# This code is adapted from https://github.com/THUDM/CogVideo/blob/ff423aa169978fb2f636f761e348631fa3178b03/cogvideo_pipeline.py
from __future__ import annotations
import argparse
import logging
import os
import pathlib
import shutil
import subprocess
import sys
import tempfile
import time
import zipfile
from typing import Any
if os.getenv('SYSTEM') == 'spaces':
subprocess.run('pip install icetk==0.0.4'.split())
subprocess.run('pip install SwissArmyTransformer==0.2.9'.split())
subprocess.run(
'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
.split())
#subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
#subprocess.run('git checkout 1403c21'.split(), cwd='apex')
#with open('patch.apex') as f:
# subprocess.run('patch -p1'.split(), cwd='apex', stdin=f)
#subprocess.run(
# 'pip install -v --disable-pip-version-check --no-cache-dir --global-option --cpp_ext --global-option --cuda_ext ./'
# .split(),
# cwd='apex')
#subprocess.run('rm -rf apex'.split())
with open('patch') as f:
subprocess.run('patch -p1'.split(), cwd='CogVideo', stdin=f)
from huggingface_hub import hf_hub_download
def download_and_extract_icetk_models() -> None:
icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
icetk_model_dir.mkdir()
path = hf_hub_download('THUDM/icetk',
'models.zip',
use_auth_token=os.getenv('HF_TOKEN'))
with zipfile.ZipFile(path) as f:
f.extractall(path=icetk_model_dir.as_posix())
def download_and_extract_cogvideo_models(name: str) -> None:
path = hf_hub_download('THUDM/CogVideo',
name,
use_auth_token=os.getenv('HF_TOKEN'))
with zipfile.ZipFile(path) as f:
f.extractall('pretrained')
os.remove(path)
def download_and_extract_cogview2_models(name: str) -> None:
path = hf_hub_download('THUDM/CogView2', name)
with zipfile.ZipFile(path) as f:
f.extractall()
shutil.move('/home/user/app/sharefs/cogview-new/cogview2-dsr',
'pretrained')
shutil.rmtree('/home/user/app/sharefs/')
os.remove(path)
download_and_extract_icetk_models()
download_and_extract_cogvideo_models('cogvideo-stage1.zip')
#download_and_extract_cogvideo_models('cogvideo-stage2.zip')
#download_and_extract_cogview2_models('cogview2-dsr.zip')
os.environ['SAT_HOME'] = '/home/user/app/pretrained'
import gradio as gr
import imageio.v2 as iio
import numpy as np
import torch
from icetk import IceTokenizer
from SwissArmyTransformer import get_args
from SwissArmyTransformer.arguments import set_random_seed
from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
from SwissArmyTransformer.resources import auto_create
app_dir = pathlib.Path(__file__).parent
submodule_dir = app_dir / 'CogVideo'
sys.path.insert(0, submodule_dir.as_posix())
from coglm_strategy import CoglmStrategy
from models.cogvideo_cache_model import CogVideoCacheModel
from sr_pipeline import DirectSuperResolution
formatter = logging.Formatter(
'[%(asctime)s] %(name)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setLevel(logging.INFO)
stream_handler.setFormatter(formatter)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.propagate = False
logger.addHandler(stream_handler)
ICETK_MODEL_DIR = app_dir / 'icetk_models'
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 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,
tokenizer,
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:
logger.debug('Using Guidance In Inference')
if limited_spatial_channel_mem:
logger.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 get_default_args() -> argparse.Namespace:
known = argparse.Namespace(generate_frame_num=5,
coglm_temperature2=0.89,
use_guidance_stage1=True,
use_guidance_stage2=False,
guidance_alpha=3.0,
stage_1=True,
stage_2=False,
both_stages=False,
parallel_size=1,
stage1_max_inference_batch_size=-1,
multi_gpu=False,
layout='64, 464, 2064',
window_size=10,
additional_seqlen=2000,
cogvideo_stage=1)
args_list = [
'--tokenizer-type',
'fake',
'--mode',
'inference',
'--distributed-backend',
'nccl',
'--fp16',
'--model-parallel-size',
'1',
'--temperature',
'1.05',
'--top_k',
'12',
'--sandwich-ln',
'--seed',
'1234',
'--num-workers',
'0',
'--batch-size',
'1',
'--max-inference-batch-size',
'8',
]
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
return args
class Model:
def __init__(self, only_first_stage: bool = False):
self.args = get_default_args()
if only_first_stage:
self.args.stage_1 = True
self.args.both_stages = False
else:
self.args.stage_1 = False
self.args.both_stages = True
self.tokenizer = self.load_tokenizer()
self.model_stage1, self.args = self.load_model_stage1()
self.model_stage2, self.args = self.load_model_stage2()
self.strategy_cogview2, self.strategy_cogvideo = self.load_strategies()
self.dsr = self.load_dsr()
self.device = torch.device(self.args.device)
def load_tokenizer(self) -> IceTokenizer:
logger.info('--- load_tokenizer ---')
start = time.perf_counter()
tokenizer = IceTokenizer(ICETK_MODEL_DIR.as_posix())
tokenizer.add_special_tokens(
['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
elapsed = time.perf_counter() - start
logger.info(f'--- done ({elapsed=:.3f}) ---')
return tokenizer
def load_model_stage1(
self) -> tuple[CogVideoCacheModel, argparse.Namespace]:
logger.info('--- load_model_stage1 ---')
start = time.perf_counter()
args = self.args
model_stage1, args = InferenceModel_Sequential.from_pretrained(
args, 'cogvideo-stage1')
model_stage1.eval()
if args.both_stages:
model_stage1 = model_stage1.cpu()
elapsed = time.perf_counter() - start
logger.info(f'--- done ({elapsed=:.3f}) ---')
return model_stage1, args
def load_model_stage2(
self) -> tuple[CogVideoCacheModel | None, argparse.Namespace]:
logger.info('--- load_model_stage2 ---')
start = time.perf_counter()
args = self.args
if 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()
else:
model_stage2 = None
elapsed = time.perf_counter() - start
logger.info(f'--- done ({elapsed=:.3f}) ---')
return model_stage2, args
def load_strategies(self) -> tuple[CoglmStrategy, CoglmStrategy]:
logger.info('--- load_strategies ---')
start = time.perf_counter()
invalid_slices = [slice(self.tokenizer.num_image_tokens, None)]
strategy_cogview2 = CoglmStrategy(invalid_slices,
temperature=1.0,
top_k=16)
strategy_cogvideo = CoglmStrategy(
invalid_slices,
temperature=self.args.temperature,
top_k=self.args.top_k,
temperature2=self.args.coglm_temperature2)
elapsed = time.perf_counter() - start
logger.info(f'--- done ({elapsed=:.3f}) ---')
return strategy_cogview2, strategy_cogvideo
def load_dsr(self) -> DirectSuperResolution | None:
logger.info('--- load_dsr ---')
start = time.perf_counter()
if self.args.both_stages:
path = auto_create('cogview2-dsr', path=None)
dsr = DirectSuperResolution(self.args,
path,
max_bz=12,
onCUDA=False)
else:
dsr = None
elapsed = time.perf_counter() - start
logger.info(f'--- done ({elapsed=:.3f}) ---')
return dsr
@torch.inference_mode()
def process_stage1(self,
model,
seq_text,
duration,
video_raw_text=None,
video_guidance_text='视频',
image_text_suffix='',
batch_size=1):
process_start_time = time.perf_counter()
generate_frame_num = self.args.generate_frame_num
tokenizer = self.tokenizer
use_guide = self.args.use_guidance_stage1
if next(model.parameters()).device != self.device:
move_start_time = time.perf_counter()
logger.debug('moving stage 1 model to cuda')
model = model.to(self.device)
elapsed = time.perf_counter() - move_start_time
logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
if video_raw_text is None:
video_raw_text = seq_text
mbz = self.args.stage1_max_inference_batch_size if self.args.stage1_max_inference_batch_size > 0 else self.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
logger.info(
f'[Generating First Frame with CogView2] Raw text: {tokenizer.decode(enc_text):s}'
)
text_len_1st = len(seq_1st) - frame_len * 1 - 1
seq_1st = torch.tensor(seq_1st, dtype=torch.long,
device=self.device).unsqueeze(0)
output_list_1st = []
for tim in range(max(batch_size // mbz, 1)):
start_time = time.perf_counter()
output_list_1st.append(
my_filling_sequence(
model,
tokenizer,
self.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])
elapsed = time.perf_counter() - start_time
logger.info(f'[First Frame] Elapsed: {elapsed:.2f}')
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(f'{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
logger.info(
f'[Stage1: Generating Subsequent Frames, Frame Rate {4/duration:.1f}] raw text: {tokenizer.decode(enc_text_video):s}'
)
text_len = len(seq) - frame_len * generate_frame_num - 1
guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
seq = torch.tensor(seq, dtype=torch.long,
device=self.device).unsqueeze(0).repeat(
batch_size, 1)
guider_seq = torch.tensor(guider_seq,
dtype=torch.long,
device=self.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)):
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,
tokenizer,
self.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=self.args.guidance_alpha,
limited_spatial_channel_mem=True,
mode_stage1=True,
)[0])
output_tokens = torch.cat(output_list, dim=0)[:, 1 + text_len:]
if self.args.both_stages:
move_start_time = time.perf_counter()
logger.debug('moving stage 1 model to cpu')
model = model.cpu()
torch.cuda.empty_cache()
elapsed = time.perf_counter() - move_start_time
logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
# decoding
res = []
for seq in output_tokens:
decoded_imgs = [
self.postprocess(
torch.nn.functional.interpolate(tokenizer.decode(
image_ids=seq.tolist()[i * 400:(i + 1) * 400]),
size=(480, 480))[0])
for i in range(total_frames)
]
res.append(decoded_imgs) # only the last image (target)
assert len(res) == batch_size
tokens = output_tokens[:, :+total_frames * 400].reshape(
-1, total_frames, 400).cpu()
elapsed = time.perf_counter() - process_start_time
logger.info(f'--- done ({elapsed=:.3f}) ---')
return tokens, res[0]
@torch.inference_mode()
def process_stage2(self,
model,
seq_text,
duration,
parent_given_tokens,
video_raw_text=None,
video_guidance_text='视频',
gpu_rank=0,
gpu_parallel_size=1):
process_start_time = time.perf_counter()
generate_frame_num = self.args.generate_frame_num
tokenizer = self.tokenizer
use_guidance = self.args.use_guidance_stage2
stage2_start_time = time.perf_counter()
if next(model.parameters()).device != self.device:
move_start_time = time.perf_counter()
logger.debug('moving stage-2 model to cuda')
model = model.to(self.device)
elapsed = time.perf_counter() - move_start_time
logger.debug(f'moving in stage-2 model takes time: {elapsed:.2f}')
try:
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:
logger.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
logger.info(
f'[Stage2: Generating Frames, Frame Rate {int(4/duration):d}] raw text: {tokenizer.decode(enc_text):s}'
)
# generation
seq = torch.tensor(seq, dtype=torch.long,
device=self.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.tensor(
guider_seq, dtype=torch.long,
device=self.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 = self.args.max_inference_batch_size
assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
output_list = []
start_time = time.perf_counter()
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,
tokenizer,
self.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=self.args.guidance_alpha,
limited_spatial_channel_mem=True,
)[0])
elapsed = time.perf_counter() - start_time
logger.info(f'Duration {duration:.2f}, Elapsed: {elapsed:.2f}\n')
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 self.args.both_stages:
move_start_time = time.perf_counter()
logger.debug('moving stage 2 model to cpu')
model = model.cpu()
torch.cuda.empty_cache()
elapsed = time.perf_counter() - move_start_time
logger.debug(f'moving out model2 takes time: {elapsed:.2f}')
elapsed = time.perf_counter() - stage2_start_time
logger.info(f'CogVideo Stage2 completed. Elapsed: {elapsed:.2f}\n')
# direct super-resolution by CogView2
logger.info('[Direct super-resolution]')
dsr_start_time = time.perf_counter()
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.tensor(enc_text, dtype=torch.long,
device=self.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(
self.postprocess(
torch.nn.functional.interpolate(decoded_sr_img,
size=(480, 480))[0]))
decoded_sr_videos.append(decoded_sr_imgs)
elapsed = time.perf_counter() - dsr_start_time
logger.info(
f'Direct super-resolution completed. Elapsed: {elapsed:.2f}')
elapsed = time.perf_counter() - process_start_time
logger.info(f'--- done ({elapsed=:.3f}) ---')
return True, decoded_sr_videos[0]
@staticmethod
def postprocess(tensor: torch.Tensor) -> np.ndarray:
return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
1, 2, 0).to(torch.uint8).numpy()
def run(self, text: str, seed: int,
only_first_stage: bool) -> list[np.ndarray]:
logger.info('==================== run ====================')
start = time.perf_counter()
set_random_seed(seed)
self.args.seed = seed
if only_first_stage:
self.args.stage_1 = True
self.args.both_stages = False
else:
self.args.stage_1 = False
self.args.both_stages = True
parent_given_tokens, res = self.process_stage1(
self.model_stage1,
text,
duration=4.0,
video_raw_text=text,
video_guidance_text='视频',
image_text_suffix=' 高清摄影',
batch_size=self.args.batch_size)
if not only_first_stage:
_, res = self.process_stage2(
self.model_stage2,
text,
duration=2.0,
parent_given_tokens=parent_given_tokens,
video_raw_text=text + ' 视频',
video_guidance_text='视频',
gpu_rank=0,
gpu_parallel_size=1) # TODO: 修改
elapsed = time.perf_counter() - start
logger.info(f'Elapsed: {elapsed:.3f}')
logger.info('==================== done ====================')
return res
class AppModel(Model):
def __init__(self, only_first_stage: bool):
super().__init__(only_first_stage)
self.translator = gr.Interface.load(
'spaces/chinhon/translation_eng2ch')
def to_video(self, frames: list[np.ndarray]) -> str:
out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
if self.args.stage_1:
fps = 4
else:
fps = 8
writer = iio.get_writer(out_file.name, fps=fps)
for frame in frames:
writer.append_data(frame)
writer.close()
return out_file.name
def run_with_translation(
self, text: str, translate: bool, seed: int, only_first_stage: bool
) -> tuple[str | None, str | None, list[np.ndarray] | None]:
logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=}')
if translate:
text = translated_text = self.translator(text)
else:
translated_text = None
frames = self.run(text, seed, only_first_stage)
video_path = self.to_video(frames)
return translated_text, video_path