round gamma to avoid covariance_matrix error on Windows

pyramid_dit/pyramid_dit_for_video_gen_pipeline.py

@@ -191,22 +191,12 @@ class PyramidDiTForVideoGeneration:
         return latents
 
     def sample_block_noise(self, bs, ch, temp, height, width):
-        gamma = self.scheduler.config.gamma
+        gamma = round(self.scheduler.config.gamma, 5)
         dist = torch.distributions.multivariate_normal.MultivariateNormal(torch.zeros(4), torch.eye(4) * (1 + gamma) - torch.ones(4, 4) * gamma)
         block_number = bs * ch * temp * (height // 2) * (width // 2)
         noise = torch.stack([dist.sample() for _ in range(block_number)]) # [block number, 4]
         noise = rearrange(noise, '(b c t h w) (p q) -> b c t (h p) (w q)',b=bs,c=ch,t=temp,h=height//2,w=width//2,p=2,q=2)
         return noise
-    
-    # def sample_block_noise(self, bs, ch, temp, height, width):
-    #     gamma = self.scheduler.config.gamma
-    #     epsilon = 1e-5  # Small value to ensure positive definiteness
-    #     covariance_matrix = torch.eye(4) * (1 + gamma) - torch.ones(4, 4) * gamma + torch.eye(4) * epsilon
-    #     dist = torch.distributions.multivariate_normal.MultivariateNormal(torch.zeros(4), covariance_matrix)
-    #     block_number = bs * ch * temp * (height // 2) * (width // 2)
-    #     noise = torch.stack([dist.sample() for _ in range(block_number)])  # [block number, 4]
-    #     noise = rearrange(noise, '(b c t h w) (p q) -> b c t (h p) (w q)', b=bs, c=ch, t=temp, h=height//2, w=width//2, p=2, q=2)
-    #     return noise
 
     @torch.no_grad()
     def generate_one_unit(