fix tiny vae with batched results

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -10,6 +10,7 @@ Post-release fixes:
 - Video implement dynamic and manual sampler shift  
 - Fix interrupt batch processing  
 - Delay import of control processors until used  
+- Fix tiny VAE with batched results    
 
 ## Update for 2025-10-17
 

modules/processing_vae.py

@@ -216,7 +216,7 @@ def taesd_vae_decode(latents):
     t0 = time.time()
     if len(latents) == 0:
         return []
-    if shared.opts.diffusers_vae_slicing and len(latents) > 1:
+    if len(latents) > 1:
         decoded = torch.zeros((len(latents), 3, latents.shape[2] * 8, latents.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
         for i in range(latents.shape[0]):
             decoded[i] = sd_vae_taesd.decode(latents[i])

modules/upscaler.py

@@ -64,7 +64,7 @@ class Upscaler:
                 scaler.custom = True
                 scalers.append(scaler)
                 loaded.append(file_name)
-                shared.log.debug(f'Upscaler type={self.name} folder="{folder}" model="{model_name}" path="{file_name}"')
+                # shared.log.debug(f'Upscaler type={self.name} folder="{folder}" model="{model_name}" path="{file_name}"')
 
     def find_scalers(self):
         scalers = []