revert refactor

predict.py

@@ -485,34 +485,25 @@ class Predictor(BasePredictor):
     ) -> typing.Iterator[Path]:
         if translate:
             prompt = self.translator.translate(prompt.strip())
-
-        if seed == -1:
-            seed = randint(0, 2**32)
-
-        self.args.seed = seed
-        self.args.use_guidance_stage1 = use_guidance        
-        self.prompt = prompt
-        self.args.both_stages = both_stages
-
-        yield [Path(file) for file in self.run()]
-
-        logging.debug("complete, exiting")
-        return
-
-    def run(self):
-        invalid_slices = [slice(tokenizer.num_image_tokens, None)]
-        strategy_cogview2 = CoglmStrategy(invalid_slices, 
-            temperature=1.0, top_k=16)
-        strategy_cogvideo = CoglmStrategy(invalid_slices, 
-            temperature=self.args.temperature, top_k=self.args.top_k,
-            temperature2=self.args.coglm_temperature2)
-
-        torch.manual_seed(self.args.seed)
-        random.seed(self.args.seed)
         workdir = tempfile.mkdtemp()
         os.makedirs(f"{workdir}/output/stage1", exist_ok=True)
         os.makedirs(f"{workdir}/output/stage2", exist_ok=True)
 
+        if seed == -1:
+            seed = randint(0, 2**32)
+
+        invalid_slices = [slice(tokenizer.num_image_tokens, None)]
+        self.strategy_cogview2 = CoglmStrategy(invalid_slices, 
+            temperature=1.0, top_k=16)
+        self.strategy_cogvideo = CoglmStrategy(invalid_slices, 
+            temperature=self.args.temperature, top_k=self.args.top_k,
+            temperature2=self.args.coglm_temperature2)
+
+
+        torch.manual_seed(seed)
+        random.seed(seed)
+        self.args.seed = seed
+        self.args.use_guidance_stage1 = use_guidance        
         move_start_time = time.time()
         logging.debug("moving stage 2 model to cpu")
         self.model_stage2 = self.model_stage2.cpu()
@@ -520,17 +511,35 @@ class Predictor(BasePredictor):
         logging.debug("moving stage 1 model to cuda")
         self.model_stage1 = self.model_stage1.cuda()
         logging.debug("moving in model1 takes time: {:.2f}".format(time.time()-move_start_time))
-                       
+                
+        parent_given_tokens = self.process_stage1(self.model_stage1, prompt, duration=4.0, video_raw_text=prompt, video_guidance_text="视频",
+                                                    image_text_suffix=" 高清摄影",
+                                                    outputdir=f'{workdir}/output/stage1', batch_size=1)
+        
+        logging.debug("moving stage 1 model to cpu")
+        self.model_stage1 = self.model_stage1.cpu()
+        torch.cuda.empty_cache()
+        yield Path(f"{workdir}/output/stage1/0.gif")
+        if both_stages:                                        
+            move_start_time = time.time()
+            logging.debug("moving stage 2 model to cuda")
+            self.model_stage2 = self.model_stage2.cuda()
+            logging.debug("moving in model2 takes time: {:.2f}".format(time.time()-move_start_time))
+            self.process_stage2(self.model_stage2, prompt, duration=2.0, video_raw_text=prompt+" 视频", 
+                                video_guidance_text="视频", parent_given_tokens=parent_given_tokens, 
+                                outputdir=f'{workdir}/output/stage2',
+                                gpu_rank=0, gpu_parallel_size=1)        
+            yield Path(f"{workdir}/output/stage2/0.gif")
+
+        logging.debug("complete, exiting")
+                
+    def process_stage1(self, model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", image_text_suffix="", outputdir=None, batch_size=1):
         process_start_time = time.time()
         args = self.args
         use_guide = args.use_guidance_stage1        
-        batch_size = 1
+            
-        seq_text = self.prompt
+        if video_raw_text is None: 
-        video_raw_text = self.prompt
+            video_raw_text = seq_text
-        duration=4.0
-        video_guidance_text="视频"
-        image_text_suffix=" 高清摄影"
-        outputdir=f'{workdir}/output/stage1'
         mbz = args.stage1_max_inference_batch_size if args.stage1_max_inference_batch_size > 0 else args.max_inference_batch_size
         assert batch_size < mbz or batch_size % mbz == 0
         frame_len = 400
@@ -546,13 +555,13 @@ class Predictor(BasePredictor):
         for tim in range(max(batch_size // mbz, 1)):
             start_time = time.time()
             output_list_1st.append(
-                my_filling_sequence(self.model_stage1, args,seq_1st.clone(),
+                my_filling_sequence(model, args,seq_1st.clone(),
                     batch_size=min(batch_size, mbz),
                     get_masks_and_position_ids=get_masks_and_position_ids_stage1,
                     text_len=text_len_1st, 
                     frame_len=frame_len,
-                    strategy=strategy_cogview2,
+                    strategy=self.strategy_cogview2,
-                    strategy2=strategy_cogvideo,
+                    strategy2=self.strategy_cogvideo,
                     log_text_attention_weights=1.4,
                     enforce_no_swin=True,
                     mode_stage1=True,
@@ -593,12 +602,12 @@ class Predictor(BasePredictor):
             input_seq = seq[:min(batch_size, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone()
             guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None
             output_list.append(
-                my_filling_sequence(self.model_stage1, args,input_seq,
+                my_filling_sequence(model, args,input_seq,
                     batch_size=min(batch_size, mbz),
                     get_masks_and_position_ids=get_masks_and_position_ids_stage1,
                     text_len=text_len, frame_len=frame_len,
-                    strategy=strategy_cogview2,
+                    strategy=self.strategy_cogview2,
-                    strategy2=strategy_cogvideo,
+                    strategy2=self.strategy_cogvideo,
                     log_text_attention_weights=video_log_text_attention_weights,
                     guider_seq=guider_seq2,
                     guider_text_len=guider_text_len,
@@ -622,37 +631,27 @@ class Predictor(BasePredictor):
             for clip_i in range(len(imgs)):
                 # os.makedirs(output_dir_full_paths[clip_i], exist_ok=True)
                 my_save_multiple_images(imgs[clip_i], outputdir, subdir=f"frames/{clip_i}", debug=False)
-                out_filename = f'{outputdir}/{clip_i}.gif'
+                os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25")
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{out_filename}' -d 0.25")
-                yield out_filename
             torch.save(save_tokens, os.path.join(outputdir, 'frame_tokens.pt'))
         
         logging.info("CogVideo Stage1 completed. Taken time {:.2f}\n".format(time.time() - process_start_time))
-            
+        
-        logging.debug("moving stage 1 model to cpu")
+        return save_tokens
-        self.model_stage1 = self.model_stage1.cpu()
-        torch.cuda.empty_cache()        
 
-        if not self.args.both_stages:
+    def process_stage2(self, model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", parent_given_tokens=None, conddir=None, outputdir=None, gpu_rank=0, gpu_parallel_size=1):
-            logging.info("only stage 1 selected, exiting")        
+        stage2_starttime = time.time()
-            return
+        args = self.args
-                
+        use_guidance = args.use_guidance_stage2
-        gpu_rank=0
+        if args.both_stages:
-        gpu_parallel_size=1
+            move_start_time = time.time()
-        video_raw_text=self.prompt+" 视频"            
+            logging.debug("moving stage-2 model to cuda")
-        duration=2.0
+            model = model.cuda()
-        video_guidance_text="视频"
+            logging.debug("moving in stage-2 model takes time: {:.2f}".format(time.time()-move_start_time))
-        outputdir=f'{workdir}/output/stage2'
-        parent_given_tokens = save_tokens
-        stage2_starttime = time.time()        
-        use_guidance = args.use_guidance_stage2        
-
-        move_start_time = time.time()
-        logging.debug("moving stage-2 model to cuda")
-        self.model_stage2 = self.model_stage2.cuda()
-        logging.debug("moving in stage-2 model takes time: {:.2f}".format(time.time()-move_start_time))
             
         try:
+            if parent_given_tokens is None:
+                assert conddir is not None
+                parent_given_tokens = torch.load(os.path.join(conddir, 'frame_tokens.pt'), map_location='cpu')
             sample_num_allgpu = parent_given_tokens.shape[0]
             sample_num = sample_num_allgpu // gpu_parallel_size
             assert sample_num * gpu_parallel_size == sample_num_allgpu
@@ -707,12 +706,12 @@ class Predictor(BasePredictor):
                 input_seq = seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone()
                 guider_seq2 = (guider_seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None
                 output_list.append(
-                    my_filling_sequence(self.model_stage2, args, input_seq,
+                    my_filling_sequence(model, args, input_seq,
                         batch_size=min(generate_batchsize_total, mbz),
                         get_masks_and_position_ids=get_masks_and_position_ids_stage2,
                         text_len=text_len, frame_len=frame_len,
-                        strategy=strategy_cogview2,
+                        strategy=self.strategy_cogview2,
-                        strategy2=strategy_cogvideo,
+                        strategy2=self.strategy_cogvideo,
                         log_text_attention_weights=video_log_text_attention_weights,
                         mode_stage1=False,
                         guider_seq=guider_seq2,
@@ -741,7 +740,8 @@ class Predictor(BasePredictor):
         parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400)        
             
         logging.debug("moving stage 2 model to cpu")
-        self.model_stage2 = self.model_stage2.cpu()        
+        self.model_stage2 = self.model_stage2.cpu()
+        model = model.cpu()
         torch.cuda.empty_cache()
             
         # use dsr if loaded
@@ -762,9 +762,7 @@ class Predictor(BasePredictor):
 
             for sample_i in range(sample_num):
                 my_save_multiple_images(decoded_sr_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False)
-                output_file = f'{outputdir}/{sample_i+sample_num*gpu_rank}.gif'
+                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125")
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{output_file}' -d 0.125")
-                yield output_file
             
             logging.info("Direct super-resolution completed. Taken time {:.2f}\n".format(time.time() - dsr_starttime))
         else:            
@@ -786,6 +784,18 @@ class Predictor(BasePredictor):
 
             for sample_i in range(sample_num):
                 my_save_multiple_images(decoded_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False)
-                output_file = f'{outputdir}/{sample_i+sample_num*gpu_rank}.gif'
+                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125")
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{output_file}' -d 0.125")
+
-                yield output_file
+            
+            #imgs = []
+            #for seq in output_tokens:
+            #    decoded_imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()[i*400: (i+1)*400]), size=(480, 480)) for i in range(total_frames)]
+            #    decoded_video.extend(decoded_imgs) # only the last image (target)
+                    
+            #for sample_i in range(sample_num):
+            #    my_save_multiple_images(decoded_video, outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False)
+            #    os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125")
+            
+        return True                    
+
+