# -*- encoding: utf-8 -*- """ @File : cogvideo_pipeline.py @Time : 2022/07/15 11:24:56 @Author : Wenyi Hong @Version : 1.0 @Contact : hwy22@mails.tsinghua.edu.cn """ # here put the import lib import os import sys import torch import argparse import time from torchvision.utils import save_image import stat from videogen_hub.depend.icetk import icetk as tokenizer import logging, sys import torch.distributed as dist tokenizer.add_special_tokens( ["", "", ""] ) 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[""] if guider_seq is not None: guider_input_tokens[:, guider_text_len + 400 * fid] = tokenizer[ "" ] 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[""] if guider_seq is not None: guider_input_tokens[:, boi_idx - guider_index_delta] = tokenizer[ "" ] 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 def main(args): 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=(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 ) ) 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=(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 # ====================================================================================================== 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 = 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.both_stages: 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 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 if __name__ == "__main__": logging.basicConfig(stream=sys.stderr, level=logging.DEBUG) py_parser = argparse.ArgumentParser(add_help=False) py_parser.add_argument("--generate-frame-num", type=int, default=5) py_parser.add_argument("--coglm-temperature2", type=float, default=0.89) # py_parser.add_argument("--interp-duration", type=float, default=-1) # -1是顺序生成，0是超分，0.5/1/2是插帧 # py_parser.add_argument("--total-duration", type=float, default=4.0) # 整个的时间 py_parser.add_argument("--use-guidance-stage1", action="store_true") py_parser.add_argument("--use-guidance-stage2", action="store_true") py_parser.add_argument("--guidance-alpha", type=float, default=3.0) py_parser.add_argument( "--stage-1", action="store_true" ) # stage 1: sequential generation py_parser.add_argument("--stage-2", action="store_true") # stage 2: interp + dsr py_parser.add_argument( "--both-stages", action="store_true" ) # stage 1&2: sequential generation; interp + dsr py_parser.add_argument("--parallel-size", type=int, default=1) py_parser.add_argument( "--stage1-max-inference-batch-size", type=int, default=-1 ) # -1: use max-inference-batch-size py_parser.add_argument("--multi-gpu", action="store_true") CogVideoCacheModel.add_model_specific_args(py_parser) known, args_list = py_parser.parse_known_args() args = get_args(args_list) args = argparse.Namespace(**vars(args), **vars(known)) args.layout = [int(x) for x in args.layout.split(",")] args.do_train = False torch.cuda.set_device(args.device) with torch.no_grad(): main(args)