diff --git a/cog.yaml b/cog.yaml
index 0e8039f..c2af953 100644
--- a/cog.yaml
+++ b/cog.yaml
@@ -23,7 +23,7 @@ build:
   run:
     - "mkdir -p /sharefs/cogview-new; cd /sharefs/cogview-new; wget https://models.nmb.ai/cogvideo/cogvideo-stage1.tar.gz -O - | tar xz"
     - "cd /sharefs/cogview-new; wget https://models.nmb.ai/cogvideo/cogvideo-stage2.tar.gz -O - | tar xz"
-    - "cd /sharefs/cogview-new; wget https://models.nmb.ai/cogview2/cogview2-dsr.tar.gz -O - | tar xz"
+    #- "cd /sharefs/cogview-new; wget https://models.nmb.ai/cogview2/cogview2-dsr.tar.gz -O - | tar xz"
     - "mkdir -p /root/.icetk_models; wget -O /root/.icetk_models/ice_text.model https://models.nmb.ai/cogvideo/ice_text.model"
     - "mkdir -p /root/.tcetk_models; wget -O /root/.icetk_models/ice_image.pt https://models.nmb.ai/cogvideo/ice_image.pt"
 
diff --git a/predict.py b/predict.py
index 419e0c2..3901597 100644
--- a/predict.py
+++ b/predict.py
@@ -4,14 +4,477 @@ import subprocess
 import tempfile
 import glob
 import typing
+from typing_extensions import Self
 from deep_translator import GoogleTranslator
 from cog import BasePredictor, Input, Path
 
+import torch
+import time
+import logging,sys
+import stat
+from torchvision.utils import save_image
+from icetk import icetk as tokenizer
+import torch.distributed as dist
+tokenizer.add_special_tokens(['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
+
+from SwissArmyTransformer import get_args
+from SwissArmyTransformer.data_utils import BinaryDataset, make_loaders
+from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
+from SwissArmyTransformer.generation.utils import timed_name, save_multiple_images, generate_continually
+from SwissArmyTransformer.resources import auto_create
+
+from models.cogvideo_cache_model import CogVideoCacheModel
+from coglm_strategy import CoglmStrategy
+
+
+def get_masks_and_position_ids_stage1(data, textlen, framelen):
+    # Extract batch size and sequence length.
+    tokens = data
+    seq_length = len(data[0])
+    # Attention mask (lower triangular).
+    attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device)
+    attention_mask[:, :textlen, textlen:] = 0
+    attention_mask[:, textlen:, textlen:].tril_()
+    attention_mask.unsqueeze_(1)
+    # Unaligned version
+    position_ids = torch.zeros(seq_length, dtype=torch.long,
+                                device=data.device)
+    torch.arange(textlen, out=position_ids[:textlen], 
+        dtype=torch.long, device=data.device)
+    torch.arange(512, 512+seq_length-textlen, out=position_ids[textlen:], 
+        dtype=torch.long, device=data.device)
+    position_ids = position_ids.unsqueeze(0)
+
+    return tokens, attention_mask, position_ids
+
+def get_masks_and_position_ids_stage2(data, textlen, framelen):
+    # Extract batch size and sequence length.
+    tokens = data
+    seq_length = len(data[0])
+
+    # Attention mask (lower triangular).
+    attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device)
+    attention_mask[:, :textlen, textlen:] = 0
+    attention_mask[:, textlen:, textlen:].tril_()
+    attention_mask.unsqueeze_(1)
+    
+    # Unaligned version
+    position_ids = torch.zeros(seq_length, dtype=torch.long,
+                                device=data.device)
+    torch.arange(textlen, out=position_ids[:textlen], 
+        dtype=torch.long, device=data.device)
+    frame_num = (seq_length-textlen)//framelen
+    assert frame_num == 5
+    torch.arange(512, 512+framelen, out=position_ids[textlen:textlen+framelen], 
+            dtype=torch.long, device=data.device)
+    torch.arange(512+framelen*2, 512+framelen*3, out=position_ids[textlen+framelen:textlen+framelen*2], 
+            dtype=torch.long, device=data.device)
+    torch.arange(512+framelen*(frame_num-1), 512+framelen*frame_num, out=position_ids[textlen+framelen*2:textlen+framelen*3], 
+            dtype=torch.long, device=data.device)
+    torch.arange(512+framelen*1, 512+framelen*2, out=position_ids[textlen+framelen*3:textlen+framelen*4], 
+            dtype=torch.long, device=data.device)
+    torch.arange(512+framelen*3, 512+framelen*4, out=position_ids[textlen+framelen*4:textlen+framelen*5], 
+            dtype=torch.long, device=data.device)
+
+    position_ids = position_ids.unsqueeze(0)
+
+    return tokens, attention_mask, position_ids
+
+def my_update_mems(hiddens, mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len):
+    if hiddens is None:
+        return None, mems_indexs
+    mem_num = len(hiddens)
+    ret_mem = []
+    with torch.no_grad():
+        for id in range(mem_num):
+            if hiddens[id][0] is None:
+                ret_mem.append(None)
+            else: 
+                if id == 0 and limited_spatial_channel_mem and mems_indexs[id]+hiddens[0][0].shape[1] >= text_len+frame_len:
+                    if mems_indexs[id] == 0:
+                        for layer, hidden in enumerate(hiddens[id]):
+                            mems_buffers[id][layer, :, :text_len] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, :text_len]
+                    new_mem_len_part2 = (mems_indexs[id]+hiddens[0][0].shape[1]-text_len)%frame_len 
+                    if new_mem_len_part2 > 0:
+                        for layer, hidden in enumerate(hiddens[id]):
+                            mems_buffers[id][layer, :, text_len:text_len+new_mem_len_part2] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, -new_mem_len_part2:]
+                    mems_indexs[id] = text_len+new_mem_len_part2
+                else:
+                    for layer, hidden in enumerate(hiddens[id]):
+                        mems_buffers[id][layer, :, mems_indexs[id]:mems_indexs[id]+hidden.shape[1]] = hidden.expand(mems_buffers[id].shape[1], -1, -1)
+                    mems_indexs[id] += hidden.shape[1]
+                ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]])
+    return ret_mem, mems_indexs
+
+
+def my_save_multiple_images(imgs, path, subdir, debug=True):
+    # imgs: list of tensor images
+    if debug:
+        imgs = torch.cat(imgs, dim=0)
+        print("\nSave to: ", path, flush=True)
+        save_image(imgs, path, normalize=True)
+    else:
+        print("\nSave to: ", path, flush=True)
+        single_frame_path = os.path.join(path, subdir)
+        os.makedirs(single_frame_path, exist_ok=True)
+        for i in range(len(imgs)):
+            save_image(imgs[i], os.path.join(single_frame_path, f'{str(i).rjust(4,"0")}.jpg'), normalize=True)
+            os.chmod(os.path.join(single_frame_path,f'{str(i).rjust(4,"0")}.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU)
+        save_image(torch.cat(imgs, dim=0), os.path.join(single_frame_path,f'frame_concat.jpg'), normalize=True)
+        os.chmod(os.path.join(single_frame_path,f'frame_concat.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU)
+        
+def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len):
+    # The fisrt token's position id of the frame that the next token belongs to; 
+    if total_len < text_len:
+        return None
+    return (total_len-text_len)//frame_len * frame_len + text_len
+
+def my_filling_sequence(
+        model, 
+        args,
+        seq, 
+        batch_size,
+        get_masks_and_position_ids,
+        text_len,
+        frame_len,
+        strategy=BaseStrategy(),
+        strategy2=BaseStrategy(),
+        mems=None,
+        log_text_attention_weights=0, # default to 0: no artificial change
+        mode_stage1=True,
+        enforce_no_swin=False,
+        guider_seq=None,
+        guider_text_len=0,
+        guidance_alpha=1,
+        limited_spatial_channel_mem=False, # 空间通道的存储限制在本帧内
+        **kw_args
+        ):
+    '''
+        seq: [2, 3, 5, ..., -1(to be generated), -1, ...]
+        mems: [num_layers, batch_size, len_mems(index), mem_hidden_size]
+            cache, should be first mems.shape[1] parts of context_tokens.
+            mems are the first-level citizens here, but we don't assume what is memorized.
+            input mems are used when multi-phase generation.
+    '''
+    if guider_seq is not None:
+        logging.debug("Using Guidance In Inference")
+    if limited_spatial_channel_mem:
+        logging.debug("Limit spatial-channel's mem to current frame")
+    assert len(seq.shape) == 2
+
+    # building the initial tokens, attention_mask, and position_ids
+    actual_context_length = 0
+    
+    while seq[-1][actual_context_length] >= 0: # the last seq has least given tokens
+        actual_context_length += 1 # [0, context_length-1] are given
+    assert actual_context_length > 0
+    current_frame_num = (actual_context_length-text_len) // frame_len
+    assert current_frame_num >= 0
+    context_length = text_len + current_frame_num * frame_len
+    
+    tokens, attention_mask, position_ids = get_masks_and_position_ids(seq, text_len, frame_len)
+    tokens = tokens[..., :context_length]
+    input_tokens = tokens.clone()
+    
+    if guider_seq is not None:
+        guider_index_delta = text_len - guider_text_len
+        guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(guider_seq, guider_text_len, frame_len)
+        guider_tokens = guider_tokens[..., :context_length-guider_index_delta]
+        guider_input_tokens = guider_tokens.clone()
+        
+    for fid in range(current_frame_num):
+        input_tokens[:, text_len+400*fid] = tokenizer['<start_of_image>']
+        if guider_seq is not None:
+            guider_input_tokens[:, guider_text_len+400*fid] = tokenizer['<start_of_image>']
+
+    attention_mask = attention_mask.type_as(next(model.parameters())) # if fp16
+    # initialize generation
+    counter = context_length - 1 # Last fixed index is ``counter'' 
+    index = 0 # Next forward starting index, also the length of cache.
+    mems_buffers_on_GPU = False
+    mems_indexs = [0, 0]
+    mems_len = [(400+74) if limited_spatial_channel_mem else 5*400+74, 5*400+74]
+    mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype)
+                        for mem_len in mems_len]
+
+    
+    if guider_seq is not None: 
+        guider_attention_mask = guider_attention_mask.type_as(next(model.parameters())) # if fp16
+        guider_mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype)
+                        for mem_len in mems_len]
+        guider_mems_indexs = [0, 0]
+        guider_mems = None
+    
+    torch.cuda.empty_cache()
+    # step-by-step generation
+    while counter < len(seq[0]) - 1:
+        # we have generated counter+1 tokens
+        # Now, we want to generate seq[counter + 1],
+        # token[:, index: counter+1] needs forwarding.
+        if index == 0:
+            group_size = 2 if (input_tokens.shape[0] == batch_size and not mode_stage1) else batch_size
+            
+            logits_all = None
+            for batch_idx in range(0, input_tokens.shape[0], group_size):
+                logits, *output_per_layers = model(
+                    input_tokens[batch_idx:batch_idx+group_size, index:],
+                    position_ids[..., index: counter+1],
+                    attention_mask, # TODO memlen
+                    mems=mems,
+                    text_len=text_len,
+                    frame_len=frame_len,
+                    counter=counter,
+                    log_text_attention_weights=log_text_attention_weights,
+                    enforce_no_swin=enforce_no_swin,
+                    **kw_args
+                )
+                logits_all = torch.cat((logits_all, logits), dim=0) if logits_all is not None else logits
+                mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers]]
+                next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(text_len, frame_len, mem_kv01[0][0].shape[1])
+                for id, mem_kv in enumerate(mem_kv01):
+                    for layer, mem_kv_perlayer in enumerate(mem_kv):
+                        if limited_spatial_channel_mem and id == 0:
+                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, :text_len] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :text_len]
+                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\
+                                mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:]
+                        else:
+                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, :mem_kv_perlayer.shape[1]] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)
+                mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[1], mem_kv01[1][0].shape[1]
+                if limited_spatial_channel_mem:
+                    mems_indexs[0] -= (next_tokens_frame_begin_id - text_len)
+
+            mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)]
+            logits = logits_all
+            
+            # Guider 
+            if guider_seq is not None:
+                guider_logits_all = None
+                for batch_idx in range(0, guider_input_tokens.shape[0], group_size):
+                    guider_logits, *guider_output_per_layers = model(
+                        guider_input_tokens[batch_idx:batch_idx+group_size, max(index-guider_index_delta, 0):],
+                        guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta],
+                        guider_attention_mask,
+                        mems=guider_mems,
+                        text_len=guider_text_len,
+                        frame_len=frame_len,
+                        counter=counter-guider_index_delta,
+                        log_text_attention_weights=log_text_attention_weights,
+                        enforce_no_swin=enforce_no_swin,
+                        **kw_args
+                    )
+                    guider_logits_all = torch.cat((guider_logits_all, guider_logits), dim=0) if guider_logits_all is not None else guider_logits
+                    guider_mem_kv01 = [[o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers]]
+                    for id, guider_mem_kv in enumerate(guider_mem_kv01):
+                        for layer, guider_mem_kv_perlayer in enumerate(guider_mem_kv):
+                            if limited_spatial_channel_mem and id == 0:
+                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_text_len] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :guider_text_len]
+                                guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(guider_text_len, frame_len, guider_mem_kv_perlayer.shape[1])
+                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\
+                                    guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:]
+                            else:
+                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_mem_kv_perlayer.shape[1]] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)
+                    guider_mems_indexs[0], guider_mems_indexs[1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[1][0].shape[1]
+                    if limited_spatial_channel_mem:
+                        guider_mems_indexs[0] -= (guider_next_tokens_frame_begin_id-guider_text_len)
+                guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)]
+                guider_logits = guider_logits_all
+        else:
+            if not mems_buffers_on_GPU:
+                if not mode_stage1:
+                    torch.cuda.empty_cache()
+                    for idx, mem in enumerate(mems):
+                        mems[idx] = mem.to(next(model.parameters()).device)
+                    if guider_seq is not None:
+                        for idx, mem in enumerate(guider_mems):
+                            guider_mems[idx] = mem.to(next(model.parameters()).device) 
+                else: 
+                    torch.cuda.empty_cache()
+                    for idx, mem_buffer in enumerate(mems_buffers):
+                        mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device)
+                    mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)]
+                    if guider_seq is not None:
+                        for idx, guider_mem_buffer in enumerate(guider_mems_buffers):
+                            guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device)
+                        guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)]
+                    mems_buffers_on_GPU = True
+                    
+            logits, *output_per_layers = model(
+                input_tokens[:, index:],
+                position_ids[..., index: counter+1],
+                attention_mask, # TODO memlen
+                mems=mems,
+                text_len=text_len,
+                frame_len=frame_len,
+                counter=counter,
+                log_text_attention_weights=log_text_attention_weights,
+                enforce_no_swin=enforce_no_swin,
+                limited_spatial_channel_mem=limited_spatial_channel_mem,
+                **kw_args
+            )
+            mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers]
+            
+            if guider_seq is not None:
+                guider_logits, *guider_output_per_layers = model(
+                    guider_input_tokens[:, max(index-guider_index_delta, 0):],
+                    guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta],
+                    guider_attention_mask,
+                    mems=guider_mems,
+                    text_len=guider_text_len,
+                    frame_len=frame_len,
+                    counter=counter-guider_index_delta,
+                    log_text_attention_weights=0,
+                    enforce_no_swin=enforce_no_swin,
+                    limited_spatial_channel_mem=limited_spatial_channel_mem,
+                    **kw_args
+                )
+                guider_mem_kv0, guider_mem_kv1 = [o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers]
+            
+            if not mems_buffers_on_GPU:
+                torch.cuda.empty_cache()
+                for idx, mem_buffer in enumerate(mems_buffers):
+                    mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device)
+                if guider_seq is not None:
+                    for idx, guider_mem_buffer in enumerate(guider_mems_buffers):
+                        guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device)
+                mems_buffers_on_GPU = True
+
+            mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1], mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len) 
+            if guider_seq is not None: 
+                guider_mems, guider_mems_indexs = my_update_mems([guider_mem_kv0, guider_mem_kv1], guider_mems_buffers, guider_mems_indexs, limited_spatial_channel_mem, guider_text_len, frame_len)
+
+       
+        counter += 1 
+        index = counter        
+
+        logits = logits[:, -1].expand(batch_size, -1) # [batch size, vocab size]
+        tokens = tokens.expand(batch_size, -1)
+        if guider_seq is not None:
+            guider_logits = guider_logits[:, -1].expand(batch_size, -1)
+            guider_tokens = guider_tokens.expand(batch_size, -1)
+        
+        if seq[-1][counter].item() < 0:
+            # sampling
+            guided_logits = guider_logits+(logits-guider_logits)*guidance_alpha if guider_seq is not None else logits
+            if mode_stage1 and counter < text_len + 400:
+                tokens, mems = strategy.forward(guided_logits, tokens, mems)
+            else:
+                tokens, mems = strategy2.forward(guided_logits, tokens, mems)
+            if guider_seq is not None:
+                guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]), dim=1)
+                
+            if seq[0][counter].item() >= 0:
+                for si in range(seq.shape[0]):
+                    if seq[si][counter].item() >= 0:
+                        tokens[si, -1] = seq[si, counter]
+                        if guider_seq is not None:
+                            guider_tokens[si, -1] = guider_seq[si, counter-guider_index_delta]
+                    
+        else:
+            tokens = torch.cat((tokens, seq[:, counter:counter+1].clone().expand(tokens.shape[0], 1).to(device=tokens.device, dtype=tokens.dtype)), dim=1)
+            if guider_seq is not None: 
+                guider_tokens = torch.cat((guider_tokens, 
+                                           guider_seq[:, counter-guider_index_delta:counter+1-guider_index_delta]
+                                           .clone().expand(guider_tokens.shape[0], 1).to(device=guider_tokens.device, dtype=guider_tokens.dtype)), dim=1)
+        
+        input_tokens = tokens.clone()
+        if guider_seq is not None:
+            guider_input_tokens = guider_tokens.clone()
+        if (index-text_len-1)//400 < (input_tokens.shape[-1]-text_len-1)//400:
+            boi_idx = ((index-text_len-1)//400 +1)*400+text_len
+            while boi_idx < input_tokens.shape[-1]:
+                input_tokens[:, boi_idx] = tokenizer['<start_of_image>']
+                if guider_seq is not None:
+                    guider_input_tokens[:, boi_idx-guider_index_delta] = tokenizer['<start_of_image>']
+                boi_idx += 400
+        
+        if strategy.is_done:
+            break
+    return strategy.finalize(tokens, mems)
+
+class InferenceModel_Sequential(CogVideoCacheModel):
+    def __init__(self, args, transformer=None, parallel_output=True):
+        super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=-1, cogvideo_stage=1)
+    # TODO: check it 
+    
+    def final_forward(self, logits, **kwargs):
+        logits_parallel = logits
+        logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float())
+        return logits_parallel
+    
+class InferenceModel_Interpolate(CogVideoCacheModel):
+    def __init__(self, args, transformer=None, parallel_output=True):
+        super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=10, cogvideo_stage=2)
+    # TODO: check it 
+
+    def final_forward(self, logits, **kwargs):
+        logits_parallel = logits
+        logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float())
+        return logits_parallel
 
 class Predictor(BasePredictor):
     def setup(self):
-        subprocess.call("python setup.py install", cwd="/src/Image-Local-Attention", shell=True)
-        self.translator = GoogleTranslator(source="en", target="zh-CN")
+        logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.DEBUG, datefmt='%Y-%m-%d %H:%M:%S')
+        subprocess.call("python setup.py develop", cwd="/src/Image-Local-Attention", shell=True)
+        sys.path.append('./Image-Local-Attention')
+        os.environ["SAT_HOME"] = "/sharefs/cogview-new"
+        args = get_args([
+            "--batch-size", "1",                                               
+            "--tokenizer-type", "fake", 
+            "--mode", "inference", 
+            "--distributed-backend", "nccl",
+            "--model-parallel-size", "1",
+            "--fp16",
+            "--sandwich-ln",            
+            "--temperature", "1.05",                        
+            "--max-inference-batch-size", "8"])
+        args.layout = [64,464,2064]
+        args.window_size = 10
+        args.additional_seqlen = 2000
+        args.cogvideo_stage = 1
+        args.do_train = False
+        args.parallel_size = 1
+        args.guidance_alpha = 3.0
+        args.generate_frame_num = 5
+        args.coglm_temperature = 0.89
+        args.coglm_temperature2 = 0.89
+        args.generate_frame_num = 5
+        args.stage1_max_inference_batch_size = -1
+        args.top_k = 12
+        args.use_guidance_stage1 = True
+        args.use_guidance_stage2 = False
+        args.both_stages = True           
+        args.device = torch.device('cuda')
+        
+        self.translator = GoogleTranslator(source="en", target="zh-CN")        
+        self.model_stage1, args = InferenceModel_Sequential.from_pretrained(args, 'cogvideo-stage1')
+        self.model_stage1.eval()
+        self.model_stage1 = self.model_stage1.cpu()
+        self.model_stage2, args = InferenceModel_Interpolate.from_pretrained(args, 'cogvideo-stage2')
+        self.model_stage2.eval()
+        self.model_stage2 = self.model_stage2.cpu()
+        
+        # enable dsr if model exists
+        if os.path.exists('/sharefs/cogview-new/cogview2-dsr'):
+            from sr_pipeline import DirectSuperResolution 
+            dsr_path = auto_create('cogview2-dsr', path=None)
+            self.dsr = DirectSuperResolution(args, dsr_path,
+                                        max_bz=12, onCUDA=False)
+        else:
+            self.dsr = None
+        
+        self.args = args                
+        torch.cuda.empty_cache()
+        
+        
+        self.generate_frame_num = 5
+        
+        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=args.temperature, top_k=args.top_k,
+            temperature2=args.coglm_temperature2)
+
 
     def predict(
         self,
@@ -21,65 +484,296 @@ class Predictor(BasePredictor):
             description="Translate prompt from English to Simplified Chinese (required if not entering Chinese text)",
             default=True,
         ),
-        # both_stages: bool = Input(
-        #    description="Run both stages (uncheck to run only stage 1 for quicker results)", default=True
-        # ),
+        both_stages: bool = Input(
+            description="Run both stages (uncheck to run more quickly and output only a few frames)", default=True
+        ),
         use_guidance: bool = Input(description="Use stage 1 guidance (recommended)", default=True),
-    ) -> typing.List[Path]:
+    ) -> typing.Iterator[Path]:
         if translate:
-            prompt = self.translator.translate(prompt)
+            prompt = self.translator.translate(prompt.strip())
         workdir = tempfile.mkdtemp()
-        os.makedirs(f"{workdir}/output")
-        with open(f"{workdir}/input.txt", "w") as f:
-            f.write(prompt)
+        os.makedirs(f"{workdir}/output/stage1", exist_ok=True)
+        os.makedirs(f"{workdir}/output/stage2", exist_ok=True)
+
         if seed is None:
             seed = randint(0, 2**32)
-        args = [
-            "python",
-            "cogvideo_pipeline.py",
-            "--input-source",
-            f"{workdir}/input.txt",
-            "--output-path",
-            f"{workdir}/output",
-            "--batch-size",
-            "1",
-            "--parallel-size",
-            "1",
-            "--guidance-alpha",
-            "3.0",
-            "--generate-frame-num",
-            "4",
-            "--tokenizer-type",
-            "fake",
-            "--mode",
-            "inference",
-            "--distributed-backend",
-            "nccl",
-            "--fp16",
-            "--model-parallel-size",
-            "1",
-            "--temperature",
-            "1.05",
-            "--coglm-temperature",
-            "0.89",
-            "--top_k",
-            "12",
-            "--sandwich-ln",
-            "--seed",
-            str(seed),
-            "--num-workers",
-            "0",
-            "--batch-size",
-            "1",
-            "--max-inference-batch-size",
-            "8",
-            "--both-stages",
-        ]
-        if use_guidance:
-            args.append("--use-guidance-stage1")
-        print(args)
-        os.environ["SAT_HOME"] = "/sharefs/cogview-new"
-        if subprocess.check_output(args, shell=False, cwd="/src"):
-            output = glob.glob(f"{workdir}/output/**/*.gif")
-            for f in output:
-                yield Path(f)
+
+        self.args.seed = seed
+        self.args.use_guidance_stage1 = use_guidance
+        with torch.no_grad():
+            move_start_time = time.time()
+            logging.debug("moving stage 2 model to cpu")
+            self.model_stage2 = self.model_stage2.cpu()
+            torch.cuda.empty_cache()
+            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")
+        return                            
+                
+    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        
+            
+        if video_raw_text is None: 
+            video_raw_text = seq_text
+        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
+        
+        # generate the first frame:
+        enc_text = tokenizer.encode(seq_text+image_text_suffix)
+        seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1]*400 # IV!!  # test local!!! # test randboi!!!
+        logging.info("[Generating First Frame with CogView2]Raw text: {:s}".format(tokenizer.decode(enc_text)))
+        text_len_1st = len(seq_1st) - frame_len*1 - 1
+
+        seq_1st = torch.cuda.LongTensor(seq_1st, device=args.device).unsqueeze(0)
+        output_list_1st = []
+        for tim in range(max(batch_size // mbz, 1)):
+            start_time = time.time()
+            output_list_1st.append(
+                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=self.strategy_cogview2,
+                    strategy2=self.strategy_cogvideo,
+                    log_text_attention_weights=1.4,
+                    enforce_no_swin=True,
+                    mode_stage1=True,
+                    )[0]
+                )
+            logging.info("[First Frame]Taken time {:.2f}\n".format(time.time() - start_time))
+        output_tokens_1st = torch.cat(output_list_1st, dim=0)
+        given_tokens = output_tokens_1st[:, text_len_1st+1:text_len_1st+401].unsqueeze(1) # given_tokens.shape: [bs, frame_num, 400]
+
+        # generate subsequent frames:
+        total_frames = self.generate_frame_num
+        enc_duration = tokenizer.encode(str(float(duration))+"秒")
+        if use_guide:
+            video_raw_text = video_raw_text + " 视频"
+        enc_text_video = tokenizer.encode(video_raw_text)
+        seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [tokenizer['<start_of_image>']] + [-1]*400*self.generate_frame_num
+        guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-1]*400*self.generate_frame_num
+        logging.info("[Stage1: Generating Subsequent Frames, Frame Rate {:.1f}]\nraw text: {:s}".format(4/duration, tokenizer.decode(enc_text_video)))
+
+        text_len = len(seq) - frame_len*self.generate_frame_num - 1
+        guider_text_len = len(guider_seq) - frame_len*self.generate_frame_num - 1
+        seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(batch_size, 1)
+        guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(batch_size, 1)
+
+        for given_frame_id in range(given_tokens.shape[1]):
+            seq[:, text_len+1+given_frame_id*400: text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id]
+            guider_seq[:, guider_text_len+1+given_frame_id*400:guider_text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id]
+        output_list = []
+        
+        if use_guide:
+            video_log_text_attention_weights = 0
+        else: 
+            guider_seq = None
+            video_log_text_attention_weights = 1.4
+            
+        for tim in range(max(batch_size // mbz, 1)):
+            start_time = time.time()
+            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(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=self.strategy_cogview2,
+                    strategy2=self.strategy_cogvideo,
+                    log_text_attention_weights=video_log_text_attention_weights,
+                    guider_seq=guider_seq2,
+                    guider_text_len=guider_text_len,
+                    guidance_alpha=args.guidance_alpha,
+                    limited_spatial_channel_mem=True,
+                    mode_stage1=True,
+                    )[0]
+                )
+
+        output_tokens = torch.cat(output_list, dim=0)[:, 1+text_len:]
+                
+        # decoding
+        imgs, sred_imgs, txts = [], [], []
+        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)]
+            imgs.append(decoded_imgs) # only the last image (target)
+
+        assert len(imgs) == batch_size
+        save_tokens = output_tokens[:, :+total_frames*400].reshape(-1, total_frames, 400).cpu()
+        if outputdir is not None:
+            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)
+                os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25")
+            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))
+        
+        return save_tokens
+
+    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):
+        stage2_starttime = time.time()
+        args = self.args
+        use_guidance = args.use_guidance_stage2
+        if args.both_stages:
+            move_start_time = time.time()
+            logging.debug("moving stage-2 model to cuda")
+            model = model.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
+            parent_given_tokens = parent_given_tokens[gpu_rank*sample_num:(gpu_rank+1)*sample_num]
+        except:
+            logging.critical("No frame_tokens found in interpolation, skip")
+            return False
+        
+        # CogVideo Stage2 Generation
+        while duration >= 0.5: # TODO: You can change the boundary to change the frame rate
+            parent_given_tokens_num = parent_given_tokens.shape[1]
+            generate_batchsize_persample = (parent_given_tokens_num-1)//2
+            generate_batchsize_total = generate_batchsize_persample * sample_num
+            total_frames = self.generate_frame_num
+            frame_len = 400
+            enc_text = tokenizer.encode(seq_text)
+            enc_duration = tokenizer.encode(str(float(duration))+"秒")
+            seq = enc_duration + [tokenizer['<n>']] + enc_text + [tokenizer['<start_of_image>']] + [-1]*400*self.generate_frame_num
+            text_len = len(seq) - frame_len*self.generate_frame_num - 1
+            
+            logging.info("[Stage2: Generating Frames, Frame Rate {:d}]\nraw text: {:s}".format(int(4/duration), tokenizer.decode(enc_text)))
+            
+            # generation
+            seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1)
+            for sample_i in range(sample_num):
+                for i in range(generate_batchsize_persample):
+                    seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i]
+                    seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1]
+                    seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2]
+                
+            if use_guidance:
+                guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-1]*400*self.generate_frame_num                 
+                guider_text_len = len(guider_seq) - frame_len*self.generate_frame_num - 1
+                guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1)
+                for sample_i in range(sample_num):
+                    for i in range(generate_batchsize_persample):
+                        guider_seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i]
+                        guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1]
+                        guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2]
+                video_log_text_attention_weights = 0
+            else:
+                guider_seq=None
+                guider_text_len=0
+                video_log_text_attention_weights = 1.4
+
+            mbz = args.max_inference_batch_size
+
+            assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
+            output_list = []
+            start_time = time.time()
+            for tim in range(max(generate_batchsize_total // mbz, 1)):
+                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(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=self.strategy_cogview2,
+                        strategy2=self.strategy_cogvideo,
+                        log_text_attention_weights=video_log_text_attention_weights,
+                        mode_stage1=False,
+                        guider_seq=guider_seq2,
+                        guider_text_len=guider_text_len,
+                        guidance_alpha=args.guidance_alpha,
+                        limited_spatial_channel_mem=True,
+                        )[0]
+                    )
+            logging.info("Duration {:.2f}, Taken time {:.2f}\n".format(duration, time.time() - start_time))
+
+            output_tokens = torch.cat(output_list, dim=0)
+            output_tokens = output_tokens[:, text_len+1:text_len+1+(total_frames)*400].reshape(sample_num, -1, 400*total_frames)
+            output_tokens_merge = torch.cat((output_tokens[:, :, :1*400], 
+                                            output_tokens[:, :, 400*3:4*400],
+                                            output_tokens[:, :, 400*1:2*400],
+                                            output_tokens[:, :, 400*4:(total_frames)*400]), dim=2).reshape(sample_num, -1, 400)     
+
+            output_tokens_merge = torch.cat((output_tokens_merge, output_tokens[:, -1:, 400*2:3*400]), dim=1)
+            duration /= 2
+            parent_given_tokens = output_tokens_merge                        
+        
+        logging.info("CogVideo Stage2 completed. Taken time {:.2f}\n".format(time.time() - stage2_starttime))    
+
+        enc_text = tokenizer.encode(seq_text)
+        frame_num_per_sample = parent_given_tokens.shape[1]
+        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()
+        torch.cuda.empty_cache()
+            
+        # use dsr if loaded
+        if (self.dsr):
+            # direct super-resolution by CogView2
+            logging.info("[Direct super-resolution]")
+            dsr_starttime = time.time()
+            text_seq = torch.cuda.LongTensor(enc_text, device=args.device).unsqueeze(0).repeat(parent_given_tokens_2d.shape[0], 1)
+            sred_tokens = self.dsr(text_seq, parent_given_tokens_2d)
+            decoded_sr_videos = []
+            
+            for sample_i in range(sample_num):
+                decoded_sr_imgs = []
+                for frame_i in range(frame_num_per_sample):
+                    decoded_sr_img = tokenizer.decode(image_ids=sred_tokens[frame_i+sample_i*frame_num_per_sample][-3600:])
+                    decoded_sr_imgs.append(torch.nn.functional.interpolate(decoded_sr_img, size=(480, 480)))
+                decoded_sr_videos.append(decoded_sr_imgs)
+
+            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)
+                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")
+            
+            logging.info("Direct super-resolution completed. Taken time {:.2f}\n".format(time.time() - dsr_starttime))
+        else:            
+            output_tokens = torch.cat(output_list, dim=0)[:, 1+text_len:]
+            
+            imgs, sred_imgs, txts = [], [], []
+            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)]
+                imgs.append(decoded_imgs) # only the last image (target)
+
+            if outputdir is not None:
+                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)
+                    os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25")
+
+