diff --git a/tile_methods/abstractdiffusion.py b/tile_methods/abstractdiffusion.py
index 1b3d8c5..9b153ed 100644
--- a/tile_methods/abstractdiffusion.py
+++ b/tile_methods/abstractdiffusion.py
@@ -690,29 +690,33 @@ class AbstractDiffusion:
 
         x = self.p.init_latent
         s_in = x.new_ones([x.shape[0]])
-        if shared.sd_model.parameterization == "v":
-            skip = 1
-        else:
-            skip = 0
-        cond = self.p.sd_model.get_learned_conditioning(prompts)
-        # NOTE: should be List[Tensor]
-        cond_dict_dummy = {
-            'c_crossattn': [],
-            'c_concat': [],
-        }
-        cond_in = self.make_cond_dict(cond_dict_dummy, cond, self.p.image_conditioning)
+        skip = 1 if shared.sd_model.parameterization == "v" else 0
         sigmas = dnw.get_sigmas(steps).flip(0)
-        state.sampling_steps = steps
+    
+        cond = self.p.sd_model.get_learned_conditioning(prompts)
+        if isinstance(cond, Tensor):    # SD1/SD2
+            cond_dict_dummy = {
+                'c_crossattn': [],      # List[Tensor]
+                'c_concat': [],         # List[Tensor]
+            }
+            cond_in = self.make_cond_dict(cond_dict_dummy, cond, self.p.image_conditioning)
+        else:                           # SDXL
+            cond_dict_dummy = {
+                'crossattn': None,      # Tensor
+                'vector': None,         # Tensor
+                'c_concat': [],         # List[Tensor]
+            }
+            cond_in = self.make_cond_dict(cond_dict_dummy, cond['crossattn'], self.p.image_conditioning, cond['vector'])
 
+        state.sampling_steps = steps
         pbar = tqdm(total=steps, desc='Noise Inversion')
         for i in range(1, len(sigmas)):
-            if state.interrupted:
-                return x
+            if state.interrupted: return x
+
             state.sampling_step += 1
 
             x_in = x
             sigma_in = torch.cat([sigmas[i] * s_in])
-
             c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
 
             t = dnw.sigma_to_t(sigma_in)