diff --git a/converters/ConvertAvatar.py b/converters/ConvertAvatar.py
index 0f62657..08f8394 100644
--- a/converters/ConvertAvatar.py
+++ b/converters/ConvertAvatar.py
@@ -11,7 +11,7 @@ def process_frame_info(frame_info, inp_sh):
     img = img_uint8.astype(np.float32) / 255.0        
 
     img_mat = LandmarksProcessor.get_transform_mat (frame_info.landmarks_list[0], inp_sh[0], face_type=FaceType.FULL_NO_ALIGN)
-    img = cv2.warpAffine( img, img_mat, inp_sh[0:2], flags=cv2.INTER_CUBIC )
+    img = cv2.warpAffine( img, img_mat, inp_sh[0:2], borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
     return img
     
 def ConvertFaceAvatar (cfg, prev_temporal_frame_infos, frame_info, next_temporal_frame_infos):
diff --git a/mainscripts/Converter.py b/mainscripts/Converter.py
index ba556d4..d075613 100644
--- a/mainscripts/Converter.py
+++ b/mainscripts/Converter.py
@@ -214,12 +214,7 @@ class ConvertSubprocessor(Subprocessor):
         for i in range( len(self.frames) ):
             frame = self.frames[i]
             frame.idx = i
-
-            inp_stem = Path(frame.frame_info.filename).stem
-            if len([ True for symbol in inp_stem if symbol not in digits ]) > 0:
-                frame.output_filename = self.output_path / ('%.5d.png' % (i+1)  )
-            else:
-                frame.output_filename = self.output_path / ( inp_stem + '.png' )
+            frame.output_filename = self.output_path / ( Path(frame.frame_info.filename).stem + '.png' )
 
         frames[0].cfg = self.converter_config.copy()
 
diff --git a/mainscripts/VideoEd.py b/mainscripts/VideoEd.py
index 2951a06..3b9ee4f 100644
--- a/mainscripts/VideoEd.py
+++ b/mainscripts/VideoEd.py
@@ -167,8 +167,10 @@ def video_from_sequence( input_dir, output_file, reference_file=None, ext=None,
     if not lossless and bitrate is None:
         bitrate = max (1, io.input_int ("Bitrate of output file in MB/s ? (default:16) : ", 16) )
 
-    i_in = ffmpeg.input(str (input_path / ('%5d.'+ext)), r=fps)
+    input_image_paths = Path_utils.get_image_paths(input_path)
 
+    i_in = ffmpeg.input('pipe:', format='image2pipe', r=fps)
+    
     output_args = [i_in]
 
     if ref_in_a is not None:
@@ -193,7 +195,16 @@ def video_from_sequence( input_dir, output_file, reference_file=None, ext=None,
                           })
 
     job = ( ffmpeg.output(*output_args, **output_kwargs).overwrite_output() )
-    try:
-        job = job.run()
+
+    try:        
+        job_run = job.run_async(pipe_stdin=True)
+        
+        for image_path in input_image_paths:
+            with open (image_path, "rb") as f:
+                image_bytes = f.read()            
+                job_run.stdin.write (image_bytes)
+        
+        job_run.stdin.close()
+        job_run.wait()
     except:
         io.log_err ("ffmpeg fail, job commandline:" + str(job.compile()) )
diff --git a/samplelib/SampleProcessor.py b/samplelib/SampleProcessor.py
index 59799b2..ac78ead 100644
--- a/samplelib/SampleProcessor.py
+++ b/samplelib/SampleProcessor.py
@@ -210,19 +210,19 @@ class SampleProcessor(object):
                     cached_images[img_type] = img
 
                 if is_face_sample and target_face_type != SPTF.NONE:
-                    ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
-                    if ft > sample.face_type:
-                        raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, ft) )
+                    target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
+                    if target_ft > sample.face_type:
+                        raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_ft) )
 
                     if sample.face_type == FaceType.MARK_ONLY:
-                        img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
+                        img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
 
                         mask = img[...,3:4] if img.shape[2] > 3 else None
                         img  = img[...,0:3]
                         img = do_transform (img, mask)
                         img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
                     else:
-                        img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, ft), (resolution,resolution), flags=cv2.INTER_CUBIC )
+                        img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft), (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
 
                 else:
                     img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )