Update app.py

app.py

@@ -185,7 +185,10 @@ class LTX23NegativePromptTwoStagePipeline:
         ctx_p, ctx_n = self.prompt_encoder(
             [prompt, negative_prompt],
             enhance_first_prompt=enhance_prompt,
-            enhance_prompt_image=images[0][0] if len(images) > 0 else None,
+            enhance_prompt_image=(
+                __import__('PIL.Image', fromlist=['Image']).open(images[0].path)
+                if (len(images) > 0 and enhance_prompt) else None
+            ),
             enhance_prompt_seed=seed,
             streaming_prefetch_count=streaming_prefetch_count,
         )
@@ -354,6 +357,15 @@ print(f"Checkpoint: {checkpoint_path}")
 print(f"Spatial upsampler: {spatial_upsampler_path}")
 print(f"Gemma root: {gemma_root}")
 
+del pipeline
+gc.collect()
+torch.cuda.empty_cache()
+
+pipeline = LTX23NegativePromptTwoStagePipeline(
+    checkpoint_path=str(checkpoint_path),
+    ...
+)
+
 # Initialize pipeline WITH text encoder and optional audio support
 # ---- Replace block (pipeline init) lines 275-281 ----
 pipeline = LTX23NegativePromptTwoStagePipeline(
@@ -380,7 +392,7 @@ def _make_lora_key(pose_strength: float, general_strength: float, motion_strengt
     rt = round(float(transition_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}|{voice_lora_path}:{rv}|{realism_lora_path}:{re}|{transition_lora_path}:{rt}"
     key = hashlib.sha256(key_str.encode("utf-8")).hexdigest()
-    return key, key_str
+    return key
 
 
 def prepare_lora_cache(