Update app.py

app.py

@@ -64,25 +64,25 @@ print("=== Carregando componentes da pipeline... ===")
 vae = AutoModel.from_pretrained(
     "Lightricks/LTX-Video",
     subfolder="vae",
-    dtype=torch_dtype,
+    dtype=dtype,
     cache_dir=CACHE_DIR
 )
 text_encoder = AutoModel.from_pretrained(
     "Lightricks/LTX-Video",
     subfolder="text_encoder",
-    dtype=torch_dtype,
+    dtype=dtype,
     cache_dir=CACHE_DIR
 )
 scheduler = AutoModel.from_pretrained(
     "Lightricks/LTX-Video",
     subfolder="scheduler",
-    dtype=torch_dtype,
+    dtype=dtype,
     cache_dir=CACHE_DIR
 )
 tokenizer = AutoModel.from_pretrained(
     "Lightricks/LTX-Video",
     subfolder="tokenizer",
-    dtype=torch_dtype,
+    dtype=dtype,
     cache_dir=CACHE_DIR
 )
 
@@ -94,11 +94,11 @@ if hasattr(scheduler.config, 'use_dynamic_shifting') and scheduler.config.use_dy
 transformer = AutoModel.from_pretrained(
     "Lightricks/LTX-Video",
     subfolder="transformer",
-    dtype=torch.bfloat16,
+    dtype=dtype,
     cache_dir=CACHE_DIR
 )
 transformer.enable_layerwise_casting(
-    storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
+    storage_dtype=torch.float8_e4m3fn, compute_dtype=dtype,
 )
 
 
@@ -116,7 +116,7 @@ pipeline.vae.enable_tiling()
 pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained(
     "Lightricks/ltxv-spatial-upscaler-0.9.7",
     cache_dir=os.getenv("HF_HOME_CACHE"),
-    vae=pipeline.vae, torch_dtype=dtype
+    vae=pipeline.vae, dtype=dtype
 )
 
 pipeline.to(device)