diff --git a/api/onnx_web/diffusers/pipelines/panorama_xl.py b/api/onnx_web/diffusers/pipelines/panorama_xl.py
index fe8a6532..dc178e30 100644
--- a/api/onnx_web/diffusers/pipelines/panorama_xl.py
+++ b/api/onnx_web/diffusers/pipelines/panorama_xl.py
@@ -1,5 +1,6 @@
 import inspect
 import logging
+from math import ceil
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -13,9 +14,9 @@ from optimum.pipelines.diffusers.pipeline_stable_diffusion_xl_img2img import (
 )
 from optimum.pipelines.diffusers.pipeline_utils import rescale_noise_cfg
 
-from onnx_web.chain.tile import make_tile_mask
-
-from ..utils import LATENT_FACTOR, parse_regions, repair_nan
+from ...chain.tile import make_tile_mask
+from ...params import Size
+from ..utils import LATENT_FACTOR, expand_latents, parse_regions, repair_nan
 
 logger = logging.getLogger(__name__)
 
@@ -41,13 +42,16 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
         self.window = window
         self.stride = stride
 
-    def get_views(self, panorama_height, panorama_width, window_size, stride):
+    def get_views(
+        self, panorama_height: int, panorama_width: int, window_size: int, stride: int
+    ) -> Tuple[List[Tuple[int, int, int, int]], Tuple[int, int]]:
         # Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
         panorama_height /= 8
         panorama_width /= 8
 
-        num_blocks_height = abs((panorama_height - window_size) // stride) + 1
-        num_blocks_width = abs((panorama_width - window_size) // stride) + 1
+        num_blocks_height = ceil(abs((panorama_height - window_size) / stride)) + 1
+        num_blocks_width = ceil(abs((panorama_width - window_size) / stride)) + 1
+
         total_num_blocks = int(num_blocks_height * num_blocks_width)
         logger.debug(
             "panorama generated %s views, %s by %s blocks",
@@ -64,7 +68,7 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
             w_end = w_start + window_size
             views.append((h_start, h_end, w_start, w_end))
 
-        return views
+        return (views, (h_end, w_end))
 
     # Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents_img2img(
@@ -382,9 +386,12 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
         timestep_dtype = self.unet.input_dtype.get("timestep", np.float32)
 
         # 8. Panorama additions
-        views = self.get_views(height, width, self.window, self.stride)
-        count = np.zeros_like(latents)
-        value = np.zeros_like(latents)
+        views, resize = self.get_views(height, width, self.window, self.stride)
+        count = np.zeros_like((latents[0], latents[1], *resize))
+        value = np.zeros_like((latents[0], latents[1], *resize))
+
+        # adjust latents
+        latents = expand_latents(latents, generator.randint(), Size(width, height))
 
         # 8. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
@@ -560,6 +567,8 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                 if callback is not None and i % callback_steps == 0:
                     callback(i, t, latents)
 
+        # remove extra margins
+        latents = latents[:, :, 0:height, 0:width]
         if output_type == "latent":
             image = latents
         else:
diff --git a/api/onnx_web/diffusers/utils.py b/api/onnx_web/diffusers/utils.py
index 140ece45..f01c6703 100644
--- a/api/onnx_web/diffusers/utils.py
+++ b/api/onnx_web/diffusers/utils.py
@@ -276,6 +276,17 @@ def get_latents_from_seed(seed: int, size: Size, batch: int = 1) -> np.ndarray:
     return image_latents
 
 
+def expand_latents(
+    latents: np.ndarray,
+    seed: int,
+    size: Size,
+) -> np.ndarray:
+    batch, _channels, height, width = latents.shape
+    extra_latents = get_latents_from_seed(seed, size, batch=batch)
+    extra_latents[:, :, 0:height, 0:width] = latents
+    return extra_latents
+
+
 def get_tile_latents(
     full_latents: np.ndarray,
     seed: int,
@@ -301,11 +312,7 @@ def get_tile_latents(
     tile_latents = full_latents[:, :, y:yt, x:xt]
 
     if tile_latents.shape[2] < t or tile_latents.shape[3] < t:
-        extra_latents = get_latents_from_seed(seed, size, batch=tile_latents.shape[0])
-        extra_latents[
-            :, :, 0 : tile_latents.shape[2], 0 : tile_latents.shape[3]
-        ] = tile_latents
-        tile_latents = extra_latents
+        tile_latents = expand_latents(tile_latents, seed, size)
 
     return tile_latents