
+            LTXV_LORA_COMFY_RENAMING_MAP,
+        )
+    ],
     quantization=QuantizationPolicy.fp8_cast(),  # keep FP8 quantization unchanged
 )
 # ----------------------------------------------------------------
@@ -382,7 +391,12 @@ def _make_lora_key(pose_strength: float, general_strength: float, motion_strengt
     rf = round(float(fluid_strength), 2)
     rl = round(float(liquid_strength), 2)
     ro = round(float(demopose_strength), 2)
-    key_str = f"{pose_lora_path}:{rp}|{general_lora_path}:{rg}|{motion_lora_path}:{rm}|{dreamlay_lora_path}:{rd}|{mself_lora_path}:{rs}|{dramatic_lora_path}:{rr}|{fluid_lora_path}:{rf}|{liquid_lora_path}:{rl}|{demopose_lora_path}:{ro}"
+    key_str = (
+        f"{distilled_lora_path}:1.0|"
+        f"{pose_lora_path}:{rp}|{general_lora_path}:{rg}|{motion_lora_path}:{rm}|"
+        f"{dreamlay_lora_path}:{rd}|{mself_lora_path}:{rs}|{dramatic_lora_path}:{rr}|"
+        f"{fluid_lora_path}:{rf}|{liquid_lora_path}:{rl}|{demopose_lora_path}:{ro}"
+    )   
     key = hashlib.sha256(key_str.encode("utf-8")).hexdigest()
     return key, key_str
 
@@ -425,6 +439,7 @@ def prepare_lora_cache(
             print(f"[LoRA] Cache load failed: {type(e).__name__}: {e}")
 
     entries = [
+        (distilled_lora_path, 1.0),
         (pose_lora_path, round(float(pose_strength), 2)),
         (general_lora_path, round(float(general_strength), 2)),
         (motion_lora_path, round(float(motion_strength), 2)),