diff --git a/NN_server/Models/ensemble_1200_v44.py b/NN_server/Models/ensemble_1200_v44.py
index aa1b561..db22dfa 100644
--- a/NN_server/Models/ensemble_1200_v44.py
+++ b/NN_server/Models/ensemble_1200_v44.py
@@ -10,6 +10,20 @@ import os
 import re
 
 
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
 def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
@@ -232,7 +246,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
 
         if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -241,7 +255,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
diff --git a/NN_server/Models/ensemble_2400_v44.py b/NN_server/Models/ensemble_2400_v44.py
index aa1b561..db22dfa 100644
--- a/NN_server/Models/ensemble_2400_v44.py
+++ b/NN_server/Models/ensemble_2400_v44.py
@@ -10,6 +10,20 @@ import os
 import re
 
 
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
 def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
@@ -232,7 +246,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
 
         if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -241,7 +255,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
diff --git a/NN_server/Models/ensemble_915_v44.py b/NN_server/Models/ensemble_915_v44.py
index d805a4f..a4067de 100644
--- a/NN_server/Models/ensemble_915_v44.py
+++ b/NN_server/Models/ensemble_915_v44.py
@@ -8,6 +8,20 @@ import gc
 import io
 
 
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
 def _render_plot(values, figsize=(16, 16), dpi=16):
     import matplotlib.pyplot as plt
 
@@ -166,7 +180,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
 
         if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -175,7 +189,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()