CogVideoX-2B-Space / cogvideo_pipeline.py
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# -*- 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 icetk import icetk as tokenizer
import logging, sys
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
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['<n>']] + enc_text + [tokenizer['<start_of_image>']] + [-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['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-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['<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=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['<n>']] + enc_text_video + [tokenizer['<start_of_image>']] + [-1]*400*generate_frame_num
guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-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_false')
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_false') # stage 2: interp + dsr
py_parser.add_argument('--both-stages', action='store_false') # 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_false')
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)