[Automated Commit] Format Codebase

Author: github-actions[bot]

text_to_video/wan2.2-t2v-14b/download_model.py

@@ -16,29 +16,31 @@
     sys.exit(1)
 
 
-def download_model(download_path: str, model_name: str = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"):
+def download_model(download_path: str,
+                   model_name: str = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"):
     """
     Download Wan T2V model from HuggingFace.
-    
+
     Args:
         download_path: Directory to download the model
         model_name: HuggingFace model identifier
     """
     download_path = Path(download_path).resolve()
-    # Extract model name without org prefix (e.g., "Wan-AI/Wan2.2-T2V-A14B-Diffusers" -> "Wan2.2-T2V-A14B-Diffusers")
+    # Extract model name without org prefix (e.g.,
+    # "Wan-AI/Wan2.2-T2V-A14B-Diffusers" -> "Wan2.2-T2V-A14B-Diffusers")
     model_dir_name = model_name.split("/")[-1]
     model_path = download_path / model_dir_name
-    
+
     # Create download directory
     download_path.mkdir(parents=True, exist_ok=True)
-    
+
     print("=" * 60)
     print(f"{model_name} Model Download")
     print("=" * 60)
     print(f"Download path: {model_path}")
     print("=" * 60)
     print()
-    
+
     try:
         print("Starting download...")
         snapshot_download(
@@ -47,13 +49,13 @@ def download_model(download_path: str, model_name: str = "Wan-AI/Wan2.2-T2V-A14B
             local_dir_use_symlinks=False,
             resume_download=True,
         )
-        
+
         print()
         print("=" * 60)
         print("? Download completed successfully!")
         print("=" * 60)
         print(f"Model location: {model_path}")
-        
+
     except Exception as e:
         print()
         print("=" * 60)
@@ -67,21 +69,21 @@ def main():
     parser = argparse.ArgumentParser(
         description="Download Wan2.2 T2V-A14B-Diffusers model from HuggingFace",
     )
-    
+
     parser.add_argument(
         "-d", "--download-path",
         default=os.environ.get("DOWNLOAD_PATH", "./models"),
         help="Download directory (default: ./models or $DOWNLOAD_PATH)"
     )
-    
+
     parser.add_argument(
         "--model-name",
         default="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
         help="HuggingFace model identifier (default: Wan-AI/Wan2.2-T2V-A14B-Diffusers)"
     )
-    
+
     args = parser.parse_args()
-    
+
     download_model(args.download_path, args.model_name)
 
 

text_to_video/wan2.2-t2v-14b/run_evaluation.py

@@ -12,6 +12,7 @@
 import sys
 from pathlib import Path
 
+
 def setup_logging():
     """Setup logging configuration."""
     logging.basicConfig(
@@ -24,36 +25,37 @@ def setup_logging():
 def parse_results(output_path):
     """
     Parse VBench evaluation results and print summary.
-    
+
     Args:
         output_path: Path to evaluation results directory
     """
     output_path = Path(output_path)
-    
+
     # Find the most recent eval_results file (contains scores)
     result_files = sorted(output_path.glob("results_*_eval_results.json"))
     if not result_files:
         logging.warning(f"No results found in {output_path}")
         return
-    
+
     result_file = result_files[-1]
-    
+
     try:
         with open(result_file, 'r') as f:
             results = json.load(f)
-        
+
         # Print summary in MLPerf-style format
-        print("\n" + "="*60)
+        print("\n" + "=" * 60)
         print("VBench Evaluation Results")
-        print("="*60)
-        
-        # Extract dimension scores (VBench format: {dimension_name: [avg_score, [video_results]], ...})
+        print("=" * 60)
+
+        # Extract dimension scores (VBench format: {dimension_name: [avg_score,
+        # [video_results]], ...})
         if results:
             print("\nDimension Scores:")
             print("-" * 60)
             total_score = 0
             num_dimensions = 0
-            
+
             for dimension, value in sorted(results.items()):
                 # VBench stores [avg_score, list_of_video_results]
                 if isinstance(value, list) and len(value) > 0:
@@ -62,24 +64,25 @@ def parse_results(output_path):
                         total_score += score
                         num_dimensions += 1
                         print(f"  {dimension:30s}: {score:6.4f}")
-            
+
             if num_dimensions > 0:
                 overall_avg = total_score / num_dimensions
                 print("-" * 60)
                 print(f"  {'Overall Average':30s}: {overall_avg:6.4f}")
-        
-        print("="*60)
+
+        print("=" * 60)
         print(f"Detailed results: {result_file}")
-        print("="*60 + "\n")
-        
+        print("=" * 60 + "\n")
+
     except Exception as e:
         logging.error(f"Failed to parse results: {e}")
         import traceback
         traceback.print_exc()
 
 
 def main():
-    parser = argparse.ArgumentParser(description="VBench evaluation for Wan2.2 T2V videos")
+    parser = argparse.ArgumentParser(
+        description="VBench evaluation for Wan2.2 T2V videos")
     parser.add_argument(
         "--videos-path",
         type=str,
@@ -111,20 +114,20 @@ def main():
         default=8,
         help="Number of GPUs to use for evaluation (default: 8)"
     )
-    
+
     args = parser.parse_args()
-    
+
     setup_logging()
-    
+
     # Validate inputs
     videos_path = Path(args.videos_path)
     if not videos_path.exists():
         logging.error(f"Videos path does not exist: {videos_path}")
         return 1
-    
+
     output_path = Path(args.output_path)
     output_path.mkdir(parents=True, exist_ok=True)
-    
+
     logging.info("=" * 60)
     logging.info("VBench Evaluation")
     logging.info("=" * 60)
@@ -133,8 +136,9 @@ def main():
     logging.info(f"GPUs: {args.num_gpus}")
     logging.info(f"Dimensions: {', '.join(args.dimensions)}")
     logging.info("=" * 60)
-    
-    vbench_script = Path(__file__).parent / "submodules" / "VBench" / "evaluate.py"
+
+    vbench_script = Path(__file__).parent / "submodules" / \
+        "VBench" / "evaluate.py"
     cmd = [
         "python", "-m", "torch.distributed.run",
         f"--nproc_per_node={args.num_gpus}",
@@ -144,21 +148,21 @@ def main():
         "--load_ckpt_from_local=True",
         "--dimension"
     ] + args.dimensions
-    
+
     logging.info("\nExecuting VBench evaluation...")
     logging.info(f"Command: {' '.join(cmd)}")
     logging.info("")
-    
+
     # Run evaluation
     try:
         result = subprocess.run(cmd, check=True)
-        
+
         # Parse and print results
         logging.info("\nParsing evaluation results...")
         parse_results(output_path)
-        
+
         return 0
-        
+
     except subprocess.CalledProcessError as e:
         logging.error(f"Evaluation failed with exit code {e.returncode}")
         return e.returncode