@@ -7,12 +7,14 @@ from io import BytesIO
 
 import filetype
 import requests
+from PIL import Image
 from django.core.files import File
 from django.core.files.images import get_image_dimensions
 from replicate.exceptions import ModelError
 
 from messages.models import Message
-from ml_model.exceptions import PredictionInterruptedError, RequestBlocked, GenerationException
+from ml_model.exceptions import PredictionInterruptedError, RequestBlocked, GenerationException, \
+    FileExtensionNotSupported
 from ml_model.services.base import SimpleService
 from ml_model.tasks import replicate_run
 
@@ -79,13 +81,20 @@ class Flux_2(SimpleService):
         }
         input_mp = 0
         if input_message.file:
-            file_width, file_height = get_image_dimensions(input_message.file)
-            input_mp = math.ceil((file_width*file_height) / 1_000_000)
-            kind = filetype.guess(input_message.file.read(20))
-            mime = kind.mime if kind else 'application/octet-stream'
-            input_message.file.seek(0)
-            image = f'data:{mime};base64,{base64.b64encode(input_message.file.read()).decode("utf-8")}'
+            file_bytes = input_message.file.read()
             input_message.file.close()
+            kind = filetype.guess(file_bytes[:20])
+            extension = kind.extension
+            if extension.upper() not in (extensions := ['JPG', 'JPEG', 'PNG', 'WEBP']):
+                raise FileExtensionNotSupported(extensions)
+            normalized_image = Image.open(BytesIO(file_bytes)).convert('RGB')
+            buf = BytesIO()
+            format = 'jpeg' if extension not in ('png', 'jpeg', 'webp') else extension
+            normalized_image.save(buf, format=format)
+            file_width, file_height = get_image_dimensions(buf)
+            input_mp = math.ceil((file_width*file_height) / 1_000_000)
+            image = f'data:image/{format};base64,{base64.b64encode(buf.getvalue()).decode("utf-8")}'
+            buf.close()
             callback_data.update({'input_images': [image]})
         try:
             images = [replicate_run(f'black-forest-labs/{version}', callback_data)]
@@ -7,8 +7,10 @@ from io import BytesIO
 import filetype
 import requests
 from django.core.files import File
+from replicate.exceptions import ModelError
 
 from messages.models import Message
+from ml_model.exceptions import RequestBlocked, GenerationException
 from ml_model.services.base import SimpleService
 from ml_model.tasks import replicate_run
 
@@ -48,7 +50,12 @@ class Minimaxvideo(SimpleService):
             input_message.file.close()
             callback_data.update({'subject_reference': image})
         start_time = time.time()
-        video = replicate_run(f'minimax/{version}', callback_data)
+        try:
+            video = replicate_run(f'minimax/{version}', callback_data)
+        except ModelError as exc:
+            if any(error in str(exc) for error in ('E005', 'E006', 'sexual')):
+                raise RequestBlocked
+            raise GenerationException from exc
         process_time = timedelta(seconds=(time.time() - start_time))
         self.handle_invoice(input_message.content_object.model, version)
         msgs = self.save_results(input_message.content, process_time, video, save)
@@ -18,6 +18,7 @@ from ml_model.exceptions import (
     InvalidParameterError,
     InvalidStyleCombinationError,
     PromptLengthExceeded,
+    FileExtensionNotSupported,
 )
 from ml_model.models import NeuronModel
 from ml_model.services.base import SimpleService
@@ -170,6 +171,7 @@ class MediaAPIView(APIView):
                         InvalidStyleCombinationError,
                         InvalidParameterError,
                         PromptLengthExceeded,
+                        FileExtensionNotSupported,
                     ),
                 ):
                     return Response({'detail': f'{exc}'}, status=HTTP_400_BAD_REQUEST)