from videogen_hub.pipelines.cogvideo.cogvideo_src.cogvideo_pipeline import ( InferenceModel_Interpolate, InferenceModel_Sequential, my_filling_sequence, get_masks_and_position_ids_stage1, get_masks_and_position_ids_stage2, my_save_multiple_images, ) from videogen_hub.depend.icetk import icetk as tokenizer from videogen_hub.pipelines.cogvideo.cogvideo_src.coglm_strategy import ( CoglmStrategy, ) from videogen_hub.pipelines.cogvideo.cogvideo_src.sr_pipeline import ( DirectSuperResolution, ) from SwissArmyTransformer.resources import auto_create import time, logging, sys, os, torch import torch.distributed as dist # path = os.path.join(args.output_path, f"{now_qi}_{raw_text}") def pipeline(args, raw_text, height, width, duration): # model_stage1, args = InferenceModel_Sequential.from_pretrained(args, 'cogvideo-stage1') # model_stage1.eval() # parent_givan_tokens = process_stage1(model_stage1, raw_text, duration=4.0, video_raw_text=raw_text, video_guidance_text="视频", # image_text_suffix=" 高清摄影", # outputdir=None, batch_size=args.batch_size) # process_stage2(model_stage2, raw_text, duration=2.0, video_raw_text=raw_text+" 视频", # video_guidance_text="视频", parent_given_tokens=parent_given_tokens, # outputdir=path, # gpu_rank=0, gpu_parallel_size=1) # TODO: 修改 assert int(args.stage_1) + int(args.stage_2) + int(args.both_stages) == 1 rank_id = args.device % args.parallel_size generate_frame_num = args.generate_frame_num if args.stage_1 or args.both_stages: model_stage1, args = InferenceModel_Sequential.from_pretrained( args, "cogvideo-stage1" ) model_stage1.eval() if args.both_stages: model_stage1 = model_stage1.cpu() if args.stage_2 or args.both_stages: model_stage2, args = InferenceModel_Interpolate.from_pretrained( args, "cogvideo-stage2" ) model_stage2.eval() if args.both_stages: model_stage2 = model_stage2.cpu() 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=args.temperature, top_k=args.top_k, temperature2=args.coglm_temperature2, ) if not args.stage_1: # from sr_pipeline import DirectSuperResolution dsr_path = auto_create( "cogview2-dsr", path=None ) # path=os.getenv('SAT_HOME', '~/.sat_models') dsr = DirectSuperResolution(args, dsr_path, max_bz=12, onCUDA=False) def process_stage2( 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() 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 = generate_frame_num frame_len = 400 enc_text = tokenizer.encode(seq_text) enc_duration = tokenizer.encode(str(float(duration)) + "秒") seq = ( enc_duration + [tokenizer[""]] + enc_text + [tokenizer[""]] + [-1] * 400 * generate_frame_num ) text_len = len(seq) - frame_len * 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[""]] + tokenizer.encode(video_guidance_text) + [tokenizer[""]] + [-1] * 400 * generate_frame_num ) guider_text_len = len(guider_seq) - frame_len * 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=strategy_cogview2, strategy2=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 if args.both_stages: move_start_time = time.time() logging.debug("moving stage 2 model to cpu") model = model.cpu() torch.cuda.empty_cache() logging.debug( "moving out model2 takes time: {:.2f}".format( time.time() - move_start_time ) ) logging.info( "CogVideo Stage2 completed. Taken time {:.2f}\n".format( time.time() - stage2_starttime ) ) # decoding # imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()), size=(480, 480)) for seq in output_tokens_merge] # os.makedirs(output_dir_full_path, exist_ok=True) # my_save_multiple_images(imgs, output_dir_full_path,subdir="frames", debug=False) # torch.save(output_tokens_merge.cpu(), os.path.join(output_dir_full_path, 'frame_token.pt')) # os.system(f"gifmaker -i '{output_dir_full_path}'/frames/0*.jpg -o '{output_dir_full_path}/{str(float(duration))}_concat.gif' -d 0.2") # direct super-resolution by CogView2 logging.info("[Direct super-resolution]") dsr_starttime = time.time() 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) text_seq = ( torch.cuda.LongTensor(enc_text, device=args.device) .unsqueeze(0) .repeat(parent_given_tokens_2d.shape[0], 1) ) sred_tokens = 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=(height, width) ) ) decoded_sr_videos.append(decoded_sr_imgs) return decoded_sr_videos # 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)) # return True def process_stage1( 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() use_guide = args.use_guidance_stage1 if args.both_stages: move_start_time = time.time() logging.debug("moving stage 1 model to cuda") model = model.cuda() logging.debug( "moving in model1 takes time: {:.2f}".format( time.time() - move_start_time ) ) 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[""]] + [-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=strategy_cogview2, strategy2=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 = 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[""]] + enc_text_video + [tokenizer[""]] + [-1] * 400 * generate_frame_num ) guider_seq = ( enc_duration + [tokenizer[""]] + tokenizer.encode(video_guidance_text) + [tokenizer[""]] + [-1] * 400 * 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 * generate_frame_num - 1 guider_text_len = len(guider_seq) - frame_len * 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=strategy_cogview2, strategy2=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 :] if args.both_stages: move_start_time = time.time() logging.debug("moving stage 1 model to cpu") model = model.cpu() torch.cuda.empty_cache() logging.debug( "moving in model1 takes time: {:.2f}".format( time.time() - move_start_time ) ) # 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=(height, width), ) for i in range(total_frames) ] imgs.append(decoded_imgs) # only the last image (target) assert len(imgs) == batch_size return imgs # 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 # ====================================================================================================== if args.stage_1 or args.both_stages: if args.input_source != "interactive": with open(args.input_source, "r") as fin: promptlist = fin.readlines() promptlist = [p.strip() for p in promptlist] else: promptlist = None now_qi = -1 while True: now_qi += 1 if promptlist is not None: # with input-source if args.multi_gpu: if now_qi % dist.get_world_size() != dist.get_rank(): continue rk = dist.get_rank() else: rk = 0 raw_text = promptlist[now_qi] raw_text = raw_text.strip() print(f"Working on Line No. {now_qi} on {rk}... [{raw_text}]") else: # interactive raw_text = input("\nPlease Input Query (stop to exit) >>> ") raw_text = raw_text.strip() if not raw_text: print("Query should not be empty!") continue if raw_text == "stop": return try: path = os.path.join(args.output_path, f"{now_qi}_{raw_text}") parent_given_tokens, imgs = process_stage1( model_stage1, raw_text, duration=4.0, video_raw_text=raw_text, video_guidance_text="视频", image_text_suffix=" 高清摄影", outputdir=path if args.stage_1 else None, batch_size=args.batch_size, ) if args.stage_1 and not args.both_stages: print("only stage 1") return imgs if args.both_stages: videos = process_stage2( model_stage2, raw_text, duration=duration, video_raw_text=raw_text + " 视频", video_guidance_text="视频", parent_given_tokens=parent_given_tokens, outputdir=path, gpu_rank=0, gpu_parallel_size=1, ) # TODO: 修改 return videos except (ValueError, FileNotFoundError) as e: print(e) continue elif args.stage_2: sample_dirs = os.listdir(args.output_path) for sample in sample_dirs: raw_text = sample.split("_")[-1] path = os.path.join(args.output_path, sample, "Interp") parent_given_tokens = torch.load( os.path.join(args.output_path, sample, "frame_tokens.pt") ) process_stage2( raw_text, duration=2.0, video_raw_text=raw_text + " 视频", video_guidance_text="视频", parent_given_tokens=parent_given_tokens, outputdir=path, gpu_rank=0, gpu_parallel_size=1, ) # TODO: 修改 else: assert False