diff --git a/CHANGELOG.md b/CHANGELOG.md
index d6b7f0cd6..d8e022c8e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,7 +9,8 @@
   - Increase the medvram mode threshold from 8GB to 12GB  
   - Set CPU backend to use FP32 by default  
   - Relax Python version checks for Zluda  
-  - don't override user set gfx version with ROCm  
+  - Don't override user set gfx version with ROCm  
+  - Make VAE options not require model reload  
 
 - **Torch**  
   - Set default to `torch==2.7.1`  
diff --git a/modules/processing_vae.py b/modules/processing_vae.py
index b8e5dc4ae..290c0d489 100644
--- a/modules/processing_vae.py
+++ b/modules/processing_vae.py
@@ -117,6 +117,7 @@ def full_vae_decode(latents, model):
     elif shared.opts.diffusers_offload_mode != "sequential":
         sd_models.move_model(model.vae, devices.device)
 
+    sd_models.set_vae_options(model, vae=None, op='decode')
     upcast = (model.vae.dtype == torch.float16) and (getattr(model.vae.config, 'force_upcast', False) or shared.opts.no_half_vae)
     if upcast:
         if hasattr(model, 'upcast_vae'): # this is done by diffusers automatically if output_type != 'latent'
@@ -193,6 +194,7 @@ def full_vae_encode(image, model):
     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)}')
 
+    sd_models.set_vae_options(model, vae=None, op='encode')
     upcast = (model.vae.dtype == torch.float16) and (getattr(model.vae.config, 'force_upcast', False) or shared.opts.no_half_vae)
     if upcast:
         if hasattr(model, 'upcast_vae'): # this is done by diffusers automatically if output_type != 'latent'
diff --git a/modules/sd_models.py b/modules/sd_models.py
index ce35d0aba..c81b50366 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -92,7 +92,7 @@ def set_vae_options(sd_model, vae=None, op:str='model', quiet:bool=False):
         if shared.opts.diffusers_vae_upcast != 'default':
             sd_model.vae.config.force_upcast = True if shared.opts.diffusers_vae_upcast == 'true' else False
             shared.log.quiet(quiet, f'Setting {op}: component=VAE upcast={sd_model.vae.config.force_upcast}')
-        if shared.opts.no_half_vae:
+        if shared.opts.no_half_vae and op not in {'decode', 'encode'}:
             devices.dtype_vae = torch.float32
             sd_model.vae.to(devices.dtype_vae)
             shared.log.quiet(quiet, f'Setting {op}: component=VAE no-half=True')
@@ -105,11 +105,20 @@ def set_vae_options(sd_model, vae=None, op:str='model', quiet:bool=False):
     if hasattr(sd_model, "enable_vae_tiling"):
         if shared.opts.diffusers_vae_tiling:
             if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'config') and hasattr(sd_model.vae.config, 'sample_size') and isinstance(sd_model.vae.config.sample_size, int):
+                if getattr(sd_model.vae, "tile_sample_min_size_backup", None) is None:
+                    sd_model.vae.tile_sample_min_size_backup = sd_model.vae.tile_sample_min_size
+                    sd_model.vae.tile_latent_min_size_backup = sd_model.vae.tile_latent_min_size
+                    sd_model.vae.tile_overlap_factor_backup = sd_model.vae.tile_overlap_factor
                 if shared.opts.diffusers_vae_tile_size > 0:
                     sd_model.vae.tile_sample_min_size = int(shared.opts.diffusers_vae_tile_size)
                     sd_model.vae.tile_latent_min_size = int(shared.opts.diffusers_vae_tile_size / (2 ** (len(sd_model.vae.config.block_out_channels) - 1)))
+                else:
+                    sd_model.vae.tile_sample_min_size = getattr(sd_model.vae, "tile_sample_min_size_backup", sd_model.vae.tile_sample_min_size)
+                    sd_model.vae.tile_latent_min_size = getattr(sd_model.vae, "tile_latent_min_size_backup", sd_model.vae.tile_latent_min_size)
                 if shared.opts.diffusers_vae_tile_overlap != 0.25:
                     sd_model.vae.tile_overlap_factor = float(shared.opts.diffusers_vae_tile_overlap)
+                else:
+                    sd_model.vae.tile_overlap_factor = getattr(sd_model.vae, "tile_overlap_factor_backup", sd_model.vae.tile_overlap_factor)
                 shared.log.quiet(quiet, f'Setting {op}: component=VAE tiling=True tile={sd_model.vae.tile_sample_min_size} overlap={sd_model.vae.tile_overlap_factor}')
             else:
                 shared.log.quiet(quiet, f'Setting {op}: component=VAE tiling=True')