removed extra debug from pipeline

Author: prishajain1

src/maxdiffusion/configs/base_wan_27b.yml

@@ -304,7 +304,7 @@ boundary_ratio: 0.875
 
 # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
 guidance_rescale: 0.0
-num_inference_steps: 30
+num_inference_steps: 40
 fps: 24
 save_final_checkpoint: False
 

src/maxdiffusion/configs/base_wan_i2v_27b.yml

@@ -300,7 +300,7 @@ boundary_ratio: 0.875
 
 # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
 guidance_rescale: 0.0
-num_inference_steps: 50
+num_inference_steps: 40
 fps: 16
 save_final_checkpoint: False
 

src/maxdiffusion/pipelines/ltx2/ltx2_pipeline.py

@@ -633,9 +633,6 @@ def get_fp8_config(cls, config: HyperParameters):
   @classmethod
   def get_qt_provider(cls, config: HyperParameters) -> Optional[qwix.QtProvider]:
     """Get quantization rules based on the config."""
-    max_logging.log(
-        f"DEBUG: use_qwix_quantization={getattr(config, 'use_qwix_quantization', None)}, quantization={getattr(config, 'quantization', None)}"
-    )
     if not getattr(config, "use_qwix_quantization", False):
       return None
 
@@ -652,17 +649,13 @@ def quantize_transformer(cls, config: HyperParameters, model: Any, pipeline: "LT
     """Quantizes the transformer model."""
     q_rules = cls.get_qt_provider(config)
     if not q_rules:
-      max_logging.log("DEBUG: Transformer is NOT being quantized. (q_rules is None)")
       return model
-    max_logging.log("Quantizing transformer with Qwix.")
 
     batch_size = config.global_batch_size_to_train_on
     model_inputs = get_dummy_ltx2_inputs(config, pipeline, batch_size)
 
     with mesh:
       quantized_model = qwix.quantize_model(model, q_rules, *model_inputs)
-    max_logging.log("DEBUG: Transformer WAS successfully quantized.")
-    max_logging.log("Qwix Quantization complete.")
     return quantized_model
 
   def _get_gemma_prompt_embeds(