Update app.py

app.py

@@ -26,15 +26,16 @@ import csv
 from datetime import datetime
 from openai import OpenAI
 
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
+torch.backends.cuda.matmul.allow_tf32 = False
+torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
 torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
-torch.backends.cudnn.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = False
 torch.backends.cudnn.deterministic = False
-
 torch.backends.cudnn.benchmark = False
+torch.backends.cuda.preferred_blas_library="cublas"
+torch.backends.cuda.preferred_linalg_library="cusolver"
 
-torch.set_float32_matmul_precision("high")
+torch.set_float32_matmul_precision("highest")
 
 # Load Hugging Face token if needed
 hf_token = os.getenv("HF_TOKEN")
@@ -229,7 +230,7 @@ def generate_video_from_text(
     txt2vid_analytics_toggle=True,
     negative_prompt="",
     frame_rate=25,
-    seed=646373,
+    seed=random.randint(0, MAX_SEED),
     num_inference_steps=30,
     guidance_scale=3,
     height=512,
@@ -253,7 +254,7 @@ def generate_video_from_text(
         "media_items": None,
     }
 
-    generator = torch.Generator(device="cuda").manual_seed(seed)
+    generator = torch.Generator(device="cpu").manual_seed(seed)
 
     def gradio_progress_callback(self, step, timestep, kwargs):
         progress((step + 1) / num_inference_steps)
@@ -300,6 +301,8 @@ def generate_video_from_text(
     del video_np
     torch.cuda.empty_cache()
     return output_path
+    
+MAX_SEED = np.iinfo(np.int64).max
 
 @spaces.GPU(duration=80)
 def generate_video_from_image(
@@ -309,7 +312,7 @@ def generate_video_from_image(
     img2vid_analytics_toggle=True,
     negative_prompt="",
     frame_rate=25,
-    seed=646373,
+    seed=random.randint(0, MAX_SEED),
     num_inference_steps=30,
     guidance_scale=3,
     height=512,
@@ -349,7 +352,7 @@ def generate_video_from_image(
         "media_items": media_items,
     }
 
-    generator = torch.Generator(device="cuda").manual_seed(seed)
+    generator = torch.Generator(device="cpu").manual_seed(seed)
 
     def gradio_progress_callback(self, step, timestep, kwargs):
         progress((step + 1) / num_inference_steps)