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CogVideo / model.py

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	# 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,
	image_prompt=None):
	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)
	if image_prompt is None:
	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]
	else:
	given_tokens = tokenizer.encode(image_path=image_prompt, image_size=160).repeat(batch_size, 1).unsqueeze(1)

	# 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,image_prompt: None) -> 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,
	image_prompt=image_prompt)
	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,image_prompt: None) -> tuple[str \| None, str \| None]:

	logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=},{image_prompt=}')
	if translate:
	text = translated_text = self.translator(text)
	else:
	translated_text = None
	frames = self.run(text, seed, only_first_stage,image_prompt)
	video_path = self.to_video(frames)
	return translated_text, video_path