diff --git a/modules/processing.py b/modules/processing.py
index cb41288a..92894d67 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -172,7 +172,7 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_unet) if devices.unet_needs_upcast else source_image))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_vae) if devices.unet_needs_upcast else source_image))
         conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
@@ -217,7 +217,7 @@ class StableDiffusionProcessing:
         )
 
         # Encode the new masked image using first stage of network.
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_unet) if devices.unet_needs_upcast else conditioning_image))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_vae) if devices.unet_needs_upcast else conditioning_image))
 
         # Create the concatenated conditioning tensor to be fed to `c_concat`
         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -417,7 +417,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
 
 def decode_first_stage(model, x):
     with devices.autocast(disable=x.dtype == devices.dtype_vae):
-        x = model.decode_first_stage(x)
+        x = model.decode_first_stage(x.to(devices.dtype_vae) if devices.unet_needs_upcast else x)
 
     return x
 
@@ -1001,7 +1001,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         image = torch.from_numpy(batch_images)
         image = 2. * image - 1.
-        image = image.to(device=shared.device, dtype=devices.dtype_unet if devices.unet_needs_upcast else None)
+        image = image.to(device=shared.device, dtype=devices.dtype_vae if devices.unet_needs_upcast else None)
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py
index f81b169a..f8684475 100644
--- a/modules/sd_hijack_utils.py
+++ b/modules/sd_hijack_utils.py
@@ -5,7 +5,7 @@ class CondFunc:
         self = super(CondFunc, cls).__new__(cls)
         if isinstance(orig_func, str):
             func_path = orig_func.split('.')
-            for i in range(len(func_path)-2, -1, -1):
+            for i in range(len(func_path)-1, -1, -1):
                 try:
                     resolved_obj = importlib.import_module('.'.join(func_path[:i]))
                     break