# 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[''] if guider_seq is not None: guider_input_tokens[:, guider_text_len + 400 * fid] = tokenizer[''] attention_mask = attention_mask.type_as(next( model.parameters())) # if fp16 # initialize generation counter = context_length - 1 # Last fixed index is ``counter'' index = 0 # Next forward starting index, also the length of cache. mems_buffers_on_GPU = False mems_indexs = [0, 0] mems_len = [(400 + 74) if limited_spatial_channel_mem else 5 * 400 + 74, 5 * 400 + 74] mems_buffers = [ torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size * 2, dtype=next(model.parameters()).dtype) for mem_len in mems_len ] if guider_seq is not None: guider_attention_mask = guider_attention_mask.type_as( next(model.parameters())) # if fp16 guider_mems_buffers = [ torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size * 2, dtype=next(model.parameters()).dtype) for mem_len in mems_len ] guider_mems_indexs = [0, 0] guider_mems = None torch.cuda.empty_cache() # step-by-step generation while counter < len(seq[0]) - 1: # we have generated counter+1 tokens # Now, we want to generate seq[counter + 1], # token[:, index: counter+1] needs forwarding. if index == 0: group_size = 2 if (input_tokens.shape[0] == batch_size and not mode_stage1) else batch_size logits_all = None for batch_idx in range(0, input_tokens.shape[0], group_size): logits, *output_per_layers = model( input_tokens[batch_idx:batch_idx + group_size, index:], position_ids[..., index:counter + 1], attention_mask, # TODO memlen mems=mems, text_len=text_len, frame_len=frame_len, counter=counter, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, **kw_args) logits_all = torch.cat( (logits_all, logits), dim=0) if logits_all is not None else logits mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers]] next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id( text_len, frame_len, mem_kv01[0][0].shape[1]) for id, mem_kv in enumerate(mem_kv01): for layer, mem_kv_perlayer in enumerate(mem_kv): if limited_spatial_channel_mem and id == 0: mems_buffers[id][ layer, batch_idx:batch_idx + group_size, : text_len] = mem_kv_perlayer.expand( min(group_size, input_tokens.shape[0] - batch_idx), -1, -1)[:, :text_len] mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\ mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:] else: mems_buffers[id][ layer, batch_idx:batch_idx + group_size, :mem_kv_perlayer. shape[1]] = mem_kv_perlayer.expand( min(group_size, input_tokens.shape[0] - batch_idx), -1, -1) mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[ 1], mem_kv01[1][0].shape[1] if limited_spatial_channel_mem: mems_indexs[0] -= (next_tokens_frame_begin_id - text_len) mems = [ mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2) ] logits = logits_all # Guider if guider_seq is not None: guider_logits_all = None for batch_idx in range(0, guider_input_tokens.shape[0], group_size): guider_logits, *guider_output_per_layers = model( guider_input_tokens[batch_idx:batch_idx + group_size, max(index - guider_index_delta, 0):], guider_position_ids[ ..., max(index - guider_index_delta, 0):counter + 1 - guider_index_delta], guider_attention_mask, mems=guider_mems, text_len=guider_text_len, frame_len=frame_len, counter=counter - guider_index_delta, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, **kw_args) guider_logits_all = torch.cat( (guider_logits_all, guider_logits), dim=0 ) if guider_logits_all is not None else guider_logits guider_mem_kv01 = [[ o['mem_kv'][0] for o in guider_output_per_layers ], [o['mem_kv'][1] for o in guider_output_per_layers]] for id, guider_mem_kv in enumerate(guider_mem_kv01): for layer, guider_mem_kv_perlayer in enumerate( guider_mem_kv): if limited_spatial_channel_mem and id == 0: guider_mems_buffers[id][ layer, batch_idx:batch_idx + group_size, : guider_text_len] = guider_mem_kv_perlayer.expand( min(group_size, input_tokens.shape[0] - batch_idx), -1, -1)[:, :guider_text_len] guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id( guider_text_len, frame_len, guider_mem_kv_perlayer.shape[1]) guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\ guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:] else: guider_mems_buffers[id][ layer, batch_idx:batch_idx + group_size, :guider_mem_kv_perlayer. shape[1]] = guider_mem_kv_perlayer.expand( min(group_size, input_tokens.shape[0] - batch_idx), -1, -1) guider_mems_indexs[0], guider_mems_indexs[ 1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[ 1][0].shape[1] if limited_spatial_channel_mem: guider_mems_indexs[0] -= ( guider_next_tokens_frame_begin_id - guider_text_len) guider_mems = [ guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2) ] guider_logits = guider_logits_all else: if not mems_buffers_on_GPU: if not mode_stage1: torch.cuda.empty_cache() for idx, mem in enumerate(mems): mems[idx] = mem.to(next(model.parameters()).device) if guider_seq is not None: for idx, mem in enumerate(guider_mems): guider_mems[idx] = mem.to( next(model.parameters()).device) else: torch.cuda.empty_cache() for idx, mem_buffer in enumerate(mems_buffers): mems_buffers[idx] = mem_buffer.to( next(model.parameters()).device) mems = [ mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2) ] if guider_seq is not None: for idx, guider_mem_buffer in enumerate( guider_mems_buffers): guider_mems_buffers[idx] = guider_mem_buffer.to( next(model.parameters()).device) guider_mems = [ guider_mems_buffers[id] [:, :, :guider_mems_indexs[id]] for id in range(2) ] mems_buffers_on_GPU = True logits, *output_per_layers = model( input_tokens[:, index:], position_ids[..., index:counter + 1], attention_mask, # TODO memlen mems=mems, text_len=text_len, frame_len=frame_len, counter=counter, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, limited_spatial_channel_mem=limited_spatial_channel_mem, **kw_args) mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers ], [o['mem_kv'][1] for o in output_per_layers] if guider_seq is not None: guider_logits, *guider_output_per_layers = model( guider_input_tokens[:, max(index - guider_index_delta, 0):], guider_position_ids[..., max(index - guider_index_delta, 0):counter + 1 - guider_index_delta], guider_attention_mask, mems=guider_mems, text_len=guider_text_len, frame_len=frame_len, counter=counter - guider_index_delta, log_text_attention_weights=0, enforce_no_swin=enforce_no_swin, limited_spatial_channel_mem=limited_spatial_channel_mem, **kw_args) guider_mem_kv0, guider_mem_kv1 = [ o['mem_kv'][0] for o in guider_output_per_layers ], [o['mem_kv'][1] for o in guider_output_per_layers] if not mems_buffers_on_GPU: torch.cuda.empty_cache() for idx, mem_buffer in enumerate(mems_buffers): mems_buffers[idx] = mem_buffer.to( next(model.parameters()).device) if guider_seq is not None: for idx, guider_mem_buffer in enumerate( guider_mems_buffers): guider_mems_buffers[idx] = guider_mem_buffer.to( next(model.parameters()).device) mems_buffers_on_GPU = True mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1], mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len) if guider_seq is not None: guider_mems, guider_mems_indexs = my_update_mems( [guider_mem_kv0, guider_mem_kv1], guider_mems_buffers, guider_mems_indexs, limited_spatial_channel_mem, guider_text_len, frame_len) counter += 1 index = counter logits = logits[:, -1].expand(batch_size, -1) # [batch size, vocab size] tokens = tokens.expand(batch_size, -1) if guider_seq is not None: guider_logits = guider_logits[:, -1].expand(batch_size, -1) guider_tokens = guider_tokens.expand(batch_size, -1) if seq[-1][counter].item() < 0: # sampling guided_logits = guider_logits + ( logits - guider_logits ) * guidance_alpha if guider_seq is not None else logits if mode_stage1 and counter < text_len + 400: tokens, mems = strategy.forward(guided_logits, tokens, mems) else: tokens, mems = strategy2.forward(guided_logits, tokens, mems) if guider_seq is not None: guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]), dim=1) if seq[0][counter].item() >= 0: for si in range(seq.shape[0]): if seq[si][counter].item() >= 0: tokens[si, -1] = seq[si, counter] if guider_seq is not None: guider_tokens[si, -1] = guider_seq[si, counter - guider_index_delta] else: tokens = torch.cat( (tokens, seq[:, counter:counter + 1].clone().expand( tokens.shape[0], 1).to(device=tokens.device, dtype=tokens.dtype)), dim=1) if guider_seq is not None: guider_tokens = torch.cat( (guider_tokens, guider_seq[:, counter - guider_index_delta:counter + 1 - guider_index_delta].clone().expand( guider_tokens.shape[0], 1).to( device=guider_tokens.device, dtype=guider_tokens.dtype)), dim=1) input_tokens = tokens.clone() if guider_seq is not None: guider_input_tokens = guider_tokens.clone() if (index - text_len - 1) // 400 < (input_tokens.shape[-1] - text_len - 1) // 400: boi_idx = ((index - text_len - 1) // 400 + 1) * 400 + text_len while boi_idx < input_tokens.shape[-1]: input_tokens[:, boi_idx] = tokenizer[''] if guider_seq is not None: guider_input_tokens[:, boi_idx - guider_index_delta] = tokenizer[ ''] boi_idx += 400 if strategy.is_done: break return strategy.finalize(tokens, mems) class InferenceModel_Sequential(CogVideoCacheModel): def __init__(self, args, transformer=None, parallel_output=True): super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=-1, cogvideo_stage=1) # TODO: check it def final_forward(self, logits, **kwargs): logits_parallel = logits logits_parallel = torch.nn.functional.linear( logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) return logits_parallel class InferenceModel_Interpolate(CogVideoCacheModel): def __init__(self, args, transformer=None, parallel_output=True): super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=10, cogvideo_stage=2) # TODO: check it def final_forward(self, logits, **kwargs): logits_parallel = logits logits_parallel = torch.nn.functional.linear( logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) return logits_parallel def 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( ['', '', '']) 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['']] + [-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['']] + enc_text_video + [ tokenizer[''] ] + [-1] * 400 * generate_frame_num guider_seq = enc_duration + [tokenizer['']] + tokenizer.encode( video_guidance_text) + [tokenizer[''] ] + [-1] * 400 * generate_frame_num 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['']] + enc_text + [ tokenizer[''] ] + [-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[''] ] + tokenizer.encode(video_guidance_text) + [ tokenizer[''] ] + [-1] * 400 * generate_frame_num guider_text_len = len( guider_seq) - frame_len * generate_frame_num - 1 guider_seq = torch.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