Update generate.py

generate.py

@@ -1,6 +1,7 @@
 import argparse
 import subprocess
 import os
+import torch
 from huggingface_hub import snapshot_download
 
 # Arguments
@@ -8,28 +9,34 @@ parser = argparse.ArgumentParser()
 parser.add_argument("--task", type=str, default="t2v-14B")
 parser.add_argument("--size", type=str, default="832*480")
 parser.add_argument("--frame_num", type=int, default=60)
-parser.add_argument("--sample_steps", type=int, default=25)
+parser.add_argument("--sample_steps", type=int, default=20)
 parser.add_argument("--ckpt_dir", type=str, default="./Wan2.1-T2V-14B")
 parser.add_argument("--offload_model", type=str, default="True")
-parser.add_argument("--precision", type=str, default="bf16")
 parser.add_argument("--prompt", type=str, required=True)
 args = parser.parse_args()
 
-# Download model if not available
+# Ensure the model is downloaded
 if not os.path.exists(args.ckpt_dir):
-    print("🔄 Downloading WAN 2.1 model...")
+    print("🔄 Downloading WAN 2.1 - 14B model from Hugging Face...")
     snapshot_download(repo_id="Wan-AI/Wan2.1-T2V-14B", local_dir=args.ckpt_dir)
 
-# Run Model
-command = f"python run_model.py --task {args.task} --size {args.size} --frame_num {args.frame_num} --sample_steps {args.sample_steps} --ckpt_dir {args.ckpt_dir} --offload_model {args.offload_model} --precision {args.precision} --prompt \"{args.prompt}\""
+# Free up GPU memory
+if torch.cuda.is_available():
+    torch.cuda.empty_cache()
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+
+# Run WAN 2.1 - 14B Model
+command = f"python run_model.py --task {args.task} --size {args.size} --frame_num {args.frame_num} --sample_steps {args.sample_steps} --ckpt_dir {args.ckpt_dir} --offload_model {args.offload_model} --prompt \"{args.prompt}\""
 
 process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
 stdout, stderr = process.communicate()
 
+# Print logs for debugging
 print("🔹 Output:", stdout.decode())
 print("🔺 Error:", stderr.decode())
 
-# Check if video was created
+# Verify if video was created
 if os.path.exists("output.mp4"):
     print("✅ Video generated successfully: output.mp4")
 else: