diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index faaacb21e..4d988e73b 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -58,16 +58,11 @@ def full_vqgan_decode(latents, model):
     if scaling_factor:
         latents = latents * scaling_factor
 
-    vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
-    vae_stats = f'name="{vae_name}" dtype={model.vqgan.dtype} device={model.vqgan.device}'
-    latents_stats = f'shape={latents.shape} dtype={latents.dtype} device={latents.device}'
-    stats = f'vae {vae_stats} latents {latents_stats}'
-
     log_debug(f'VAE config: {model.vqgan.config}')
     try:
         decoded = model.vqgan.decode(latents).sample.clamp(0, 1)
     except Exception as e:
-        shared.log.error(f'VAE decode: {stats} {e}')
+        shared.log.error(f'VAE decode: {e}')
         errors.display(e, 'VAE decode')
         decoded = []
 
@@ -85,7 +80,8 @@ def full_vqgan_decode(latents, model):
     t1 = time.time()
     if debug:
         log_debug(f'VAE memory: {shared.mem_mon.read()}')
-    shared.log.debug(f'VAE decode: {stats} time={round(t1-t0, 3)}')
+    vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
+    shared.log.debug(f'VAE decode: vae="{vae_name}" type="vqgan" dtype={model.vqgan.dtype} device={model.vqgan.device} time={round(t1-t0, 3)}')
     return decoded
 
 
@@ -103,7 +99,6 @@ def full_vae_decode(latents, model):
     base_device = None
     if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False):
         base_device = sd_models.move_base(model, devices.cpu)
-
     elif shared.opts.diffusers_offload_mode != "sequential":
         sd_models.move_model(model.vae, devices.device)
 
@@ -134,17 +129,12 @@ def full_vae_decode(latents, model):
     if shift_factor:
         latents = latents + shift_factor
 
-    vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
-    vae_stats = f'vae="{vae_name}" dtype={model.vae.dtype} device={model.vae.device} upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)}'
-    latents_stats = f'latents={latents.shape}:{latents.device}:{latents.dtype}'
-    stats = f'{vae_stats} {latents_stats}'
-
     log_debug(f'VAE config: {model.vae.config}')
     try:
         with devices.inference_context():
             decoded = model.vae.decode(latents, return_dict=False)[0]
     except Exception as e:
-        shared.log.error(f'VAE decode: {stats} {e}')
+        shared.log.error(f'VAE decode: {e}')
         if 'out of memory' not in str(e):
             errors.display(e, 'VAE decode')
         decoded = []
@@ -162,21 +152,24 @@ def full_vae_decode(latents, model):
 
     elif shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and base_device is not None:
         sd_models.move_base(model, base_device)
+
     t1 = time.time()
     if debug:
         log_debug(f'VAE memory: {shared.mem_mon.read()}')
-    shared.log.debug(f'Decode: {stats} time={round(t1-t0, 3)}')
+    vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
+    shared.log.debug(f'Decode: vae="{vae_name}" upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={latents.shape}:{latents.device}:{latents.dtype} time={t1-t0:.3f}')
     return decoded
 
 
 def full_vae_encode(image, model):
-    log_debug(f'VAE encode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)}')
     if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and hasattr(model, 'unet'):
         log_debug('Moving to CPU: model=UNet')
         unet_device = model.unet.device
         sd_models.move_model(model.unet, devices.cpu)
     if not shared.opts.diffusers_offload_mode == "sequential" and hasattr(model, 'vae'):
         sd_models.move_model(model.vae, devices.device)
+    vae_name = sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "default"
+    log_debug(f'Encode vae="{vae_name}" dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)}')
     encoded = model.vae.encode(image.to(model.vae.device, model.vae.dtype)).latent_dist.sample()
     if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and hasattr(model, 'unet'):
         sd_models.move_model(model.unet, unet_device)
@@ -184,7 +177,7 @@ def full_vae_encode(image, model):
 
 
 def taesd_vae_decode(latents):
-    log_debug(f'VAE decode: name=TAESD images={len(latents)} latents={latents.shape} slicing={shared.opts.diffusers_vae_slicing}')
+    t0 = time.time()
     if len(latents) == 0:
         return []
     if shared.opts.diffusers_vae_slicing and len(latents) > 1:
@@ -193,11 +186,13 @@ def taesd_vae_decode(latents):
             decoded[i] = sd_vae_taesd.decode(latents[i])
     else:
         decoded = sd_vae_taesd.decode(latents)
+    t1 = time.time()
+    shared.log.debug(f'Decode: vae="taesd" latents={latents.shape}:{latents.dtype}:{latents.device} time={t1-t0:.3f}')
     return decoded
 
 
 def taesd_vae_encode(image):
-    log_debug(f'VAE encode: name=TAESD image={image.shape}')
+    shared.log.debug(f'Encode: vae="taesd" image={image.shape}')
     encoded = sd_vae_taesd.encode(image)
     return encoded
 
@@ -243,6 +238,8 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
             decoded = 2.0 * decoded - 1.0 # typical normalized range
 
     if torch.is_tensor(decoded):
+        if len(decoded.shape) == 3 and decoded.shape[0] == 3:
+            decoded = decoded.unsqueeze(0)
         if hasattr(model, 'video_processor'):
             imgs = model.video_processor.postprocess_video(decoded, output_type='pil')
         elif hasattr(model, 'image_processor'):