VAE优化，模型直接加载至GPU

src/unifolm_wma/models/autoencoder.py

@@ -99,13 +99,16 @@ class AutoencoderKL(pl.LightningModule):
         print(f"Restored from {path}")
 
     def encode(self, x, **kwargs):
-
+        if getattr(self, '_channels_last', False):
+            x = x.to(memory_format=torch.channels_last)
         h = self.encoder(x)
         moments = self.quant_conv(h)
         posterior = DiagonalGaussianDistribution(moments)
         return posterior
 
     def decode(self, z, **kwargs):
+        if getattr(self, '_channels_last', False):
+            z = z.to(memory_format=torch.channels_last)
         z = self.post_quant_conv(z)
         dec = self.decoder(z)
         return dec

src/unifolm_wma/models/ddpms.py

@@ -1073,15 +1073,19 @@ class LatentDiffusion(DDPM):
         if not self.perframe_ae:
             encoder_posterior = self.first_stage_model.encode(x)
             results = self.get_first_stage_encoding(encoder_posterior).detach()
-        else:  ## Consume less GPU memory but slower
-            results = []
-            for index in range(x.shape[0]):
-                frame_batch = self.first_stage_model.encode(x[index:index +
-                                                              1, :, :, :])
-                frame_result = self.get_first_stage_encoding(
-                    frame_batch).detach()
-                results.append(frame_result)
-            results = torch.cat(results, dim=0)
+        else:  ## Batch encode with configurable batch size
+            bs = getattr(self, 'vae_encode_bs', 1)
+            if bs >= x.shape[0]:
+                encoder_posterior = self.first_stage_model.encode(x)
+                results = self.get_first_stage_encoding(encoder_posterior).detach()
+            else:
+                results = []
+                for i in range(0, x.shape[0], bs):
+                    frame_batch = self.first_stage_model.encode(x[i:i + bs])
+                    frame_result = self.get_first_stage_encoding(
+                        frame_batch).detach()
+                    results.append(frame_result)
+                results = torch.cat(results, dim=0)
 
         if reshape_back:
             results = rearrange(results, '(b t) c h w -> b c t h w', b=b, t=t)
@@ -1105,16 +1109,21 @@ class LatentDiffusion(DDPM):
         else:
             reshape_back = False
 
+        z = 1. / self.scale_factor * z
+
         if not self.perframe_ae:
-            z = 1. / self.scale_factor * z
             results = self.first_stage_model.decode(z, **kwargs)
         else:
-            results = []
-            for index in range(z.shape[0]):
-                frame_z = 1. / self.scale_factor * z[index:index + 1, :, :, :]
-                frame_result = self.first_stage_model.decode(frame_z, **kwargs)
-                results.append(frame_result)
-            results = torch.cat(results, dim=0)
+            bs = getattr(self, 'vae_decode_bs', 1)
+            if bs >= z.shape[0]:
+                # all frames in one batch
+                results = self.first_stage_model.decode(z, **kwargs)
+            else:
+                results = []
+                for i in range(0, z.shape[0], bs):
+                    results.append(
+                        self.first_stage_model.decode(z[i:i + bs], **kwargs))
+                results = torch.cat(results, dim=0)
 
         if reshape_back:
             results = rearrange(results, '(b t) c h w -> b c t h w', b=b, t=t)

src/unifolm_wma/modules/networks/ae_modules.py

@@ -10,8 +10,8 @@ from unifolm_wma.utils.utils import instantiate_from_config
 
 
 def nonlinearity(x):
-    # swish
-    return x * torch.sigmoid(x)
+    # swish / SiLU — single fused CUDA kernel instead of x * sigmoid(x)
+    return torch.nn.functional.silu(x)
 
 
 def Normalize(in_channels, num_groups=32):