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Browse files- .gitmodules +3 -0
- .pre-commit-config.yaml +46 -0
- .style.yapf +5 -0
- CogVideo +1 -0
- app.py +93 -0
- icetk_models/.gitkeep +0 -0
- model.py +1180 -0
- patch +51 -0
- pretrained/.gitkeep +0 -0
- requirements.txt +10 -0
- style.css +7 -0
.gitmodules
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[submodule "CogVideo"]
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path = CogVideo
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url = https://github.com/THUDM/CogVideo
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.pre-commit-config.yaml
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exclude: ^patch
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.2.0
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hooks:
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- id: check-executables-have-shebangs
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- id: check-json
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- id: check-merge-conflict
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- id: check-shebang-scripts-are-executable
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- id: check-toml
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- id: check-yaml
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- id: double-quote-string-fixer
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- id: end-of-file-fixer
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- id: mixed-line-ending
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args: ['--fix=lf']
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- id: requirements-txt-fixer
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- id: trailing-whitespace
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- repo: https://github.com/myint/docformatter
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rev: v1.4
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hooks:
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- id: docformatter
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args: ['--in-place']
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- repo: https://github.com/pycqa/isort
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rev: 5.10.1
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hooks:
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- id: isort
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v0.812
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hooks:
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- id: mypy
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args: ['--ignore-missing-imports']
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- repo: https://github.com/google/yapf
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rev: v0.32.0
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hooks:
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- id: yapf
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args: ['--parallel', '--in-place']
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- repo: https://github.com/kynan/nbstripout
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rev: 0.5.0
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hooks:
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- id: nbstripout
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args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
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- repo: https://github.com/nbQA-dev/nbQA
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rev: 1.3.1
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hooks:
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- id: nbqa-isort
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- id: nbqa-yapf
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.style.yapf
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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spaces_before_comment = 2
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split_before_logical_operator = true
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CogVideo
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Subproject commit ff423aa169978fb2f636f761e348631fa3178b03
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app.py
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#!/usr/bin/env python
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from __future__ import annotations
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import argparse
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import gradio as gr
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from model import AppModel
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DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVideo">CogVideo</a>
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The model takes only Chinese as input.
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If you check the "Translate to Chinese" checkbox, the app will use the English to Chinese translation results with [this Space](https://huggingface.co/spaces/chinhon/translation_eng2ch) as input.
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But the translation model may mistranslate and the results could be poor.
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So, it is also a good idea to input the translation results from other translation services.
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'''
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser()
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parser.add_argument('--only-first-stage', action='store_true')
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parser.add_argument('--share', action='store_true')
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return parser.parse_args()
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def set_example_text(example: list) -> dict:
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return gr.Textbox.update(value=example[0])
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def main():
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args = parse_args()
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model = AppModel(args.only_first_stage)
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with gr.Blocks(css='style.css') as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Row():
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with gr.Column():
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with gr.Group():
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text = gr.Textbox(label='Input Text')
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translate = gr.Checkbox(label='Translate to Chinese',
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value=False)
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seed = gr.Slider(0,
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100000,
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step=1,
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value=1234,
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label='Seed')
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only_first_stage = gr.Checkbox(
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label='Only First Stage',
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value=args.only_first_stage,
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visible=not args.only_first_stage)
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run_button = gr.Button('Run')
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with open('samples.txt') as f:
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samples = [
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line.strip().split('\t') for line in f.readlines()
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]
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examples = gr.Dataset(components=[text], samples=samples)
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with gr.Column():
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with gr.Group():
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translated_text = gr.Textbox(label='Translated Text')
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with gr.Tabs():
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with gr.TabItem('Output (Video)'):
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result_video = gr.Video(show_label=False)
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with gr.TabItem('Output (Gallery)'):
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result_gallery = gr.Gallery(show_label=False)
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run_button.click(fn=model.run_with_translation,
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inputs=[
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text,
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translate,
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seed,
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only_first_stage,
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],
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outputs=[
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translated_text,
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result_video,
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result_gallery,
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])
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examples.click(fn=set_example_text,
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inputs=examples,
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outputs=examples.components)
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demo.launch(
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enable_queue=True,
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share=args.share,
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)
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if __name__ == '__main__':
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main()
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icetk_models/.gitkeep
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model.py
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1 |
+
# This code is adapted from https://github.com/THUDM/CogView2/blob/4e55cce981eb94b9c8c1f19ba9f632fd3ee42ba8/cogview2_text2image.py
|
2 |
+
|
3 |
+
from __future__ import annotations
|
4 |
+
|
5 |
+
import argparse
|
6 |
+
import functools
|
7 |
+
import logging
|
8 |
+
import pathlib
|
9 |
+
import sys
|
10 |
+
import tempfile
|
11 |
+
import time
|
12 |
+
from typing import Any
|
13 |
+
|
14 |
+
import gradio as gr
|
15 |
+
import imageio.v2 as iio
|
16 |
+
import numpy as np
|
17 |
+
import torch
|
18 |
+
from icetk import IceTokenizer
|
19 |
+
from SwissArmyTransformer import get_args
|
20 |
+
from SwissArmyTransformer.arguments import set_random_seed
|
21 |
+
from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
|
22 |
+
from SwissArmyTransformer.resources import auto_create
|
23 |
+
|
24 |
+
app_dir = pathlib.Path(__file__).parent
|
25 |
+
submodule_dir = app_dir / 'CogVideo'
|
26 |
+
sys.path.insert(0, submodule_dir.as_posix())
|
27 |
+
|
28 |
+
from coglm_strategy import CoglmStrategy
|
29 |
+
from models.cogvideo_cache_model import CogVideoCacheModel
|
30 |
+
from sr_pipeline import DirectSuperResolution
|
31 |
+
|
32 |
+
formatter = logging.Formatter(
|
33 |
+
'[%(asctime)s] %(name)s %(levelname)s: %(message)s',
|
34 |
+
datefmt='%Y-%m-%d %H:%M:%S')
|
35 |
+
stream_handler = logging.StreamHandler(stream=sys.stdout)
|
36 |
+
stream_handler.setLevel(logging.INFO)
|
37 |
+
stream_handler.setFormatter(formatter)
|
38 |
+
logger = logging.getLogger(__name__)
|
39 |
+
logger.setLevel(logging.INFO)
|
40 |
+
logger.propagate = False
|
41 |
+
logger.addHandler(stream_handler)
|
42 |
+
|
43 |
+
ICETK_MODEL_DIR = app_dir / 'icetk_models'
|
44 |
+
|
45 |
+
|
46 |
+
def get_masks_and_position_ids_stage1(data, textlen, framelen):
|
47 |
+
# Extract batch size and sequence length.
|
48 |
+
tokens = data
|
49 |
+
seq_length = len(data[0])
|
50 |
+
# Attention mask (lower triangular).
|
51 |
+
attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
|
52 |
+
device=data.device)
|
53 |
+
attention_mask[:, :textlen, textlen:] = 0
|
54 |
+
attention_mask[:, textlen:, textlen:].tril_()
|
55 |
+
attention_mask.unsqueeze_(1)
|
56 |
+
# Unaligned version
|
57 |
+
position_ids = torch.zeros(seq_length,
|
58 |
+
dtype=torch.long,
|
59 |
+
device=data.device)
|
60 |
+
torch.arange(textlen,
|
61 |
+
out=position_ids[:textlen],
|
62 |
+
dtype=torch.long,
|
63 |
+
device=data.device)
|
64 |
+
torch.arange(512,
|
65 |
+
512 + seq_length - textlen,
|
66 |
+
out=position_ids[textlen:],
|
67 |
+
dtype=torch.long,
|
68 |
+
device=data.device)
|
69 |
+
position_ids = position_ids.unsqueeze(0)
|
70 |
+
|
71 |
+
return tokens, attention_mask, position_ids
|
72 |
+
|
73 |
+
|
74 |
+
def get_masks_and_position_ids_stage2(data, textlen, framelen):
|
75 |
+
# Extract batch size and sequence length.
|
76 |
+
tokens = data
|
77 |
+
seq_length = len(data[0])
|
78 |
+
|
79 |
+
# Attention mask (lower triangular).
|
80 |
+
attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
|
81 |
+
device=data.device)
|
82 |
+
attention_mask[:, :textlen, textlen:] = 0
|
83 |
+
attention_mask[:, textlen:, textlen:].tril_()
|
84 |
+
attention_mask.unsqueeze_(1)
|
85 |
+
|
86 |
+
# Unaligned version
|
87 |
+
position_ids = torch.zeros(seq_length,
|
88 |
+
dtype=torch.long,
|
89 |
+
device=data.device)
|
90 |
+
torch.arange(textlen,
|
91 |
+
out=position_ids[:textlen],
|
92 |
+
dtype=torch.long,
|
93 |
+
device=data.device)
|
94 |
+
frame_num = (seq_length - textlen) // framelen
|
95 |
+
assert frame_num == 5
|
96 |
+
torch.arange(512,
|
97 |
+
512 + framelen,
|
98 |
+
out=position_ids[textlen:textlen + framelen],
|
99 |
+
dtype=torch.long,
|
100 |
+
device=data.device)
|
101 |
+
torch.arange(512 + framelen * 2,
|
102 |
+
512 + framelen * 3,
|
103 |
+
out=position_ids[textlen + framelen:textlen + framelen * 2],
|
104 |
+
dtype=torch.long,
|
105 |
+
device=data.device)
|
106 |
+
torch.arange(512 + framelen * (frame_num - 1),
|
107 |
+
512 + framelen * frame_num,
|
108 |
+
out=position_ids[textlen + framelen * 2:textlen +
|
109 |
+
framelen * 3],
|
110 |
+
dtype=torch.long,
|
111 |
+
device=data.device)
|
112 |
+
torch.arange(512 + framelen * 1,
|
113 |
+
512 + framelen * 2,
|
114 |
+
out=position_ids[textlen + framelen * 3:textlen +
|
115 |
+
framelen * 4],
|
116 |
+
dtype=torch.long,
|
117 |
+
device=data.device)
|
118 |
+
torch.arange(512 + framelen * 3,
|
119 |
+
512 + framelen * 4,
|
120 |
+
out=position_ids[textlen + framelen * 4:textlen +
|
121 |
+
framelen * 5],
|
122 |
+
dtype=torch.long,
|
123 |
+
device=data.device)
|
124 |
+
|
125 |
+
position_ids = position_ids.unsqueeze(0)
|
126 |
+
|
127 |
+
return tokens, attention_mask, position_ids
|
128 |
+
|
129 |
+
|
130 |
+
def my_update_mems(hiddens, mems_buffers, mems_indexs,
|
131 |
+
limited_spatial_channel_mem, text_len, frame_len):
|
132 |
+
if hiddens is None:
|
133 |
+
return None, mems_indexs
|
134 |
+
mem_num = len(hiddens)
|
135 |
+
ret_mem = []
|
136 |
+
with torch.no_grad():
|
137 |
+
for id in range(mem_num):
|
138 |
+
if hiddens[id][0] is None:
|
139 |
+
ret_mem.append(None)
|
140 |
+
else:
|
141 |
+
if id == 0 and limited_spatial_channel_mem and mems_indexs[
|
142 |
+
id] + hiddens[0][0].shape[1] >= text_len + frame_len:
|
143 |
+
if mems_indexs[id] == 0:
|
144 |
+
for layer, hidden in enumerate(hiddens[id]):
|
145 |
+
mems_buffers[id][
|
146 |
+
layer, :, :text_len] = hidden.expand(
|
147 |
+
mems_buffers[id].shape[1], -1,
|
148 |
+
-1)[:, :text_len]
|
149 |
+
new_mem_len_part2 = (mems_indexs[id] +
|
150 |
+
hiddens[0][0].shape[1] -
|
151 |
+
text_len) % frame_len
|
152 |
+
if new_mem_len_part2 > 0:
|
153 |
+
for layer, hidden in enumerate(hiddens[id]):
|
154 |
+
mems_buffers[id][
|
155 |
+
layer, :, text_len:text_len +
|
156 |
+
new_mem_len_part2] = hidden.expand(
|
157 |
+
mems_buffers[id].shape[1], -1,
|
158 |
+
-1)[:, -new_mem_len_part2:]
|
159 |
+
mems_indexs[id] = text_len + new_mem_len_part2
|
160 |
+
else:
|
161 |
+
for layer, hidden in enumerate(hiddens[id]):
|
162 |
+
mems_buffers[id][layer, :,
|
163 |
+
mems_indexs[id]:mems_indexs[id] +
|
164 |
+
hidden.shape[1]] = hidden.expand(
|
165 |
+
mems_buffers[id].shape[1], -1, -1)
|
166 |
+
mems_indexs[id] += hidden.shape[1]
|
167 |
+
ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]])
|
168 |
+
return ret_mem, mems_indexs
|
169 |
+
|
170 |
+
|
171 |
+
def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len):
|
172 |
+
# The fisrt token's position id of the frame that the next token belongs to;
|
173 |
+
if total_len < text_len:
|
174 |
+
return None
|
175 |
+
return (total_len - text_len) // frame_len * frame_len + text_len
|
176 |
+
|
177 |
+
|
178 |
+
def my_filling_sequence(
|
179 |
+
model,
|
180 |
+
tokenizer,
|
181 |
+
args,
|
182 |
+
seq,
|
183 |
+
batch_size,
|
184 |
+
get_masks_and_position_ids,
|
185 |
+
text_len,
|
186 |
+
frame_len,
|
187 |
+
strategy=BaseStrategy(),
|
188 |
+
strategy2=BaseStrategy(),
|
189 |
+
mems=None,
|
190 |
+
log_text_attention_weights=0, # default to 0: no artificial change
|
191 |
+
mode_stage1=True,
|
192 |
+
enforce_no_swin=False,
|
193 |
+
guider_seq=None,
|
194 |
+
guider_text_len=0,
|
195 |
+
guidance_alpha=1,
|
196 |
+
limited_spatial_channel_mem=False, # 空间通道的存储限制在本帧内
|
197 |
+
**kw_args):
|
198 |
+
'''
|
199 |
+
seq: [2, 3, 5, ..., -1(to be generated), -1, ...]
|
200 |
+
mems: [num_layers, batch_size, len_mems(index), mem_hidden_size]
|
201 |
+
cache, should be first mems.shape[1] parts of context_tokens.
|
202 |
+
mems are the first-level citizens here, but we don't assume what is memorized.
|
203 |
+
input mems are used when multi-phase generation.
|
204 |
+
'''
|
205 |
+
if guider_seq is not None:
|
206 |
+
logger.debug('Using Guidance In Inference')
|
207 |
+
if limited_spatial_channel_mem:
|
208 |
+
logger.debug("Limit spatial-channel's mem to current frame")
|
209 |
+
assert len(seq.shape) == 2
|
210 |
+
|
211 |
+
# building the initial tokens, attention_mask, and position_ids
|
212 |
+
actual_context_length = 0
|
213 |
+
|
214 |
+
while seq[-1][
|
215 |
+
actual_context_length] >= 0: # the last seq has least given tokens
|
216 |
+
actual_context_length += 1 # [0, context_length-1] are given
|
217 |
+
assert actual_context_length > 0
|
218 |
+
current_frame_num = (actual_context_length - text_len) // frame_len
|
219 |
+
assert current_frame_num >= 0
|
220 |
+
context_length = text_len + current_frame_num * frame_len
|
221 |
+
|
222 |
+
tokens, attention_mask, position_ids = get_masks_and_position_ids(
|
223 |
+
seq, text_len, frame_len)
|
224 |
+
tokens = tokens[..., :context_length]
|
225 |
+
input_tokens = tokens.clone()
|
226 |
+
|
227 |
+
if guider_seq is not None:
|
228 |
+
guider_index_delta = text_len - guider_text_len
|
229 |
+
guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(
|
230 |
+
guider_seq, guider_text_len, frame_len)
|
231 |
+
guider_tokens = guider_tokens[..., :context_length -
|
232 |
+
guider_index_delta]
|
233 |
+
guider_input_tokens = guider_tokens.clone()
|
234 |
+
|
235 |
+
for fid in range(current_frame_num):
|
236 |
+
input_tokens[:, text_len + 400 * fid] = tokenizer['<start_of_image>']
|
237 |
+
if guider_seq is not None:
|
238 |
+
guider_input_tokens[:, guider_text_len +
|
239 |
+
400 * fid] = tokenizer['<start_of_image>']
|
240 |
+
|
241 |
+
attention_mask = attention_mask.type_as(next(
|
242 |
+
model.parameters())) # if fp16
|
243 |
+
# initialize generation
|
244 |
+
counter = context_length - 1 # Last fixed index is ``counter''
|
245 |
+
index = 0 # Next forward starting index, also the length of cache.
|
246 |
+
mems_buffers_on_GPU = False
|
247 |
+
mems_indexs = [0, 0]
|
248 |
+
mems_len = [(400 + 74) if limited_spatial_channel_mem else 5 * 400 + 74,
|
249 |
+
5 * 400 + 74]
|
250 |
+
mems_buffers = [
|
251 |
+
torch.zeros(args.num_layers,
|
252 |
+
batch_size,
|
253 |
+
mem_len,
|
254 |
+
args.hidden_size * 2,
|
255 |
+
dtype=next(model.parameters()).dtype)
|
256 |
+
for mem_len in mems_len
|
257 |
+
]
|
258 |
+
|
259 |
+
if guider_seq is not None:
|
260 |
+
guider_attention_mask = guider_attention_mask.type_as(
|
261 |
+
next(model.parameters())) # if fp16
|
262 |
+
guider_mems_buffers = [
|
263 |
+
torch.zeros(args.num_layers,
|
264 |
+
batch_size,
|
265 |
+
mem_len,
|
266 |
+
args.hidden_size * 2,
|
267 |
+
dtype=next(model.parameters()).dtype)
|
268 |
+
for mem_len in mems_len
|
269 |
+
]
|
270 |
+
guider_mems_indexs = [0, 0]
|
271 |
+
guider_mems = None
|
272 |
+
|
273 |
+
torch.cuda.empty_cache()
|
274 |
+
# step-by-step generation
|
275 |
+
while counter < len(seq[0]) - 1:
|
276 |
+
# we have generated counter+1 tokens
|
277 |
+
# Now, we want to generate seq[counter + 1],
|
278 |
+
# token[:, index: counter+1] needs forwarding.
|
279 |
+
if index == 0:
|
280 |
+
group_size = 2 if (input_tokens.shape[0] == batch_size
|
281 |
+
and not mode_stage1) else batch_size
|
282 |
+
|
283 |
+
logits_all = None
|
284 |
+
for batch_idx in range(0, input_tokens.shape[0], group_size):
|
285 |
+
logits, *output_per_layers = model(
|
286 |
+
input_tokens[batch_idx:batch_idx + group_size, index:],
|
287 |
+
position_ids[..., index:counter + 1],
|
288 |
+
attention_mask, # TODO memlen
|
289 |
+
mems=mems,
|
290 |
+
text_len=text_len,
|
291 |
+
frame_len=frame_len,
|
292 |
+
counter=counter,
|
293 |
+
log_text_attention_weights=log_text_attention_weights,
|
294 |
+
enforce_no_swin=enforce_no_swin,
|
295 |
+
**kw_args)
|
296 |
+
logits_all = torch.cat(
|
297 |
+
(logits_all,
|
298 |
+
logits), dim=0) if logits_all is not None else logits
|
299 |
+
mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers],
|
300 |
+
[o['mem_kv'][1] for o in output_per_layers]]
|
301 |
+
next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
|
302 |
+
text_len, frame_len, mem_kv01[0][0].shape[1])
|
303 |
+
for id, mem_kv in enumerate(mem_kv01):
|
304 |
+
for layer, mem_kv_perlayer in enumerate(mem_kv):
|
305 |
+
if limited_spatial_channel_mem and id == 0:
|
306 |
+
mems_buffers[id][
|
307 |
+
layer, batch_idx:batch_idx + group_size, :
|
308 |
+
text_len] = mem_kv_perlayer.expand(
|
309 |
+
min(group_size,
|
310 |
+
input_tokens.shape[0] - batch_idx), -1,
|
311 |
+
-1)[:, :text_len]
|
312 |
+
mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\
|
313 |
+
mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:]
|
314 |
+
else:
|
315 |
+
mems_buffers[id][
|
316 |
+
layer, batch_idx:batch_idx +
|
317 |
+
group_size, :mem_kv_perlayer.
|
318 |
+
shape[1]] = mem_kv_perlayer.expand(
|
319 |
+
min(group_size,
|
320 |
+
input_tokens.shape[0] - batch_idx), -1,
|
321 |
+
-1)
|
322 |
+
mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[
|
323 |
+
1], mem_kv01[1][0].shape[1]
|
324 |
+
if limited_spatial_channel_mem:
|
325 |
+
mems_indexs[0] -= (next_tokens_frame_begin_id - text_len)
|
326 |
+
|
327 |
+
mems = [
|
328 |
+
mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)
|
329 |
+
]
|
330 |
+
logits = logits_all
|
331 |
+
|
332 |
+
# Guider
|
333 |
+
if guider_seq is not None:
|
334 |
+
guider_logits_all = None
|
335 |
+
for batch_idx in range(0, guider_input_tokens.shape[0],
|
336 |
+
group_size):
|
337 |
+
guider_logits, *guider_output_per_layers = model(
|
338 |
+
guider_input_tokens[batch_idx:batch_idx + group_size,
|
339 |
+
max(index -
|
340 |
+
guider_index_delta, 0):],
|
341 |
+
guider_position_ids[
|
342 |
+
...,
|
343 |
+
max(index - guider_index_delta, 0):counter + 1 -
|
344 |
+
guider_index_delta],
|
345 |
+
guider_attention_mask,
|
346 |
+
mems=guider_mems,
|
347 |
+
text_len=guider_text_len,
|
348 |
+
frame_len=frame_len,
|
349 |
+
counter=counter - guider_index_delta,
|
350 |
+
log_text_attention_weights=log_text_attention_weights,
|
351 |
+
enforce_no_swin=enforce_no_swin,
|
352 |
+
**kw_args)
|
353 |
+
guider_logits_all = torch.cat(
|
354 |
+
(guider_logits_all, guider_logits), dim=0
|
355 |
+
) if guider_logits_all is not None else guider_logits
|
356 |
+
guider_mem_kv01 = [[
|
357 |
+
o['mem_kv'][0] for o in guider_output_per_layers
|
358 |
+
], [o['mem_kv'][1] for o in guider_output_per_layers]]
|
359 |
+
for id, guider_mem_kv in enumerate(guider_mem_kv01):
|
360 |
+
for layer, guider_mem_kv_perlayer in enumerate(
|
361 |
+
guider_mem_kv):
|
362 |
+
if limited_spatial_channel_mem and id == 0:
|
363 |
+
guider_mems_buffers[id][
|
364 |
+
layer, batch_idx:batch_idx + group_size, :
|
365 |
+
guider_text_len] = guider_mem_kv_perlayer.expand(
|
366 |
+
min(group_size,
|
367 |
+
input_tokens.shape[0] - batch_idx),
|
368 |
+
-1, -1)[:, :guider_text_len]
|
369 |
+
guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
|
370 |
+
guider_text_len, frame_len,
|
371 |
+
guider_mem_kv_perlayer.shape[1])
|
372 |
+
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] =\
|
373 |
+
guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:]
|
374 |
+
else:
|
375 |
+
guider_mems_buffers[id][
|
376 |
+
layer, batch_idx:batch_idx +
|
377 |
+
group_size, :guider_mem_kv_perlayer.
|
378 |
+
shape[1]] = guider_mem_kv_perlayer.expand(
|
379 |
+
min(group_size,
|
380 |
+
input_tokens.shape[0] - batch_idx),
|
381 |
+
-1, -1)
|
382 |
+
guider_mems_indexs[0], guider_mems_indexs[
|
383 |
+
1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[
|
384 |
+
1][0].shape[1]
|
385 |
+
if limited_spatial_channel_mem:
|
386 |
+
guider_mems_indexs[0] -= (
|
387 |
+
guider_next_tokens_frame_begin_id -
|
388 |
+
guider_text_len)
|
389 |
+
guider_mems = [
|
390 |
+
guider_mems_buffers[id][:, :, :guider_mems_indexs[id]]
|
391 |
+
for id in range(2)
|
392 |
+
]
|
393 |
+
guider_logits = guider_logits_all
|
394 |
+
else:
|
395 |
+
if not mems_buffers_on_GPU:
|
396 |
+
if not mode_stage1:
|
397 |
+
torch.cuda.empty_cache()
|
398 |
+
for idx, mem in enumerate(mems):
|
399 |
+
mems[idx] = mem.to(next(model.parameters()).device)
|
400 |
+
if guider_seq is not None:
|
401 |
+
for idx, mem in enumerate(guider_mems):
|
402 |
+
guider_mems[idx] = mem.to(
|
403 |
+
next(model.parameters()).device)
|
404 |
+
else:
|
405 |
+
torch.cuda.empty_cache()
|
406 |
+
for idx, mem_buffer in enumerate(mems_buffers):
|
407 |
+
mems_buffers[idx] = mem_buffer.to(
|
408 |
+
next(model.parameters()).device)
|
409 |
+
mems = [
|
410 |
+
mems_buffers[id][:, :, :mems_indexs[id]]
|
411 |
+
for id in range(2)
|
412 |
+
]
|
413 |
+
if guider_seq is not None:
|
414 |
+
for idx, guider_mem_buffer in enumerate(
|
415 |
+
guider_mems_buffers):
|
416 |
+
guider_mems_buffers[idx] = guider_mem_buffer.to(
|
417 |
+
next(model.parameters()).device)
|
418 |
+
guider_mems = [
|
419 |
+
guider_mems_buffers[id]
|
420 |
+
[:, :, :guider_mems_indexs[id]] for id in range(2)
|
421 |
+
]
|
422 |
+
mems_buffers_on_GPU = True
|
423 |
+
|
424 |
+
logits, *output_per_layers = model(
|
425 |
+
input_tokens[:, index:],
|
426 |
+
position_ids[..., index:counter + 1],
|
427 |
+
attention_mask, # TODO memlen
|
428 |
+
mems=mems,
|
429 |
+
text_len=text_len,
|
430 |
+
frame_len=frame_len,
|
431 |
+
counter=counter,
|
432 |
+
log_text_attention_weights=log_text_attention_weights,
|
433 |
+
enforce_no_swin=enforce_no_swin,
|
434 |
+
limited_spatial_channel_mem=limited_spatial_channel_mem,
|
435 |
+
**kw_args)
|
436 |
+
mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers
|
437 |
+
], [o['mem_kv'][1] for o in output_per_layers]
|
438 |
+
|
439 |
+
if guider_seq is not None:
|
440 |
+
guider_logits, *guider_output_per_layers = model(
|
441 |
+
guider_input_tokens[:,
|
442 |
+
max(index - guider_index_delta, 0):],
|
443 |
+
guider_position_ids[...,
|
444 |
+
max(index -
|
445 |
+
guider_index_delta, 0):counter +
|
446 |
+
1 - guider_index_delta],
|
447 |
+
guider_attention_mask,
|
448 |
+
mems=guider_mems,
|
449 |
+
text_len=guider_text_len,
|
450 |
+
frame_len=frame_len,
|
451 |
+
counter=counter - guider_index_delta,
|
452 |
+
log_text_attention_weights=0,
|
453 |
+
enforce_no_swin=enforce_no_swin,
|
454 |
+
limited_spatial_channel_mem=limited_spatial_channel_mem,
|
455 |
+
**kw_args)
|
456 |
+
guider_mem_kv0, guider_mem_kv1 = [
|
457 |
+
o['mem_kv'][0] for o in guider_output_per_layers
|
458 |
+
], [o['mem_kv'][1] for o in guider_output_per_layers]
|
459 |
+
|
460 |
+
if not mems_buffers_on_GPU:
|
461 |
+
torch.cuda.empty_cache()
|
462 |
+
for idx, mem_buffer in enumerate(mems_buffers):
|
463 |
+
mems_buffers[idx] = mem_buffer.to(
|
464 |
+
next(model.parameters()).device)
|
465 |
+
if guider_seq is not None:
|
466 |
+
for idx, guider_mem_buffer in enumerate(
|
467 |
+
guider_mems_buffers):
|
468 |
+
guider_mems_buffers[idx] = guider_mem_buffer.to(
|
469 |
+
next(model.parameters()).device)
|
470 |
+
mems_buffers_on_GPU = True
|
471 |
+
|
472 |
+
mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1],
|
473 |
+
mems_buffers, mems_indexs,
|
474 |
+
limited_spatial_channel_mem,
|
475 |
+
text_len, frame_len)
|
476 |
+
if guider_seq is not None:
|
477 |
+
guider_mems, guider_mems_indexs = my_update_mems(
|
478 |
+
[guider_mem_kv0, guider_mem_kv1], guider_mems_buffers,
|
479 |
+
guider_mems_indexs, limited_spatial_channel_mem,
|
480 |
+
guider_text_len, frame_len)
|
481 |
+
|
482 |
+
counter += 1
|
483 |
+
index = counter
|
484 |
+
|
485 |
+
logits = logits[:, -1].expand(batch_size,
|
486 |
+
-1) # [batch size, vocab size]
|
487 |
+
tokens = tokens.expand(batch_size, -1)
|
488 |
+
if guider_seq is not None:
|
489 |
+
guider_logits = guider_logits[:, -1].expand(batch_size, -1)
|
490 |
+
guider_tokens = guider_tokens.expand(batch_size, -1)
|
491 |
+
|
492 |
+
if seq[-1][counter].item() < 0:
|
493 |
+
# sampling
|
494 |
+
guided_logits = guider_logits + (
|
495 |
+
logits - guider_logits
|
496 |
+
) * guidance_alpha if guider_seq is not None else logits
|
497 |
+
if mode_stage1 and counter < text_len + 400:
|
498 |
+
tokens, mems = strategy.forward(guided_logits, tokens, mems)
|
499 |
+
else:
|
500 |
+
tokens, mems = strategy2.forward(guided_logits, tokens, mems)
|
501 |
+
if guider_seq is not None:
|
502 |
+
guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]),
|
503 |
+
dim=1)
|
504 |
+
|
505 |
+
if seq[0][counter].item() >= 0:
|
506 |
+
for si in range(seq.shape[0]):
|
507 |
+
if seq[si][counter].item() >= 0:
|
508 |
+
tokens[si, -1] = seq[si, counter]
|
509 |
+
if guider_seq is not None:
|
510 |
+
guider_tokens[si,
|
511 |
+
-1] = guider_seq[si, counter -
|
512 |
+
guider_index_delta]
|
513 |
+
|
514 |
+
else:
|
515 |
+
tokens = torch.cat(
|
516 |
+
(tokens, seq[:, counter:counter + 1].clone().expand(
|
517 |
+
tokens.shape[0], 1).to(device=tokens.device,
|
518 |
+
dtype=tokens.dtype)),
|
519 |
+
dim=1)
|
520 |
+
if guider_seq is not None:
|
521 |
+
guider_tokens = torch.cat(
|
522 |
+
(guider_tokens,
|
523 |
+
guider_seq[:, counter - guider_index_delta:counter + 1 -
|
524 |
+
guider_index_delta].clone().expand(
|
525 |
+
guider_tokens.shape[0], 1).to(
|
526 |
+
device=guider_tokens.device,
|
527 |
+
dtype=guider_tokens.dtype)),
|
528 |
+
dim=1)
|
529 |
+
|
530 |
+
input_tokens = tokens.clone()
|
531 |
+
if guider_seq is not None:
|
532 |
+
guider_input_tokens = guider_tokens.clone()
|
533 |
+
if (index - text_len - 1) // 400 < (input_tokens.shape[-1] - text_len -
|
534 |
+
1) // 400:
|
535 |
+
boi_idx = ((index - text_len - 1) // 400 + 1) * 400 + text_len
|
536 |
+
while boi_idx < input_tokens.shape[-1]:
|
537 |
+
input_tokens[:, boi_idx] = tokenizer['<start_of_image>']
|
538 |
+
if guider_seq is not None:
|
539 |
+
guider_input_tokens[:, boi_idx -
|
540 |
+
guider_index_delta] = tokenizer[
|
541 |
+
'<start_of_image>']
|
542 |
+
boi_idx += 400
|
543 |
+
|
544 |
+
if strategy.is_done:
|
545 |
+
break
|
546 |
+
return strategy.finalize(tokens, mems)
|
547 |
+
|
548 |
+
|
549 |
+
class InferenceModel_Sequential(CogVideoCacheModel):
|
550 |
+
def __init__(self, args, transformer=None, parallel_output=True):
|
551 |
+
super().__init__(args,
|
552 |
+
transformer=transformer,
|
553 |
+
parallel_output=parallel_output,
|
554 |
+
window_size=-1,
|
555 |
+
cogvideo_stage=1)
|
556 |
+
|
557 |
+
# TODO: check it
|
558 |
+
|
559 |
+
def final_forward(self, logits, **kwargs):
|
560 |
+
logits_parallel = logits
|
561 |
+
logits_parallel = torch.nn.functional.linear(
|
562 |
+
logits_parallel.float(),
|
563 |
+
self.transformer.word_embeddings.weight[:20000].float())
|
564 |
+
return logits_parallel
|
565 |
+
|
566 |
+
|
567 |
+
class InferenceModel_Interpolate(CogVideoCacheModel):
|
568 |
+
def __init__(self, args, transformer=None, parallel_output=True):
|
569 |
+
super().__init__(args,
|
570 |
+
transformer=transformer,
|
571 |
+
parallel_output=parallel_output,
|
572 |
+
window_size=10,
|
573 |
+
cogvideo_stage=2)
|
574 |
+
|
575 |
+
# TODO: check it
|
576 |
+
|
577 |
+
def final_forward(self, logits, **kwargs):
|
578 |
+
logits_parallel = logits
|
579 |
+
logits_parallel = torch.nn.functional.linear(
|
580 |
+
logits_parallel.float(),
|
581 |
+
self.transformer.word_embeddings.weight[:20000].float())
|
582 |
+
return logits_parallel
|
583 |
+
|
584 |
+
|
585 |
+
def get_default_args() -> argparse.Namespace:
|
586 |
+
known = argparse.Namespace(generate_frame_num=5,
|
587 |
+
coglm_temperature2=0.89,
|
588 |
+
use_guidance_stage1=True,
|
589 |
+
use_guidance_stage2=False,
|
590 |
+
guidance_alpha=3.0,
|
591 |
+
stage_1=True,
|
592 |
+
stage_2=False,
|
593 |
+
both_stages=False,
|
594 |
+
parallel_size=1,
|
595 |
+
stage1_max_inference_batch_size=-1,
|
596 |
+
multi_gpu=False,
|
597 |
+
layout='64, 464, 2064',
|
598 |
+
window_size=10,
|
599 |
+
additional_seqlen=2000,
|
600 |
+
cogvideo_stage=1)
|
601 |
+
|
602 |
+
args_list = [
|
603 |
+
'--tokenizer-type',
|
604 |
+
'fake',
|
605 |
+
'--mode',
|
606 |
+
'inference',
|
607 |
+
'--distributed-backend',
|
608 |
+
'nccl',
|
609 |
+
'--fp16',
|
610 |
+
'--model-parallel-size',
|
611 |
+
'1',
|
612 |
+
'--temperature',
|
613 |
+
'1.05',
|
614 |
+
'--top_k',
|
615 |
+
'12',
|
616 |
+
'--sandwich-ln',
|
617 |
+
'--seed',
|
618 |
+
'1234',
|
619 |
+
'--num-workers',
|
620 |
+
'0',
|
621 |
+
'--batch-size',
|
622 |
+
'1',
|
623 |
+
'--max-inference-batch-size',
|
624 |
+
'8',
|
625 |
+
]
|
626 |
+
args = get_args(args_list)
|
627 |
+
args = argparse.Namespace(**vars(args), **vars(known))
|
628 |
+
args.layout = [int(x) for x in args.layout.split(',')]
|
629 |
+
args.do_train = False
|
630 |
+
return args
|
631 |
+
|
632 |
+
|
633 |
+
class Model:
|
634 |
+
def __init__(self, only_first_stage: bool = False):
|
635 |
+
self.args = get_default_args()
|
636 |
+
if only_first_stage:
|
637 |
+
self.args.stage_1 = True
|
638 |
+
self.args.both_stages = False
|
639 |
+
else:
|
640 |
+
self.args.stage_1 = False
|
641 |
+
self.args.both_stages = True
|
642 |
+
|
643 |
+
self.tokenizer = self.load_tokenizer()
|
644 |
+
|
645 |
+
self.model_stage1, self.args = self.load_model_stage1()
|
646 |
+
self.model_stage2, self.args = self.load_model_stage2()
|
647 |
+
|
648 |
+
self.strategy_cogview2, self.strategy_cogvideo = self.load_strategies()
|
649 |
+
self.dsr = self.load_dsr()
|
650 |
+
|
651 |
+
self.device = torch.device(self.args.device)
|
652 |
+
|
653 |
+
def load_tokenizer(self) -> IceTokenizer:
|
654 |
+
logger.info('--- load_tokenizer ---')
|
655 |
+
start = time.perf_counter()
|
656 |
+
|
657 |
+
tokenizer = IceTokenizer(ICETK_MODEL_DIR.as_posix())
|
658 |
+
tokenizer.add_special_tokens(
|
659 |
+
['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
|
660 |
+
|
661 |
+
elapsed = time.perf_counter() - start
|
662 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
663 |
+
return tokenizer
|
664 |
+
|
665 |
+
def load_model_stage1(
|
666 |
+
self) -> tuple[CogVideoCacheModel, argparse.Namespace]:
|
667 |
+
logger.info('--- load_model_stage1 ---')
|
668 |
+
start = time.perf_counter()
|
669 |
+
|
670 |
+
args = self.args
|
671 |
+
model_stage1, args = InferenceModel_Sequential.from_pretrained(
|
672 |
+
args, 'cogvideo-stage1')
|
673 |
+
model_stage1.eval()
|
674 |
+
if args.both_stages:
|
675 |
+
model_stage1 = model_stage1.cpu()
|
676 |
+
|
677 |
+
elapsed = time.perf_counter() - start
|
678 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
679 |
+
return model_stage1, args
|
680 |
+
|
681 |
+
def load_model_stage2(
|
682 |
+
self) -> tuple[CogVideoCacheModel | None, argparse.Namespace]:
|
683 |
+
logger.info('--- load_model_stage2 ---')
|
684 |
+
start = time.perf_counter()
|
685 |
+
|
686 |
+
args = self.args
|
687 |
+
if args.both_stages:
|
688 |
+
model_stage2, args = InferenceModel_Interpolate.from_pretrained(
|
689 |
+
args, 'cogvideo-stage2')
|
690 |
+
model_stage2.eval()
|
691 |
+
if args.both_stages:
|
692 |
+
model_stage2 = model_stage2.cpu()
|
693 |
+
else:
|
694 |
+
model_stage2 = None
|
695 |
+
|
696 |
+
elapsed = time.perf_counter() - start
|
697 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
698 |
+
return model_stage2, args
|
699 |
+
|
700 |
+
def load_strategies(self) -> tuple[CoglmStrategy, CoglmStrategy]:
|
701 |
+
logger.info('--- load_strategies ---')
|
702 |
+
start = time.perf_counter()
|
703 |
+
|
704 |
+
invalid_slices = [slice(self.tokenizer.num_image_tokens, None)]
|
705 |
+
strategy_cogview2 = CoglmStrategy(invalid_slices,
|
706 |
+
temperature=1.0,
|
707 |
+
top_k=16)
|
708 |
+
strategy_cogvideo = CoglmStrategy(
|
709 |
+
invalid_slices,
|
710 |
+
temperature=self.args.temperature,
|
711 |
+
top_k=self.args.top_k,
|
712 |
+
temperature2=self.args.coglm_temperature2)
|
713 |
+
|
714 |
+
elapsed = time.perf_counter() - start
|
715 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
716 |
+
return strategy_cogview2, strategy_cogvideo
|
717 |
+
|
718 |
+
def load_dsr(self) -> DirectSuperResolution | None:
|
719 |
+
logger.info('--- load_dsr ---')
|
720 |
+
start = time.perf_counter()
|
721 |
+
|
722 |
+
if self.args.both_stages:
|
723 |
+
path = auto_create('cogview2-dsr', path=None)
|
724 |
+
dsr = DirectSuperResolution(self.args,
|
725 |
+
path,
|
726 |
+
max_bz=12,
|
727 |
+
onCUDA=False)
|
728 |
+
else:
|
729 |
+
dsr = None
|
730 |
+
|
731 |
+
elapsed = time.perf_counter() - start
|
732 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
733 |
+
return dsr
|
734 |
+
|
735 |
+
@torch.inference_mode()
|
736 |
+
def process_stage1(self,
|
737 |
+
model,
|
738 |
+
seq_text,
|
739 |
+
duration,
|
740 |
+
video_raw_text=None,
|
741 |
+
video_guidance_text='视频',
|
742 |
+
image_text_suffix='',
|
743 |
+
batch_size=1):
|
744 |
+
process_start_time = time.perf_counter()
|
745 |
+
|
746 |
+
generate_frame_num = self.args.generate_frame_num
|
747 |
+
tokenizer = self.tokenizer
|
748 |
+
use_guide = self.args.use_guidance_stage1
|
749 |
+
|
750 |
+
if next(model.parameters()).device != self.device:
|
751 |
+
move_start_time = time.perf_counter()
|
752 |
+
logger.debug('moving stage 1 model to cuda')
|
753 |
+
|
754 |
+
model = model.to(self.device)
|
755 |
+
|
756 |
+
elapsed = time.perf_counter() - move_start_time
|
757 |
+
logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
|
758 |
+
|
759 |
+
if video_raw_text is None:
|
760 |
+
video_raw_text = seq_text
|
761 |
+
mbz = self.args.stage1_max_inference_batch_size if self.args.stage1_max_inference_batch_size > 0 else self.args.max_inference_batch_size
|
762 |
+
assert batch_size < mbz or batch_size % mbz == 0
|
763 |
+
frame_len = 400
|
764 |
+
|
765 |
+
# generate the first frame:
|
766 |
+
enc_text = tokenizer.encode(seq_text + image_text_suffix)
|
767 |
+
seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1] * 400
|
768 |
+
logger.info(
|
769 |
+
f'[Generating First Frame with CogView2] Raw text: {tokenizer.decode(enc_text):s}'
|
770 |
+
)
|
771 |
+
text_len_1st = len(seq_1st) - frame_len * 1 - 1
|
772 |
+
|
773 |
+
seq_1st = torch.tensor(seq_1st, dtype=torch.long,
|
774 |
+
device=self.device).unsqueeze(0)
|
775 |
+
output_list_1st = []
|
776 |
+
for tim in range(max(batch_size // mbz, 1)):
|
777 |
+
start_time = time.perf_counter()
|
778 |
+
output_list_1st.append(
|
779 |
+
my_filling_sequence(
|
780 |
+
model,
|
781 |
+
tokenizer,
|
782 |
+
self.args,
|
783 |
+
seq_1st.clone(),
|
784 |
+
batch_size=min(batch_size, mbz),
|
785 |
+
get_masks_and_position_ids=
|
786 |
+
get_masks_and_position_ids_stage1,
|
787 |
+
text_len=text_len_1st,
|
788 |
+
frame_len=frame_len,
|
789 |
+
strategy=self.strategy_cogview2,
|
790 |
+
strategy2=self.strategy_cogvideo,
|
791 |
+
log_text_attention_weights=1.4,
|
792 |
+
enforce_no_swin=True,
|
793 |
+
mode_stage1=True,
|
794 |
+
)[0])
|
795 |
+
elapsed = time.perf_counter() - start_time
|
796 |
+
logger.info(f'[First Frame] Elapsed: {elapsed:.2f}')
|
797 |
+
output_tokens_1st = torch.cat(output_list_1st, dim=0)
|
798 |
+
given_tokens = output_tokens_1st[:, text_len_1st + 1:text_len_1st +
|
799 |
+
401].unsqueeze(
|
800 |
+
1
|
801 |
+
) # given_tokens.shape: [bs, frame_num, 400]
|
802 |
+
|
803 |
+
# generate subsequent frames:
|
804 |
+
total_frames = generate_frame_num
|
805 |
+
enc_duration = tokenizer.encode(f'{float(duration)}秒')
|
806 |
+
if use_guide:
|
807 |
+
video_raw_text = video_raw_text + ' 视频'
|
808 |
+
enc_text_video = tokenizer.encode(video_raw_text)
|
809 |
+
seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [
|
810 |
+
tokenizer['<start_of_image>']
|
811 |
+
] + [-1] * 400 * generate_frame_num
|
812 |
+
guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(
|
813 |
+
video_guidance_text) + [tokenizer['<start_of_image>']
|
814 |
+
] + [-1] * 400 * generate_frame_num
|
815 |
+
logger.info(
|
816 |
+
f'[Stage1: Generating Subsequent Frames, Frame Rate {4/duration:.1f}] raw text: {tokenizer.decode(enc_text_video):s}'
|
817 |
+
)
|
818 |
+
|
819 |
+
text_len = len(seq) - frame_len * generate_frame_num - 1
|
820 |
+
guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
|
821 |
+
seq = torch.tensor(seq, dtype=torch.long,
|
822 |
+
device=self.device).unsqueeze(0).repeat(
|
823 |
+
batch_size, 1)
|
824 |
+
guider_seq = torch.tensor(guider_seq,
|
825 |
+
dtype=torch.long,
|
826 |
+
device=self.device).unsqueeze(0).repeat(
|
827 |
+
batch_size, 1)
|
828 |
+
|
829 |
+
for given_frame_id in range(given_tokens.shape[1]):
|
830 |
+
seq[:, text_len + 1 + given_frame_id * 400:text_len + 1 +
|
831 |
+
(given_frame_id + 1) * 400] = given_tokens[:, given_frame_id]
|
832 |
+
guider_seq[:, guider_text_len + 1 +
|
833 |
+
given_frame_id * 400:guider_text_len + 1 +
|
834 |
+
(given_frame_id + 1) *
|
835 |
+
400] = given_tokens[:, given_frame_id]
|
836 |
+
output_list = []
|
837 |
+
|
838 |
+
if use_guide:
|
839 |
+
video_log_text_attention_weights = 0
|
840 |
+
else:
|
841 |
+
guider_seq = None
|
842 |
+
video_log_text_attention_weights = 1.4
|
843 |
+
|
844 |
+
for tim in range(max(batch_size // mbz, 1)):
|
845 |
+
input_seq = seq[:min(batch_size, mbz)].clone(
|
846 |
+
) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
|
847 |
+
guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone()
|
848 |
+
if tim == 0 else guider_seq[mbz * tim:mbz *
|
849 |
+
(tim + 1)].clone()
|
850 |
+
) if guider_seq is not None else None
|
851 |
+
output_list.append(
|
852 |
+
my_filling_sequence(
|
853 |
+
model,
|
854 |
+
tokenizer,
|
855 |
+
self.args,
|
856 |
+
input_seq,
|
857 |
+
batch_size=min(batch_size, mbz),
|
858 |
+
get_masks_and_position_ids=
|
859 |
+
get_masks_and_position_ids_stage1,
|
860 |
+
text_len=text_len,
|
861 |
+
frame_len=frame_len,
|
862 |
+
strategy=self.strategy_cogview2,
|
863 |
+
strategy2=self.strategy_cogvideo,
|
864 |
+
log_text_attention_weights=video_log_text_attention_weights,
|
865 |
+
guider_seq=guider_seq2,
|
866 |
+
guider_text_len=guider_text_len,
|
867 |
+
guidance_alpha=self.args.guidance_alpha,
|
868 |
+
limited_spatial_channel_mem=True,
|
869 |
+
mode_stage1=True,
|
870 |
+
)[0])
|
871 |
+
|
872 |
+
output_tokens = torch.cat(output_list, dim=0)[:, 1 + text_len:]
|
873 |
+
|
874 |
+
if self.args.both_stages:
|
875 |
+
move_start_time = time.perf_counter()
|
876 |
+
logger.debug('moving stage 1 model to cpu')
|
877 |
+
model = model.cpu()
|
878 |
+
torch.cuda.empty_cache()
|
879 |
+
elapsed = time.perf_counter() - move_start_time
|
880 |
+
logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
|
881 |
+
|
882 |
+
# decoding
|
883 |
+
res = []
|
884 |
+
for seq in output_tokens:
|
885 |
+
decoded_imgs = [
|
886 |
+
self.postprocess(
|
887 |
+
torch.nn.functional.interpolate(tokenizer.decode(
|
888 |
+
image_ids=seq.tolist()[i * 400:(i + 1) * 400]),
|
889 |
+
size=(480, 480))[0])
|
890 |
+
for i in range(total_frames)
|
891 |
+
]
|
892 |
+
res.append(decoded_imgs) # only the last image (target)
|
893 |
+
|
894 |
+
assert len(res) == batch_size
|
895 |
+
tokens = output_tokens[:, :+total_frames * 400].reshape(
|
896 |
+
-1, total_frames, 400).cpu()
|
897 |
+
|
898 |
+
elapsed = time.perf_counter() - process_start_time
|
899 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
900 |
+
return tokens, res[0]
|
901 |
+
|
902 |
+
@torch.inference_mode()
|
903 |
+
def process_stage2(self,
|
904 |
+
model,
|
905 |
+
seq_text,
|
906 |
+
duration,
|
907 |
+
parent_given_tokens,
|
908 |
+
video_raw_text=None,
|
909 |
+
video_guidance_text='视频',
|
910 |
+
gpu_rank=0,
|
911 |
+
gpu_parallel_size=1):
|
912 |
+
process_start_time = time.perf_counter()
|
913 |
+
|
914 |
+
generate_frame_num = self.args.generate_frame_num
|
915 |
+
tokenizer = self.tokenizer
|
916 |
+
use_guidance = self.args.use_guidance_stage2
|
917 |
+
|
918 |
+
stage2_start_time = time.perf_counter()
|
919 |
+
|
920 |
+
if next(model.parameters()).device != self.device:
|
921 |
+
move_start_time = time.perf_counter()
|
922 |
+
logger.debug('moving stage-2 model to cuda')
|
923 |
+
|
924 |
+
model = model.to(self.device)
|
925 |
+
|
926 |
+
elapsed = time.perf_counter() - move_start_time
|
927 |
+
logger.debug(f'moving in stage-2 model takes time: {elapsed:.2f}')
|
928 |
+
|
929 |
+
try:
|
930 |
+
sample_num_allgpu = parent_given_tokens.shape[0]
|
931 |
+
sample_num = sample_num_allgpu // gpu_parallel_size
|
932 |
+
assert sample_num * gpu_parallel_size == sample_num_allgpu
|
933 |
+
parent_given_tokens = parent_given_tokens[gpu_rank *
|
934 |
+
sample_num:(gpu_rank +
|
935 |
+
1) *
|
936 |
+
sample_num]
|
937 |
+
except:
|
938 |
+
logger.critical('No frame_tokens found in interpolation, skip')
|
939 |
+
return False, []
|
940 |
+
|
941 |
+
# CogVideo Stage2 Generation
|
942 |
+
while duration >= 0.5: # TODO: You can change the boundary to change the frame rate
|
943 |
+
parent_given_tokens_num = parent_given_tokens.shape[1]
|
944 |
+
generate_batchsize_persample = (parent_given_tokens_num - 1) // 2
|
945 |
+
generate_batchsize_total = generate_batchsize_persample * sample_num
|
946 |
+
total_frames = generate_frame_num
|
947 |
+
frame_len = 400
|
948 |
+
enc_text = tokenizer.encode(seq_text)
|
949 |
+
enc_duration = tokenizer.encode(str(float(duration)) + '秒')
|
950 |
+
seq = enc_duration + [tokenizer['<n>']] + enc_text + [
|
951 |
+
tokenizer['<start_of_image>']
|
952 |
+
] + [-1] * 400 * generate_frame_num
|
953 |
+
text_len = len(seq) - frame_len * generate_frame_num - 1
|
954 |
+
|
955 |
+
logger.info(
|
956 |
+
f'[Stage2: Generating Frames, Frame Rate {int(4/duration):d}] raw text: {tokenizer.decode(enc_text):s}'
|
957 |
+
)
|
958 |
+
|
959 |
+
# generation
|
960 |
+
seq = torch.tensor(seq, dtype=torch.long,
|
961 |
+
device=self.device).unsqueeze(0).repeat(
|
962 |
+
generate_batchsize_total, 1)
|
963 |
+
for sample_i in range(sample_num):
|
964 |
+
for i in range(generate_batchsize_persample):
|
965 |
+
seq[sample_i * generate_batchsize_persample +
|
966 |
+
i][text_len + 1:text_len + 1 +
|
967 |
+
400] = parent_given_tokens[sample_i][2 * i]
|
968 |
+
seq[sample_i * generate_batchsize_persample +
|
969 |
+
i][text_len + 1 + 400:text_len + 1 +
|
970 |
+
800] = parent_given_tokens[sample_i][2 * i + 1]
|
971 |
+
seq[sample_i * generate_batchsize_persample +
|
972 |
+
i][text_len + 1 + 800:text_len + 1 +
|
973 |
+
1200] = parent_given_tokens[sample_i][2 * i + 2]
|
974 |
+
|
975 |
+
if use_guidance:
|
976 |
+
guider_seq = enc_duration + [
|
977 |
+
tokenizer['<n>']
|
978 |
+
] + tokenizer.encode(video_guidance_text) + [
|
979 |
+
tokenizer['<start_of_image>']
|
980 |
+
] + [-1] * 400 * generate_frame_num
|
981 |
+
guider_text_len = len(
|
982 |
+
guider_seq) - frame_len * generate_frame_num - 1
|
983 |
+
guider_seq = torch.tensor(
|
984 |
+
guider_seq, dtype=torch.long,
|
985 |
+
device=self.device).unsqueeze(0).repeat(
|
986 |
+
generate_batchsize_total, 1)
|
987 |
+
for sample_i in range(sample_num):
|
988 |
+
for i in range(generate_batchsize_persample):
|
989 |
+
guider_seq[sample_i * generate_batchsize_persample +
|
990 |
+
i][text_len + 1:text_len + 1 +
|
991 |
+
400] = parent_given_tokens[sample_i][2 *
|
992 |
+
i]
|
993 |
+
guider_seq[sample_i * generate_batchsize_persample +
|
994 |
+
i][text_len + 1 + 400:text_len + 1 +
|
995 |
+
800] = parent_given_tokens[sample_i][2 *
|
996 |
+
i +
|
997 |
+
1]
|
998 |
+
guider_seq[sample_i * generate_batchsize_persample +
|
999 |
+
i][text_len + 1 + 800:text_len + 1 +
|
1000 |
+
1200] = parent_given_tokens[sample_i][2 *
|
1001 |
+
i +
|
1002 |
+
2]
|
1003 |
+
video_log_text_attention_weights = 0
|
1004 |
+
else:
|
1005 |
+
guider_seq = None
|
1006 |
+
guider_text_len = 0
|
1007 |
+
video_log_text_attention_weights = 1.4
|
1008 |
+
|
1009 |
+
mbz = self.args.max_inference_batch_size
|
1010 |
+
|
1011 |
+
assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
|
1012 |
+
output_list = []
|
1013 |
+
start_time = time.perf_counter()
|
1014 |
+
for tim in range(max(generate_batchsize_total // mbz, 1)):
|
1015 |
+
input_seq = seq[:min(generate_batchsize_total, mbz)].clone(
|
1016 |
+
) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
|
1017 |
+
guider_seq2 = (
|
1018 |
+
guider_seq[:min(generate_batchsize_total, mbz)].clone()
|
1019 |
+
if tim == 0 else guider_seq[mbz * tim:mbz *
|
1020 |
+
(tim + 1)].clone()
|
1021 |
+
) if guider_seq is not None else None
|
1022 |
+
output_list.append(
|
1023 |
+
my_filling_sequence(
|
1024 |
+
model,
|
1025 |
+
tokenizer,
|
1026 |
+
self.args,
|
1027 |
+
input_seq,
|
1028 |
+
batch_size=min(generate_batchsize_total, mbz),
|
1029 |
+
get_masks_and_position_ids=
|
1030 |
+
get_masks_and_position_ids_stage2,
|
1031 |
+
text_len=text_len,
|
1032 |
+
frame_len=frame_len,
|
1033 |
+
strategy=self.strategy_cogview2,
|
1034 |
+
strategy2=self.strategy_cogvideo,
|
1035 |
+
log_text_attention_weights=
|
1036 |
+
video_log_text_attention_weights,
|
1037 |
+
mode_stage1=False,
|
1038 |
+
guider_seq=guider_seq2,
|
1039 |
+
guider_text_len=guider_text_len,
|
1040 |
+
guidance_alpha=self.args.guidance_alpha,
|
1041 |
+
limited_spatial_channel_mem=True,
|
1042 |
+
)[0])
|
1043 |
+
elapsed = time.perf_counter() - start_time
|
1044 |
+
logger.info(f'Duration {duration:.2f}, Elapsed: {elapsed:.2f}\n')
|
1045 |
+
|
1046 |
+
output_tokens = torch.cat(output_list, dim=0)
|
1047 |
+
output_tokens = output_tokens[:, text_len + 1:text_len + 1 +
|
1048 |
+
(total_frames) * 400].reshape(
|
1049 |
+
sample_num, -1,
|
1050 |
+
400 * total_frames)
|
1051 |
+
output_tokens_merge = torch.cat(
|
1052 |
+
(output_tokens[:, :, :1 * 400], output_tokens[:, :,
|
1053 |
+
400 * 3:4 * 400],
|
1054 |
+
output_tokens[:, :, 400 * 1:2 * 400],
|
1055 |
+
output_tokens[:, :, 400 * 4:(total_frames) * 400]),
|
1056 |
+
dim=2).reshape(sample_num, -1, 400)
|
1057 |
+
|
1058 |
+
output_tokens_merge = torch.cat(
|
1059 |
+
(output_tokens_merge, output_tokens[:, -1:, 400 * 2:3 * 400]),
|
1060 |
+
dim=1)
|
1061 |
+
duration /= 2
|
1062 |
+
parent_given_tokens = output_tokens_merge
|
1063 |
+
|
1064 |
+
if self.args.both_stages:
|
1065 |
+
move_start_time = time.perf_counter()
|
1066 |
+
logger.debug('moving stage 2 model to cpu')
|
1067 |
+
model = model.cpu()
|
1068 |
+
torch.cuda.empty_cache()
|
1069 |
+
elapsed = time.perf_counter() - move_start_time
|
1070 |
+
logger.debug(f'moving out model2 takes time: {elapsed:.2f}')
|
1071 |
+
|
1072 |
+
elapsed = time.perf_counter() - stage2_start_time
|
1073 |
+
logger.info(f'CogVideo Stage2 completed. Elapsed: {elapsed:.2f}\n')
|
1074 |
+
|
1075 |
+
# direct super-resolution by CogView2
|
1076 |
+
logger.info('[Direct super-resolution]')
|
1077 |
+
dsr_start_time = time.perf_counter()
|
1078 |
+
|
1079 |
+
enc_text = tokenizer.encode(seq_text)
|
1080 |
+
frame_num_per_sample = parent_given_tokens.shape[1]
|
1081 |
+
parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400)
|
1082 |
+
text_seq = torch.tensor(enc_text, dtype=torch.long,
|
1083 |
+
device=self.device).unsqueeze(0).repeat(
|
1084 |
+
parent_given_tokens_2d.shape[0], 1)
|
1085 |
+
sred_tokens = self.dsr(text_seq, parent_given_tokens_2d)
|
1086 |
+
|
1087 |
+
decoded_sr_videos = []
|
1088 |
+
for sample_i in range(sample_num):
|
1089 |
+
decoded_sr_imgs = []
|
1090 |
+
for frame_i in range(frame_num_per_sample):
|
1091 |
+
decoded_sr_img = tokenizer.decode(
|
1092 |
+
image_ids=sred_tokens[frame_i + sample_i *
|
1093 |
+
frame_num_per_sample][-3600:])
|
1094 |
+
decoded_sr_imgs.append(
|
1095 |
+
self.postprocess(
|
1096 |
+
torch.nn.functional.interpolate(decoded_sr_img,
|
1097 |
+
size=(480, 480))[0]))
|
1098 |
+
decoded_sr_videos.append(decoded_sr_imgs)
|
1099 |
+
|
1100 |
+
elapsed = time.perf_counter() - dsr_start_time
|
1101 |
+
logger.info(
|
1102 |
+
f'Direct super-resolution completed. Elapsed: {elapsed:.2f}')
|
1103 |
+
|
1104 |
+
elapsed = time.perf_counter() - process_start_time
|
1105 |
+
logger.info(f'--- done ({elapsed=:.3f}) ---')
|
1106 |
+
return True, decoded_sr_videos[0]
|
1107 |
+
|
1108 |
+
@staticmethod
|
1109 |
+
def postprocess(tensor: torch.Tensor) -> np.ndarray:
|
1110 |
+
return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
|
1111 |
+
1, 2, 0).to(torch.uint8).numpy()
|
1112 |
+
|
1113 |
+
def run(self, text: str, seed: int,
|
1114 |
+
only_first_stage: bool) -> list[np.ndarray]:
|
1115 |
+
logger.info('==================== run ====================')
|
1116 |
+
start = time.perf_counter()
|
1117 |
+
|
1118 |
+
set_random_seed(seed)
|
1119 |
+
|
1120 |
+
if only_first_stage:
|
1121 |
+
self.args.stage_1 = True
|
1122 |
+
self.args.both_stages = False
|
1123 |
+
else:
|
1124 |
+
self.args.stage_1 = False
|
1125 |
+
self.args.both_stages = True
|
1126 |
+
|
1127 |
+
parent_given_tokens, res = self.process_stage1(
|
1128 |
+
self.model_stage1,
|
1129 |
+
text,
|
1130 |
+
duration=4.0,
|
1131 |
+
video_raw_text=text,
|
1132 |
+
video_guidance_text='视频',
|
1133 |
+
image_text_suffix=' 高清摄影',
|
1134 |
+
batch_size=self.args.batch_size)
|
1135 |
+
if not only_first_stage:
|
1136 |
+
_, res = self.process_stage2(
|
1137 |
+
self.model_stage2,
|
1138 |
+
text,
|
1139 |
+
duration=2.0,
|
1140 |
+
parent_given_tokens=parent_given_tokens,
|
1141 |
+
video_raw_text=text + ' 视频',
|
1142 |
+
video_guidance_text='视频',
|
1143 |
+
gpu_rank=0,
|
1144 |
+
gpu_parallel_size=1) # TODO: 修改
|
1145 |
+
|
1146 |
+
elapsed = time.perf_counter() - start
|
1147 |
+
logger.info(f'Elapsed: {elapsed:.3f}')
|
1148 |
+
logger.info('==================== done ====================')
|
1149 |
+
return res
|
1150 |
+
|
1151 |
+
|
1152 |
+
class AppModel(Model):
|
1153 |
+
def __init__(self, only_first_stage: bool):
|
1154 |
+
super().__init__(only_first_stage)
|
1155 |
+
self.translator = gr.Interface.load(
|
1156 |
+
'spaces/chinhon/translation_eng2ch')
|
1157 |
+
|
1158 |
+
def to_video(self, frames: list[np.ndarray]) -> str:
|
1159 |
+
out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
|
1160 |
+
if self.args.stage_1:
|
1161 |
+
fps = 4
|
1162 |
+
else:
|
1163 |
+
fps = 8
|
1164 |
+
writer = iio.get_writer(out_file.name, fps=fps)
|
1165 |
+
for frame in frames:
|
1166 |
+
writer.append_data(frame)
|
1167 |
+
writer.close()
|
1168 |
+
return out_file.name
|
1169 |
+
|
1170 |
+
def run_with_translation(
|
1171 |
+
self, text: str, translate: bool, seed: int, only_first_stage: bool
|
1172 |
+
) -> tuple[str | None, np.ndarray | None, list[np.ndarray] | None]:
|
1173 |
+
logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=}')
|
1174 |
+
if translate:
|
1175 |
+
text = translated_text = self.translator(text)
|
1176 |
+
else:
|
1177 |
+
translated_text = None
|
1178 |
+
frames = self.run(text, seed, only_first_stage)
|
1179 |
+
video_path = self.to_video(frames)
|
1180 |
+
return translated_text, video_path, frames
|
patch
ADDED
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
diff --git a/coglm_strategy.py b/coglm_strategy.py
|
2 |
+
index d485715..a9eab3b 100644
|
3 |
+
--- a/coglm_strategy.py
|
4 |
+
+++ b/coglm_strategy.py
|
5 |
+
@@ -8,6 +8,7 @@
|
6 |
+
|
7 |
+
# here put the import lib
|
8 |
+
import os
|
9 |
+
+import pathlib
|
10 |
+
import sys
|
11 |
+
import math
|
12 |
+
import random
|
13 |
+
@@ -58,7 +59,8 @@ class CoglmStrategy:
|
14 |
+
self._is_done = False
|
15 |
+
self.outlier_count_down = torch.zeros(16)
|
16 |
+
self.vis_list = [[]for i in range(16)]
|
17 |
+
- self.cluster_labels = torch.tensor(np.load('cluster_label2.npy'), device='cuda', dtype=torch.long)
|
18 |
+
+ cluster_label_path = pathlib.Path(__file__).parent / 'cluster_label2.npy'
|
19 |
+
+ self.cluster_labels = torch.tensor(np.load(cluster_label_path), device='cuda', dtype=torch.long)
|
20 |
+
self.start_pos = -1
|
21 |
+
self.white_cluster = []
|
22 |
+
# self.fout = open('tmp.txt', 'w')
|
23 |
+
@@ -98,4 +100,4 @@ class CoglmStrategy:
|
24 |
+
|
25 |
+
def finalize(self, tokens, mems):
|
26 |
+
self._is_done = False
|
27 |
+
- return tokens, mems
|
28 |
+
|
29 |
+
+ return tokens, mems
|
30 |
+
diff --git a/sr_pipeline/dsr_sampling.py b/sr_pipeline/dsr_sampling.py
|
31 |
+
index 5b8dded..07e97fd 100644
|
32 |
+
--- a/sr_pipeline/dsr_sampling.py
|
33 |
+
+++ b/sr_pipeline/dsr_sampling.py
|
34 |
+
@@ -8,6 +8,7 @@
|
35 |
+
|
36 |
+
# here put the import lib
|
37 |
+
import os
|
38 |
+
+import pathlib
|
39 |
+
import sys
|
40 |
+
import math
|
41 |
+
import random
|
42 |
+
@@ -28,7 +29,8 @@ class IterativeEntfilterStrategy:
|
43 |
+
self.invalid_slices = invalid_slices
|
44 |
+
self.temperature = temperature
|
45 |
+
self.topk = topk
|
46 |
+
- self.cluster_labels = torch.tensor(np.load('cluster_label2.npy'), device='cuda', dtype=torch.long)
|
47 |
+
+ cluster_label_path = pathlib.Path(__file__).parents[1] / 'cluster_label2.npy'
|
48 |
+
+ self.cluster_labels = torch.tensor(np.load(cluster_label_path), device='cuda', dtype=torch.long)
|
49 |
+
|
50 |
+
|
51 |
+
def forward(self, logits_, tokens, temperature=None, entfilter=None, filter_topk=5, temperature2=None):
|
pretrained/.gitkeep
ADDED
File without changes
|
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
git+https://github.com/Sleepychord/Image-Local-Attention@43fee31
|
2 |
+
gradio==3.1.0
|
3 |
+
icetk==0.0.4
|
4 |
+
imageio==2.19.5
|
5 |
+
imageio-ffmpeg==0.4.7
|
6 |
+
numpy==1.22.4
|
7 |
+
opencv-python-headless==4.6.0.66
|
8 |
+
SwissArmyTransformer==0.2.9
|
9 |
+
torch==1.12.0
|
10 |
+
torchvision==0.13.0
|
style.css
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
h1 {
|
2 |
+
text-align: center;
|
3 |
+
}
|
4 |
+
img#visitor-badge {
|
5 |
+
display: block;
|
6 |
+
margin: auto;
|
7 |
+
}
|