diff --git a/.gitignore b/.gitignore
index 7803c36..b4f316f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,3 @@
-*.pkl
 *.mp4
 *.pth
 *.pyc
diff --git a/asr/sherpa_ncnn_asr.py b/asr/sherpa_ncnn_asr.py
index 1cc81d3..4124e76 100644
--- a/asr/sherpa_ncnn_asr.py
+++ b/asr/sherpa_ncnn_asr.py
@@ -63,15 +63,6 @@ class SherpaNcnnAsr(AsrBase):
         logger.info(f'_recognize_loop')
         print(f'_recognize_loop')
 
-        while self._stop_event.is_set():
-            logger.info(f'_recognize_loop000')
-            self._notify_complete('介绍中国5000年历史文学')
-            logger.info(f'_recognize_loop111')
-            segment_id += 1
-            time.sleep(150)
-            logger.info(f'_recognize_loop222')
-        logger.info(f'_recognize_loop exit')
-'''
         with sd.InputStream(channels=1, dtype="float32", samplerate=self._sample_rate) as s:
             while self._stop_event.is_set():
                 samples, _ = s.read(self._samples_per_read)  # a blocking read
@@ -94,3 +85,13 @@ class SherpaNcnnAsr(AsrBase):
                     self._recognizer.reset()
 '''
 
+        while self._stop_event.is_set():
+            logger.info(f'_recognize_loop000')
+            self._notify_complete('介绍中国5000年历史文学')
+            logger.info(f'_recognize_loop111')
+            segment_id += 1
+            time.sleep(150)
+            logger.info(f'_recognize_loop222')
+        logger.info(f'_recognize_loop exit')
+'''
+
diff --git a/checkpoints/weights/wav2lip/model_a_general.onnx b/checkpoints/weights/wav2lip/model_a_general.onnx
new file mode 100644
index 0000000..90697bc
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diff --git a/checkpoints/weights/wav2lip/model_g_general.onnx b/checkpoints/weights/wav2lip/model_g_general.onnx
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index 0000000..557bfcf
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diff --git a/checkpoints/weights/wflw/hrnet18_256x256_p1/config.yaml b/checkpoints/weights/wflw/hrnet18_256x256_p1/config.yaml
new file mode 100644
index 0000000..b2222bd
--- /dev/null
+++ b/checkpoints/weights/wflw/hrnet18_256x256_p1/config.yaml
@@ -0,0 +1,12 @@
+INPUT:
+  SIZE: [256, 256]
+  BBOX: 'P1'
+
+BACKBONE:
+  ARCH: 'hrnet18'
+
+HEATMAP:
+  IN_CHANNEL: 270
+  PROJ_CHANNEL: 270
+  OUT_CHANNEL: 98
+  TOPK: 9
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diff --git a/human/__init__.py b/human/__init__.py
index 966011f..503a041 100644
--- a/human/__init__.py
+++ b/human/__init__.py
@@ -3,4 +3,5 @@
 from .human_context import HumanContext
 from .audio_mal_handler import AudioMalHandler
 from .audio_inference_handler import AudioInferenceHandler
+from .audio_inference_onnx_handler import AudioInferenceOnnxHandler
 from .human_render import HumanRender
diff --git a/human/audio_inference_handler.py b/human/audio_inference_handler.py
index 30e430b..82a8d51 100644
--- a/human/audio_inference_handler.py
+++ b/human/audio_inference_handler.py
@@ -59,7 +59,9 @@ class AudioInferenceHandler(AudioHandler):
         super().on_message(message)
 
     def __on_run(self):
-        wav2lip_path = os.path.join(current_file_path, '..', 'checkpoints', 'wav2lip_gan.pth')
+        # wav2lip_path = os.path.join(current_file_path, '..', 'checkpoints', 'wav2lip.pth')
+        wav2lip_path = os.path.join(current_file_path, '..', 'checkpoints', 'weights', 'wav2lip',
+                                    'ema_checkpoint_step000300000.pth')
         logger.info(f'AudioInferenceHandler init, path:{wav2lip_path}')
         model = load_model(wav2lip_path)
         logger.info("Model loaded")
diff --git a/human/audio_inference_onnx_handler.py b/human/audio_inference_onnx_handler.py
new file mode 100644
index 0000000..f7d28db
--- /dev/null
+++ b/human/audio_inference_onnx_handler.py
@@ -0,0 +1,207 @@
+#encoding = utf8
+import logging
+import os
+import queue
+import time
+from threading import Event, Thread
+
+from gfpgan import GFPGANer
+from eventbus import EventBus
+from human_handler import AudioHandler
+from utils import load_model, mirror_index, get_device, SyncQueue
+
+logger = logging.getLogger(__name__)
+current_file_path = os.path.dirname(os.path.abspath(__file__))
+
+
+def load_gfpgan_model(model_path):
+    logger.info(f'load_gfpgan_model, path:{model_path}')
+    model = GFPGANer(
+        model_path=model_path,
+        upscale=1,
+        arch='clean',
+        channel_multiplier=2,
+        bg_upsampler=None,
+    )
+    return model
+
+
+def load_model(model_path):
+    import onnxruntime as ort
+
+    sess_opt = ort.SessionOptions()
+    sess_opt.intra_op_num_threads = 8
+    sess = ort.InferenceSession(model_path, sess_options=sess_opt, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+
+    return sess
+
+
+class AudioInferenceOnnxHandler(AudioHandler):
+    def __init__(self, context, handler):
+        super().__init__(context, handler)
+
+        EventBus().register('stop', self._on_stop)
+        EventBus().register('clear_cache', self.on_clear_cache)
+        self._mal_queue = SyncQueue(1, "AudioInferenceHandler_Mel")
+        self._audio_queue = SyncQueue(context.batch_size * 2, "AudioInferenceHandler_Audio")
+
+        self._is_running = True
+        self._exit_event = Event()
+        self._run_thread = Thread(target=self.__on_run, name="AudioInferenceHandlerThread")
+        self._exit_event.set()
+        self._run_thread.start()
+
+        logger.info("AudioInferenceHandler init")
+
+    def __del__(self):
+        EventBus().unregister('stop', self._on_stop)
+        EventBus().unregister('clear_cache', self.on_clear_cache)
+
+    def _on_stop(self, *args, **kwargs):
+        self.stop()
+
+    def on_clear_cache(self, *args, **kwargs):
+        self._mal_queue.clear()
+        self._audio_queue.clear()
+
+    def on_handle(self, stream, type_):
+        if not self._is_running:
+            return
+
+        if type_ == 1:
+            self._mal_queue.put(stream)
+        elif type_ == 0:
+            self._audio_queue.put(stream)
+        # print('AudioInferenceHandler on_handle', type_, self._audio_queue.size())
+
+    def on_message(self, message):
+        super().on_message(message)
+
+    def __on_run(self):
+        model_path = os.path.join(current_file_path, '..', 'checkpoints', 'weights', 'wav2lip')
+        model_a_path = os.path.join(model_path, 'model_a_general.onnx')
+        logger.info(f'AudioInferenceHandler model_a_path, path:{model_a_path}')
+        model_g_path = os.path.join(model_path, 'model_g_general.onnx')
+        logger.info(f'AudioInferenceHandler model_g_path, path:{model_g_path}')
+
+        model_a = load_model(model_a_path)
+        model_g = load_model(model_g_path)
+        logger.info("Model loaded")
+
+        gfpgan_model_path = os.path.join(current_file_path, '..', 'checkpoints', 'gfpgan', 'weights', 'GFPGANv1.4.pth')
+        gfpgan_model = load_gfpgan_model(gfpgan_model_path)
+
+        face_list_cycle = self._context.face_list_cycle
+
+
+        length = len(face_list_cycle)
+        index = 0
+        count = 0
+        count_time = 0
+        logger.info('start inference')
+
+        device = get_device()
+        logger.info(f'use device:{device}')
+
+        while self._is_running:
+            if self._exit_event.is_set():
+                start_time = time.perf_counter()
+                batch_size = self._context.batch_size
+                try:
+                    mel_batch = self._mal_queue.get(timeout=0.02)
+                    # print('AudioInferenceHandler mel_batch:', len(mel_batch), 'size:', self._mal_queue.size())
+                except queue.Empty:
+                    continue
+
+                # print('origin mel_batch:', len(mel_batch))
+                is_all_silence = True
+                audio_frames = []
+                current_text = ''
+                for _ in range(batch_size * 2):
+                    frame, type_ = self._audio_queue.get()
+                    # print('AudioInferenceHandler type_', type_)
+                    current_text = frame[1]
+                    audio_frames.append((frame, type_))
+                    if type_ == 0:
+                        is_all_silence = False
+
+                if not self._is_running:
+                    print('AudioInferenceHandler not running')
+                    break
+
+                if is_all_silence:
+                    for i in range(batch_size):
+                        if not self._is_running:
+                            break
+                        self.on_next_handle((None, mirror_index(length, index), audio_frames[i * 2:i * 2 + 2]),
+                                            0)
+                        index = index + 1
+                else:
+                    logger.info(f'infer======= {current_text}')
+                    t = time.perf_counter()
+                    img_batch = []
+                    # for i in range(batch_size):
+                    for i in range(len(mel_batch)):
+                        idx = mirror_index(length, index + i)
+                        face = face_list_cycle[idx]
+                        img_batch.append(face)
+
+                    # print('orign img_batch:', len(img_batch), 'origin mel_batch:', len(mel_batch))
+                    onnx_input = {'audio_seqs__0': mel_batch, }
+                    onnx_names = [output.name for output in model_a.get_outputs()]
+                    embeddings = model_a.run(onnx_names, onnx_input)[0]
+
+                    onnx_input = {"audio_embedings__0": embeddings, "img_seqs__1": img_batch}
+                    onnx_names = [output.name for output in model_g.get_outputs()]
+                    onnx_out = model_g.run(onnx_names, onnx_input)[0]
+                    pred = onnx_out
+
+                    # onnxruntime_inputs = {"audio_seqs__0": mel_batch, }
+                    # onnxruntime_names = [output.name for output in model_a.get_outputs()]
+                    # embeddings = model_a.run(onnxruntime_names, onnxruntime_inputs)[0]
+                    #
+                    # onnxruntime_inputs = {"audio_embedings__0": embeddings, "img_seqs__1": img_batch}
+                    # onnxruntime_names = [output.name for output in model_g.get_outputs()]
+                    #
+                    # start_model = time.time()
+                    # onnxruntime_output = model_g.run(onnxruntime_names, onnxruntime_inputs)[0]
+                    # end_model = time.time()
+                    # pred = onnxruntime_output
+
+                    count_time += (time.perf_counter() - t)
+                    count += batch_size
+
+                    if count >= 100:
+                        logger.info(f"------actual avg infer fps:{count / count_time:.4f}")
+                        count = 0
+                        count_time = 0
+
+                    for res_frame in zip(pred):
+                        if not self._is_running:
+                            break
+
+                        self.on_next_handle(
+                            (res_frame[0], mirror_index(length, index), audio_frames[i * 2:i * 2 + 2]),
+                            0)
+                        index = index + 1
+                    logger.info(f'total batch time: {time.perf_counter() - start_time}')
+            else:
+                time.sleep(1)
+                break
+        logger.info('AudioInferenceHandler inference processor stop')
+
+    def stop(self):
+        logger.info('AudioInferenceHandler stop')
+        self._is_running = False
+        self._exit_event.clear()
+        if self._run_thread.is_alive():
+            logger.info('AudioInferenceHandler stop join')
+            self._run_thread.join()
+        logger.info('AudioInferenceHandler stop exit')
+
+    def pause_talk(self):
+        print('AudioInferenceHandler pause_talk', self._audio_queue.size(), self._mal_queue.size())
+        self._audio_queue.clear()
+        print('AudioInferenceHandler111')
+        self._mal_queue.clear()
+        print('AudioInferenceHandler222')
diff --git a/human/audio_mal_handler.py b/human/audio_mal_handler.py
index 53fc14b..679f27d 100644
--- a/human/audio_mal_handler.py
+++ b/human/audio_mal_handler.py
@@ -79,7 +79,7 @@ class AudioMalHandler(AudioHandler):
         mel = melspectrogram(inputs)
         # print(mel.shape[0],mel.shape,len(mel[0]),len(self.frames))
         # cut off stride
-        left = max(0, self._context.stride_left_size * 80 / 50)
+        left = max(0, self._context.stride_left_size * 80 / self._context.fps)
         right = min(len(mel[0]), len(mel[0]) - self._context.stride_right_size * 80 / 50)
         mel_idx_multiplier = 80. * 2 / self._context.fps
         mel_step_size = 16
diff --git a/human/human_context.py b/human/human_context.py
index cca030d..e76e8ad 100644
--- a/human/human_context.py
+++ b/human/human_context.py
@@ -4,12 +4,13 @@ import os
 
 from asr import SherpaNcnnAsr
 from eventbus import EventBus
+from .audio_inference_onnx_handler import AudioInferenceOnnxHandler
 from .audio_inference_handler import AudioInferenceHandler
 from .audio_mal_handler import AudioMalHandler
 from .human_render import HumanRender
 from nlp import PunctuationSplit, DouBao
 from tts import TTSEdge, TTSAudioSplitHandle, TTSEdgeHttp
-from utils import load_avatar, get_device, object_stop
+from utils import load_avatar, get_device, object_stop, load_avatar_from_processed, load_avatar_from_256_processed
 
 logger = logging.getLogger(__name__)
 current_file_path = os.path.dirname(os.path.abspath(__file__))
@@ -17,8 +18,8 @@ current_file_path = os.path.dirname(os.path.abspath(__file__))
 
 class HumanContext:
     def __init__(self):
-        self._fps = 50  # 20 ms per frame
-        self._image_size = 96
+        self._fps = 25  # 20 ms per frame
+        self._image_size = 288
         self._batch_size = 16
         self._sample_rate = 16000
         self._stride_left_size = 10
@@ -35,12 +36,19 @@ class HumanContext:
 
         self._device = get_device()
         print(f'device:{self._device}')
-        base_path = os.path.join(current_file_path, '..', 'face')
-        logger.info(f'_create_recognizer init, path:{base_path}')
-        full_images, face_frames, coord_frames = load_avatar(base_path, self._image_size, self._device)
+        base_path = os.path.join(current_file_path, '..')
+        logger.info(f'base path:{base_path}')
+        # full_images, face_frames, coord_frames = load_avatar(base_path, self._image_size, self._device)
+        # full_images, face_frames, coord_frames = load_avatar_from_processed(base_path,
+        #                                                                     'wav2lip_avatar3')
+        full_images, face_frames, coord_frames, align_frames, m_frames, inv_m_frames = load_avatar_from_256_processed(
+            base_path, 'wav2lip_avatar4', '26.pkl')
         self._frame_list_cycle = full_images
         self._face_list_cycle = face_frames
         self._coord_list_cycle = coord_frames
+        self._align_frames = align_frames
+        self._m_frames = m_frames
+        self._inv_m_frames = inv_m_frames
         face_images_length = len(self._face_list_cycle)
         logging.info(f'face images length: {face_images_length}')
         print(f'face images length: {face_images_length}')
@@ -92,6 +100,14 @@ class HumanContext:
     def coord_list_cycle(self):
         return self._coord_list_cycle
 
+    @property
+    def align_frames(self):
+        return self._align_frames
+
+    @property
+    def inv_m_frames(self):
+        return self._inv_m_frames
+
     @property
     def render_handler(self):
         return self._render_handler
diff --git a/human/human_render.py b/human/human_render.py
index 87c1666..186ddbc 100644
--- a/human/human_render.py
+++ b/human/human_render.py
@@ -47,13 +47,8 @@ class HumanRender(AudioHandler):
     def _on_run(self):
         logging.info('human render run')
         while self._exit_event.is_set() and self._is_running:
-            # t = time.time()
             self._run_step()
-            # delay = time.time() - t
-            delay = 0.038 # - delay
-            # print(delay)
-            # if delay <= 0.0:
-            #     continue
+            delay = 0.04
             time.sleep(delay)
 
         logging.info('human render exit')
@@ -64,11 +59,9 @@ class HumanRender(AudioHandler):
             if value is None:
                 return
             res_frame, idx, audio_frames = value
-            # print('render queue size', self._queue.size())
             if not self._empty_log:
                 self._empty_log = True
                 logging.info('render render:')
-            # print('voice render queue size', self._queue.size())
         except Empty:
             if self._empty_log:
                 self._empty_log = False
diff --git a/models/__init__.py b/models/__init__.py
index 1c144be..8b8b3be 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -2,3 +2,4 @@
 
 from .wav2lip import Wav2Lip, Wav2Lip_disc_qual
 from .syncnet import SyncNet_color
+from .wav2lip_v2 import Wav2LipV2
diff --git a/models/wav2lip.py b/models/wav2lip.py
index ae5d691..0a4fb78 100644
--- a/models/wav2lip.py
+++ b/models/wav2lip.py
@@ -5,6 +5,7 @@ import math
 
 from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d
 
+
 class Wav2Lip(nn.Module):
     def __init__(self):
         super(Wav2Lip, self).__init__()
diff --git a/models/wav2lip_v2.py b/models/wav2lip_v2.py
new file mode 100644
index 0000000..bf6cbd1
--- /dev/null
+++ b/models/wav2lip_v2.py
@@ -0,0 +1,221 @@
+import torch
+from torch import nn
+from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d
+
+
+class Wav2LipV2(nn.Module):
+    def __init__(self):
+        super(Wav2LipV2, self).__init__()
+
+        self.face_encoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)),
+
+            nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
+                          Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2d(512, 512, kernel_size=4, stride=1, padding=0),
+                          Conv2d(512, 512, kernel_size=1, stride=1, padding=0)), ])
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0), )
+
+        self.face_decoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0), ),
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=4, stride=1, padding=0),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), ),
+
+            nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), ), ])
+
+        self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1),
+                                          nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
+                                          nn.Sigmoid())
+        
+    def audio_forward(self, audio_sequences, a_alpha=1.):
+        audio_embedding = self.audio_encoder(audio_sequences)  # B, 512, 1, 1
+        if a_alpha != 1.:
+            audio_embedding *= a_alpha
+        return audio_embedding
+    
+    def inference(self, audio_embedding, face_sequences):
+        feats = []
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+            feats.append(x)
+
+        x = audio_embedding
+        for f in self.face_decoder_blocks:
+            x = f(x)
+            try:
+                x = torch.cat((x, feats[-1]), dim=1)
+            except Exception as e:
+                print(x.size())
+                print(feats[-1].size())
+                raise e
+
+            feats.pop()
+
+        x = self.output_block(x)
+        outputs = x
+
+        return outputs
+
+    def forward(self, audio_sequences, face_sequences, a_alpha=1.):
+        # audio_sequences = (B, T, 1, 80, 16)
+        B = audio_sequences.size(0)
+
+        input_dim_size = len(face_sequences.size())
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)#[bz, 5, 1, 80, 16]->[bz*5, 1, 80, 16]
+            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)#[bz, 6, 5, 256, 256]->[bz*5, 6, 256, 256]
+
+        audio_embedding = self.audio_encoder(audio_sequences)  # [bz*5, 1, 80, 16]->[bz*5, 512, 1, 1]
+        if a_alpha != 1.:
+            audio_embedding *= a_alpha                         #放大音频强度
+
+        feats = []
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+            feats.append(x)
+
+        x = audio_embedding
+        for f in self.face_decoder_blocks:
+            x = f(x)
+            try:
+                x = torch.cat((x, feats[-1]), dim=1)
+            except Exception as e:
+                print(x.size())
+                print(feats[-1].size())
+                raise e
+
+            feats.pop()
+
+        x = self.output_block(x)                              #[bz*5, 80, 256, 256]->[bz*5, 3, 256, 256]
+
+        if input_dim_size > 4:                                #[bz*5, 3, 256, 256]->[B, 3, 5, 256, 256]
+            x = torch.split(x, B, dim=0)   
+            outputs = torch.stack(x, dim=2)   
+
+        else:
+            outputs = x
+
+        return outputs
+
+
+class Wav2Lip_disc_qual(nn.Module):
+    def __init__(self):
+        super(Wav2Lip_disc_qual, self).__init__()
+
+        self.face_encoder_blocks = nn.ModuleList([
+            nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)),
+
+            nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2),
+                          nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2),
+                          nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2),
+                          nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)),
+
+            nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
+                          nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)),
+
+            nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                          nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1), ),
+
+            nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
+                          nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1), ),
+
+            nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=4, stride=1, padding=0),
+                          nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)), ])
+
+        self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid())
+        self.label_noise = .0
+
+    def get_lower_half(self, face_sequences):                      #取得输入图片的下半部分。
+        return face_sequences[:, :, face_sequences.size(2) // 2:]
+
+    def to_2d(self, face_sequences):                               #将输入的图片序列连接起来，形成一个二维的tensor。
+        B = face_sequences.size(0)
+        face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+        return face_sequences
+
+    def perceptual_forward(self, false_face_sequences):            #前传生成图像
+        false_face_sequences = self.to_2d(false_face_sequences)    #[bz, 3, 5, 256, 256]->[bz*5, 3, 256, 256]
+        false_face_sequences = self.get_lower_half(false_face_sequences)#[bz*5, 3, 256, 256]->[bz*5, 3, 128, 256]
+
+        false_feats = false_face_sequences
+        for f in self.face_encoder_blocks:                         #[bz*5, 3, 128, 256]->[bz*5, 512, 1, 1]
+            false_feats = f(false_feats)
+
+        return self.binary_pred(false_feats).view(len(false_feats), -1) #[bz*5, 512, 1, 1]->[bz*5, 1, 1]
+
+    def forward(self, face_sequences):                             #前传真值图像
+        face_sequences = self.to_2d(face_sequences)
+        face_sequences = self.get_lower_half(face_sequences)
+
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+
+        return self.binary_pred(x).view(len(x), -1)
diff --git a/render/video_render.py b/render/video_render.py
index 2b53137..91426bd 100644
--- a/render/video_render.py
+++ b/render/video_render.py
@@ -1,38 +1,23 @@
 #encoding = utf8
 import copy
+import logging
 import time
-from queue import Empty
-from enum import Enum
 
 import cv2
 import numpy as np
 
 from .base_render import BaseRender
-from human.message_type import MessageType
 
 
 class VideoRender(BaseRender):
     def __init__(self, play_clock, context, human_render):
         super().__init__(play_clock, context, 'Video')
         self._human_render = human_render
+        self.index = 0
 
     def render(self, frame, ps):
-        res_frame, idx, type_ = frame
-
-        if type_ == 0:
-            combine_frame = self._context.frame_list_cycle[idx]
-        else:
-            bbox = self._context.coord_list_cycle[idx]
-            combine_frame = copy.deepcopy(self._context.frame_list_cycle[idx])
-            y1, y2, x1, x2 = bbox
-            try:
-                res_frame = cv2.resize(res_frame.astype(np.uint8), (x2 - x1, y2 - y1))
-            except:
-                print('resize error')
-                return
-            combine_frame[y1:y2, x1:x2, :3] = res_frame
-
-        image = combine_frame
-        # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
         if self._human_render is not None:
-            self._human_render.put_image(image)
+            self._human_render.put_image(frame)
+
+        # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+
diff --git a/test/test_mzzsfy_tts.py b/test/test_mzzsfy_tts.py
index f62c643..ecd3895 100644
--- a/test/test_mzzsfy_tts.py
+++ b/test/test_mzzsfy_tts.py
@@ -25,7 +25,7 @@ def download_tts(url):
 
 def __create_bytes_stream(byte_stream):
     stream, sample_rate = sf.read(byte_stream)  # [T*sample_rate,] float64
-    print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
+    # print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
     stream = stream.astype(np.float32)
 
     if stream.ndim > 1:
diff --git a/tts/tts_edge_http.py b/tts/tts_edge_http.py
index 84b5301..c3ad86a 100644
--- a/tts/tts_edge_http.py
+++ b/tts/tts_edge_http.py
@@ -79,15 +79,15 @@ class TTSEdgeHttp(TTSBase):
 
     def __create_bytes_stream(self, byte_stream):
         stream, sample_rate = sf.read(byte_stream)  # [T*sample_rate,] float64
-        print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
+        logging.info(f'tts audio stream {sample_rate}: {stream.shape}')
         stream = stream.astype(np.float32)
 
         if stream.ndim > 1:
-            print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
+            logging.warning(f'audio has {stream.shape[1]} channels, only use the first.')
             stream = stream[:, 0]
 
         if sample_rate != self._handle.sample_rate and stream.shape[0] > 0:
-            print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self._handle.sample_rate}.')
+            logging.warning(f'audio sample rate is {sample_rate}, resampling into {self._handle.sample_rate}.')
             stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self._handle.sample_rate)
 
         return stream
diff --git a/ui/pygame_ui.py b/ui/pygame_ui.py
index de4e5dd..134b580 100644
--- a/ui/pygame_ui.py
+++ b/ui/pygame_ui.py
@@ -1,8 +1,12 @@
 #encoding = utf8
+import copy
 import logging
 import os
+import time
 from queue import Queue
 
+import cv2
+import numpy as np
 import pygame
 from pygame.locals import *
 
@@ -13,17 +17,54 @@ logger = logging.getLogger(__name__)
 current_file_path = os.path.dirname(os.path.abspath(__file__))
 
 
+def img_warp_back_inv_m(img, img_to, inv_m):
+    h_up, w_up, c = img_to.shape
+    mask = np.ones_like(img).astype(np.float32)
+    inv_mask = cv2.warpAffine(mask, inv_m, (w_up, h_up))
+    inv_img = cv2.warpAffine(img, inv_m, (w_up, h_up))
+    mask_indices = inv_mask == 1
+    if 4 == c:
+        img_to[:, :, :3][mask_indices] = inv_img[mask_indices]
+    else:
+        img_to[inv_mask == 1] = inv_img[inv_mask == 1]
+    return img_to
+
+
+def render_image(context, frame):
+    res_frame, idx, type_ = frame
+
+    if type_ == 0:
+        combine_frame = context.frame_list_cycle[idx]
+    else:
+        bbox = context.coord_list_cycle[idx]
+        combine_frame = copy.deepcopy(context.frame_list_cycle[idx])
+        af = context.align_frames[idx]
+        inv_m = context.inv_m_frames[idx]
+        y1, y2, x1, x2 = bbox
+        try:
+            t = time.perf_counter()
+            res_frame = cv2.resize(res_frame.astype(np.uint8), (x2 - x1, y2 - y1))
+            af[y1:y2, x1:x2] = res_frame
+            combine_frame = img_warp_back_inv_m(af, combine_frame, inv_m)
+        except Exception as e:
+            logging.error(f'resize error:{e}')
+            return
+
+    image = combine_frame
+    return image
+
+
 class PyGameUI:
     def __init__(self):
         self._human_context = None
         self._queue = None
-        self.screen_ = pygame.display.set_mode((800, 600), HWSURFACE | DOUBLEBUF | RESIZABLE)
+        self.screen_ = pygame.display.set_mode((1920, 1080), HWSURFACE | DOUBLEBUF | RESIZABLE)
         self.clock = pygame.time.Clock()
 
         background = os.path.join(current_file_path, '..', 'data', 'background', 'background.jpg')
         logger.info(f'background: {background}')
         self._background = pygame.image.load(background).convert()
-        self.background_display_ = pygame.transform.scale(self._background, (800, 600))
+        self.background_display_ = pygame.transform.scale(self._background, (1920, 1080))
         self._human_image = None
         self.running = True
 
@@ -46,7 +87,10 @@ class PyGameUI:
             self.screen_.blit(self.background_display_, (0, 0))
             self._update_human()
             if self._human_image is not None:
-                self.screen_.blit(self._human_image, (0, 0))
+                self.screen_.blit(self._human_image, (760, -300))
+
+            fps = self.clock.get_fps()
+            pygame.display.set_caption('fps:{:.2f}'.format(fps))
             pygame.display.flip()
         self.stop()
         pygame.quit()
@@ -54,10 +98,18 @@ class PyGameUI:
     def _update_human(self):
         if self._queue.empty():
             return
+
+        if self._queue.qsize() > 5:
+            print('render queue is slower')
+
         image = self._queue.get()
+        image = render_image(self._human_context, image)
         color_format = "RGB"
         if 4 == image.shape[2]:
             color_format = "RGBA"
+            image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+        else:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
         self._human_image = pygame.image.frombuffer(image.tobytes(), image.shape[1::-1], color_format)
 
diff --git a/utils/__init__.py b/utils/__init__.py
index 3a9238c..2f3ca11 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -4,4 +4,5 @@ from .async_task_queue import AsyncTaskQueue
 from .sync_queue import SyncQueue
 from .utils import mirror_index, load_model, get_device, load_avatar, config_logging
 from .utils import read_image, object_stop
+from .utils import load_avatar_from_processed, load_avatar_from_256_processed
 from .audio_utils import melspectrogram, save_wav
diff --git a/utils/utils.py b/utils/utils.py
index 28c3a19..eb307ba 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -1,6 +1,8 @@
 #encoding = utf8
+import glob
 import logging
 import os
+import pickle
 
 import cv2
 import numpy as np
@@ -9,7 +11,7 @@ from tqdm import tqdm
 from PIL import Image
 
 import face_detection
-from models import Wav2Lip
+from models import Wav2Lip, Wav2LipV2
 
 logger = logging.getLogger(__name__)
 
@@ -33,7 +35,6 @@ def read_images(img_list):
     frames = []
     print('reading images...')
     for img_path in tqdm(img_list):
-        print(f'read image path:{img_path}')
         # frame = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
         frame = Image.open(img_path)
         frame = np.array(frame)
@@ -143,7 +144,7 @@ def get_device():
 
 
 def _load(checkpoint_path):
-    device = get_device
+    device = get_device()
     if device == 'cuda':
         checkpoint = torch.load(checkpoint_path)
     else:
@@ -153,7 +154,7 @@ def _load(checkpoint_path):
 
 
 def load_model(path):
-    model = Wav2Lip()
+    model = Wav2LipV2()
     print("Load checkpoint from: {}".format(path))
     logging.info(f'Load checkpoint from {path}')
     checkpoint = _load(path)
@@ -169,7 +170,7 @@ def load_model(path):
 
 def load_avatar(path, img_size, device):
     print(f'load avatar:{path}')
-    face_images_path = path
+    face_images_path = os.path.join(path, 'face')
     face_images_path = read_files_path(face_images_path)
     full_list_cycle = read_images(face_images_path)
 
@@ -185,6 +186,71 @@ def load_avatar(path, img_size, device):
     return full_list_cycle, face_frames, coord_frames
 
 
+def load_avatar_from_processed(base_path, avatar_name):
+    avatar_path = os.path.join(base_path, 'data', 'avatars', avatar_name)
+    print(f'load avatar from processed:{avatar_path}')
+    coord_path = os.path.join(avatar_path, 'coords.pkl')
+    print(f'load avatar_path from processed:{avatar_path}')
+    face_image_path = os.path.join(avatar_path, 'face_imgs')
+    print(f'load face_image_path from processed:{face_image_path}')
+    full_image_path = os.path.join(avatar_path, 'full_imgs')
+    print(f'load full_image_path from processed:{full_image_path}')
+
+    with open(coord_path, 'rb') as f:
+        coord_list_frames = pickle.load(f)
+
+    face_image_list = glob.glob(os.path.join(face_image_path, '*.[jpJP][pnPN]*[gG]'))
+    face_image_list = sorted(face_image_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+    face_list_cycle = read_images(face_image_list)
+
+    full_image_list = glob.glob(os.path.join(full_image_path, '*.[jpJP][pnPN]*[gG]'))
+    full_image_list = sorted(full_image_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+    frame_list_cycle = read_images(full_image_list)
+
+    return frame_list_cycle, face_list_cycle, coord_list_frames
+
+
+def jpeg_to_png(image):
+    min_green = np.array([50, 100, 100])
+    max_green = np.array([70, 255, 255])
+
+    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
+    mask = cv2.inRange(hsv, min_green, max_green)
+    mask_not = cv2.bitwise_not(mask)
+    green_not = cv2.bitwise_and(image, image, mask=mask_not)
+    b, g, r = cv2.split(green_not)
+
+    # todo 合成四通道
+    image = cv2.merge([b, g, r, mask_not])
+    return image
+
+
+def load_avatar_from_256_processed(base_path, avatar_name, pkl):
+    avatar_path = os.path.join(base_path, 'data', 'avatars', avatar_name, pkl)
+    print(f'load avatar from processed:{avatar_path}')
+
+    with open(avatar_path, "rb") as f:
+        avatar_data = pickle.load(f)
+
+    face_list_cycle = []
+    frame_list_cycle = []
+    coord_list_frames = []
+    align_frames = []
+    m_frames = []
+    inv_m_frames = []
+
+    frame_info_list = avatar_data['frame_info_list']
+    for frame_info in tqdm(frame_info_list):
+        face_list_cycle.append(frame_info['img'])
+        frame_list_cycle.append(jpeg_to_png(frame_info['frame']))
+        coord_list_frames.append(frame_info['coords'])
+        align_frames.append(frame_info['align_frame'])
+        m_frames.append(frame_info['m'])
+        inv_m_frames.append(frame_info['inv_m'])
+
+    return frame_list_cycle, face_list_cycle, coord_list_frames, align_frames, m_frames, inv_m_frames
+
+
 def config_logging(file_name: str, console_level: int = logging.INFO, file_level: int = logging.DEBUG):
     file_handler = logging.FileHandler(file_name, mode='a', encoding="utf8")
     file_handler.setFormatter(logging.Formatter(