Spaces:
Paused
Paused
Sylvain Filoni
commited on
Commit
•
8502051
1
Parent(s):
869bd9e
init
Browse files- .DS_Store +0 -0
- GITHUB_README.md +159 -0
- LICENSE +21 -0
- assets/.DS_Store +0 -0
- cog.yaml +53 -0
- inference.py +133 -0
- inference.sh +10 -0
- models/RIFE/IFNet_HDv3.py +130 -0
- models/RIFE/warplayer.py +22 -0
- models/attention.py +478 -0
- models/controlnet.py +605 -0
- models/controlnet_attention.py +483 -0
- models/controlnet_unet_blocks.py +589 -0
- models/pipeline_controlvideo.py +1351 -0
- models/resnet.py +217 -0
- models/unet.py +472 -0
- models/unet_blocks.py +588 -0
- models/util.py +120 -0
- predict.py +164 -0
- requirements.txt +38 -0
.DS_Store
ADDED
Binary file (8.2 kB). View file
|
|
GITHUB_README.md
ADDED
@@ -0,0 +1,159 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ControlVideo
|
2 |
+
|
3 |
+
Official pytorch implementation of "ControlVideo: Training-free Controllable Text-to-Video Generation"
|
4 |
+
|
5 |
+
[![arXiv](https://img.shields.io/badge/arXiv-2305.13077-b31b1b.svg)](https://arxiv.org/abs/2305.13077)
|
6 |
+
![visitors](https://visitor-badge.laobi.icu/badge?page_id=YBYBZhang/ControlVideo)
|
7 |
+
[![Replicate](https://replicate.com/cjwbw/controlvideo/badge)](https://replicate.com/cjwbw/controlvideo)
|
8 |
+
|
9 |
+
<p align="center">
|
10 |
+
<img src="assets/overview.png" width="1080px"/>
|
11 |
+
<br>
|
12 |
+
<em>ControlVideo adapts ControlNet to the video counterpart without any finetuning, aiming to directly inherit its high-quality and consistent generation </em>
|
13 |
+
</p>
|
14 |
+
|
15 |
+
## News
|
16 |
+
|
17 |
+
* [05/28/2023] Thanks [chenxwh](https://github.com/chenxwh), add a [Replicate demo](https://replicate.com/cjwbw/controlvideo)!
|
18 |
+
* [05/25/2023] Code [ControlVideo](https://github.com/YBYBZhang/ControlVideo/) released!
|
19 |
+
* [05/23/2023] Paper [ControlVideo](https://arxiv.org/abs/2305.13077) released!
|
20 |
+
|
21 |
+
## Setup
|
22 |
+
|
23 |
+
### 1. Download Weights
|
24 |
+
All pre-trained weights are downloaded to `checkpoints/` directory, including the pre-trained weights of [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), ControlNet conditioned on [canny edges](https://huggingface.co/lllyasviel/sd-controlnet-canny), [depth maps](https://huggingface.co/lllyasviel/sd-controlnet-depth), [human poses](https://huggingface.co/lllyasviel/sd-controlnet-openpose).
|
25 |
+
The `flownet.pkl` is the weights of [RIFE](https://github.com/megvii-research/ECCV2022-RIFE).
|
26 |
+
The final file tree likes:
|
27 |
+
|
28 |
+
```none
|
29 |
+
checkpoints
|
30 |
+
├── stable-diffusion-v1-5
|
31 |
+
├── sd-controlnet-canny
|
32 |
+
├── sd-controlnet-depth
|
33 |
+
├── sd-controlnet-openpose
|
34 |
+
├── flownet.pkl
|
35 |
+
```
|
36 |
+
### 2. Requirements
|
37 |
+
|
38 |
+
```shell
|
39 |
+
conda create -n controlvideo python=3.10
|
40 |
+
conda activate controlvideo
|
41 |
+
pip install -r requirements.txt
|
42 |
+
```
|
43 |
+
`xformers` is recommended to save memory and running time.
|
44 |
+
|
45 |
+
## Inference
|
46 |
+
|
47 |
+
To perform text-to-video generation, just run this command in `inference.sh`:
|
48 |
+
```bash
|
49 |
+
python inference.py \
|
50 |
+
--prompt "A striking mallard floats effortlessly on the sparkling pond." \
|
51 |
+
--condition "depth" \
|
52 |
+
--video_path "data/mallard-water.mp4" \
|
53 |
+
--output_path "outputs/" \
|
54 |
+
--video_length 15 \
|
55 |
+
--smoother_steps 19 20 \
|
56 |
+
--width 512 \
|
57 |
+
--height 512 \
|
58 |
+
# --is_long_video
|
59 |
+
```
|
60 |
+
where `--video_length` is the length of synthesized video, `--condition` represents the type of structure sequence,
|
61 |
+
`--smoother_steps` determines at which timesteps to perform smoothing, and `--is_long_video` denotes whether to enable efficient long-video synthesis.
|
62 |
+
|
63 |
+
## Visualizations
|
64 |
+
|
65 |
+
### ControlVideo on depth maps
|
66 |
+
|
67 |
+
<table class="center">
|
68 |
+
<tr>
|
69 |
+
<td width=30% align="center"><img src="assets/depth/A_charming_flamingo_gracefully_wanders_in_the_calm_and_serene_water,_its_delicate_neck_curving_into_an_elegant_shape..gif" raw=true></td>
|
70 |
+
<td width=30% align="center"><img src="assets/depth/A_striking_mallard_floats_effortlessly_on_the_sparkling_pond..gif" raw=true></td>
|
71 |
+
<td width=30% align="center"><img src="assets/depth/A_gigantic_yellow_jeep_slowly_turns_on_a_wide,_smooth_road_in_the_city..gif" raw=true></td>
|
72 |
+
</tr>
|
73 |
+
<tr>
|
74 |
+
<td width=30% align="center">"A charming flamingo gracefully wanders in the calm and serene water, its delicate neck curving into an elegant shape."</td>
|
75 |
+
<td width=30% align="center">"A striking mallard floats effortlessly on the sparkling pond."</td>
|
76 |
+
<td width=30% align="center">"A gigantic yellow jeep slowly turns on a wide, smooth road in the city."</td>
|
77 |
+
</tr>
|
78 |
+
<tr>
|
79 |
+
<td width=30% align="center"><img src="assets/depth/A_sleek_boat_glides_effortlessly_through_the_shimmering_river,_van_gogh_style..gif" raw=true></td>
|
80 |
+
<td width=30% align="center"><img src="assets/depth/A_majestic_sailing_boat_cruises_along_the_vast,_azure_sea..gif" raw=true></td>
|
81 |
+
<td width=30% align="center"><img src="assets/depth/A_contented_cow_ambles_across_the_dewy,_verdant_pasture..gif" raw=true></td>
|
82 |
+
</tr>
|
83 |
+
<tr>
|
84 |
+
<td width=30% align="center">"A sleek boat glides effortlessly through the shimmering river, van gogh style."</td>
|
85 |
+
<td width=30% align="center">"A majestic sailing boat cruises along the vast, azure sea."</td>
|
86 |
+
<td width=30% align="center">"A contented cow ambles across the dewy, verdant pasture."</td>
|
87 |
+
</tr>
|
88 |
+
</table>
|
89 |
+
|
90 |
+
### ControlVideo on canny edges
|
91 |
+
|
92 |
+
<table class="center">
|
93 |
+
<tr>
|
94 |
+
<td width=30% align="center"><img src="assets/canny/A_young_man_riding_a_sleek,_black_motorbike_through_the_winding_mountain_roads..gif" raw=true></td>
|
95 |
+
<td width=30% align="center"><img src="assets/canny/A_white_swan_moving_on_the_lake,_cartoon_style..gif" raw=true></td>
|
96 |
+
<td width=30% align="center"><img src="assets/canny/A_dusty_old_jeep_was_making_its_way_down_the_winding_forest_road,_creaking_and_groaning_with_each_bump_and_turn..gif" raw=true></td>
|
97 |
+
</tr>
|
98 |
+
<tr>
|
99 |
+
<td width=30% align="center">"A young man riding a sleek, black motorbike through the winding mountain roads."</td>
|
100 |
+
<td width=30% align="center">"A white swan movingon the lake, cartoon style."</td>
|
101 |
+
<td width=30% align="center">"A dusty old jeep was making its way down the winding forest road, creaking and groaning with each bump and turn."</td>
|
102 |
+
</tr>
|
103 |
+
<tr>
|
104 |
+
<td width=30% align="center"><img src="assets/canny/A_shiny_red_jeep_smoothly_turns_on_a_narrow,_winding_road_in_the_mountains..gif" raw=true></td>
|
105 |
+
<td width=30% align="center"><img src="assets/canny/A_majestic_camel_gracefully_strides_across_the_scorching_desert_sands..gif" raw=true></td>
|
106 |
+
<td width=30% align="center"><img src="assets/canny/A_fit_man_is_leisurely_hiking_through_a_lush_and_verdant_forest..gif" raw=true></td>
|
107 |
+
</tr>
|
108 |
+
<tr>
|
109 |
+
<td width=30% align="center">"A shiny red jeep smoothly turns on a narrow, winding road in the mountains."</td>
|
110 |
+
<td width=30% align="center">"A majestic camel gracefully strides across the scorching desert sands."</td>
|
111 |
+
<td width=30% align="center">"A fit man is leisurely hiking through a lush and verdant forest."</td>
|
112 |
+
</tr>
|
113 |
+
</table>
|
114 |
+
|
115 |
+
|
116 |
+
### ControlVideo on human poses
|
117 |
+
|
118 |
+
<table class="center">
|
119 |
+
<tr>
|
120 |
+
<td width=25% align="center"><img src="assets/pose/James_bond_moonwalk_on_the_beach,_animation_style.gif" raw=true></td>
|
121 |
+
<td width=25% align="center"><img src="assets/pose/Goku_in_a_mountain_range,_surreal_style..gif" raw=true></td>
|
122 |
+
<td width=25% align="center"><img src="assets/pose/Hulk_is_jumping_on_the_street,_cartoon_style.gif" raw=true></td>
|
123 |
+
<td width=25% align="center"><img src="assets/pose/A_robot_dances_on_a_road,_animation_style.gif" raw=true></td>
|
124 |
+
</tr>
|
125 |
+
<tr>
|
126 |
+
<td width=25% align="center">"James bond moonwalk on the beach, animation style."</td>
|
127 |
+
<td width=25% align="center">"Goku in a mountain range, surreal style."</td>
|
128 |
+
<td width=25% align="center">"Hulk is jumping on the street, cartoon style."</td>
|
129 |
+
<td width=25% align="center">"A robot dances on a road, animation style."</td>
|
130 |
+
</tr></table>
|
131 |
+
|
132 |
+
### Long video generation
|
133 |
+
|
134 |
+
<table class="center">
|
135 |
+
<tr>
|
136 |
+
<td width=60% align="center"><img src="assets/long/A_steamship_on_the_ocean,_at_sunset,_sketch_style.gif" raw=true></td>
|
137 |
+
<td width=40% align="center"><img src="assets/long/Hulk_is_dancing_on_the_beach,_cartoon_style.gif" raw=true></td>
|
138 |
+
</tr>
|
139 |
+
<tr>
|
140 |
+
<td width=60% align="center">"A steamship on the ocean, at sunset, sketch style."</td>
|
141 |
+
<td width=40% align="center">"Hulk is dancing on the beach, cartoon style."</td>
|
142 |
+
</tr>
|
143 |
+
</table>
|
144 |
+
|
145 |
+
## Citation
|
146 |
+
If you make use of our work, please cite our paper.
|
147 |
+
```bibtex
|
148 |
+
@article{zhang2023controlvideo,
|
149 |
+
title={ControlVideo: Training-free Controllable Text-to-Video Generation},
|
150 |
+
author={Zhang, Yabo and Wei, Yuxiang and Jiang, Dongsheng and Zhang, Xiaopeng and Zuo, Wangmeng and Tian, Qi},
|
151 |
+
journal={arXiv preprint arXiv:2305.13077},
|
152 |
+
year={2023}
|
153 |
+
}
|
154 |
+
```
|
155 |
+
|
156 |
+
## Acknowledgement
|
157 |
+
This work repository borrows heavily from [Diffusers](https://github.com/huggingface/diffusers), [ControlNet](https://github.com/lllyasviel/ControlNet), [Tune-A-Video](https://github.com/showlab/Tune-A-Video), and [RIFE](https://github.com/megvii-research/ECCV2022-RIFE).
|
158 |
+
|
159 |
+
There are also many interesting works on video generation: [Tune-A-Video](https://github.com/showlab/Tune-A-Video), [Text2Video-Zero](https://github.com/Picsart-AI-Research/Text2Video-Zero), [Follow-Your-Pose](https://github.com/mayuelala/FollowYourPose), [Control-A-Video](https://github.com/Weifeng-Chen/control-a-video), et al.
|
LICENSE
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
MIT License
|
2 |
+
|
3 |
+
Copyright (c) 2023 YaboZhang
|
4 |
+
|
5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
6 |
+
of this software and associated documentation files (the "Software"), to deal
|
7 |
+
in the Software without restriction, including without limitation the rights
|
8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
9 |
+
copies of the Software, and to permit persons to whom the Software is
|
10 |
+
furnished to do so, subject to the following conditions:
|
11 |
+
|
12 |
+
The above copyright notice and this permission notice shall be included in all
|
13 |
+
copies or substantial portions of the Software.
|
14 |
+
|
15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
21 |
+
SOFTWARE.
|
assets/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|
cog.yaml
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Configuration for Cog ⚙️
|
2 |
+
# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
|
3 |
+
|
4 |
+
build:
|
5 |
+
gpu: true
|
6 |
+
cuda: "11.6"
|
7 |
+
system_packages:
|
8 |
+
- "libgl1-mesa-glx"
|
9 |
+
- "libglib2.0-0"
|
10 |
+
python_version: "3.8"
|
11 |
+
python_packages:
|
12 |
+
- "accelerate==0.17.1"
|
13 |
+
- "addict==2.4.0"
|
14 |
+
- "basicsr==1.4.2"
|
15 |
+
- "bitsandbytes==0.35.4"
|
16 |
+
- "clip==0.2.0"
|
17 |
+
- "cmake==3.25.2"
|
18 |
+
- "controlnet-aux==0.0.4"
|
19 |
+
- "decord==0.6.0"
|
20 |
+
- "deepspeed==0.8.0"
|
21 |
+
- "diffusers==0.14.0"
|
22 |
+
- "easydict==1.10"
|
23 |
+
- "einops==0.6.0"
|
24 |
+
- "ffmpy==0.3.0"
|
25 |
+
- "ftfy==6.1.1"
|
26 |
+
- "imageio==2.25.1"
|
27 |
+
- "imageio-ffmpeg==0.4.8"
|
28 |
+
- "moviepy==1.0.3"
|
29 |
+
- "numpy==1.24.2"
|
30 |
+
- "omegaconf==2.3.0"
|
31 |
+
- "opencv-python==4.7.0.68"
|
32 |
+
- "pandas==1.5.3"
|
33 |
+
- "pillow==9.4.0"
|
34 |
+
- "scikit-image==0.19.3"
|
35 |
+
- "scipy==1.10.1"
|
36 |
+
- "tensorboard==2.12.0"
|
37 |
+
- "tensorboard-data-server==0.7.0"
|
38 |
+
- "tensorboard-plugin-wit==1.8.1"
|
39 |
+
- "termcolor==2.2.0"
|
40 |
+
- "thinc==8.1.10"
|
41 |
+
- "timm==0.6.12"
|
42 |
+
- "tokenizers==0.13.2"
|
43 |
+
- "torch==1.13.1"
|
44 |
+
- "torchvision==0.14.1"
|
45 |
+
- "tqdm==4.64.1"
|
46 |
+
- "transformers==4.26.1"
|
47 |
+
- "wandb==0.13.10"
|
48 |
+
- "xformers==0.0.16"
|
49 |
+
- "positional_encodings==6.0.1"
|
50 |
+
- "mediapipe==0.10.0"
|
51 |
+
- "triton==2.0.0.post1"
|
52 |
+
|
53 |
+
predict: "predict.py:Predictor"
|
inference.py
ADDED
@@ -0,0 +1,133 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import numpy as np
|
3 |
+
import argparse
|
4 |
+
import imageio
|
5 |
+
import torch
|
6 |
+
|
7 |
+
from einops import rearrange
|
8 |
+
from diffusers import DDIMScheduler, AutoencoderKL
|
9 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
10 |
+
# from annotator.canny import CannyDetector
|
11 |
+
# from annotator.openpose import OpenposeDetector
|
12 |
+
# from annotator.midas import MidasDetector
|
13 |
+
# import sys
|
14 |
+
# sys.path.insert(0, ".")
|
15 |
+
from huggingface_hub import hf_hub_download
|
16 |
+
import controlnet_aux
|
17 |
+
from controlnet_aux import OpenposeDetector, CannyDetector, MidasDetector
|
18 |
+
from controlnet_aux.open_pose.body import Body
|
19 |
+
|
20 |
+
from models.pipeline_controlvideo import ControlVideoPipeline
|
21 |
+
from models.util import save_videos_grid, read_video, get_annotation
|
22 |
+
from models.unet import UNet3DConditionModel
|
23 |
+
from models.controlnet import ControlNetModel3D
|
24 |
+
from models.RIFE.IFNet_HDv3 import IFNet
|
25 |
+
|
26 |
+
|
27 |
+
device = "cuda"
|
28 |
+
sd_path = "checkpoints/stable-diffusion-v1-5"
|
29 |
+
inter_path = "checkpoints/flownet.pkl"
|
30 |
+
controlnet_dict = {
|
31 |
+
"pose": "checkpoints/sd-controlnet-openpose",
|
32 |
+
"depth": "checkpoints/sd-controlnet-depth",
|
33 |
+
"canny": "checkpoints/sd-controlnet-canny",
|
34 |
+
}
|
35 |
+
|
36 |
+
controlnet_parser_dict = {
|
37 |
+
"pose": OpenposeDetector,
|
38 |
+
"depth": MidasDetector,
|
39 |
+
"canny": CannyDetector,
|
40 |
+
}
|
41 |
+
|
42 |
+
POS_PROMPT = " ,best quality, extremely detailed, HD, ultra-realistic, 8K, HQ, masterpiece, trending on artstation, art, smooth"
|
43 |
+
NEG_PROMPT = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer difits, cropped, worst quality, low quality, deformed body, bloated, ugly, unrealistic"
|
44 |
+
|
45 |
+
|
46 |
+
|
47 |
+
def get_args():
|
48 |
+
parser = argparse.ArgumentParser()
|
49 |
+
parser.add_argument("--prompt", type=str, required=True, help="Text description of target video")
|
50 |
+
parser.add_argument("--video_path", type=str, required=True, help="Path to a source video")
|
51 |
+
parser.add_argument("--output_path", type=str, default="./outputs", help="Directory of output")
|
52 |
+
parser.add_argument("--condition", type=str, default="depth", help="Condition of structure sequence")
|
53 |
+
parser.add_argument("--video_length", type=int, default=15, help="Length of synthesized video")
|
54 |
+
parser.add_argument("--height", type=int, default=512, help="Height of synthesized video, and should be a multiple of 32")
|
55 |
+
parser.add_argument("--width", type=int, default=512, help="Width of synthesized video, and should be a multiple of 32")
|
56 |
+
parser.add_argument("--smoother_steps", nargs='+', default=[19, 20], type=int, help="Timesteps at which using interleaved-frame smoother")
|
57 |
+
parser.add_argument("--is_long_video", action='store_true', help="Whether to use hierarchical sampler to produce long video")
|
58 |
+
parser.add_argument("--seed", type=int, default=42, help="Random seed of generator")
|
59 |
+
|
60 |
+
args = parser.parse_args()
|
61 |
+
return args
|
62 |
+
|
63 |
+
if __name__ == "__main__":
|
64 |
+
args = get_args()
|
65 |
+
os.makedirs(args.output_path, exist_ok=True)
|
66 |
+
|
67 |
+
# Height and width should be a multiple of 32
|
68 |
+
args.height = (args.height // 32) * 32
|
69 |
+
args.width = (args.width // 32) * 32
|
70 |
+
|
71 |
+
if args.condition == "pose":
|
72 |
+
pretrained_model_or_path = "lllyasviel/ControlNet"
|
73 |
+
body_model_path = hf_hub_download(pretrained_model_or_path, "annotator/ckpts/body_pose_model.pth", cache_dir="checkpoints")
|
74 |
+
body_estimation = Body(body_model_path)
|
75 |
+
annotator = controlnet_parser_dict[args.condition](body_estimation)
|
76 |
+
else:
|
77 |
+
annotator = controlnet_parser_dict[args.condition]()
|
78 |
+
|
79 |
+
tokenizer = CLIPTokenizer.from_pretrained(sd_path, subfolder="tokenizer")
|
80 |
+
text_encoder = CLIPTextModel.from_pretrained(sd_path, subfolder="text_encoder").to(dtype=torch.float16)
|
81 |
+
vae = AutoencoderKL.from_pretrained(sd_path, subfolder="vae").to(dtype=torch.float16)
|
82 |
+
unet = UNet3DConditionModel.from_pretrained_2d(sd_path, subfolder="unet").to(dtype=torch.float16)
|
83 |
+
controlnet = ControlNetModel3D.from_pretrained_2d(controlnet_dict[args.condition]).to(dtype=torch.float16)
|
84 |
+
interpolater = IFNet(ckpt_path=inter_path).to(dtype=torch.float16)
|
85 |
+
scheduler=DDIMScheduler.from_pretrained(sd_path, subfolder="scheduler")
|
86 |
+
|
87 |
+
pipe = ControlVideoPipeline(
|
88 |
+
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
|
89 |
+
controlnet=controlnet, interpolater=interpolater, scheduler=scheduler,
|
90 |
+
)
|
91 |
+
pipe.enable_vae_slicing()
|
92 |
+
pipe.enable_xformers_memory_efficient_attention()
|
93 |
+
pipe.to(device)
|
94 |
+
|
95 |
+
generator = torch.Generator(device="cuda")
|
96 |
+
generator.manual_seed(args.seed)
|
97 |
+
|
98 |
+
# Step 1. Read a video
|
99 |
+
video = read_video(video_path=args.video_path, video_length=args.video_length, width=args.width, height=args.height)
|
100 |
+
|
101 |
+
# Save source video
|
102 |
+
original_pixels = rearrange(video, "(b f) c h w -> b c f h w", b=1)
|
103 |
+
save_videos_grid(original_pixels, os.path.join(args.output_path, "source_video.mp4"), rescale=True)
|
104 |
+
|
105 |
+
|
106 |
+
# Step 2. Parse a video to conditional frames
|
107 |
+
pil_annotation = get_annotation(video, annotator)
|
108 |
+
if args.condition == "depth" and controlnet_aux.__version__ == '0.0.1':
|
109 |
+
pil_annotation = [pil_annot[0] for pil_annot in pil_annotation]
|
110 |
+
|
111 |
+
# Save condition video
|
112 |
+
video_cond = [np.array(p).astype(np.uint8) for p in pil_annotation]
|
113 |
+
imageio.mimsave(os.path.join(args.output_path, f"{args.condition}_condition.mp4"), video_cond, fps=8)
|
114 |
+
|
115 |
+
# Reduce memory (optional)
|
116 |
+
del annotator; torch.cuda.empty_cache()
|
117 |
+
|
118 |
+
# Step 3. inference
|
119 |
+
|
120 |
+
if args.is_long_video:
|
121 |
+
window_size = int(np.sqrt(args.video_length))
|
122 |
+
sample = pipe.generate_long_video(args.prompt + POS_PROMPT, video_length=args.video_length, frames=pil_annotation,
|
123 |
+
num_inference_steps=50, smooth_steps=args.smoother_steps, window_size=window_size,
|
124 |
+
generator=generator, guidance_scale=12.5, negative_prompt=NEG_PROMPT,
|
125 |
+
width=args.width, height=args.height
|
126 |
+
).videos
|
127 |
+
else:
|
128 |
+
sample = pipe(args.prompt + POS_PROMPT, video_length=args.video_length, frames=pil_annotation,
|
129 |
+
num_inference_steps=50, smooth_steps=args.smoother_steps,
|
130 |
+
generator=generator, guidance_scale=12.5, negative_prompt=NEG_PROMPT,
|
131 |
+
width=args.width, height=args.height
|
132 |
+
).videos
|
133 |
+
save_videos_grid(sample, f"{args.output_path}/{args.prompt}.mp4")
|
inference.sh
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
python inference.py \
|
2 |
+
--prompt "A striking mallard floats effortlessly on the sparkling pond." \
|
3 |
+
--condition "depth" \
|
4 |
+
--video_path "data/mallard-water.mp4" \
|
5 |
+
--output_path "outputs/" \
|
6 |
+
--video_length 15 \
|
7 |
+
--smoother_steps 19 20 \
|
8 |
+
--width 512 \
|
9 |
+
--height 512 \
|
10 |
+
# --is_long_video
|
models/RIFE/IFNet_HDv3.py
ADDED
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.nn.functional as F
|
4 |
+
from diffusers import ModelMixin
|
5 |
+
|
6 |
+
from .warplayer import warp
|
7 |
+
|
8 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
9 |
+
|
10 |
+
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
11 |
+
return nn.Sequential(
|
12 |
+
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
|
13 |
+
padding=padding, dilation=dilation, bias=True),
|
14 |
+
nn.PReLU(out_planes)
|
15 |
+
)
|
16 |
+
|
17 |
+
def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
18 |
+
return nn.Sequential(
|
19 |
+
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
|
20 |
+
padding=padding, dilation=dilation, bias=False),
|
21 |
+
nn.BatchNorm2d(out_planes),
|
22 |
+
nn.PReLU(out_planes)
|
23 |
+
)
|
24 |
+
|
25 |
+
def convert(param):
|
26 |
+
return {
|
27 |
+
k.replace("module.", ""): v
|
28 |
+
for k, v in param.items()
|
29 |
+
if "module." in k
|
30 |
+
}
|
31 |
+
|
32 |
+
class IFBlock(nn.Module):
|
33 |
+
def __init__(self, in_planes, c=64):
|
34 |
+
super(IFBlock, self).__init__()
|
35 |
+
self.conv0 = nn.Sequential(
|
36 |
+
conv(in_planes, c//2, 3, 2, 1),
|
37 |
+
conv(c//2, c, 3, 2, 1),
|
38 |
+
)
|
39 |
+
self.convblock0 = nn.Sequential(
|
40 |
+
conv(c, c),
|
41 |
+
conv(c, c)
|
42 |
+
)
|
43 |
+
self.convblock1 = nn.Sequential(
|
44 |
+
conv(c, c),
|
45 |
+
conv(c, c)
|
46 |
+
)
|
47 |
+
self.convblock2 = nn.Sequential(
|
48 |
+
conv(c, c),
|
49 |
+
conv(c, c)
|
50 |
+
)
|
51 |
+
self.convblock3 = nn.Sequential(
|
52 |
+
conv(c, c),
|
53 |
+
conv(c, c)
|
54 |
+
)
|
55 |
+
self.conv1 = nn.Sequential(
|
56 |
+
nn.ConvTranspose2d(c, c//2, 4, 2, 1),
|
57 |
+
nn.PReLU(c//2),
|
58 |
+
nn.ConvTranspose2d(c//2, 4, 4, 2, 1),
|
59 |
+
)
|
60 |
+
self.conv2 = nn.Sequential(
|
61 |
+
nn.ConvTranspose2d(c, c//2, 4, 2, 1),
|
62 |
+
nn.PReLU(c//2),
|
63 |
+
nn.ConvTranspose2d(c//2, 1, 4, 2, 1),
|
64 |
+
)
|
65 |
+
|
66 |
+
def forward(self, x, flow, scale=1):
|
67 |
+
x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
68 |
+
flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale
|
69 |
+
feat = self.conv0(torch.cat((x, flow), 1))
|
70 |
+
feat = self.convblock0(feat) + feat
|
71 |
+
feat = self.convblock1(feat) + feat
|
72 |
+
feat = self.convblock2(feat) + feat
|
73 |
+
feat = self.convblock3(feat) + feat
|
74 |
+
flow = self.conv1(feat)
|
75 |
+
mask = self.conv2(feat)
|
76 |
+
flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale
|
77 |
+
mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
78 |
+
return flow, mask
|
79 |
+
|
80 |
+
class IFNet(ModelMixin):
|
81 |
+
def __init__(self, ckpt_path="checkpoints/flownet.pkl"):
|
82 |
+
super(IFNet, self).__init__()
|
83 |
+
self.block0 = IFBlock(7+4, c=90)
|
84 |
+
self.block1 = IFBlock(7+4, c=90)
|
85 |
+
self.block2 = IFBlock(7+4, c=90)
|
86 |
+
self.block_tea = IFBlock(10+4, c=90)
|
87 |
+
if ckpt_path is not None:
|
88 |
+
self.load_state_dict(convert(torch.load(ckpt_path, map_location ='cpu')))
|
89 |
+
|
90 |
+
def inference(self, img0, img1, scale=1.0):
|
91 |
+
imgs = torch.cat((img0, img1), 1)
|
92 |
+
scale_list = [4/scale, 2/scale, 1/scale]
|
93 |
+
flow, mask, merged = self.forward(imgs, scale_list)
|
94 |
+
return merged[2]
|
95 |
+
|
96 |
+
def forward(self, x, scale_list=[4, 2, 1], training=False):
|
97 |
+
if training == False:
|
98 |
+
channel = x.shape[1] // 2
|
99 |
+
img0 = x[:, :channel]
|
100 |
+
img1 = x[:, channel:]
|
101 |
+
flow_list = []
|
102 |
+
merged = []
|
103 |
+
mask_list = []
|
104 |
+
warped_img0 = img0
|
105 |
+
warped_img1 = img1
|
106 |
+
flow = (x[:, :4]).detach() * 0
|
107 |
+
mask = (x[:, :1]).detach() * 0
|
108 |
+
loss_cons = 0
|
109 |
+
block = [self.block0, self.block1, self.block2]
|
110 |
+
for i in range(3):
|
111 |
+
f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
|
112 |
+
f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
|
113 |
+
flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
|
114 |
+
mask = mask + (m0 + (-m1)) / 2
|
115 |
+
mask_list.append(mask)
|
116 |
+
flow_list.append(flow)
|
117 |
+
warped_img0 = warp(img0, flow[:, :2])
|
118 |
+
warped_img1 = warp(img1, flow[:, 2:4])
|
119 |
+
merged.append((warped_img0, warped_img1))
|
120 |
+
'''
|
121 |
+
c0 = self.contextnet(img0, flow[:, :2])
|
122 |
+
c1 = self.contextnet(img1, flow[:, 2:4])
|
123 |
+
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
124 |
+
res = tmp[:, 1:4] * 2 - 1
|
125 |
+
'''
|
126 |
+
for i in range(3):
|
127 |
+
mask_list[i] = torch.sigmoid(mask_list[i])
|
128 |
+
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
129 |
+
# merged[i] = torch.clamp(merged[i] + res, 0, 1)
|
130 |
+
return flow_list, mask_list[2], merged
|
models/RIFE/warplayer.py
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
|
4 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
5 |
+
backwarp_tenGrid = {}
|
6 |
+
|
7 |
+
|
8 |
+
def warp(tenInput, tenFlow):
|
9 |
+
k = (str(tenFlow.device), str(tenFlow.size()))
|
10 |
+
if k not in backwarp_tenGrid:
|
11 |
+
tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
|
12 |
+
1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
|
13 |
+
tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
|
14 |
+
1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
|
15 |
+
backwarp_tenGrid[k] = torch.cat(
|
16 |
+
[tenHorizontal, tenVertical], 1).to(device)
|
17 |
+
|
18 |
+
tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
|
19 |
+
tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
|
20 |
+
|
21 |
+
g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1).to(dtype=tenInput.dtype)
|
22 |
+
return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
|
models/attention.py
ADDED
@@ -0,0 +1,478 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
|
2 |
+
|
3 |
+
from dataclasses import dataclass
|
4 |
+
from typing import Optional, Callable
|
5 |
+
import math
|
6 |
+
import torch
|
7 |
+
import torch.nn.functional as F
|
8 |
+
from torch import nn
|
9 |
+
from positional_encodings.torch_encodings import PositionalEncoding2D
|
10 |
+
|
11 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
12 |
+
from diffusers import ModelMixin
|
13 |
+
from diffusers.utils import BaseOutput
|
14 |
+
from diffusers.utils.import_utils import is_xformers_available
|
15 |
+
from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm
|
16 |
+
from einops import rearrange, repeat
|
17 |
+
|
18 |
+
|
19 |
+
@dataclass
|
20 |
+
class Transformer3DModelOutput(BaseOutput):
|
21 |
+
sample: torch.FloatTensor
|
22 |
+
|
23 |
+
|
24 |
+
if is_xformers_available():
|
25 |
+
import xformers
|
26 |
+
import xformers.ops
|
27 |
+
else:
|
28 |
+
xformers = None
|
29 |
+
|
30 |
+
|
31 |
+
class Transformer3DModel(ModelMixin, ConfigMixin):
|
32 |
+
@register_to_config
|
33 |
+
def __init__(
|
34 |
+
self,
|
35 |
+
num_attention_heads: int = 16,
|
36 |
+
attention_head_dim: int = 88,
|
37 |
+
in_channels: Optional[int] = None,
|
38 |
+
num_layers: int = 1,
|
39 |
+
dropout: float = 0.0,
|
40 |
+
norm_num_groups: int = 32,
|
41 |
+
cross_attention_dim: Optional[int] = None,
|
42 |
+
attention_bias: bool = False,
|
43 |
+
activation_fn: str = "geglu",
|
44 |
+
num_embeds_ada_norm: Optional[int] = None,
|
45 |
+
use_linear_projection: bool = False,
|
46 |
+
only_cross_attention: bool = False,
|
47 |
+
upcast_attention: bool = False,
|
48 |
+
):
|
49 |
+
super().__init__()
|
50 |
+
self.use_linear_projection = use_linear_projection
|
51 |
+
self.num_attention_heads = num_attention_heads
|
52 |
+
self.attention_head_dim = attention_head_dim
|
53 |
+
inner_dim = num_attention_heads * attention_head_dim
|
54 |
+
|
55 |
+
# Define input layers
|
56 |
+
self.in_channels = in_channels
|
57 |
+
|
58 |
+
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
59 |
+
if use_linear_projection:
|
60 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
61 |
+
else:
|
62 |
+
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
|
63 |
+
|
64 |
+
# Define transformers blocks
|
65 |
+
self.transformer_blocks = nn.ModuleList(
|
66 |
+
[
|
67 |
+
BasicTransformerBlock(
|
68 |
+
inner_dim,
|
69 |
+
num_attention_heads,
|
70 |
+
attention_head_dim,
|
71 |
+
dropout=dropout,
|
72 |
+
cross_attention_dim=cross_attention_dim,
|
73 |
+
activation_fn=activation_fn,
|
74 |
+
num_embeds_ada_norm=num_embeds_ada_norm,
|
75 |
+
attention_bias=attention_bias,
|
76 |
+
only_cross_attention=only_cross_attention,
|
77 |
+
upcast_attention=upcast_attention,
|
78 |
+
)
|
79 |
+
for d in range(num_layers)
|
80 |
+
]
|
81 |
+
)
|
82 |
+
|
83 |
+
# 4. Define output layers
|
84 |
+
if use_linear_projection:
|
85 |
+
self.proj_out = nn.Linear(in_channels, inner_dim)
|
86 |
+
else:
|
87 |
+
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
|
88 |
+
|
89 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True, \
|
90 |
+
inter_frame=False):
|
91 |
+
# Input
|
92 |
+
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
|
93 |
+
video_length = hidden_states.shape[2]
|
94 |
+
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
|
95 |
+
encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
|
96 |
+
|
97 |
+
batch, channel, height, weight = hidden_states.shape
|
98 |
+
residual = hidden_states
|
99 |
+
|
100 |
+
hidden_states = self.norm(hidden_states)
|
101 |
+
if not self.use_linear_projection:
|
102 |
+
hidden_states = self.proj_in(hidden_states)
|
103 |
+
inner_dim = hidden_states.shape[1]
|
104 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
105 |
+
else:
|
106 |
+
inner_dim = hidden_states.shape[1]
|
107 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
108 |
+
hidden_states = self.proj_in(hidden_states)
|
109 |
+
|
110 |
+
# Blocks
|
111 |
+
for block in self.transformer_blocks:
|
112 |
+
hidden_states = block(
|
113 |
+
hidden_states,
|
114 |
+
encoder_hidden_states=encoder_hidden_states,
|
115 |
+
timestep=timestep,
|
116 |
+
video_length=video_length,
|
117 |
+
inter_frame=inter_frame
|
118 |
+
)
|
119 |
+
|
120 |
+
# Output
|
121 |
+
if not self.use_linear_projection:
|
122 |
+
hidden_states = (
|
123 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
124 |
+
)
|
125 |
+
hidden_states = self.proj_out(hidden_states)
|
126 |
+
else:
|
127 |
+
hidden_states = self.proj_out(hidden_states)
|
128 |
+
hidden_states = (
|
129 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
130 |
+
)
|
131 |
+
|
132 |
+
output = hidden_states + residual
|
133 |
+
|
134 |
+
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
|
135 |
+
if not return_dict:
|
136 |
+
return (output,)
|
137 |
+
|
138 |
+
return Transformer3DModelOutput(sample=output)
|
139 |
+
|
140 |
+
|
141 |
+
class BasicTransformerBlock(nn.Module):
|
142 |
+
def __init__(
|
143 |
+
self,
|
144 |
+
dim: int,
|
145 |
+
num_attention_heads: int,
|
146 |
+
attention_head_dim: int,
|
147 |
+
dropout=0.0,
|
148 |
+
cross_attention_dim: Optional[int] = None,
|
149 |
+
activation_fn: str = "geglu",
|
150 |
+
num_embeds_ada_norm: Optional[int] = None,
|
151 |
+
attention_bias: bool = False,
|
152 |
+
only_cross_attention: bool = False,
|
153 |
+
upcast_attention: bool = False,
|
154 |
+
):
|
155 |
+
super().__init__()
|
156 |
+
self.only_cross_attention = only_cross_attention
|
157 |
+
self.use_ada_layer_norm = num_embeds_ada_norm is not None
|
158 |
+
|
159 |
+
# Fully
|
160 |
+
self.attn1 = FullyFrameAttention(
|
161 |
+
query_dim=dim,
|
162 |
+
heads=num_attention_heads,
|
163 |
+
dim_head=attention_head_dim,
|
164 |
+
dropout=dropout,
|
165 |
+
bias=attention_bias,
|
166 |
+
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
167 |
+
upcast_attention=upcast_attention,
|
168 |
+
)
|
169 |
+
|
170 |
+
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
171 |
+
|
172 |
+
# Cross-Attn
|
173 |
+
if cross_attention_dim is not None:
|
174 |
+
self.attn2 = CrossAttention(
|
175 |
+
query_dim=dim,
|
176 |
+
cross_attention_dim=cross_attention_dim,
|
177 |
+
heads=num_attention_heads,
|
178 |
+
dim_head=attention_head_dim,
|
179 |
+
dropout=dropout,
|
180 |
+
bias=attention_bias,
|
181 |
+
upcast_attention=upcast_attention,
|
182 |
+
)
|
183 |
+
else:
|
184 |
+
self.attn2 = None
|
185 |
+
|
186 |
+
if cross_attention_dim is not None:
|
187 |
+
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
188 |
+
else:
|
189 |
+
self.norm2 = None
|
190 |
+
|
191 |
+
# Feed-forward
|
192 |
+
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
|
193 |
+
self.norm3 = nn.LayerNorm(dim)
|
194 |
+
|
195 |
+
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None):
|
196 |
+
if not is_xformers_available():
|
197 |
+
print("Here is how to install it")
|
198 |
+
raise ModuleNotFoundError(
|
199 |
+
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
200 |
+
" xformers",
|
201 |
+
name="xformers",
|
202 |
+
)
|
203 |
+
elif not torch.cuda.is_available():
|
204 |
+
raise ValueError(
|
205 |
+
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
|
206 |
+
" available for GPU "
|
207 |
+
)
|
208 |
+
else:
|
209 |
+
try:
|
210 |
+
# Make sure we can run the memory efficient attention
|
211 |
+
_ = xformers.ops.memory_efficient_attention(
|
212 |
+
torch.randn((1, 2, 40), device="cuda"),
|
213 |
+
torch.randn((1, 2, 40), device="cuda"),
|
214 |
+
torch.randn((1, 2, 40), device="cuda"),
|
215 |
+
)
|
216 |
+
except Exception as e:
|
217 |
+
raise e
|
218 |
+
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
219 |
+
if self.attn2 is not None:
|
220 |
+
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
221 |
+
|
222 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None, \
|
223 |
+
inter_frame=False):
|
224 |
+
# SparseCausal-Attention
|
225 |
+
norm_hidden_states = (
|
226 |
+
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
|
227 |
+
)
|
228 |
+
|
229 |
+
if self.only_cross_attention:
|
230 |
+
hidden_states = (
|
231 |
+
self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask, inter_frame=inter_frame) + hidden_states
|
232 |
+
)
|
233 |
+
else:
|
234 |
+
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length, inter_frame=inter_frame) + hidden_states
|
235 |
+
|
236 |
+
if self.attn2 is not None:
|
237 |
+
# Cross-Attention
|
238 |
+
norm_hidden_states = (
|
239 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
240 |
+
)
|
241 |
+
hidden_states = (
|
242 |
+
self.attn2(
|
243 |
+
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
244 |
+
)
|
245 |
+
+ hidden_states
|
246 |
+
)
|
247 |
+
|
248 |
+
# Feed-forward
|
249 |
+
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
|
250 |
+
|
251 |
+
return hidden_states
|
252 |
+
|
253 |
+
class FullyFrameAttention(nn.Module):
|
254 |
+
r"""
|
255 |
+
A cross attention layer.
|
256 |
+
|
257 |
+
Parameters:
|
258 |
+
query_dim (`int`): The number of channels in the query.
|
259 |
+
cross_attention_dim (`int`, *optional*):
|
260 |
+
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
|
261 |
+
heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
|
262 |
+
dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.
|
263 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
264 |
+
bias (`bool`, *optional*, defaults to False):
|
265 |
+
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
|
266 |
+
"""
|
267 |
+
|
268 |
+
def __init__(
|
269 |
+
self,
|
270 |
+
query_dim: int,
|
271 |
+
cross_attention_dim: Optional[int] = None,
|
272 |
+
heads: int = 8,
|
273 |
+
dim_head: int = 64,
|
274 |
+
dropout: float = 0.0,
|
275 |
+
bias=False,
|
276 |
+
upcast_attention: bool = False,
|
277 |
+
upcast_softmax: bool = False,
|
278 |
+
added_kv_proj_dim: Optional[int] = None,
|
279 |
+
norm_num_groups: Optional[int] = None,
|
280 |
+
):
|
281 |
+
super().__init__()
|
282 |
+
inner_dim = dim_head * heads
|
283 |
+
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
|
284 |
+
self.upcast_attention = upcast_attention
|
285 |
+
self.upcast_softmax = upcast_softmax
|
286 |
+
|
287 |
+
self.scale = dim_head**-0.5
|
288 |
+
|
289 |
+
self.heads = heads
|
290 |
+
# for slice_size > 0 the attention score computation
|
291 |
+
# is split across the batch axis to save memory
|
292 |
+
# You can set slice_size with `set_attention_slice`
|
293 |
+
self.sliceable_head_dim = heads
|
294 |
+
self._slice_size = None
|
295 |
+
self._use_memory_efficient_attention_xformers = False
|
296 |
+
self.added_kv_proj_dim = added_kv_proj_dim
|
297 |
+
|
298 |
+
if norm_num_groups is not None:
|
299 |
+
self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
|
300 |
+
else:
|
301 |
+
self.group_norm = None
|
302 |
+
|
303 |
+
self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
|
304 |
+
self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
305 |
+
self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
306 |
+
|
307 |
+
if self.added_kv_proj_dim is not None:
|
308 |
+
self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
309 |
+
self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
310 |
+
|
311 |
+
self.to_out = nn.ModuleList([])
|
312 |
+
self.to_out.append(nn.Linear(inner_dim, query_dim))
|
313 |
+
self.to_out.append(nn.Dropout(dropout))
|
314 |
+
|
315 |
+
def reshape_heads_to_batch_dim(self, tensor):
|
316 |
+
batch_size, seq_len, dim = tensor.shape
|
317 |
+
head_size = self.heads
|
318 |
+
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
|
319 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
|
320 |
+
return tensor
|
321 |
+
|
322 |
+
def reshape_batch_dim_to_heads(self, tensor):
|
323 |
+
batch_size, seq_len, dim = tensor.shape
|
324 |
+
head_size = self.heads
|
325 |
+
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
|
326 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
|
327 |
+
return tensor
|
328 |
+
|
329 |
+
def set_attention_slice(self, slice_size):
|
330 |
+
if slice_size is not None and slice_size > self.sliceable_head_dim:
|
331 |
+
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
|
332 |
+
|
333 |
+
self._slice_size = slice_size
|
334 |
+
|
335 |
+
def _attention(self, query, key, value, attention_mask=None):
|
336 |
+
if self.upcast_attention:
|
337 |
+
query = query.float()
|
338 |
+
key = key.float()
|
339 |
+
|
340 |
+
attention_scores = torch.baddbmm(
|
341 |
+
torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
|
342 |
+
query,
|
343 |
+
key.transpose(-1, -2),
|
344 |
+
beta=0,
|
345 |
+
alpha=self.scale,
|
346 |
+
)
|
347 |
+
if attention_mask is not None:
|
348 |
+
attention_scores = attention_scores + attention_mask
|
349 |
+
|
350 |
+
if self.upcast_softmax:
|
351 |
+
attention_scores = attention_scores.float()
|
352 |
+
|
353 |
+
attention_probs = attention_scores.softmax(dim=-1)
|
354 |
+
|
355 |
+
# cast back to the original dtype
|
356 |
+
attention_probs = attention_probs.to(value.dtype)
|
357 |
+
|
358 |
+
# compute attention output
|
359 |
+
hidden_states = torch.bmm(attention_probs, value)
|
360 |
+
|
361 |
+
# reshape hidden_states
|
362 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
363 |
+
return hidden_states
|
364 |
+
|
365 |
+
def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask):
|
366 |
+
batch_size_attention = query.shape[0]
|
367 |
+
hidden_states = torch.zeros(
|
368 |
+
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
|
369 |
+
)
|
370 |
+
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
|
371 |
+
for i in range(hidden_states.shape[0] // slice_size):
|
372 |
+
start_idx = i * slice_size
|
373 |
+
end_idx = (i + 1) * slice_size
|
374 |
+
|
375 |
+
query_slice = query[start_idx:end_idx]
|
376 |
+
key_slice = key[start_idx:end_idx]
|
377 |
+
|
378 |
+
if self.upcast_attention:
|
379 |
+
query_slice = query_slice.float()
|
380 |
+
key_slice = key_slice.float()
|
381 |
+
|
382 |
+
attn_slice = torch.baddbmm(
|
383 |
+
torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device),
|
384 |
+
query_slice,
|
385 |
+
key_slice.transpose(-1, -2),
|
386 |
+
beta=0,
|
387 |
+
alpha=self.scale,
|
388 |
+
)
|
389 |
+
|
390 |
+
if attention_mask is not None:
|
391 |
+
attn_slice = attn_slice + attention_mask[start_idx:end_idx]
|
392 |
+
|
393 |
+
if self.upcast_softmax:
|
394 |
+
attn_slice = attn_slice.float()
|
395 |
+
|
396 |
+
attn_slice = attn_slice.softmax(dim=-1)
|
397 |
+
|
398 |
+
# cast back to the original dtype
|
399 |
+
attn_slice = attn_slice.to(value.dtype)
|
400 |
+
attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
|
401 |
+
|
402 |
+
hidden_states[start_idx:end_idx] = attn_slice
|
403 |
+
|
404 |
+
# reshape hidden_states
|
405 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
406 |
+
return hidden_states
|
407 |
+
|
408 |
+
def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
|
409 |
+
# TODO attention_mask
|
410 |
+
query = query.contiguous()
|
411 |
+
key = key.contiguous()
|
412 |
+
value = value.contiguous()
|
413 |
+
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
|
414 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
415 |
+
return hidden_states
|
416 |
+
|
417 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, inter_frame=False):
|
418 |
+
batch_size, sequence_length, _ = hidden_states.shape
|
419 |
+
|
420 |
+
encoder_hidden_states = encoder_hidden_states
|
421 |
+
|
422 |
+
if self.group_norm is not None:
|
423 |
+
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
424 |
+
|
425 |
+
query = self.to_q(hidden_states) # (bf) x d(hw) x c
|
426 |
+
dim = query.shape[-1]
|
427 |
+
|
428 |
+
# All frames
|
429 |
+
query = rearrange(query, "(b f) d c -> b (f d) c", f=video_length)
|
430 |
+
|
431 |
+
query = self.reshape_heads_to_batch_dim(query)
|
432 |
+
|
433 |
+
if self.added_kv_proj_dim is not None:
|
434 |
+
raise NotImplementedError
|
435 |
+
|
436 |
+
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
|
437 |
+
key = self.to_k(encoder_hidden_states)
|
438 |
+
value = self.to_v(encoder_hidden_states)
|
439 |
+
|
440 |
+
if inter_frame:
|
441 |
+
key = rearrange(key, "(b f) d c -> b f d c", f=video_length)[:, [0, -1]]
|
442 |
+
value = rearrange(value, "(b f) d c -> b f d c", f=video_length)[:, [0, -1]]
|
443 |
+
key = rearrange(key, "b f d c -> b (f d) c",)
|
444 |
+
value = rearrange(value, "b f d c -> b (f d) c")
|
445 |
+
else:
|
446 |
+
# All frames
|
447 |
+
key = rearrange(key, "(b f) d c -> b (f d) c", f=video_length)
|
448 |
+
value = rearrange(value, "(b f) d c -> b (f d) c", f=video_length)
|
449 |
+
|
450 |
+
key = self.reshape_heads_to_batch_dim(key)
|
451 |
+
value = self.reshape_heads_to_batch_dim(value)
|
452 |
+
|
453 |
+
if attention_mask is not None:
|
454 |
+
if attention_mask.shape[-1] != query.shape[1]:
|
455 |
+
target_length = query.shape[1]
|
456 |
+
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
|
457 |
+
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
|
458 |
+
|
459 |
+
# attention, what we cannot get enough of
|
460 |
+
if self._use_memory_efficient_attention_xformers:
|
461 |
+
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
|
462 |
+
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
|
463 |
+
hidden_states = hidden_states.to(query.dtype)
|
464 |
+
else:
|
465 |
+
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
|
466 |
+
hidden_states = self._attention(query, key, value, attention_mask)
|
467 |
+
else:
|
468 |
+
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
|
469 |
+
|
470 |
+
# linear proj
|
471 |
+
hidden_states = self.to_out[0](hidden_states)
|
472 |
+
|
473 |
+
# dropout
|
474 |
+
hidden_states = self.to_out[1](hidden_states)
|
475 |
+
|
476 |
+
# All frames
|
477 |
+
hidden_states = rearrange(hidden_states, "b (f d) c -> (b f) d c", f=video_length)
|
478 |
+
return hidden_states
|
models/controlnet.py
ADDED
@@ -0,0 +1,605 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
from dataclasses import dataclass
|
15 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
16 |
+
import os
|
17 |
+
import json
|
18 |
+
|
19 |
+
import torch
|
20 |
+
from torch import nn
|
21 |
+
from torch.nn import functional as F
|
22 |
+
|
23 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
24 |
+
from diffusers.utils import BaseOutput, logging
|
25 |
+
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
|
26 |
+
from diffusers import ModelMixin
|
27 |
+
from .controlnet_unet_blocks import (
|
28 |
+
CrossAttnDownBlock3D,
|
29 |
+
DownBlock3D,
|
30 |
+
UNetMidBlock3DCrossAttn,
|
31 |
+
get_down_block,
|
32 |
+
)
|
33 |
+
from .resnet import InflatedConv3d
|
34 |
+
|
35 |
+
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
36 |
+
from diffusers.models.cross_attention import AttnProcessor
|
37 |
+
|
38 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
39 |
+
|
40 |
+
|
41 |
+
@dataclass
|
42 |
+
class ControlNetOutput(BaseOutput):
|
43 |
+
down_block_res_samples: Tuple[torch.Tensor]
|
44 |
+
mid_block_res_sample: torch.Tensor
|
45 |
+
|
46 |
+
|
47 |
+
class ControlNetConditioningEmbedding(nn.Module):
|
48 |
+
"""
|
49 |
+
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
|
50 |
+
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
|
51 |
+
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
|
52 |
+
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
|
53 |
+
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
|
54 |
+
model) to encode image-space conditions ... into feature maps ..."
|
55 |
+
"""
|
56 |
+
|
57 |
+
def __init__(
|
58 |
+
self,
|
59 |
+
conditioning_embedding_channels: int,
|
60 |
+
conditioning_channels: int = 3,
|
61 |
+
block_out_channels: Tuple[int] = (16, 32, 96, 256),
|
62 |
+
):
|
63 |
+
super().__init__()
|
64 |
+
|
65 |
+
self.conv_in = InflatedConv3d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
|
66 |
+
|
67 |
+
self.blocks = nn.ModuleList([])
|
68 |
+
|
69 |
+
for i in range(len(block_out_channels) - 1):
|
70 |
+
channel_in = block_out_channels[i]
|
71 |
+
channel_out = block_out_channels[i + 1]
|
72 |
+
self.blocks.append(InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1))
|
73 |
+
self.blocks.append(InflatedConv3d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
|
74 |
+
|
75 |
+
self.conv_out = zero_module(
|
76 |
+
InflatedConv3d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
|
77 |
+
)
|
78 |
+
|
79 |
+
def forward(self, conditioning):
|
80 |
+
embedding = self.conv_in(conditioning)
|
81 |
+
embedding = F.silu(embedding)
|
82 |
+
|
83 |
+
for block in self.blocks:
|
84 |
+
embedding = block(embedding)
|
85 |
+
embedding = F.silu(embedding)
|
86 |
+
|
87 |
+
embedding = self.conv_out(embedding)
|
88 |
+
|
89 |
+
return embedding
|
90 |
+
|
91 |
+
|
92 |
+
class ControlNetModel3D(ModelMixin, ConfigMixin):
|
93 |
+
_supports_gradient_checkpointing = True
|
94 |
+
|
95 |
+
@register_to_config
|
96 |
+
def __init__(
|
97 |
+
self,
|
98 |
+
in_channels: int = 4,
|
99 |
+
flip_sin_to_cos: bool = True,
|
100 |
+
freq_shift: int = 0,
|
101 |
+
down_block_types: Tuple[str] = (
|
102 |
+
"CrossAttnDownBlock3D",
|
103 |
+
"CrossAttnDownBlock3D",
|
104 |
+
"CrossAttnDownBlock3D",
|
105 |
+
"DownBlock3D",
|
106 |
+
),
|
107 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
108 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
109 |
+
layers_per_block: int = 2,
|
110 |
+
downsample_padding: int = 1,
|
111 |
+
mid_block_scale_factor: float = 1,
|
112 |
+
act_fn: str = "silu",
|
113 |
+
norm_num_groups: Optional[int] = 32,
|
114 |
+
norm_eps: float = 1e-5,
|
115 |
+
cross_attention_dim: int = 1280,
|
116 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
117 |
+
dual_cross_attention: bool = False,
|
118 |
+
use_linear_projection: bool = False,
|
119 |
+
class_embed_type: Optional[str] = None,
|
120 |
+
num_class_embeds: Optional[int] = None,
|
121 |
+
upcast_attention: bool = False,
|
122 |
+
resnet_time_scale_shift: str = "default",
|
123 |
+
projection_class_embeddings_input_dim: Optional[int] = None,
|
124 |
+
controlnet_conditioning_channel_order: str = "rgb",
|
125 |
+
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
126 |
+
):
|
127 |
+
super().__init__()
|
128 |
+
|
129 |
+
# Check inputs
|
130 |
+
if len(block_out_channels) != len(down_block_types):
|
131 |
+
raise ValueError(
|
132 |
+
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
133 |
+
)
|
134 |
+
|
135 |
+
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
136 |
+
raise ValueError(
|
137 |
+
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
138 |
+
)
|
139 |
+
|
140 |
+
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
|
141 |
+
raise ValueError(
|
142 |
+
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
|
143 |
+
)
|
144 |
+
|
145 |
+
# input
|
146 |
+
conv_in_kernel = 3
|
147 |
+
conv_in_padding = (conv_in_kernel - 1) // 2
|
148 |
+
self.conv_in = InflatedConv3d(
|
149 |
+
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
150 |
+
)
|
151 |
+
|
152 |
+
# time
|
153 |
+
time_embed_dim = block_out_channels[0] * 4
|
154 |
+
|
155 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
156 |
+
timestep_input_dim = block_out_channels[0]
|
157 |
+
|
158 |
+
self.time_embedding = TimestepEmbedding(
|
159 |
+
timestep_input_dim,
|
160 |
+
time_embed_dim,
|
161 |
+
act_fn=act_fn,
|
162 |
+
)
|
163 |
+
|
164 |
+
# class embedding
|
165 |
+
if class_embed_type is None and num_class_embeds is not None:
|
166 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
167 |
+
elif class_embed_type == "timestep":
|
168 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
169 |
+
elif class_embed_type == "identity":
|
170 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
171 |
+
elif class_embed_type == "projection":
|
172 |
+
if projection_class_embeddings_input_dim is None:
|
173 |
+
raise ValueError(
|
174 |
+
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
175 |
+
)
|
176 |
+
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
177 |
+
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
178 |
+
# 2. it projects from an arbitrary input dimension.
|
179 |
+
#
|
180 |
+
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
181 |
+
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
182 |
+
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
183 |
+
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
184 |
+
else:
|
185 |
+
self.class_embedding = None
|
186 |
+
|
187 |
+
# control net conditioning embedding
|
188 |
+
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
|
189 |
+
conditioning_embedding_channels=block_out_channels[0],
|
190 |
+
block_out_channels=conditioning_embedding_out_channels,
|
191 |
+
)
|
192 |
+
|
193 |
+
self.down_blocks = nn.ModuleList([])
|
194 |
+
self.controlnet_down_blocks = nn.ModuleList([])
|
195 |
+
|
196 |
+
if isinstance(only_cross_attention, bool):
|
197 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
198 |
+
|
199 |
+
if isinstance(attention_head_dim, int):
|
200 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
201 |
+
|
202 |
+
# down
|
203 |
+
output_channel = block_out_channels[0]
|
204 |
+
|
205 |
+
controlnet_block = InflatedConv3d(output_channel, output_channel, kernel_size=1)
|
206 |
+
controlnet_block = zero_module(controlnet_block)
|
207 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
208 |
+
|
209 |
+
for i, down_block_type in enumerate(down_block_types):
|
210 |
+
input_channel = output_channel
|
211 |
+
output_channel = block_out_channels[i]
|
212 |
+
is_final_block = i == len(block_out_channels) - 1
|
213 |
+
|
214 |
+
down_block = get_down_block(
|
215 |
+
down_block_type,
|
216 |
+
num_layers=layers_per_block,
|
217 |
+
in_channels=input_channel,
|
218 |
+
out_channels=output_channel,
|
219 |
+
temb_channels=time_embed_dim,
|
220 |
+
add_downsample=not is_final_block,
|
221 |
+
resnet_eps=norm_eps,
|
222 |
+
resnet_act_fn=act_fn,
|
223 |
+
resnet_groups=norm_num_groups,
|
224 |
+
cross_attention_dim=cross_attention_dim,
|
225 |
+
attn_num_head_channels=attention_head_dim[i],
|
226 |
+
downsample_padding=downsample_padding,
|
227 |
+
dual_cross_attention=dual_cross_attention,
|
228 |
+
use_linear_projection=use_linear_projection,
|
229 |
+
only_cross_attention=only_cross_attention[i],
|
230 |
+
upcast_attention=upcast_attention,
|
231 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
232 |
+
)
|
233 |
+
self.down_blocks.append(down_block)
|
234 |
+
|
235 |
+
for _ in range(layers_per_block):
|
236 |
+
controlnet_block = InflatedConv3d(output_channel, output_channel, kernel_size=1)
|
237 |
+
controlnet_block = zero_module(controlnet_block)
|
238 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
239 |
+
|
240 |
+
if not is_final_block:
|
241 |
+
controlnet_block = InflatedConv3d(output_channel, output_channel, kernel_size=1)
|
242 |
+
controlnet_block = zero_module(controlnet_block)
|
243 |
+
self.controlnet_down_blocks.append(controlnet_block)
|
244 |
+
|
245 |
+
# mid
|
246 |
+
mid_block_channel = block_out_channels[-1]
|
247 |
+
|
248 |
+
controlnet_block = InflatedConv3d(mid_block_channel, mid_block_channel, kernel_size=1)
|
249 |
+
controlnet_block = zero_module(controlnet_block)
|
250 |
+
self.controlnet_mid_block = controlnet_block
|
251 |
+
|
252 |
+
# mid
|
253 |
+
self.mid_block = UNetMidBlock3DCrossAttn(
|
254 |
+
in_channels=block_out_channels[-1],
|
255 |
+
temb_channels=time_embed_dim,
|
256 |
+
resnet_eps=norm_eps,
|
257 |
+
resnet_act_fn=act_fn,
|
258 |
+
output_scale_factor=mid_block_scale_factor,
|
259 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
260 |
+
cross_attention_dim=cross_attention_dim,
|
261 |
+
attn_num_head_channels=attention_head_dim[-1],
|
262 |
+
resnet_groups=norm_num_groups,
|
263 |
+
dual_cross_attention=dual_cross_attention,
|
264 |
+
use_linear_projection=use_linear_projection,
|
265 |
+
upcast_attention=upcast_attention,
|
266 |
+
)
|
267 |
+
|
268 |
+
@classmethod
|
269 |
+
def from_unet(
|
270 |
+
cls,
|
271 |
+
unet: UNet2DConditionModel,
|
272 |
+
controlnet_conditioning_channel_order: str = "rgb",
|
273 |
+
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
274 |
+
load_weights_from_unet: bool = True,
|
275 |
+
):
|
276 |
+
r"""
|
277 |
+
Instantiate Controlnet class from UNet2DConditionModel.
|
278 |
+
|
279 |
+
Parameters:
|
280 |
+
unet (`UNet2DConditionModel`):
|
281 |
+
UNet model which weights are copied to the ControlNet. Note that all configuration options are also
|
282 |
+
copied where applicable.
|
283 |
+
"""
|
284 |
+
controlnet = cls(
|
285 |
+
in_channels=unet.config.in_channels,
|
286 |
+
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
287 |
+
freq_shift=unet.config.freq_shift,
|
288 |
+
down_block_types=unet.config.down_block_types,
|
289 |
+
only_cross_attention=unet.config.only_cross_attention,
|
290 |
+
block_out_channels=unet.config.block_out_channels,
|
291 |
+
layers_per_block=unet.config.layers_per_block,
|
292 |
+
downsample_padding=unet.config.downsample_padding,
|
293 |
+
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
294 |
+
act_fn=unet.config.act_fn,
|
295 |
+
norm_num_groups=unet.config.norm_num_groups,
|
296 |
+
norm_eps=unet.config.norm_eps,
|
297 |
+
cross_attention_dim=unet.config.cross_attention_dim,
|
298 |
+
attention_head_dim=unet.config.attention_head_dim,
|
299 |
+
use_linear_projection=unet.config.use_linear_projection,
|
300 |
+
class_embed_type=unet.config.class_embed_type,
|
301 |
+
num_class_embeds=unet.config.num_class_embeds,
|
302 |
+
upcast_attention=unet.config.upcast_attention,
|
303 |
+
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
304 |
+
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
305 |
+
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
306 |
+
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
307 |
+
)
|
308 |
+
|
309 |
+
if load_weights_from_unet:
|
310 |
+
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
311 |
+
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
312 |
+
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
313 |
+
|
314 |
+
if controlnet.class_embedding:
|
315 |
+
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
316 |
+
|
317 |
+
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
318 |
+
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
319 |
+
|
320 |
+
return controlnet
|
321 |
+
|
322 |
+
@property
|
323 |
+
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
|
324 |
+
def attn_processors(self) -> Dict[str, AttnProcessor]:
|
325 |
+
r"""
|
326 |
+
Returns:
|
327 |
+
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
328 |
+
indexed by its weight name.
|
329 |
+
"""
|
330 |
+
# set recursively
|
331 |
+
processors = {}
|
332 |
+
|
333 |
+
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]):
|
334 |
+
if hasattr(module, "set_processor"):
|
335 |
+
processors[f"{name}.processor"] = module.processor
|
336 |
+
|
337 |
+
for sub_name, child in module.named_children():
|
338 |
+
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
339 |
+
|
340 |
+
return processors
|
341 |
+
|
342 |
+
for name, module in self.named_children():
|
343 |
+
fn_recursive_add_processors(name, module, processors)
|
344 |
+
|
345 |
+
return processors
|
346 |
+
|
347 |
+
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
348 |
+
def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]):
|
349 |
+
r"""
|
350 |
+
Parameters:
|
351 |
+
`processor (`dict` of `AttnProcessor` or `AttnProcessor`):
|
352 |
+
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
353 |
+
of **all** `Attention` layers.
|
354 |
+
In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:
|
355 |
+
|
356 |
+
"""
|
357 |
+
count = len(self.attn_processors.keys())
|
358 |
+
|
359 |
+
if isinstance(processor, dict) and len(processor) != count:
|
360 |
+
raise ValueError(
|
361 |
+
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
362 |
+
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
363 |
+
)
|
364 |
+
|
365 |
+
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
366 |
+
if hasattr(module, "set_processor"):
|
367 |
+
if not isinstance(processor, dict):
|
368 |
+
module.set_processor(processor)
|
369 |
+
else:
|
370 |
+
module.set_processor(processor.pop(f"{name}.processor"))
|
371 |
+
|
372 |
+
for sub_name, child in module.named_children():
|
373 |
+
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
374 |
+
|
375 |
+
for name, module in self.named_children():
|
376 |
+
fn_recursive_attn_processor(name, module, processor)
|
377 |
+
|
378 |
+
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
379 |
+
def set_attention_slice(self, slice_size):
|
380 |
+
r"""
|
381 |
+
Enable sliced attention computation.
|
382 |
+
|
383 |
+
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
384 |
+
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
385 |
+
|
386 |
+
Args:
|
387 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
388 |
+
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
389 |
+
`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
|
390 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
391 |
+
must be a multiple of `slice_size`.
|
392 |
+
"""
|
393 |
+
sliceable_head_dims = []
|
394 |
+
|
395 |
+
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
396 |
+
if hasattr(module, "set_attention_slice"):
|
397 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
398 |
+
|
399 |
+
for child in module.children():
|
400 |
+
fn_recursive_retrieve_sliceable_dims(child)
|
401 |
+
|
402 |
+
# retrieve number of attention layers
|
403 |
+
for module in self.children():
|
404 |
+
fn_recursive_retrieve_sliceable_dims(module)
|
405 |
+
|
406 |
+
num_sliceable_layers = len(sliceable_head_dims)
|
407 |
+
|
408 |
+
if slice_size == "auto":
|
409 |
+
# half the attention head size is usually a good trade-off between
|
410 |
+
# speed and memory
|
411 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
412 |
+
elif slice_size == "max":
|
413 |
+
# make smallest slice possible
|
414 |
+
slice_size = num_sliceable_layers * [1]
|
415 |
+
|
416 |
+
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
417 |
+
|
418 |
+
if len(slice_size) != len(sliceable_head_dims):
|
419 |
+
raise ValueError(
|
420 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
421 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
422 |
+
)
|
423 |
+
|
424 |
+
for i in range(len(slice_size)):
|
425 |
+
size = slice_size[i]
|
426 |
+
dim = sliceable_head_dims[i]
|
427 |
+
if size is not None and size > dim:
|
428 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
429 |
+
|
430 |
+
# Recursively walk through all the children.
|
431 |
+
# Any children which exposes the set_attention_slice method
|
432 |
+
# gets the message
|
433 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
434 |
+
if hasattr(module, "set_attention_slice"):
|
435 |
+
module.set_attention_slice(slice_size.pop())
|
436 |
+
|
437 |
+
for child in module.children():
|
438 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
439 |
+
|
440 |
+
reversed_slice_size = list(reversed(slice_size))
|
441 |
+
for module in self.children():
|
442 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
443 |
+
|
444 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
445 |
+
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D)):
|
446 |
+
module.gradient_checkpointing = value
|
447 |
+
|
448 |
+
def forward(
|
449 |
+
self,
|
450 |
+
sample: torch.FloatTensor,
|
451 |
+
timestep: Union[torch.Tensor, float, int],
|
452 |
+
encoder_hidden_states: torch.Tensor,
|
453 |
+
controlnet_cond: torch.FloatTensor,
|
454 |
+
conditioning_scale: float = 1.0,
|
455 |
+
class_labels: Optional[torch.Tensor] = None,
|
456 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
457 |
+
attention_mask: Optional[torch.Tensor] = None,
|
458 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
459 |
+
return_dict: bool = True,
|
460 |
+
) -> Union[ControlNetOutput, Tuple]:
|
461 |
+
# check channel order
|
462 |
+
channel_order = self.config.controlnet_conditioning_channel_order
|
463 |
+
|
464 |
+
if channel_order == "rgb":
|
465 |
+
# in rgb order by default
|
466 |
+
...
|
467 |
+
elif channel_order == "bgr":
|
468 |
+
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
469 |
+
else:
|
470 |
+
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
471 |
+
|
472 |
+
# prepare attention_mask
|
473 |
+
if attention_mask is not None:
|
474 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
475 |
+
attention_mask = attention_mask.unsqueeze(1)
|
476 |
+
|
477 |
+
# 1. time
|
478 |
+
timesteps = timestep
|
479 |
+
if not torch.is_tensor(timesteps):
|
480 |
+
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
481 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
482 |
+
is_mps = sample.device.type == "mps"
|
483 |
+
if isinstance(timestep, float):
|
484 |
+
dtype = torch.float32 if is_mps else torch.float64
|
485 |
+
else:
|
486 |
+
dtype = torch.int32 if is_mps else torch.int64
|
487 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
488 |
+
elif len(timesteps.shape) == 0:
|
489 |
+
timesteps = timesteps[None].to(sample.device)
|
490 |
+
|
491 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
492 |
+
timesteps = timesteps.expand(sample.shape[0])
|
493 |
+
|
494 |
+
t_emb = self.time_proj(timesteps)
|
495 |
+
|
496 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
497 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
498 |
+
# there might be better ways to encapsulate this.
|
499 |
+
t_emb = t_emb.to(dtype=self.dtype)
|
500 |
+
|
501 |
+
emb = self.time_embedding(t_emb, timestep_cond)
|
502 |
+
|
503 |
+
if self.class_embedding is not None:
|
504 |
+
if class_labels is None:
|
505 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
506 |
+
|
507 |
+
if self.config.class_embed_type == "timestep":
|
508 |
+
class_labels = self.time_proj(class_labels)
|
509 |
+
|
510 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
511 |
+
emb = emb + class_emb
|
512 |
+
|
513 |
+
# 2. pre-process
|
514 |
+
sample = self.conv_in(sample)
|
515 |
+
|
516 |
+
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
517 |
+
|
518 |
+
sample += controlnet_cond
|
519 |
+
|
520 |
+
# 3. down
|
521 |
+
down_block_res_samples = (sample,)
|
522 |
+
for downsample_block in self.down_blocks:
|
523 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
524 |
+
sample, res_samples = downsample_block(
|
525 |
+
hidden_states=sample,
|
526 |
+
temb=emb,
|
527 |
+
encoder_hidden_states=encoder_hidden_states,
|
528 |
+
attention_mask=attention_mask,
|
529 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
530 |
+
)
|
531 |
+
else:
|
532 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
533 |
+
|
534 |
+
down_block_res_samples += res_samples
|
535 |
+
|
536 |
+
# 4. mid
|
537 |
+
if self.mid_block is not None:
|
538 |
+
sample = self.mid_block(
|
539 |
+
sample,
|
540 |
+
emb,
|
541 |
+
encoder_hidden_states=encoder_hidden_states,
|
542 |
+
attention_mask=attention_mask,
|
543 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
544 |
+
)
|
545 |
+
|
546 |
+
# 5. Control net blocks
|
547 |
+
|
548 |
+
controlnet_down_block_res_samples = ()
|
549 |
+
|
550 |
+
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
551 |
+
down_block_res_sample = controlnet_block(down_block_res_sample)
|
552 |
+
controlnet_down_block_res_samples += (down_block_res_sample,)
|
553 |
+
|
554 |
+
down_block_res_samples = controlnet_down_block_res_samples
|
555 |
+
|
556 |
+
mid_block_res_sample = self.controlnet_mid_block(sample)
|
557 |
+
|
558 |
+
# 6. scaling
|
559 |
+
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
560 |
+
mid_block_res_sample *= conditioning_scale
|
561 |
+
|
562 |
+
if not return_dict:
|
563 |
+
return (down_block_res_samples, mid_block_res_sample)
|
564 |
+
|
565 |
+
return ControlNetOutput(
|
566 |
+
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
567 |
+
)
|
568 |
+
|
569 |
+
@classmethod
|
570 |
+
def from_pretrained_2d(cls, pretrained_model_path, control_path=None):
|
571 |
+
config_file = os.path.join(pretrained_model_path, 'config.json')
|
572 |
+
if not os.path.isfile(config_file):
|
573 |
+
raise RuntimeError(f"{config_file} does not exist")
|
574 |
+
with open(config_file, "r") as f:
|
575 |
+
config = json.load(f)
|
576 |
+
config["_class_name"] = cls.__name__
|
577 |
+
config["down_block_types"] = [
|
578 |
+
"CrossAttnDownBlock3D",
|
579 |
+
"CrossAttnDownBlock3D",
|
580 |
+
"CrossAttnDownBlock3D",
|
581 |
+
"DownBlock3D"
|
582 |
+
]
|
583 |
+
|
584 |
+
from diffusers.utils import WEIGHTS_NAME
|
585 |
+
model = cls.from_config(config)
|
586 |
+
if control_path is None:
|
587 |
+
model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
|
588 |
+
state_dict = torch.load(model_file, map_location="cpu")
|
589 |
+
else:
|
590 |
+
model_file = control_path
|
591 |
+
state_dict = torch.load(model_file, map_location="cpu")
|
592 |
+
state_dict = {k[14:]: state_dict[k] for k in state_dict.keys()}
|
593 |
+
|
594 |
+
|
595 |
+
for k, v in model.state_dict().items():
|
596 |
+
if '_temp.' in k:
|
597 |
+
state_dict.update({k: v})
|
598 |
+
model.load_state_dict(state_dict)
|
599 |
+
|
600 |
+
return model
|
601 |
+
|
602 |
+
def zero_module(module):
|
603 |
+
for p in module.parameters():
|
604 |
+
nn.init.zeros_(p)
|
605 |
+
return module
|
models/controlnet_attention.py
ADDED
@@ -0,0 +1,483 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
|
2 |
+
|
3 |
+
from dataclasses import dataclass
|
4 |
+
from typing import Optional, Callable
|
5 |
+
import math
|
6 |
+
import torch
|
7 |
+
import torch.nn.functional as F
|
8 |
+
from torch import nn
|
9 |
+
from positional_encodings.torch_encodings import PositionalEncoding2D
|
10 |
+
|
11 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
12 |
+
from diffusers import ModelMixin
|
13 |
+
from diffusers.utils import BaseOutput
|
14 |
+
from diffusers.utils.import_utils import is_xformers_available
|
15 |
+
from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm
|
16 |
+
from einops import rearrange, repeat
|
17 |
+
|
18 |
+
|
19 |
+
@dataclass
|
20 |
+
class Transformer3DModelOutput(BaseOutput):
|
21 |
+
sample: torch.FloatTensor
|
22 |
+
|
23 |
+
|
24 |
+
if is_xformers_available():
|
25 |
+
import xformers
|
26 |
+
import xformers.ops
|
27 |
+
else:
|
28 |
+
xformers = None
|
29 |
+
|
30 |
+
|
31 |
+
class Transformer3DModel(ModelMixin, ConfigMixin):
|
32 |
+
@register_to_config
|
33 |
+
def __init__(
|
34 |
+
self,
|
35 |
+
num_attention_heads: int = 16,
|
36 |
+
attention_head_dim: int = 88,
|
37 |
+
in_channels: Optional[int] = None,
|
38 |
+
num_layers: int = 1,
|
39 |
+
dropout: float = 0.0,
|
40 |
+
norm_num_groups: int = 32,
|
41 |
+
cross_attention_dim: Optional[int] = None,
|
42 |
+
attention_bias: bool = False,
|
43 |
+
activation_fn: str = "geglu",
|
44 |
+
num_embeds_ada_norm: Optional[int] = None,
|
45 |
+
use_linear_projection: bool = False,
|
46 |
+
only_cross_attention: bool = False,
|
47 |
+
upcast_attention: bool = False,
|
48 |
+
):
|
49 |
+
super().__init__()
|
50 |
+
self.use_linear_projection = use_linear_projection
|
51 |
+
self.num_attention_heads = num_attention_heads
|
52 |
+
self.attention_head_dim = attention_head_dim
|
53 |
+
inner_dim = num_attention_heads * attention_head_dim
|
54 |
+
|
55 |
+
# Define input layers
|
56 |
+
self.in_channels = in_channels
|
57 |
+
|
58 |
+
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
59 |
+
if use_linear_projection:
|
60 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
61 |
+
else:
|
62 |
+
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
|
63 |
+
|
64 |
+
# Define transformers blocks
|
65 |
+
self.transformer_blocks = nn.ModuleList(
|
66 |
+
[
|
67 |
+
BasicTransformerBlock(
|
68 |
+
inner_dim,
|
69 |
+
num_attention_heads,
|
70 |
+
attention_head_dim,
|
71 |
+
dropout=dropout,
|
72 |
+
cross_attention_dim=cross_attention_dim,
|
73 |
+
activation_fn=activation_fn,
|
74 |
+
num_embeds_ada_norm=num_embeds_ada_norm,
|
75 |
+
attention_bias=attention_bias,
|
76 |
+
only_cross_attention=only_cross_attention,
|
77 |
+
upcast_attention=upcast_attention,
|
78 |
+
)
|
79 |
+
for d in range(num_layers)
|
80 |
+
]
|
81 |
+
)
|
82 |
+
|
83 |
+
# 4. Define output layers
|
84 |
+
if use_linear_projection:
|
85 |
+
self.proj_out = nn.Linear(in_channels, inner_dim)
|
86 |
+
else:
|
87 |
+
self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
|
88 |
+
|
89 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
|
90 |
+
# Input
|
91 |
+
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
|
92 |
+
video_length = hidden_states.shape[2]
|
93 |
+
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
|
94 |
+
encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
|
95 |
+
|
96 |
+
batch, channel, height, weight = hidden_states.shape
|
97 |
+
residual = hidden_states
|
98 |
+
|
99 |
+
hidden_states = self.norm(hidden_states)
|
100 |
+
if not self.use_linear_projection:
|
101 |
+
hidden_states = self.proj_in(hidden_states)
|
102 |
+
inner_dim = hidden_states.shape[1]
|
103 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
104 |
+
else:
|
105 |
+
inner_dim = hidden_states.shape[1]
|
106 |
+
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
|
107 |
+
hidden_states = self.proj_in(hidden_states)
|
108 |
+
|
109 |
+
# Blocks
|
110 |
+
for block in self.transformer_blocks:
|
111 |
+
hidden_states = block(
|
112 |
+
hidden_states,
|
113 |
+
encoder_hidden_states=encoder_hidden_states,
|
114 |
+
timestep=timestep,
|
115 |
+
video_length=video_length
|
116 |
+
)
|
117 |
+
|
118 |
+
# Output
|
119 |
+
if not self.use_linear_projection:
|
120 |
+
hidden_states = (
|
121 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
122 |
+
)
|
123 |
+
hidden_states = self.proj_out(hidden_states)
|
124 |
+
else:
|
125 |
+
hidden_states = self.proj_out(hidden_states)
|
126 |
+
hidden_states = (
|
127 |
+
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
|
128 |
+
)
|
129 |
+
|
130 |
+
output = hidden_states + residual
|
131 |
+
|
132 |
+
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
|
133 |
+
if not return_dict:
|
134 |
+
return (output,)
|
135 |
+
|
136 |
+
return Transformer3DModelOutput(sample=output)
|
137 |
+
|
138 |
+
|
139 |
+
class BasicTransformerBlock(nn.Module):
|
140 |
+
def __init__(
|
141 |
+
self,
|
142 |
+
dim: int,
|
143 |
+
num_attention_heads: int,
|
144 |
+
attention_head_dim: int,
|
145 |
+
dropout=0.0,
|
146 |
+
cross_attention_dim: Optional[int] = None,
|
147 |
+
activation_fn: str = "geglu",
|
148 |
+
num_embeds_ada_norm: Optional[int] = None,
|
149 |
+
attention_bias: bool = False,
|
150 |
+
only_cross_attention: bool = False,
|
151 |
+
upcast_attention: bool = False,
|
152 |
+
):
|
153 |
+
super().__init__()
|
154 |
+
self.only_cross_attention = only_cross_attention
|
155 |
+
self.use_ada_layer_norm = num_embeds_ada_norm is not None
|
156 |
+
|
157 |
+
# Individual-Attn
|
158 |
+
self.attn1 = IndividualAttention(
|
159 |
+
query_dim=dim,
|
160 |
+
heads=num_attention_heads,
|
161 |
+
dim_head=attention_head_dim,
|
162 |
+
dropout=dropout,
|
163 |
+
bias=attention_bias,
|
164 |
+
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
165 |
+
upcast_attention=upcast_attention,
|
166 |
+
)
|
167 |
+
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
168 |
+
|
169 |
+
# Cross-Attn
|
170 |
+
if cross_attention_dim is not None:
|
171 |
+
self.attn2 = CrossAttention(
|
172 |
+
query_dim=dim,
|
173 |
+
cross_attention_dim=cross_attention_dim,
|
174 |
+
heads=num_attention_heads,
|
175 |
+
dim_head=attention_head_dim,
|
176 |
+
dropout=dropout,
|
177 |
+
bias=attention_bias,
|
178 |
+
upcast_attention=upcast_attention,
|
179 |
+
)
|
180 |
+
else:
|
181 |
+
self.attn2 = None
|
182 |
+
|
183 |
+
if cross_attention_dim is not None:
|
184 |
+
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
185 |
+
else:
|
186 |
+
self.norm2 = None
|
187 |
+
|
188 |
+
# Feed-forward
|
189 |
+
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
|
190 |
+
self.norm3 = nn.LayerNorm(dim)
|
191 |
+
|
192 |
+
self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
|
193 |
+
|
194 |
+
def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None):
|
195 |
+
if not is_xformers_available():
|
196 |
+
print("Here is how to install it")
|
197 |
+
raise ModuleNotFoundError(
|
198 |
+
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
|
199 |
+
" xformers",
|
200 |
+
name="xformers",
|
201 |
+
)
|
202 |
+
elif not torch.cuda.is_available():
|
203 |
+
raise ValueError(
|
204 |
+
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
|
205 |
+
" available for GPU "
|
206 |
+
)
|
207 |
+
else:
|
208 |
+
try:
|
209 |
+
# Make sure we can run the memory efficient attention
|
210 |
+
_ = xformers.ops.memory_efficient_attention(
|
211 |
+
torch.randn((1, 2, 40), device="cuda"),
|
212 |
+
torch.randn((1, 2, 40), device="cuda"),
|
213 |
+
torch.randn((1, 2, 40), device="cuda"),
|
214 |
+
)
|
215 |
+
except Exception as e:
|
216 |
+
raise e
|
217 |
+
self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
218 |
+
if self.attn2 is not None:
|
219 |
+
self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
220 |
+
# self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
|
221 |
+
|
222 |
+
def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
|
223 |
+
# Individual-Attention
|
224 |
+
norm_hidden_states = (
|
225 |
+
self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
|
226 |
+
)
|
227 |
+
|
228 |
+
if self.only_cross_attention:
|
229 |
+
hidden_states = (
|
230 |
+
self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
|
231 |
+
)
|
232 |
+
else:
|
233 |
+
hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
|
234 |
+
|
235 |
+
if self.attn2 is not None:
|
236 |
+
# Cross-Attention
|
237 |
+
norm_hidden_states = (
|
238 |
+
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
239 |
+
)
|
240 |
+
hidden_states = (
|
241 |
+
self.attn2(
|
242 |
+
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
243 |
+
)
|
244 |
+
+ hidden_states
|
245 |
+
)
|
246 |
+
|
247 |
+
# Feed-forward
|
248 |
+
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
|
249 |
+
|
250 |
+
# # Temporal-Attention
|
251 |
+
# d = hidden_states.shape[1]
|
252 |
+
# hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
|
253 |
+
# norm_hidden_states = (
|
254 |
+
# self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
|
255 |
+
# )
|
256 |
+
# hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
|
257 |
+
# hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
|
258 |
+
|
259 |
+
return hidden_states
|
260 |
+
|
261 |
+
class IndividualAttention(nn.Module):
|
262 |
+
r"""
|
263 |
+
A cross attention layer.
|
264 |
+
|
265 |
+
Parameters:
|
266 |
+
query_dim (`int`): The number of channels in the query.
|
267 |
+
cross_attention_dim (`int`, *optional*):
|
268 |
+
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
|
269 |
+
heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
|
270 |
+
dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.
|
271 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
272 |
+
bias (`bool`, *optional*, defaults to False):
|
273 |
+
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
|
274 |
+
"""
|
275 |
+
|
276 |
+
def __init__(
|
277 |
+
self,
|
278 |
+
query_dim: int,
|
279 |
+
cross_attention_dim: Optional[int] = None,
|
280 |
+
heads: int = 8,
|
281 |
+
dim_head: int = 64,
|
282 |
+
dropout: float = 0.0,
|
283 |
+
bias=False,
|
284 |
+
upcast_attention: bool = False,
|
285 |
+
upcast_softmax: bool = False,
|
286 |
+
added_kv_proj_dim: Optional[int] = None,
|
287 |
+
norm_num_groups: Optional[int] = None,
|
288 |
+
):
|
289 |
+
super().__init__()
|
290 |
+
inner_dim = dim_head * heads
|
291 |
+
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
|
292 |
+
self.upcast_attention = upcast_attention
|
293 |
+
self.upcast_softmax = upcast_softmax
|
294 |
+
|
295 |
+
self.scale = dim_head**-0.5
|
296 |
+
|
297 |
+
self.heads = heads
|
298 |
+
# for slice_size > 0 the attention score computation
|
299 |
+
# is split across the batch axis to save memory
|
300 |
+
# You can set slice_size with `set_attention_slice`
|
301 |
+
self.sliceable_head_dim = heads
|
302 |
+
self._slice_size = None
|
303 |
+
self._use_memory_efficient_attention_xformers = False
|
304 |
+
self.added_kv_proj_dim = added_kv_proj_dim
|
305 |
+
|
306 |
+
if norm_num_groups is not None:
|
307 |
+
self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
|
308 |
+
else:
|
309 |
+
self.group_norm = None
|
310 |
+
|
311 |
+
self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
|
312 |
+
self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
313 |
+
self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
|
314 |
+
|
315 |
+
if self.added_kv_proj_dim is not None:
|
316 |
+
self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
317 |
+
self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
|
318 |
+
|
319 |
+
self.to_out = nn.ModuleList([])
|
320 |
+
self.to_out.append(nn.Linear(inner_dim, query_dim))
|
321 |
+
self.to_out.append(nn.Dropout(dropout))
|
322 |
+
|
323 |
+
def reshape_heads_to_batch_dim(self, tensor):
|
324 |
+
batch_size, seq_len, dim = tensor.shape
|
325 |
+
head_size = self.heads
|
326 |
+
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
|
327 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
|
328 |
+
return tensor
|
329 |
+
|
330 |
+
def reshape_batch_dim_to_heads(self, tensor):
|
331 |
+
batch_size, seq_len, dim = tensor.shape
|
332 |
+
head_size = self.heads
|
333 |
+
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
|
334 |
+
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
|
335 |
+
return tensor
|
336 |
+
|
337 |
+
def set_attention_slice(self, slice_size):
|
338 |
+
if slice_size is not None and slice_size > self.sliceable_head_dim:
|
339 |
+
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
|
340 |
+
|
341 |
+
self._slice_size = slice_size
|
342 |
+
|
343 |
+
def _attention(self, query, key, value, attention_mask=None):
|
344 |
+
if self.upcast_attention:
|
345 |
+
query = query.float()
|
346 |
+
key = key.float()
|
347 |
+
|
348 |
+
attention_scores = torch.baddbmm(
|
349 |
+
torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
|
350 |
+
query,
|
351 |
+
key.transpose(-1, -2),
|
352 |
+
beta=0,
|
353 |
+
alpha=self.scale,
|
354 |
+
)
|
355 |
+
|
356 |
+
if attention_mask is not None:
|
357 |
+
attention_scores = attention_scores + attention_mask
|
358 |
+
|
359 |
+
if self.upcast_softmax:
|
360 |
+
attention_scores = attention_scores.float()
|
361 |
+
|
362 |
+
attention_probs = attention_scores.softmax(dim=-1)
|
363 |
+
|
364 |
+
# cast back to the original dtype
|
365 |
+
attention_probs = attention_probs.to(value.dtype)
|
366 |
+
|
367 |
+
# compute attention output
|
368 |
+
hidden_states = torch.bmm(attention_probs, value)
|
369 |
+
|
370 |
+
# reshape hidden_states
|
371 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
372 |
+
return hidden_states
|
373 |
+
|
374 |
+
def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask):
|
375 |
+
batch_size_attention = query.shape[0]
|
376 |
+
hidden_states = torch.zeros(
|
377 |
+
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
|
378 |
+
)
|
379 |
+
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
|
380 |
+
for i in range(hidden_states.shape[0] // slice_size):
|
381 |
+
start_idx = i * slice_size
|
382 |
+
end_idx = (i + 1) * slice_size
|
383 |
+
|
384 |
+
query_slice = query[start_idx:end_idx]
|
385 |
+
key_slice = key[start_idx:end_idx]
|
386 |
+
|
387 |
+
if self.upcast_attention:
|
388 |
+
query_slice = query_slice.float()
|
389 |
+
key_slice = key_slice.float()
|
390 |
+
|
391 |
+
attn_slice = torch.baddbmm(
|
392 |
+
torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device),
|
393 |
+
query_slice,
|
394 |
+
key_slice.transpose(-1, -2),
|
395 |
+
beta=0,
|
396 |
+
alpha=self.scale,
|
397 |
+
)
|
398 |
+
|
399 |
+
if attention_mask is not None:
|
400 |
+
attn_slice = attn_slice + attention_mask[start_idx:end_idx]
|
401 |
+
|
402 |
+
if self.upcast_softmax:
|
403 |
+
attn_slice = attn_slice.float()
|
404 |
+
|
405 |
+
attn_slice = attn_slice.softmax(dim=-1)
|
406 |
+
|
407 |
+
# cast back to the original dtype
|
408 |
+
attn_slice = attn_slice.to(value.dtype)
|
409 |
+
attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
|
410 |
+
|
411 |
+
hidden_states[start_idx:end_idx] = attn_slice
|
412 |
+
|
413 |
+
# reshape hidden_states
|
414 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
415 |
+
return hidden_states
|
416 |
+
|
417 |
+
def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
|
418 |
+
# TODO attention_mask
|
419 |
+
query = query.contiguous()
|
420 |
+
key = key.contiguous()
|
421 |
+
value = value.contiguous()
|
422 |
+
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
|
423 |
+
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
|
424 |
+
return hidden_states
|
425 |
+
|
426 |
+
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
|
427 |
+
batch_size, sequence_length, _ = hidden_states.shape
|
428 |
+
|
429 |
+
encoder_hidden_states = encoder_hidden_states
|
430 |
+
|
431 |
+
if self.group_norm is not None:
|
432 |
+
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
433 |
+
|
434 |
+
query = self.to_q(hidden_states) # (bf) x d(hw) x c
|
435 |
+
dim = query.shape[-1]
|
436 |
+
|
437 |
+
query = self.reshape_heads_to_batch_dim(query)
|
438 |
+
|
439 |
+
if self.added_kv_proj_dim is not None:
|
440 |
+
raise NotImplementedError
|
441 |
+
|
442 |
+
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
|
443 |
+
key = self.to_k(encoder_hidden_states)
|
444 |
+
value = self.to_v(encoder_hidden_states)
|
445 |
+
|
446 |
+
curr_frame_index = torch.arange(video_length)
|
447 |
+
|
448 |
+
key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
|
449 |
+
|
450 |
+
key = key[:, curr_frame_index]
|
451 |
+
key = rearrange(key, "b f d c -> (b f) d c")
|
452 |
+
|
453 |
+
value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
|
454 |
+
|
455 |
+
value = value[:, curr_frame_index]
|
456 |
+
value = rearrange(value, "b f d c -> (b f) d c")
|
457 |
+
|
458 |
+
key = self.reshape_heads_to_batch_dim(key)
|
459 |
+
value = self.reshape_heads_to_batch_dim(value)
|
460 |
+
|
461 |
+
if attention_mask is not None:
|
462 |
+
if attention_mask.shape[-1] != query.shape[1]:
|
463 |
+
target_length = query.shape[1]
|
464 |
+
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
|
465 |
+
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
|
466 |
+
|
467 |
+
# attention, what we cannot get enough of
|
468 |
+
if self._use_memory_efficient_attention_xformers:
|
469 |
+
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
|
470 |
+
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
|
471 |
+
hidden_states = hidden_states.to(query.dtype)
|
472 |
+
else:
|
473 |
+
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
|
474 |
+
hidden_states = self._attention(query, key, value, attention_mask)
|
475 |
+
else:
|
476 |
+
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
|
477 |
+
|
478 |
+
# linear proj
|
479 |
+
hidden_states = self.to_out[0](hidden_states)
|
480 |
+
|
481 |
+
# dropout
|
482 |
+
hidden_states = self.to_out[1](hidden_states)
|
483 |
+
return hidden_states
|
models/controlnet_unet_blocks.py
ADDED
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
|
6 |
+
from .controlnet_attention import Transformer3DModel
|
7 |
+
from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
|
8 |
+
|
9 |
+
|
10 |
+
def get_down_block(
|
11 |
+
down_block_type,
|
12 |
+
num_layers,
|
13 |
+
in_channels,
|
14 |
+
out_channels,
|
15 |
+
temb_channels,
|
16 |
+
add_downsample,
|
17 |
+
resnet_eps,
|
18 |
+
resnet_act_fn,
|
19 |
+
attn_num_head_channels,
|
20 |
+
resnet_groups=None,
|
21 |
+
cross_attention_dim=None,
|
22 |
+
downsample_padding=None,
|
23 |
+
dual_cross_attention=False,
|
24 |
+
use_linear_projection=False,
|
25 |
+
only_cross_attention=False,
|
26 |
+
upcast_attention=False,
|
27 |
+
resnet_time_scale_shift="default",
|
28 |
+
):
|
29 |
+
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
|
30 |
+
if down_block_type == "DownBlock3D":
|
31 |
+
return DownBlock3D(
|
32 |
+
num_layers=num_layers,
|
33 |
+
in_channels=in_channels,
|
34 |
+
out_channels=out_channels,
|
35 |
+
temb_channels=temb_channels,
|
36 |
+
add_downsample=add_downsample,
|
37 |
+
resnet_eps=resnet_eps,
|
38 |
+
resnet_act_fn=resnet_act_fn,
|
39 |
+
resnet_groups=resnet_groups,
|
40 |
+
downsample_padding=downsample_padding,
|
41 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
42 |
+
)
|
43 |
+
elif down_block_type == "CrossAttnDownBlock3D":
|
44 |
+
if cross_attention_dim is None:
|
45 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
|
46 |
+
return CrossAttnDownBlock3D(
|
47 |
+
num_layers=num_layers,
|
48 |
+
in_channels=in_channels,
|
49 |
+
out_channels=out_channels,
|
50 |
+
temb_channels=temb_channels,
|
51 |
+
add_downsample=add_downsample,
|
52 |
+
resnet_eps=resnet_eps,
|
53 |
+
resnet_act_fn=resnet_act_fn,
|
54 |
+
resnet_groups=resnet_groups,
|
55 |
+
downsample_padding=downsample_padding,
|
56 |
+
cross_attention_dim=cross_attention_dim,
|
57 |
+
attn_num_head_channels=attn_num_head_channels,
|
58 |
+
dual_cross_attention=dual_cross_attention,
|
59 |
+
use_linear_projection=use_linear_projection,
|
60 |
+
only_cross_attention=only_cross_attention,
|
61 |
+
upcast_attention=upcast_attention,
|
62 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
63 |
+
)
|
64 |
+
raise ValueError(f"{down_block_type} does not exist.")
|
65 |
+
|
66 |
+
|
67 |
+
def get_up_block(
|
68 |
+
up_block_type,
|
69 |
+
num_layers,
|
70 |
+
in_channels,
|
71 |
+
out_channels,
|
72 |
+
prev_output_channel,
|
73 |
+
temb_channels,
|
74 |
+
add_upsample,
|
75 |
+
resnet_eps,
|
76 |
+
resnet_act_fn,
|
77 |
+
attn_num_head_channels,
|
78 |
+
resnet_groups=None,
|
79 |
+
cross_attention_dim=None,
|
80 |
+
dual_cross_attention=False,
|
81 |
+
use_linear_projection=False,
|
82 |
+
only_cross_attention=False,
|
83 |
+
upcast_attention=False,
|
84 |
+
resnet_time_scale_shift="default",
|
85 |
+
):
|
86 |
+
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
87 |
+
if up_block_type == "UpBlock3D":
|
88 |
+
return UpBlock3D(
|
89 |
+
num_layers=num_layers,
|
90 |
+
in_channels=in_channels,
|
91 |
+
out_channels=out_channels,
|
92 |
+
prev_output_channel=prev_output_channel,
|
93 |
+
temb_channels=temb_channels,
|
94 |
+
add_upsample=add_upsample,
|
95 |
+
resnet_eps=resnet_eps,
|
96 |
+
resnet_act_fn=resnet_act_fn,
|
97 |
+
resnet_groups=resnet_groups,
|
98 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
99 |
+
)
|
100 |
+
elif up_block_type == "CrossAttnUpBlock3D":
|
101 |
+
if cross_attention_dim is None:
|
102 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
|
103 |
+
return CrossAttnUpBlock3D(
|
104 |
+
num_layers=num_layers,
|
105 |
+
in_channels=in_channels,
|
106 |
+
out_channels=out_channels,
|
107 |
+
prev_output_channel=prev_output_channel,
|
108 |
+
temb_channels=temb_channels,
|
109 |
+
add_upsample=add_upsample,
|
110 |
+
resnet_eps=resnet_eps,
|
111 |
+
resnet_act_fn=resnet_act_fn,
|
112 |
+
resnet_groups=resnet_groups,
|
113 |
+
cross_attention_dim=cross_attention_dim,
|
114 |
+
attn_num_head_channels=attn_num_head_channels,
|
115 |
+
dual_cross_attention=dual_cross_attention,
|
116 |
+
use_linear_projection=use_linear_projection,
|
117 |
+
only_cross_attention=only_cross_attention,
|
118 |
+
upcast_attention=upcast_attention,
|
119 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
120 |
+
)
|
121 |
+
raise ValueError(f"{up_block_type} does not exist.")
|
122 |
+
|
123 |
+
|
124 |
+
class UNetMidBlock3DCrossAttn(nn.Module):
|
125 |
+
def __init__(
|
126 |
+
self,
|
127 |
+
in_channels: int,
|
128 |
+
temb_channels: int,
|
129 |
+
dropout: float = 0.0,
|
130 |
+
num_layers: int = 1,
|
131 |
+
resnet_eps: float = 1e-6,
|
132 |
+
resnet_time_scale_shift: str = "default",
|
133 |
+
resnet_act_fn: str = "swish",
|
134 |
+
resnet_groups: int = 32,
|
135 |
+
resnet_pre_norm: bool = True,
|
136 |
+
attn_num_head_channels=1,
|
137 |
+
output_scale_factor=1.0,
|
138 |
+
cross_attention_dim=1280,
|
139 |
+
dual_cross_attention=False,
|
140 |
+
use_linear_projection=False,
|
141 |
+
upcast_attention=False,
|
142 |
+
):
|
143 |
+
super().__init__()
|
144 |
+
|
145 |
+
self.has_cross_attention = True
|
146 |
+
self.attn_num_head_channels = attn_num_head_channels
|
147 |
+
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
148 |
+
|
149 |
+
# there is always at least one resnet
|
150 |
+
resnets = [
|
151 |
+
ResnetBlock3D(
|
152 |
+
in_channels=in_channels,
|
153 |
+
out_channels=in_channels,
|
154 |
+
temb_channels=temb_channels,
|
155 |
+
eps=resnet_eps,
|
156 |
+
groups=resnet_groups,
|
157 |
+
dropout=dropout,
|
158 |
+
time_embedding_norm=resnet_time_scale_shift,
|
159 |
+
non_linearity=resnet_act_fn,
|
160 |
+
output_scale_factor=output_scale_factor,
|
161 |
+
pre_norm=resnet_pre_norm,
|
162 |
+
)
|
163 |
+
]
|
164 |
+
attentions = []
|
165 |
+
|
166 |
+
for _ in range(num_layers):
|
167 |
+
if dual_cross_attention:
|
168 |
+
raise NotImplementedError
|
169 |
+
attentions.append(
|
170 |
+
Transformer3DModel(
|
171 |
+
attn_num_head_channels,
|
172 |
+
in_channels // attn_num_head_channels,
|
173 |
+
in_channels=in_channels,
|
174 |
+
num_layers=1,
|
175 |
+
cross_attention_dim=cross_attention_dim,
|
176 |
+
norm_num_groups=resnet_groups,
|
177 |
+
use_linear_projection=use_linear_projection,
|
178 |
+
upcast_attention=upcast_attention,
|
179 |
+
)
|
180 |
+
)
|
181 |
+
resnets.append(
|
182 |
+
ResnetBlock3D(
|
183 |
+
in_channels=in_channels,
|
184 |
+
out_channels=in_channels,
|
185 |
+
temb_channels=temb_channels,
|
186 |
+
eps=resnet_eps,
|
187 |
+
groups=resnet_groups,
|
188 |
+
dropout=dropout,
|
189 |
+
time_embedding_norm=resnet_time_scale_shift,
|
190 |
+
non_linearity=resnet_act_fn,
|
191 |
+
output_scale_factor=output_scale_factor,
|
192 |
+
pre_norm=resnet_pre_norm,
|
193 |
+
)
|
194 |
+
)
|
195 |
+
|
196 |
+
self.attentions = nn.ModuleList(attentions)
|
197 |
+
self.resnets = nn.ModuleList(resnets)
|
198 |
+
|
199 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None):
|
200 |
+
hidden_states = self.resnets[0](hidden_states, temb)
|
201 |
+
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
202 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
203 |
+
hidden_states = resnet(hidden_states, temb)
|
204 |
+
|
205 |
+
return hidden_states
|
206 |
+
|
207 |
+
|
208 |
+
class CrossAttnDownBlock3D(nn.Module):
|
209 |
+
def __init__(
|
210 |
+
self,
|
211 |
+
in_channels: int,
|
212 |
+
out_channels: int,
|
213 |
+
temb_channels: int,
|
214 |
+
dropout: float = 0.0,
|
215 |
+
num_layers: int = 1,
|
216 |
+
resnet_eps: float = 1e-6,
|
217 |
+
resnet_time_scale_shift: str = "default",
|
218 |
+
resnet_act_fn: str = "swish",
|
219 |
+
resnet_groups: int = 32,
|
220 |
+
resnet_pre_norm: bool = True,
|
221 |
+
attn_num_head_channels=1,
|
222 |
+
cross_attention_dim=1280,
|
223 |
+
output_scale_factor=1.0,
|
224 |
+
downsample_padding=1,
|
225 |
+
add_downsample=True,
|
226 |
+
dual_cross_attention=False,
|
227 |
+
use_linear_projection=False,
|
228 |
+
only_cross_attention=False,
|
229 |
+
upcast_attention=False,
|
230 |
+
):
|
231 |
+
super().__init__()
|
232 |
+
resnets = []
|
233 |
+
attentions = []
|
234 |
+
|
235 |
+
self.has_cross_attention = True
|
236 |
+
self.attn_num_head_channels = attn_num_head_channels
|
237 |
+
|
238 |
+
for i in range(num_layers):
|
239 |
+
in_channels = in_channels if i == 0 else out_channels
|
240 |
+
resnets.append(
|
241 |
+
ResnetBlock3D(
|
242 |
+
in_channels=in_channels,
|
243 |
+
out_channels=out_channels,
|
244 |
+
temb_channels=temb_channels,
|
245 |
+
eps=resnet_eps,
|
246 |
+
groups=resnet_groups,
|
247 |
+
dropout=dropout,
|
248 |
+
time_embedding_norm=resnet_time_scale_shift,
|
249 |
+
non_linearity=resnet_act_fn,
|
250 |
+
output_scale_factor=output_scale_factor,
|
251 |
+
pre_norm=resnet_pre_norm,
|
252 |
+
)
|
253 |
+
)
|
254 |
+
if dual_cross_attention:
|
255 |
+
raise NotImplementedError
|
256 |
+
attentions.append(
|
257 |
+
Transformer3DModel(
|
258 |
+
attn_num_head_channels,
|
259 |
+
out_channels // attn_num_head_channels,
|
260 |
+
in_channels=out_channels,
|
261 |
+
num_layers=1,
|
262 |
+
cross_attention_dim=cross_attention_dim,
|
263 |
+
norm_num_groups=resnet_groups,
|
264 |
+
use_linear_projection=use_linear_projection,
|
265 |
+
only_cross_attention=only_cross_attention,
|
266 |
+
upcast_attention=upcast_attention,
|
267 |
+
)
|
268 |
+
)
|
269 |
+
self.attentions = nn.ModuleList(attentions)
|
270 |
+
self.resnets = nn.ModuleList(resnets)
|
271 |
+
|
272 |
+
if add_downsample:
|
273 |
+
self.downsamplers = nn.ModuleList(
|
274 |
+
[
|
275 |
+
Downsample3D(
|
276 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
277 |
+
)
|
278 |
+
]
|
279 |
+
)
|
280 |
+
else:
|
281 |
+
self.downsamplers = None
|
282 |
+
|
283 |
+
self.gradient_checkpointing = False
|
284 |
+
|
285 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None,cross_attention_kwargs=None):
|
286 |
+
output_states = ()
|
287 |
+
|
288 |
+
for resnet, attn in zip(self.resnets, self.attentions):
|
289 |
+
if self.training and self.gradient_checkpointing:
|
290 |
+
|
291 |
+
def create_custom_forward(module, return_dict=None):
|
292 |
+
def custom_forward(*inputs):
|
293 |
+
if return_dict is not None:
|
294 |
+
return module(*inputs, return_dict=return_dict)
|
295 |
+
else:
|
296 |
+
return module(*inputs)
|
297 |
+
|
298 |
+
return custom_forward
|
299 |
+
|
300 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
301 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
302 |
+
create_custom_forward(attn, return_dict=False),
|
303 |
+
hidden_states,
|
304 |
+
encoder_hidden_states,
|
305 |
+
)[0]
|
306 |
+
else:
|
307 |
+
hidden_states = resnet(hidden_states, temb)
|
308 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
309 |
+
|
310 |
+
output_states += (hidden_states,)
|
311 |
+
|
312 |
+
if self.downsamplers is not None:
|
313 |
+
for downsampler in self.downsamplers:
|
314 |
+
hidden_states = downsampler(hidden_states)
|
315 |
+
|
316 |
+
output_states += (hidden_states,)
|
317 |
+
|
318 |
+
return hidden_states, output_states
|
319 |
+
|
320 |
+
|
321 |
+
class DownBlock3D(nn.Module):
|
322 |
+
def __init__(
|
323 |
+
self,
|
324 |
+
in_channels: int,
|
325 |
+
out_channels: int,
|
326 |
+
temb_channels: int,
|
327 |
+
dropout: float = 0.0,
|
328 |
+
num_layers: int = 1,
|
329 |
+
resnet_eps: float = 1e-6,
|
330 |
+
resnet_time_scale_shift: str = "default",
|
331 |
+
resnet_act_fn: str = "swish",
|
332 |
+
resnet_groups: int = 32,
|
333 |
+
resnet_pre_norm: bool = True,
|
334 |
+
output_scale_factor=1.0,
|
335 |
+
add_downsample=True,
|
336 |
+
downsample_padding=1,
|
337 |
+
):
|
338 |
+
super().__init__()
|
339 |
+
resnets = []
|
340 |
+
|
341 |
+
for i in range(num_layers):
|
342 |
+
in_channels = in_channels if i == 0 else out_channels
|
343 |
+
resnets.append(
|
344 |
+
ResnetBlock3D(
|
345 |
+
in_channels=in_channels,
|
346 |
+
out_channels=out_channels,
|
347 |
+
temb_channels=temb_channels,
|
348 |
+
eps=resnet_eps,
|
349 |
+
groups=resnet_groups,
|
350 |
+
dropout=dropout,
|
351 |
+
time_embedding_norm=resnet_time_scale_shift,
|
352 |
+
non_linearity=resnet_act_fn,
|
353 |
+
output_scale_factor=output_scale_factor,
|
354 |
+
pre_norm=resnet_pre_norm,
|
355 |
+
)
|
356 |
+
)
|
357 |
+
|
358 |
+
self.resnets = nn.ModuleList(resnets)
|
359 |
+
|
360 |
+
if add_downsample:
|
361 |
+
self.downsamplers = nn.ModuleList(
|
362 |
+
[
|
363 |
+
Downsample3D(
|
364 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
365 |
+
)
|
366 |
+
]
|
367 |
+
)
|
368 |
+
else:
|
369 |
+
self.downsamplers = None
|
370 |
+
|
371 |
+
self.gradient_checkpointing = False
|
372 |
+
|
373 |
+
def forward(self, hidden_states, temb=None):
|
374 |
+
output_states = ()
|
375 |
+
|
376 |
+
for resnet in self.resnets:
|
377 |
+
if self.training and self.gradient_checkpointing:
|
378 |
+
|
379 |
+
def create_custom_forward(module):
|
380 |
+
def custom_forward(*inputs):
|
381 |
+
return module(*inputs)
|
382 |
+
|
383 |
+
return custom_forward
|
384 |
+
|
385 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
386 |
+
else:
|
387 |
+
hidden_states = resnet(hidden_states, temb)
|
388 |
+
|
389 |
+
output_states += (hidden_states,)
|
390 |
+
|
391 |
+
if self.downsamplers is not None:
|
392 |
+
for downsampler in self.downsamplers:
|
393 |
+
hidden_states = downsampler(hidden_states)
|
394 |
+
|
395 |
+
output_states += (hidden_states,)
|
396 |
+
|
397 |
+
return hidden_states, output_states
|
398 |
+
|
399 |
+
|
400 |
+
class CrossAttnUpBlock3D(nn.Module):
|
401 |
+
def __init__(
|
402 |
+
self,
|
403 |
+
in_channels: int,
|
404 |
+
out_channels: int,
|
405 |
+
prev_output_channel: int,
|
406 |
+
temb_channels: int,
|
407 |
+
dropout: float = 0.0,
|
408 |
+
num_layers: int = 1,
|
409 |
+
resnet_eps: float = 1e-6,
|
410 |
+
resnet_time_scale_shift: str = "default",
|
411 |
+
resnet_act_fn: str = "swish",
|
412 |
+
resnet_groups: int = 32,
|
413 |
+
resnet_pre_norm: bool = True,
|
414 |
+
attn_num_head_channels=1,
|
415 |
+
cross_attention_dim=1280,
|
416 |
+
output_scale_factor=1.0,
|
417 |
+
add_upsample=True,
|
418 |
+
dual_cross_attention=False,
|
419 |
+
use_linear_projection=False,
|
420 |
+
only_cross_attention=False,
|
421 |
+
upcast_attention=False,
|
422 |
+
):
|
423 |
+
super().__init__()
|
424 |
+
resnets = []
|
425 |
+
attentions = []
|
426 |
+
|
427 |
+
self.has_cross_attention = True
|
428 |
+
self.attn_num_head_channels = attn_num_head_channels
|
429 |
+
|
430 |
+
for i in range(num_layers):
|
431 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
432 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
433 |
+
|
434 |
+
resnets.append(
|
435 |
+
ResnetBlock3D(
|
436 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
437 |
+
out_channels=out_channels,
|
438 |
+
temb_channels=temb_channels,
|
439 |
+
eps=resnet_eps,
|
440 |
+
groups=resnet_groups,
|
441 |
+
dropout=dropout,
|
442 |
+
time_embedding_norm=resnet_time_scale_shift,
|
443 |
+
non_linearity=resnet_act_fn,
|
444 |
+
output_scale_factor=output_scale_factor,
|
445 |
+
pre_norm=resnet_pre_norm,
|
446 |
+
)
|
447 |
+
)
|
448 |
+
if dual_cross_attention:
|
449 |
+
raise NotImplementedError
|
450 |
+
attentions.append(
|
451 |
+
Transformer3DModel(
|
452 |
+
attn_num_head_channels,
|
453 |
+
out_channels // attn_num_head_channels,
|
454 |
+
in_channels=out_channels,
|
455 |
+
num_layers=1,
|
456 |
+
cross_attention_dim=cross_attention_dim,
|
457 |
+
norm_num_groups=resnet_groups,
|
458 |
+
use_linear_projection=use_linear_projection,
|
459 |
+
only_cross_attention=only_cross_attention,
|
460 |
+
upcast_attention=upcast_attention,
|
461 |
+
)
|
462 |
+
)
|
463 |
+
|
464 |
+
self.attentions = nn.ModuleList(attentions)
|
465 |
+
self.resnets = nn.ModuleList(resnets)
|
466 |
+
|
467 |
+
if add_upsample:
|
468 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
469 |
+
else:
|
470 |
+
self.upsamplers = None
|
471 |
+
|
472 |
+
self.gradient_checkpointing = False
|
473 |
+
|
474 |
+
def forward(
|
475 |
+
self,
|
476 |
+
hidden_states,
|
477 |
+
res_hidden_states_tuple,
|
478 |
+
temb=None,
|
479 |
+
encoder_hidden_states=None,
|
480 |
+
upsample_size=None,
|
481 |
+
attention_mask=None,
|
482 |
+
cross_attention_kwargs=None
|
483 |
+
):
|
484 |
+
for resnet, attn in zip(self.resnets, self.attentions):
|
485 |
+
# pop res hidden states
|
486 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
487 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
488 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
489 |
+
|
490 |
+
if self.training and self.gradient_checkpointing:
|
491 |
+
|
492 |
+
def create_custom_forward(module, return_dict=None):
|
493 |
+
def custom_forward(*inputs):
|
494 |
+
if return_dict is not None:
|
495 |
+
return module(*inputs, return_dict=return_dict)
|
496 |
+
else:
|
497 |
+
return module(*inputs)
|
498 |
+
|
499 |
+
return custom_forward
|
500 |
+
|
501 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
502 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
503 |
+
create_custom_forward(attn, return_dict=False),
|
504 |
+
hidden_states,
|
505 |
+
encoder_hidden_states,
|
506 |
+
)[0]
|
507 |
+
else:
|
508 |
+
hidden_states = resnet(hidden_states, temb)
|
509 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
|
510 |
+
|
511 |
+
if self.upsamplers is not None:
|
512 |
+
for upsampler in self.upsamplers:
|
513 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
514 |
+
|
515 |
+
return hidden_states
|
516 |
+
|
517 |
+
|
518 |
+
class UpBlock3D(nn.Module):
|
519 |
+
def __init__(
|
520 |
+
self,
|
521 |
+
in_channels: int,
|
522 |
+
prev_output_channel: int,
|
523 |
+
out_channels: int,
|
524 |
+
temb_channels: int,
|
525 |
+
dropout: float = 0.0,
|
526 |
+
num_layers: int = 1,
|
527 |
+
resnet_eps: float = 1e-6,
|
528 |
+
resnet_time_scale_shift: str = "default",
|
529 |
+
resnet_act_fn: str = "swish",
|
530 |
+
resnet_groups: int = 32,
|
531 |
+
resnet_pre_norm: bool = True,
|
532 |
+
output_scale_factor=1.0,
|
533 |
+
add_upsample=True,
|
534 |
+
):
|
535 |
+
super().__init__()
|
536 |
+
resnets = []
|
537 |
+
|
538 |
+
for i in range(num_layers):
|
539 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
540 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
541 |
+
|
542 |
+
resnets.append(
|
543 |
+
ResnetBlock3D(
|
544 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
545 |
+
out_channels=out_channels,
|
546 |
+
temb_channels=temb_channels,
|
547 |
+
eps=resnet_eps,
|
548 |
+
groups=resnet_groups,
|
549 |
+
dropout=dropout,
|
550 |
+
time_embedding_norm=resnet_time_scale_shift,
|
551 |
+
non_linearity=resnet_act_fn,
|
552 |
+
output_scale_factor=output_scale_factor,
|
553 |
+
pre_norm=resnet_pre_norm,
|
554 |
+
)
|
555 |
+
)
|
556 |
+
|
557 |
+
self.resnets = nn.ModuleList(resnets)
|
558 |
+
|
559 |
+
if add_upsample:
|
560 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
561 |
+
else:
|
562 |
+
self.upsamplers = None
|
563 |
+
|
564 |
+
self.gradient_checkpointing = False
|
565 |
+
|
566 |
+
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
567 |
+
for resnet in self.resnets:
|
568 |
+
# pop res hidden states
|
569 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
570 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
571 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
572 |
+
|
573 |
+
if self.training and self.gradient_checkpointing:
|
574 |
+
|
575 |
+
def create_custom_forward(module):
|
576 |
+
def custom_forward(*inputs):
|
577 |
+
return module(*inputs)
|
578 |
+
|
579 |
+
return custom_forward
|
580 |
+
|
581 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
582 |
+
else:
|
583 |
+
hidden_states = resnet(hidden_states, temb)
|
584 |
+
|
585 |
+
if self.upsamplers is not None:
|
586 |
+
for upsampler in self.upsamplers:
|
587 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
588 |
+
|
589 |
+
return hidden_states
|
models/pipeline_controlvideo.py
ADDED
@@ -0,0 +1,1351 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
|
16 |
+
import inspect
|
17 |
+
import os
|
18 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
19 |
+
from dataclasses import dataclass
|
20 |
+
|
21 |
+
import numpy as np
|
22 |
+
import PIL.Image
|
23 |
+
import torch
|
24 |
+
from torch import nn
|
25 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
26 |
+
|
27 |
+
from diffusers.models import AutoencoderKL
|
28 |
+
from .controlnet import ControlNetOutput
|
29 |
+
from diffusers import ModelMixin
|
30 |
+
from diffusers.schedulers import DDIMScheduler
|
31 |
+
from diffusers.utils import (
|
32 |
+
PIL_INTERPOLATION,
|
33 |
+
is_accelerate_available,
|
34 |
+
is_accelerate_version,
|
35 |
+
logging,
|
36 |
+
randn_tensor,
|
37 |
+
BaseOutput
|
38 |
+
)
|
39 |
+
from diffusers.pipeline_utils import DiffusionPipeline
|
40 |
+
|
41 |
+
from einops import rearrange
|
42 |
+
|
43 |
+
from .unet import UNet3DConditionModel
|
44 |
+
from .controlnet import ControlNetModel3D
|
45 |
+
from .RIFE.IFNet_HDv3 import IFNet
|
46 |
+
|
47 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
48 |
+
|
49 |
+
|
50 |
+
@dataclass
|
51 |
+
class ControlVideoPipelineOutput(BaseOutput):
|
52 |
+
videos: Union[torch.Tensor, np.ndarray]
|
53 |
+
|
54 |
+
|
55 |
+
class MultiControlNetModel3D(ModelMixin):
|
56 |
+
r"""
|
57 |
+
Multiple `ControlNetModel` wrapper class for Multi-ControlNet
|
58 |
+
|
59 |
+
This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
|
60 |
+
compatible with `ControlNetModel`.
|
61 |
+
|
62 |
+
Args:
|
63 |
+
controlnets (`List[ControlNetModel]`):
|
64 |
+
Provides additional conditioning to the unet during the denoising process. You must set multiple
|
65 |
+
`ControlNetModel` as a list.
|
66 |
+
"""
|
67 |
+
|
68 |
+
def __init__(self, controlnets: Union[List[ControlNetModel3D], Tuple[ControlNetModel3D]]):
|
69 |
+
super().__init__()
|
70 |
+
self.nets = nn.ModuleList(controlnets)
|
71 |
+
|
72 |
+
def forward(
|
73 |
+
self,
|
74 |
+
sample: torch.FloatTensor,
|
75 |
+
timestep: Union[torch.Tensor, float, int],
|
76 |
+
encoder_hidden_states: torch.Tensor,
|
77 |
+
controlnet_cond: List[List[torch.tensor]],
|
78 |
+
conditioning_scale: List[float],
|
79 |
+
class_labels: Optional[torch.Tensor] = None,
|
80 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
81 |
+
attention_mask: Optional[torch.Tensor] = None,
|
82 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
83 |
+
return_dict: bool = True,
|
84 |
+
) -> Union[ControlNetOutput, Tuple]:
|
85 |
+
for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
|
86 |
+
down_samples, mid_sample = controlnet(
|
87 |
+
sample,
|
88 |
+
timestep,
|
89 |
+
encoder_hidden_states,
|
90 |
+
torch.cat(image, dim=0),
|
91 |
+
scale,
|
92 |
+
class_labels,
|
93 |
+
timestep_cond,
|
94 |
+
attention_mask,
|
95 |
+
cross_attention_kwargs,
|
96 |
+
return_dict,
|
97 |
+
)
|
98 |
+
|
99 |
+
# merge samples
|
100 |
+
if i == 0:
|
101 |
+
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
|
102 |
+
else:
|
103 |
+
down_block_res_samples = [
|
104 |
+
samples_prev + samples_curr
|
105 |
+
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
|
106 |
+
]
|
107 |
+
mid_block_res_sample += mid_sample
|
108 |
+
|
109 |
+
return down_block_res_samples, mid_block_res_sample
|
110 |
+
|
111 |
+
|
112 |
+
class ControlVideoPipeline(DiffusionPipeline):
|
113 |
+
r"""
|
114 |
+
Pipeline for text-to-video generation using Stable Diffusion with ControlNet guidance.
|
115 |
+
|
116 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
117 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
118 |
+
|
119 |
+
Args:
|
120 |
+
vae ([`AutoencoderKL`]):
|
121 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
122 |
+
text_encoder ([`CLIPTextModel`]):
|
123 |
+
Frozen text-encoder. Stable Diffusion uses the text portion of
|
124 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
125 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
126 |
+
tokenizer (`CLIPTokenizer`):
|
127 |
+
Tokenizer of class
|
128 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
129 |
+
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
130 |
+
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
|
131 |
+
Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
|
132 |
+
as a list, the outputs from each ControlNet are added together to create one combined additional
|
133 |
+
conditioning.
|
134 |
+
scheduler ([`SchedulerMixin`]):
|
135 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
136 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
137 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
138 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
139 |
+
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
140 |
+
feature_extractor ([`CLIPImageProcessor`]):
|
141 |
+
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
142 |
+
"""
|
143 |
+
_optional_components = ["safety_checker", "feature_extractor"]
|
144 |
+
|
145 |
+
def __init__(
|
146 |
+
self,
|
147 |
+
vae: AutoencoderKL,
|
148 |
+
text_encoder: CLIPTextModel,
|
149 |
+
tokenizer: CLIPTokenizer,
|
150 |
+
unet: UNet3DConditionModel,
|
151 |
+
controlnet: Union[ControlNetModel3D, List[ControlNetModel3D], Tuple[ControlNetModel3D], MultiControlNetModel3D],
|
152 |
+
scheduler: DDIMScheduler,
|
153 |
+
interpolater: IFNet,
|
154 |
+
):
|
155 |
+
super().__init__()
|
156 |
+
|
157 |
+
if isinstance(controlnet, (list, tuple)):
|
158 |
+
controlnet = MultiControlNetModel3D(controlnet)
|
159 |
+
|
160 |
+
self.register_modules(
|
161 |
+
vae=vae,
|
162 |
+
text_encoder=text_encoder,
|
163 |
+
tokenizer=tokenizer,
|
164 |
+
unet=unet,
|
165 |
+
controlnet=controlnet,
|
166 |
+
scheduler=scheduler,
|
167 |
+
interpolater=interpolater,
|
168 |
+
)
|
169 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
170 |
+
|
171 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
172 |
+
def enable_vae_slicing(self):
|
173 |
+
r"""
|
174 |
+
Enable sliced VAE decoding.
|
175 |
+
|
176 |
+
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
|
177 |
+
steps. This is useful to save some memory and allow larger batch sizes.
|
178 |
+
"""
|
179 |
+
self.vae.enable_slicing()
|
180 |
+
|
181 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
182 |
+
def disable_vae_slicing(self):
|
183 |
+
r"""
|
184 |
+
Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
|
185 |
+
computing decoding in one step.
|
186 |
+
"""
|
187 |
+
self.vae.disable_slicing()
|
188 |
+
|
189 |
+
def enable_sequential_cpu_offload(self, gpu_id=0):
|
190 |
+
r"""
|
191 |
+
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
|
192 |
+
text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
|
193 |
+
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
|
194 |
+
Note that offloading happens on a submodule basis. Memory savings are higher than with
|
195 |
+
`enable_model_cpu_offload`, but performance is lower.
|
196 |
+
"""
|
197 |
+
if is_accelerate_available():
|
198 |
+
from accelerate import cpu_offload
|
199 |
+
else:
|
200 |
+
raise ImportError("Please install accelerate via `pip install accelerate`")
|
201 |
+
|
202 |
+
device = torch.device(f"cuda:{gpu_id}")
|
203 |
+
|
204 |
+
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
|
205 |
+
cpu_offload(cpu_offloaded_model, device)
|
206 |
+
|
207 |
+
if self.safety_checker is not None:
|
208 |
+
cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
|
209 |
+
|
210 |
+
def enable_model_cpu_offload(self, gpu_id=0):
|
211 |
+
r"""
|
212 |
+
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
|
213 |
+
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
|
214 |
+
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
|
215 |
+
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
|
216 |
+
"""
|
217 |
+
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
|
218 |
+
from accelerate import cpu_offload_with_hook
|
219 |
+
else:
|
220 |
+
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
|
221 |
+
|
222 |
+
device = torch.device(f"cuda:{gpu_id}")
|
223 |
+
|
224 |
+
hook = None
|
225 |
+
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
|
226 |
+
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
|
227 |
+
|
228 |
+
if self.safety_checker is not None:
|
229 |
+
# the safety checker can offload the vae again
|
230 |
+
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
|
231 |
+
|
232 |
+
# control net hook has be manually offloaded as it alternates with unet
|
233 |
+
cpu_offload_with_hook(self.controlnet, device)
|
234 |
+
|
235 |
+
# We'll offload the last model manually.
|
236 |
+
self.final_offload_hook = hook
|
237 |
+
|
238 |
+
@property
|
239 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
|
240 |
+
def _execution_device(self):
|
241 |
+
r"""
|
242 |
+
Returns the device on which the pipeline's models will be executed. After calling
|
243 |
+
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
|
244 |
+
hooks.
|
245 |
+
"""
|
246 |
+
if not hasattr(self.unet, "_hf_hook"):
|
247 |
+
return self.device
|
248 |
+
for module in self.unet.modules():
|
249 |
+
if (
|
250 |
+
hasattr(module, "_hf_hook")
|
251 |
+
and hasattr(module._hf_hook, "execution_device")
|
252 |
+
and module._hf_hook.execution_device is not None
|
253 |
+
):
|
254 |
+
return torch.device(module._hf_hook.execution_device)
|
255 |
+
return self.device
|
256 |
+
|
257 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
|
258 |
+
def _encode_prompt(
|
259 |
+
self,
|
260 |
+
prompt,
|
261 |
+
device,
|
262 |
+
num_videos_per_prompt,
|
263 |
+
do_classifier_free_guidance,
|
264 |
+
negative_prompt=None,
|
265 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
266 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
267 |
+
):
|
268 |
+
r"""
|
269 |
+
Encodes the prompt into text encoder hidden states.
|
270 |
+
|
271 |
+
Args:
|
272 |
+
prompt (`str` or `List[str]`, *optional*):
|
273 |
+
prompt to be encoded
|
274 |
+
device: (`torch.device`):
|
275 |
+
torch device
|
276 |
+
num_videos_per_prompt (`int`):
|
277 |
+
number of images that should be generated per prompt
|
278 |
+
do_classifier_free_guidance (`bool`):
|
279 |
+
whether to use classifier free guidance or not
|
280 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
281 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
282 |
+
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
|
283 |
+
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
|
284 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
285 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
286 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
287 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
288 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
289 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
290 |
+
argument.
|
291 |
+
"""
|
292 |
+
if prompt is not None and isinstance(prompt, str):
|
293 |
+
batch_size = 1
|
294 |
+
elif prompt is not None and isinstance(prompt, list):
|
295 |
+
batch_size = len(prompt)
|
296 |
+
else:
|
297 |
+
batch_size = prompt_embeds.shape[0]
|
298 |
+
|
299 |
+
if prompt_embeds is None:
|
300 |
+
text_inputs = self.tokenizer(
|
301 |
+
prompt,
|
302 |
+
padding="max_length",
|
303 |
+
max_length=self.tokenizer.model_max_length,
|
304 |
+
truncation=True,
|
305 |
+
return_tensors="pt",
|
306 |
+
)
|
307 |
+
text_input_ids = text_inputs.input_ids
|
308 |
+
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
309 |
+
|
310 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
311 |
+
text_input_ids, untruncated_ids
|
312 |
+
):
|
313 |
+
removed_text = self.tokenizer.batch_decode(
|
314 |
+
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
315 |
+
)
|
316 |
+
logger.warning(
|
317 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
318 |
+
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
319 |
+
)
|
320 |
+
|
321 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
322 |
+
attention_mask = text_inputs.attention_mask.to(device)
|
323 |
+
else:
|
324 |
+
attention_mask = None
|
325 |
+
|
326 |
+
prompt_embeds = self.text_encoder(
|
327 |
+
text_input_ids.to(device),
|
328 |
+
attention_mask=attention_mask,
|
329 |
+
)
|
330 |
+
prompt_embeds = prompt_embeds[0]
|
331 |
+
|
332 |
+
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
333 |
+
|
334 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
335 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
336 |
+
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
|
337 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1)
|
338 |
+
|
339 |
+
# get unconditional embeddings for classifier free guidance
|
340 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
341 |
+
uncond_tokens: List[str]
|
342 |
+
if negative_prompt is None:
|
343 |
+
uncond_tokens = [""] * batch_size
|
344 |
+
elif type(prompt) is not type(negative_prompt):
|
345 |
+
raise TypeError(
|
346 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
347 |
+
f" {type(prompt)}."
|
348 |
+
)
|
349 |
+
elif isinstance(negative_prompt, str):
|
350 |
+
uncond_tokens = [negative_prompt]
|
351 |
+
elif batch_size != len(negative_prompt):
|
352 |
+
raise ValueError(
|
353 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
354 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
355 |
+
" the batch size of `prompt`."
|
356 |
+
)
|
357 |
+
else:
|
358 |
+
uncond_tokens = negative_prompt
|
359 |
+
|
360 |
+
max_length = prompt_embeds.shape[1]
|
361 |
+
uncond_input = self.tokenizer(
|
362 |
+
uncond_tokens,
|
363 |
+
padding="max_length",
|
364 |
+
max_length=max_length,
|
365 |
+
truncation=True,
|
366 |
+
return_tensors="pt",
|
367 |
+
)
|
368 |
+
|
369 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
370 |
+
attention_mask = uncond_input.attention_mask.to(device)
|
371 |
+
else:
|
372 |
+
attention_mask = None
|
373 |
+
|
374 |
+
negative_prompt_embeds = self.text_encoder(
|
375 |
+
uncond_input.input_ids.to(device),
|
376 |
+
attention_mask=attention_mask,
|
377 |
+
)
|
378 |
+
negative_prompt_embeds = negative_prompt_embeds[0]
|
379 |
+
|
380 |
+
if do_classifier_free_guidance:
|
381 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
382 |
+
seq_len = negative_prompt_embeds.shape[1]
|
383 |
+
|
384 |
+
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
385 |
+
|
386 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)
|
387 |
+
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
|
388 |
+
|
389 |
+
# For classifier free guidance, we need to do two forward passes.
|
390 |
+
# Here we concatenate the unconditional and text embeddings into a single batch
|
391 |
+
# to avoid doing two forward passes
|
392 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
393 |
+
|
394 |
+
return prompt_embeds
|
395 |
+
|
396 |
+
|
397 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
398 |
+
def decode_latents(self, latents, return_tensor=False):
|
399 |
+
video_length = latents.shape[2]
|
400 |
+
latents = 1 / 0.18215 * latents
|
401 |
+
latents = rearrange(latents, "b c f h w -> (b f) c h w")
|
402 |
+
video = self.vae.decode(latents).sample
|
403 |
+
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
|
404 |
+
video = (video / 2 + 0.5).clamp(0, 1)
|
405 |
+
if return_tensor:
|
406 |
+
return video
|
407 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
408 |
+
video = video.cpu().float().numpy()
|
409 |
+
return video
|
410 |
+
|
411 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
412 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
413 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
414 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
415 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
416 |
+
# and should be between [0, 1]
|
417 |
+
|
418 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
419 |
+
extra_step_kwargs = {}
|
420 |
+
if accepts_eta:
|
421 |
+
extra_step_kwargs["eta"] = eta
|
422 |
+
|
423 |
+
# check if the scheduler accepts generator
|
424 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
425 |
+
if accepts_generator:
|
426 |
+
extra_step_kwargs["generator"] = generator
|
427 |
+
return extra_step_kwargs
|
428 |
+
|
429 |
+
def check_inputs(
|
430 |
+
self,
|
431 |
+
prompt,
|
432 |
+
# image,
|
433 |
+
height,
|
434 |
+
width,
|
435 |
+
callback_steps,
|
436 |
+
negative_prompt=None,
|
437 |
+
prompt_embeds=None,
|
438 |
+
negative_prompt_embeds=None,
|
439 |
+
controlnet_conditioning_scale=1.0,
|
440 |
+
):
|
441 |
+
if height % 8 != 0 or width % 8 != 0:
|
442 |
+
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
443 |
+
|
444 |
+
if (callback_steps is None) or (
|
445 |
+
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
446 |
+
):
|
447 |
+
raise ValueError(
|
448 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
449 |
+
f" {type(callback_steps)}."
|
450 |
+
)
|
451 |
+
|
452 |
+
if prompt is not None and prompt_embeds is not None:
|
453 |
+
raise ValueError(
|
454 |
+
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
455 |
+
" only forward one of the two."
|
456 |
+
)
|
457 |
+
elif prompt is None and prompt_embeds is None:
|
458 |
+
raise ValueError(
|
459 |
+
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
460 |
+
)
|
461 |
+
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
462 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
463 |
+
|
464 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
465 |
+
raise ValueError(
|
466 |
+
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
467 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
468 |
+
)
|
469 |
+
|
470 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
471 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
472 |
+
raise ValueError(
|
473 |
+
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
474 |
+
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
475 |
+
f" {negative_prompt_embeds.shape}."
|
476 |
+
)
|
477 |
+
|
478 |
+
# Check `image`
|
479 |
+
|
480 |
+
# if isinstance(self.controlnet, ControlNetModel):
|
481 |
+
# self.check_image(image, prompt, prompt_embeds)
|
482 |
+
# elif isinstance(self.controlnet, MultiControlNetModel):
|
483 |
+
# if not isinstance(image, list):
|
484 |
+
# raise TypeError("For multiple controlnets: `image` must be type `list`")
|
485 |
+
|
486 |
+
# if len(image) != len(self.controlnet.nets):
|
487 |
+
# raise ValueError(
|
488 |
+
# "For multiple controlnets: `image` must have the same length as the number of controlnets."
|
489 |
+
# )
|
490 |
+
|
491 |
+
# for image_ in image:
|
492 |
+
# self.check_image(image_, prompt, prompt_embeds)
|
493 |
+
# else:
|
494 |
+
# assert False
|
495 |
+
|
496 |
+
# Check `controlnet_conditioning_scale`
|
497 |
+
|
498 |
+
if isinstance(self.controlnet, ControlNetModel3D):
|
499 |
+
if not isinstance(controlnet_conditioning_scale, float):
|
500 |
+
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
|
501 |
+
elif isinstance(self.controlnet, MultiControlNetModel3D):
|
502 |
+
if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
|
503 |
+
self.controlnet.nets
|
504 |
+
):
|
505 |
+
raise ValueError(
|
506 |
+
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
|
507 |
+
" the same length as the number of controlnets"
|
508 |
+
)
|
509 |
+
else:
|
510 |
+
assert False
|
511 |
+
|
512 |
+
def check_image(self, image, prompt, prompt_embeds):
|
513 |
+
image_is_pil = isinstance(image, PIL.Image.Image)
|
514 |
+
image_is_tensor = isinstance(image, torch.Tensor)
|
515 |
+
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
|
516 |
+
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
|
517 |
+
|
518 |
+
if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
|
519 |
+
raise TypeError(
|
520 |
+
"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
|
521 |
+
)
|
522 |
+
|
523 |
+
if image_is_pil:
|
524 |
+
image_batch_size = 1
|
525 |
+
elif image_is_tensor:
|
526 |
+
image_batch_size = image.shape[0]
|
527 |
+
elif image_is_pil_list:
|
528 |
+
image_batch_size = len(image)
|
529 |
+
elif image_is_tensor_list:
|
530 |
+
image_batch_size = len(image)
|
531 |
+
|
532 |
+
if prompt is not None and isinstance(prompt, str):
|
533 |
+
prompt_batch_size = 1
|
534 |
+
elif prompt is not None and isinstance(prompt, list):
|
535 |
+
prompt_batch_size = len(prompt)
|
536 |
+
elif prompt_embeds is not None:
|
537 |
+
prompt_batch_size = prompt_embeds.shape[0]
|
538 |
+
|
539 |
+
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
|
540 |
+
raise ValueError(
|
541 |
+
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
|
542 |
+
)
|
543 |
+
|
544 |
+
def prepare_image(
|
545 |
+
self, image, width, height, batch_size, num_videos_per_prompt, device, dtype, do_classifier_free_guidance
|
546 |
+
):
|
547 |
+
if not isinstance(image, torch.Tensor):
|
548 |
+
if isinstance(image, PIL.Image.Image):
|
549 |
+
image = [image]
|
550 |
+
|
551 |
+
if isinstance(image[0], PIL.Image.Image):
|
552 |
+
images = []
|
553 |
+
|
554 |
+
for image_ in image:
|
555 |
+
image_ = image_.convert("RGB")
|
556 |
+
image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
|
557 |
+
image_ = np.array(image_)
|
558 |
+
image_ = image_[None, :]
|
559 |
+
images.append(image_)
|
560 |
+
|
561 |
+
image = images
|
562 |
+
|
563 |
+
image = np.concatenate(image, axis=0)
|
564 |
+
image = np.array(image).astype(np.float32) / 255.0
|
565 |
+
image = image.transpose(0, 3, 1, 2)
|
566 |
+
image = torch.from_numpy(image)
|
567 |
+
elif isinstance(image[0], torch.Tensor):
|
568 |
+
image = torch.cat(image, dim=0)
|
569 |
+
|
570 |
+
image_batch_size = image.shape[0]
|
571 |
+
|
572 |
+
if image_batch_size == 1:
|
573 |
+
repeat_by = batch_size
|
574 |
+
else:
|
575 |
+
# image batch size is the same as prompt batch size
|
576 |
+
repeat_by = num_videos_per_prompt
|
577 |
+
|
578 |
+
image = image.repeat_interleave(repeat_by, dim=0)
|
579 |
+
|
580 |
+
image = image.to(device=device, dtype=dtype)
|
581 |
+
|
582 |
+
if do_classifier_free_guidance:
|
583 |
+
image = torch.cat([image] * 2)
|
584 |
+
|
585 |
+
return image
|
586 |
+
|
587 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
588 |
+
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, \
|
589 |
+
device, generator, latents=None, same_frame_noise=True):
|
590 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
591 |
+
raise ValueError(
|
592 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
593 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
594 |
+
)
|
595 |
+
|
596 |
+
if latents is None:
|
597 |
+
if same_frame_noise:
|
598 |
+
shape = (batch_size, num_channels_latents, 1, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
599 |
+
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
600 |
+
latents = latents.repeat(1, 1, video_length, 1, 1)
|
601 |
+
else:
|
602 |
+
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
603 |
+
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
604 |
+
else:
|
605 |
+
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
606 |
+
if latents.shape != shape:
|
607 |
+
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
|
608 |
+
latents = latents.to(device)
|
609 |
+
|
610 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
611 |
+
latents = latents * self.scheduler.init_noise_sigma
|
612 |
+
return latents
|
613 |
+
|
614 |
+
def _default_height_width(self, height, width, image):
|
615 |
+
# NOTE: It is possible that a list of images have different
|
616 |
+
# dimensions for each image, so just checking the first image
|
617 |
+
# is not _exactly_ correct, but it is simple.
|
618 |
+
while isinstance(image, list):
|
619 |
+
image = image[0]
|
620 |
+
|
621 |
+
if height is None:
|
622 |
+
if isinstance(image, PIL.Image.Image):
|
623 |
+
height = image.height
|
624 |
+
elif isinstance(image, torch.Tensor):
|
625 |
+
height = image.shape[3]
|
626 |
+
|
627 |
+
height = (height // 8) * 8 # round down to nearest multiple of 8
|
628 |
+
|
629 |
+
if width is None:
|
630 |
+
if isinstance(image, PIL.Image.Image):
|
631 |
+
width = image.width
|
632 |
+
elif isinstance(image, torch.Tensor):
|
633 |
+
width = image.shape[2]
|
634 |
+
|
635 |
+
width = (width // 8) * 8 # round down to nearest multiple of 8
|
636 |
+
|
637 |
+
return height, width
|
638 |
+
|
639 |
+
# override DiffusionPipeline
|
640 |
+
def save_pretrained(
|
641 |
+
self,
|
642 |
+
save_directory: Union[str, os.PathLike],
|
643 |
+
safe_serialization: bool = False,
|
644 |
+
variant: Optional[str] = None,
|
645 |
+
):
|
646 |
+
if isinstance(self.controlnet, ControlNetModel3D):
|
647 |
+
super().save_pretrained(save_directory, safe_serialization, variant)
|
648 |
+
else:
|
649 |
+
raise NotImplementedError("Currently, the `save_pretrained()` is not implemented for Multi-ControlNet.")
|
650 |
+
|
651 |
+
def get_alpha_prev(self, timestep):
|
652 |
+
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
|
653 |
+
alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
|
654 |
+
return alpha_prod_t_prev
|
655 |
+
|
656 |
+
def get_slide_window_indices(self, video_length, window_size):
|
657 |
+
assert window_size >=3
|
658 |
+
key_frame_indices = np.arange(0, video_length, window_size-1).tolist()
|
659 |
+
|
660 |
+
# Append last index
|
661 |
+
if key_frame_indices[-1] != (video_length-1):
|
662 |
+
key_frame_indices.append(video_length-1)
|
663 |
+
|
664 |
+
slices = np.split(np.arange(video_length), key_frame_indices)
|
665 |
+
inter_frame_list = []
|
666 |
+
for s in slices:
|
667 |
+
if len(s) < 2:
|
668 |
+
continue
|
669 |
+
inter_frame_list.append(s[1:].tolist())
|
670 |
+
return key_frame_indices, inter_frame_list
|
671 |
+
|
672 |
+
@torch.no_grad()
|
673 |
+
def __call__(
|
674 |
+
self,
|
675 |
+
prompt: Union[str, List[str]] = None,
|
676 |
+
video_length: Optional[int] = 1,
|
677 |
+
frames: Union[List[torch.FloatTensor], List[PIL.Image.Image], List[List[torch.FloatTensor]], List[List[PIL.Image.Image]]] = None,
|
678 |
+
height: Optional[int] = None,
|
679 |
+
width: Optional[int] = None,
|
680 |
+
num_inference_steps: int = 50,
|
681 |
+
guidance_scale: float = 7.5,
|
682 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
683 |
+
num_videos_per_prompt: Optional[int] = 1,
|
684 |
+
eta: float = 0.0,
|
685 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
686 |
+
latents: Optional[torch.FloatTensor] = None,
|
687 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
688 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
689 |
+
output_type: Optional[str] = "tensor",
|
690 |
+
return_dict: bool = True,
|
691 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
692 |
+
callback_steps: int = 1,
|
693 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
694 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
695 |
+
smooth_steps: List = [19, 20],
|
696 |
+
**kwargs,
|
697 |
+
):
|
698 |
+
r"""
|
699 |
+
Function invoked when calling the pipeline for generation.
|
700 |
+
|
701 |
+
Args:
|
702 |
+
prompt (`str` or `List[str]`, *optional*):
|
703 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
704 |
+
instead.
|
705 |
+
frames (`List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
|
706 |
+
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
|
707 |
+
The ControlVideo input condition. ControlVideo uses this input condition to generate guidance to Unet. If
|
708 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
709 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
710 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
711 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
712 |
+
batched for input to a single controlnet.
|
713 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
714 |
+
The height in pixels of the generated image.
|
715 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
716 |
+
The width in pixels of the generated image.
|
717 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
718 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
719 |
+
expense of slower inference.
|
720 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
721 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
722 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
723 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
724 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
725 |
+
usually at the expense of lower image quality.
|
726 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
727 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
728 |
+
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
|
729 |
+
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
|
730 |
+
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
731 |
+
The number of images to generate per prompt.
|
732 |
+
eta (`float`, *optional*, defaults to 0.0):
|
733 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
734 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
735 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
736 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
737 |
+
to make generation deterministic.
|
738 |
+
latents (`torch.FloatTensor`, *optional*):
|
739 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
740 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
741 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
742 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
743 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
744 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
745 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
746 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
747 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
748 |
+
argument.
|
749 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
750 |
+
The output format of the generate image. Choose between
|
751 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
752 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
753 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
754 |
+
plain tuple.
|
755 |
+
callback (`Callable`, *optional*):
|
756 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
757 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
758 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
759 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
760 |
+
called at every step.
|
761 |
+
cross_attention_kwargs (`dict`, *optional*):
|
762 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
763 |
+
`self.processor` in
|
764 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
765 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
766 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
767 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
768 |
+
corresponding scale as a list.
|
769 |
+
smooth_steps (`List[int]`):
|
770 |
+
Perform smoother on predicted RGB frames at these timesteps.
|
771 |
+
|
772 |
+
Examples:
|
773 |
+
|
774 |
+
Returns:
|
775 |
+
[`ControlVideoPipelineOutput`] or `tuple`:
|
776 |
+
[`ControlVideoPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
777 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
778 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
779 |
+
(nsfw) content, according to the `safety_checker`.
|
780 |
+
"""
|
781 |
+
# 0. Default height and width to unet
|
782 |
+
height, width = self._default_height_width(height, width, frames)
|
783 |
+
|
784 |
+
# 1. Check inputs. Raise error if not correct
|
785 |
+
self.check_inputs(
|
786 |
+
prompt,
|
787 |
+
height,
|
788 |
+
width,
|
789 |
+
callback_steps,
|
790 |
+
negative_prompt,
|
791 |
+
prompt_embeds,
|
792 |
+
negative_prompt_embeds,
|
793 |
+
controlnet_conditioning_scale,
|
794 |
+
)
|
795 |
+
|
796 |
+
# 2. Define call parameters
|
797 |
+
if prompt is not None and isinstance(prompt, str):
|
798 |
+
batch_size = 1
|
799 |
+
elif prompt is not None and isinstance(prompt, list):
|
800 |
+
batch_size = len(prompt)
|
801 |
+
else:
|
802 |
+
batch_size = prompt_embeds.shape[0]
|
803 |
+
|
804 |
+
device = self._execution_device
|
805 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
806 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
807 |
+
# corresponds to doing no classifier free guidance.
|
808 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
809 |
+
|
810 |
+
if isinstance(self.controlnet, MultiControlNetModel3D) and isinstance(controlnet_conditioning_scale, float):
|
811 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
|
812 |
+
|
813 |
+
# 3. Encode input prompt
|
814 |
+
prompt_embeds = self._encode_prompt(
|
815 |
+
prompt,
|
816 |
+
device,
|
817 |
+
num_videos_per_prompt,
|
818 |
+
do_classifier_free_guidance,
|
819 |
+
negative_prompt,
|
820 |
+
prompt_embeds=prompt_embeds,
|
821 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
822 |
+
)
|
823 |
+
|
824 |
+
# 4. Prepare image
|
825 |
+
if isinstance(self.controlnet, ControlNetModel3D):
|
826 |
+
images = []
|
827 |
+
for i_img in frames:
|
828 |
+
i_img = self.prepare_image(
|
829 |
+
image=i_img,
|
830 |
+
width=width,
|
831 |
+
height=height,
|
832 |
+
batch_size=batch_size * num_videos_per_prompt,
|
833 |
+
num_videos_per_prompt=num_videos_per_prompt,
|
834 |
+
device=device,
|
835 |
+
dtype=self.controlnet.dtype,
|
836 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
837 |
+
)
|
838 |
+
images.append(i_img)
|
839 |
+
frames = torch.stack(images, dim=2) # b x c x f x h x w
|
840 |
+
elif isinstance(self.controlnet, MultiControlNetModel3D):
|
841 |
+
images = []
|
842 |
+
for i_img in frames:
|
843 |
+
i_images = []
|
844 |
+
for ii_img in i_img:
|
845 |
+
ii_img = self.prepare_image(
|
846 |
+
image=ii_img,
|
847 |
+
width=width,
|
848 |
+
height=height,
|
849 |
+
batch_size=batch_size * num_videos_per_prompt,
|
850 |
+
num_videos_per_prompt=num_videos_per_prompt,
|
851 |
+
device=device,
|
852 |
+
dtype=self.controlnet.dtype,
|
853 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
854 |
+
)
|
855 |
+
|
856 |
+
i_images.append(ii_img)
|
857 |
+
images.append(torch.stack(i_images, dim=2))
|
858 |
+
frames = images
|
859 |
+
else:
|
860 |
+
assert False
|
861 |
+
|
862 |
+
# 5. Prepare timesteps
|
863 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
864 |
+
timesteps = self.scheduler.timesteps
|
865 |
+
|
866 |
+
# 6. Prepare latent variables
|
867 |
+
num_channels_latents = self.unet.in_channels
|
868 |
+
latents = self.prepare_latents(
|
869 |
+
batch_size * num_videos_per_prompt,
|
870 |
+
num_channels_latents,
|
871 |
+
video_length,
|
872 |
+
height,
|
873 |
+
width,
|
874 |
+
prompt_embeds.dtype,
|
875 |
+
device,
|
876 |
+
generator,
|
877 |
+
latents,
|
878 |
+
same_frame_noise=True,
|
879 |
+
)
|
880 |
+
|
881 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
882 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
883 |
+
|
884 |
+
|
885 |
+
# Prepare video indices if performing smoothing
|
886 |
+
if len(smooth_steps) > 0:
|
887 |
+
video_indices = np.arange(video_length)
|
888 |
+
zero_indices = video_indices[0::2]
|
889 |
+
one_indices = video_indices[1::2]
|
890 |
+
|
891 |
+
# 8. Denoising loop
|
892 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
893 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
894 |
+
for i, t in enumerate(timesteps):
|
895 |
+
torch.cuda.empty_cache()
|
896 |
+
|
897 |
+
# expand the latents if we are doing classifier free guidance
|
898 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
899 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
900 |
+
|
901 |
+
# controlnet(s) inference
|
902 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
903 |
+
latent_model_input,
|
904 |
+
t,
|
905 |
+
encoder_hidden_states=prompt_embeds,
|
906 |
+
controlnet_cond=frames,
|
907 |
+
conditioning_scale=controlnet_conditioning_scale,
|
908 |
+
return_dict=False,
|
909 |
+
)
|
910 |
+
# predict the noise residual
|
911 |
+
noise_pred = self.unet(
|
912 |
+
latent_model_input,
|
913 |
+
t,
|
914 |
+
encoder_hidden_states=prompt_embeds,
|
915 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
916 |
+
down_block_additional_residuals=down_block_res_samples,
|
917 |
+
mid_block_additional_residual=mid_block_res_sample,
|
918 |
+
).sample
|
919 |
+
|
920 |
+
# perform guidance
|
921 |
+
if do_classifier_free_guidance:
|
922 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
923 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
924 |
+
|
925 |
+
# compute the previous noisy sample x_t -> x_t-1
|
926 |
+
step_dict = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
|
927 |
+
latents = step_dict.prev_sample
|
928 |
+
pred_original_sample = step_dict.pred_original_sample
|
929 |
+
|
930 |
+
# Smooth videos
|
931 |
+
if (num_inference_steps - i) in smooth_steps:
|
932 |
+
pred_video = self.decode_latents(pred_original_sample, return_tensor=True) # b c f h w
|
933 |
+
pred_video = rearrange(pred_video, "b c f h w -> b f c h w")
|
934 |
+
for b_i in range(len(pred_video)):
|
935 |
+
if i % 2 == 0:
|
936 |
+
for v_i in range(len(zero_indices)-1):
|
937 |
+
s_frame = pred_video[b_i][zero_indices[v_i]].unsqueeze(0)
|
938 |
+
e_frame = pred_video[b_i][zero_indices[v_i+1]].unsqueeze(0)
|
939 |
+
pred_video[b_i][one_indices[v_i]] = self.interpolater.inference(s_frame, e_frame)[0]
|
940 |
+
else:
|
941 |
+
if video_length % 2 == 1:
|
942 |
+
tmp_one_indices = [0] + one_indices.tolist() + [video_length-1]
|
943 |
+
else:
|
944 |
+
tmp_one_indices = [0] + one_indices.tolist()
|
945 |
+
|
946 |
+
for v_i in range(len(tmp_one_indices)-1):
|
947 |
+
s_frame = pred_video[b_i][tmp_one_indices[v_i]].unsqueeze(0)
|
948 |
+
e_frame = pred_video[b_i][tmp_one_indices[v_i+1]].unsqueeze(0)
|
949 |
+
pred_video[b_i][zero_indices[v_i]] = self.interpolater.inference(s_frame, e_frame)[0]
|
950 |
+
pred_video = rearrange(pred_video, "b f c h w -> (b f) c h w")
|
951 |
+
pred_video = 2.0 * pred_video - 1.0
|
952 |
+
# ori_pred_original_sample = pred_original_sample
|
953 |
+
pred_original_sample = self.vae.encode(pred_video).latent_dist.sample(generator)
|
954 |
+
pred_original_sample *= self.vae.config.scaling_factor
|
955 |
+
pred_original_sample = rearrange(pred_original_sample, "(b f) c h w -> b c f h w", f=video_length)
|
956 |
+
|
957 |
+
# predict xt-1 with smoothed x0
|
958 |
+
alpha_prod_t_prev =self.get_alpha_prev(t)
|
959 |
+
# preserve more details
|
960 |
+
# pred_original_sample = ori_pred_original_sample * alpha_prod_t_prev + (1 - alpha_prod_t_prev) * pred_original_sample
|
961 |
+
# compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
962 |
+
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * noise_pred
|
963 |
+
# compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
964 |
+
latents = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
|
965 |
+
|
966 |
+
|
967 |
+
# call the callback, if provided
|
968 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
969 |
+
progress_bar.update()
|
970 |
+
if callback is not None and i % callback_steps == 0:
|
971 |
+
callback(i, t, latents)
|
972 |
+
|
973 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
974 |
+
# manually for max memory savings
|
975 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
976 |
+
self.unet.to("cpu")
|
977 |
+
self.controlnet.to("cpu")
|
978 |
+
torch.cuda.empty_cache()
|
979 |
+
# Post-processing
|
980 |
+
video = self.decode_latents(latents)
|
981 |
+
|
982 |
+
# Convert to tensor
|
983 |
+
if output_type == "tensor":
|
984 |
+
video = torch.from_numpy(video)
|
985 |
+
|
986 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
987 |
+
self.final_offload_hook.offload()
|
988 |
+
|
989 |
+
if not return_dict:
|
990 |
+
return video
|
991 |
+
|
992 |
+
return ControlVideoPipelineOutput(videos=video)
|
993 |
+
|
994 |
+
@torch.no_grad()
|
995 |
+
def generate_long_video(
|
996 |
+
self,
|
997 |
+
prompt: Union[str, List[str]] = None,
|
998 |
+
video_length: Optional[int] = 1,
|
999 |
+
frames: Union[List[torch.FloatTensor], List[PIL.Image.Image], List[List[torch.FloatTensor]], List[List[PIL.Image.Image]]] = None,
|
1000 |
+
height: Optional[int] = None,
|
1001 |
+
width: Optional[int] = None,
|
1002 |
+
num_inference_steps: int = 50,
|
1003 |
+
guidance_scale: float = 7.5,
|
1004 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
1005 |
+
num_videos_per_prompt: Optional[int] = 1,
|
1006 |
+
eta: float = 0.0,
|
1007 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
1008 |
+
latents: Optional[torch.FloatTensor] = None,
|
1009 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
1010 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
1011 |
+
output_type: Optional[str] = "tensor",
|
1012 |
+
return_dict: bool = True,
|
1013 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
1014 |
+
callback_steps: int = 1,
|
1015 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
1016 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
1017 |
+
smooth_steps: List = [19, 20],
|
1018 |
+
window_size: int = 8,
|
1019 |
+
**kwargs,
|
1020 |
+
):
|
1021 |
+
r"""
|
1022 |
+
Function invoked when calling the pipeline for generation.
|
1023 |
+
|
1024 |
+
Args:
|
1025 |
+
prompt (`str` or `List[str]`, *optional*):
|
1026 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
1027 |
+
instead.
|
1028 |
+
frames (`List[torch.FloatTensor]`, `List[PIL.Image.Image]`,
|
1029 |
+
`List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
|
1030 |
+
The ControlVideo input condition. ControlVideo uses this input condition to generate guidance to Unet. If
|
1031 |
+
the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
|
1032 |
+
also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
|
1033 |
+
height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
|
1034 |
+
specified in init, images must be passed as a list such that each element of the list can be correctly
|
1035 |
+
batched for input to a single controlnet.
|
1036 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
1037 |
+
The height in pixels of the generated image.
|
1038 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
1039 |
+
The width in pixels of the generated image.
|
1040 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
1041 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
1042 |
+
expense of slower inference.
|
1043 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
1044 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
1045 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
1046 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
1047 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
1048 |
+
usually at the expense of lower image quality.
|
1049 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
1050 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
1051 |
+
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
|
1052 |
+
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
|
1053 |
+
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
1054 |
+
The number of images to generate per prompt.
|
1055 |
+
eta (`float`, *optional*, defaults to 0.0):
|
1056 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
1057 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
1058 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
1059 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
1060 |
+
to make generation deterministic.
|
1061 |
+
latents (`torch.FloatTensor`, *optional*):
|
1062 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
1063 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
1064 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
1065 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
1066 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
1067 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
1068 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
1069 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
1070 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
1071 |
+
argument.
|
1072 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
1073 |
+
The output format of the generate image. Choose between
|
1074 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
1075 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
1076 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
1077 |
+
plain tuple.
|
1078 |
+
callback (`Callable`, *optional*):
|
1079 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
1080 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
1081 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
1082 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
1083 |
+
called at every step.
|
1084 |
+
cross_attention_kwargs (`dict`, *optional*):
|
1085 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
1086 |
+
`self.processor` in
|
1087 |
+
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
|
1088 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
1089 |
+
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
|
1090 |
+
to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
|
1091 |
+
corresponding scale as a list.
|
1092 |
+
smooth_steps (`List[int]`):
|
1093 |
+
Perform smoother on predicted RGB frames at these timesteps.
|
1094 |
+
window_size ('int'):
|
1095 |
+
The length of each short clip.
|
1096 |
+
Examples:
|
1097 |
+
|
1098 |
+
Returns:
|
1099 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
1100 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
1101 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
1102 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
1103 |
+
(nsfw) content, according to the `safety_checker`.
|
1104 |
+
"""
|
1105 |
+
# 0. Default height and width to unet
|
1106 |
+
height, width = self._default_height_width(height, width, frames)
|
1107 |
+
|
1108 |
+
# 1. Check inputs. Raise error if not correct
|
1109 |
+
self.check_inputs(
|
1110 |
+
prompt,
|
1111 |
+
height,
|
1112 |
+
width,
|
1113 |
+
callback_steps,
|
1114 |
+
negative_prompt,
|
1115 |
+
prompt_embeds,
|
1116 |
+
negative_prompt_embeds,
|
1117 |
+
controlnet_conditioning_scale,
|
1118 |
+
)
|
1119 |
+
|
1120 |
+
# 2. Define call parameters
|
1121 |
+
if prompt is not None and isinstance(prompt, str):
|
1122 |
+
batch_size = 1
|
1123 |
+
elif prompt is not None and isinstance(prompt, list):
|
1124 |
+
batch_size = len(prompt)
|
1125 |
+
else:
|
1126 |
+
batch_size = prompt_embeds.shape[0]
|
1127 |
+
|
1128 |
+
device = self._execution_device
|
1129 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
1130 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
1131 |
+
# corresponds to doing no classifier free guidance.
|
1132 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
1133 |
+
|
1134 |
+
if isinstance(self.controlnet, MultiControlNetModel3D) and isinstance(controlnet_conditioning_scale, float):
|
1135 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
|
1136 |
+
|
1137 |
+
# 3. Encode input prompt
|
1138 |
+
prompt_embeds = self._encode_prompt(
|
1139 |
+
prompt,
|
1140 |
+
device,
|
1141 |
+
num_videos_per_prompt,
|
1142 |
+
do_classifier_free_guidance,
|
1143 |
+
negative_prompt,
|
1144 |
+
prompt_embeds=prompt_embeds,
|
1145 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1146 |
+
)
|
1147 |
+
|
1148 |
+
# 4. Prepare image
|
1149 |
+
if isinstance(self.controlnet, ControlNetModel3D):
|
1150 |
+
images = []
|
1151 |
+
for i_img in frames:
|
1152 |
+
i_img = self.prepare_image(
|
1153 |
+
image=i_img,
|
1154 |
+
width=width,
|
1155 |
+
height=height,
|
1156 |
+
batch_size=batch_size * num_videos_per_prompt,
|
1157 |
+
num_videos_per_prompt=num_videos_per_prompt,
|
1158 |
+
device=device,
|
1159 |
+
dtype=self.controlnet.dtype,
|
1160 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1161 |
+
)
|
1162 |
+
images.append(i_img)
|
1163 |
+
frames = torch.stack(images, dim=2) # b x c x f x h x w
|
1164 |
+
elif isinstance(self.controlnet, MultiControlNetModel3D):
|
1165 |
+
images = []
|
1166 |
+
for i_img in frames:
|
1167 |
+
i_images = []
|
1168 |
+
for ii_img in i_img:
|
1169 |
+
ii_img = self.prepare_image(
|
1170 |
+
image=ii_img,
|
1171 |
+
width=width,
|
1172 |
+
height=height,
|
1173 |
+
batch_size=batch_size * num_videos_per_prompt,
|
1174 |
+
num_videos_per_prompt=num_videos_per_prompt,
|
1175 |
+
device=device,
|
1176 |
+
dtype=self.controlnet.dtype,
|
1177 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1178 |
+
)
|
1179 |
+
|
1180 |
+
i_images.append(ii_img)
|
1181 |
+
images.append(torch.stack(i_images, dim=2))
|
1182 |
+
frames = images
|
1183 |
+
else:
|
1184 |
+
assert False
|
1185 |
+
|
1186 |
+
# 5. Prepare timesteps
|
1187 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
1188 |
+
timesteps = self.scheduler.timesteps
|
1189 |
+
|
1190 |
+
# 6. Prepare latent variables
|
1191 |
+
num_channels_latents = self.unet.in_channels
|
1192 |
+
latents = self.prepare_latents(
|
1193 |
+
batch_size * num_videos_per_prompt,
|
1194 |
+
num_channels_latents,
|
1195 |
+
video_length,
|
1196 |
+
height,
|
1197 |
+
width,
|
1198 |
+
prompt_embeds.dtype,
|
1199 |
+
device,
|
1200 |
+
generator,
|
1201 |
+
latents,
|
1202 |
+
same_frame_noise=True,
|
1203 |
+
)
|
1204 |
+
|
1205 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
1206 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
1207 |
+
|
1208 |
+
# Prepare indices of key frames and interval frames
|
1209 |
+
key_frame_indices, inter_frame_list = self.get_slide_window_indices(video_length, window_size)
|
1210 |
+
|
1211 |
+
# Prepare video indices if performing smoothing
|
1212 |
+
if len(smooth_steps) > 0:
|
1213 |
+
video_indices = np.arange(video_length)
|
1214 |
+
zero_indices = video_indices[0::2]
|
1215 |
+
one_indices = video_indices[1::2]
|
1216 |
+
|
1217 |
+
# 8. Denoising loop
|
1218 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
1219 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
1220 |
+
for i, t in enumerate(timesteps):
|
1221 |
+
torch.cuda.empty_cache()
|
1222 |
+
# expand the latents if we are doing classifier free guidance
|
1223 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
1224 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
1225 |
+
noise_pred = torch.zeros_like(latents)
|
1226 |
+
pred_original_sample = torch.zeros_like(latents)
|
1227 |
+
|
1228 |
+
# 8.1 Key frames
|
1229 |
+
# controlnet(s) inference
|
1230 |
+
key_down_block_res_samples, key_mid_block_res_sample = self.controlnet(
|
1231 |
+
latent_model_input[:, :, key_frame_indices],
|
1232 |
+
t,
|
1233 |
+
encoder_hidden_states=prompt_embeds,
|
1234 |
+
controlnet_cond=frames[:, :, key_frame_indices],
|
1235 |
+
conditioning_scale=controlnet_conditioning_scale,
|
1236 |
+
return_dict=False,
|
1237 |
+
)
|
1238 |
+
# predict the noise residual
|
1239 |
+
key_noise_pred = self.unet(
|
1240 |
+
latent_model_input[:, :, key_frame_indices],
|
1241 |
+
t,
|
1242 |
+
encoder_hidden_states=prompt_embeds,
|
1243 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1244 |
+
down_block_additional_residuals=key_down_block_res_samples,
|
1245 |
+
mid_block_additional_residual=key_mid_block_res_sample,
|
1246 |
+
inter_frame=False,
|
1247 |
+
).sample
|
1248 |
+
|
1249 |
+
# perform guidance
|
1250 |
+
if do_classifier_free_guidance:
|
1251 |
+
noise_pred_uncond, noise_pred_text = key_noise_pred.chunk(2)
|
1252 |
+
noise_pred[:, :, key_frame_indices] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
1253 |
+
|
1254 |
+
# compute the previous noisy sample x_t -> x_t-1
|
1255 |
+
key_step_dict = self.scheduler.step(noise_pred[:, :, key_frame_indices], t, latents[:, :, key_frame_indices], **extra_step_kwargs)
|
1256 |
+
latents[:, :, key_frame_indices] = key_step_dict.prev_sample
|
1257 |
+
pred_original_sample[:, :, key_frame_indices] = key_step_dict.pred_original_sample
|
1258 |
+
|
1259 |
+
# 8.2 compute interval frames
|
1260 |
+
for f_i, frame_ids in enumerate(inter_frame_list):
|
1261 |
+
input_frame_ids = key_frame_indices[f_i:f_i+2] + frame_ids
|
1262 |
+
# controlnet(s) inference
|
1263 |
+
inter_down_block_res_samples, inter_mid_block_res_sample = self.controlnet(
|
1264 |
+
latent_model_input[:, :, input_frame_ids],
|
1265 |
+
t,
|
1266 |
+
encoder_hidden_states=prompt_embeds,
|
1267 |
+
controlnet_cond=frames[:, :, input_frame_ids],
|
1268 |
+
conditioning_scale=controlnet_conditioning_scale,
|
1269 |
+
return_dict=False,
|
1270 |
+
)
|
1271 |
+
# predict the noise residual
|
1272 |
+
inter_noise_pred = self.unet(
|
1273 |
+
latent_model_input[:, :, input_frame_ids],
|
1274 |
+
t,
|
1275 |
+
encoder_hidden_states=prompt_embeds,
|
1276 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1277 |
+
down_block_additional_residuals=inter_down_block_res_samples,
|
1278 |
+
mid_block_additional_residual=inter_mid_block_res_sample,
|
1279 |
+
inter_frame=True,
|
1280 |
+
).sample
|
1281 |
+
|
1282 |
+
# perform guidance
|
1283 |
+
if do_classifier_free_guidance:
|
1284 |
+
noise_pred_uncond, noise_pred_text = inter_noise_pred[:, :, 2:].chunk(2)
|
1285 |
+
noise_pred[:, :, frame_ids] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
1286 |
+
|
1287 |
+
# compute the previous noisy sample x_t -> x_t-1
|
1288 |
+
step_dict = self.scheduler.step(noise_pred[:, :, frame_ids], t, latents[:, :, frame_ids], **extra_step_kwargs)
|
1289 |
+
latents[:, :, frame_ids] = step_dict.prev_sample
|
1290 |
+
pred_original_sample[:, :, frame_ids] = step_dict.pred_original_sample
|
1291 |
+
|
1292 |
+
# Smooth videos
|
1293 |
+
if (num_inference_steps - i) in smooth_steps:
|
1294 |
+
pred_video = self.decode_latents(pred_original_sample, return_tensor=True) # b c f h w
|
1295 |
+
pred_video = rearrange(pred_video, "b c f h w -> b f c h w")
|
1296 |
+
for b_i in range(len(pred_video)):
|
1297 |
+
if i % 2 == 0:
|
1298 |
+
for v_i in range(len(zero_indices)-1):
|
1299 |
+
s_frame = pred_video[b_i][zero_indices[v_i]].unsqueeze(0)
|
1300 |
+
e_frame = pred_video[b_i][zero_indices[v_i+1]].unsqueeze(0)
|
1301 |
+
pred_video[b_i][one_indices[v_i]] = self.interpolater.inference(s_frame, e_frame)[0]
|
1302 |
+
else:
|
1303 |
+
if video_length % 2 == 1:
|
1304 |
+
tmp_one_indices = [0] + one_indices.tolist() + [video_length-1]
|
1305 |
+
else:
|
1306 |
+
tmp_one_indices = [0] + one_indices.tolist()
|
1307 |
+
for v_i in range(len(tmp_one_indices)-1):
|
1308 |
+
s_frame = pred_video[b_i][tmp_one_indices[v_i]].unsqueeze(0)
|
1309 |
+
e_frame = pred_video[b_i][tmp_one_indices[v_i+1]].unsqueeze(0)
|
1310 |
+
pred_video[b_i][zero_indices[v_i]] = self.interpolater.inference(s_frame, e_frame)[0]
|
1311 |
+
pred_video = rearrange(pred_video, "b f c h w -> (b f) c h w")
|
1312 |
+
pred_video = 2.0 * pred_video - 1.0
|
1313 |
+
for v_i in range(len(pred_video)):
|
1314 |
+
pred_original_sample[:, :, v_i] = self.vae.encode(pred_video[v_i:v_i+1]).latent_dist.sample(generator)
|
1315 |
+
pred_original_sample[:, :, v_i] *= self.vae.config.scaling_factor
|
1316 |
+
|
1317 |
+
|
1318 |
+
# predict xt-1 with smoothed x0
|
1319 |
+
alpha_prod_t_prev =self.get_alpha_prev(t)
|
1320 |
+
# preserve more details
|
1321 |
+
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * noise_pred
|
1322 |
+
# compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
1323 |
+
latents = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
|
1324 |
+
|
1325 |
+
|
1326 |
+
# call the callback, if provided
|
1327 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
1328 |
+
progress_bar.update()
|
1329 |
+
if callback is not None and i % callback_steps == 0:
|
1330 |
+
callback(i, t, latents)
|
1331 |
+
|
1332 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
1333 |
+
# manually for max memory savings
|
1334 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
1335 |
+
self.unet.to("cpu")
|
1336 |
+
self.controlnet.to("cpu")
|
1337 |
+
torch.cuda.empty_cache()
|
1338 |
+
# Post-processing
|
1339 |
+
video = self.decode_latents(latents)
|
1340 |
+
|
1341 |
+
# Convert to tensor
|
1342 |
+
if output_type == "tensor":
|
1343 |
+
video = torch.from_numpy(video)
|
1344 |
+
|
1345 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
1346 |
+
self.final_offload_hook.offload()
|
1347 |
+
|
1348 |
+
if not return_dict:
|
1349 |
+
return video
|
1350 |
+
|
1351 |
+
return ControlVideoPipelineOutput(videos=video)
|
models/resnet.py
ADDED
@@ -0,0 +1,217 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
|
2 |
+
|
3 |
+
import torch
|
4 |
+
import torch.nn as nn
|
5 |
+
import torch.nn.functional as F
|
6 |
+
|
7 |
+
from einops import rearrange
|
8 |
+
|
9 |
+
|
10 |
+
class InflatedConv3d(nn.Conv2d):
|
11 |
+
def forward(self, x):
|
12 |
+
video_length = x.shape[2]
|
13 |
+
|
14 |
+
x = rearrange(x, "b c f h w -> (b f) c h w")
|
15 |
+
x = super().forward(x)
|
16 |
+
x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
|
17 |
+
|
18 |
+
return x
|
19 |
+
|
20 |
+
class TemporalConv1d(nn.Conv1d):
|
21 |
+
def forward(self, x):
|
22 |
+
b, c, f, h, w = x.shape
|
23 |
+
y = rearrange(x.clone(), "b c f h w -> (b h w) c f")
|
24 |
+
y = super().forward(y)
|
25 |
+
y = rearrange(y, "(b h w) c f -> b c f h w", b=b, h=h, w=w)
|
26 |
+
return y
|
27 |
+
|
28 |
+
|
29 |
+
class Upsample3D(nn.Module):
|
30 |
+
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
|
31 |
+
super().__init__()
|
32 |
+
self.channels = channels
|
33 |
+
self.out_channels = out_channels or channels
|
34 |
+
self.use_conv = use_conv
|
35 |
+
self.use_conv_transpose = use_conv_transpose
|
36 |
+
self.name = name
|
37 |
+
|
38 |
+
conv = None
|
39 |
+
if use_conv_transpose:
|
40 |
+
raise NotImplementedError
|
41 |
+
elif use_conv:
|
42 |
+
conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
|
43 |
+
|
44 |
+
if name == "conv":
|
45 |
+
self.conv = conv
|
46 |
+
else:
|
47 |
+
self.Conv2d_0 = conv
|
48 |
+
|
49 |
+
def forward(self, hidden_states, output_size=None):
|
50 |
+
assert hidden_states.shape[1] == self.channels
|
51 |
+
|
52 |
+
if self.use_conv_transpose:
|
53 |
+
raise NotImplementedError
|
54 |
+
|
55 |
+
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
|
56 |
+
dtype = hidden_states.dtype
|
57 |
+
if dtype == torch.bfloat16:
|
58 |
+
hidden_states = hidden_states.to(torch.float32)
|
59 |
+
|
60 |
+
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
|
61 |
+
if hidden_states.shape[0] >= 64:
|
62 |
+
hidden_states = hidden_states.contiguous()
|
63 |
+
|
64 |
+
# if `output_size` is passed we force the interpolation output
|
65 |
+
# size and do not make use of `scale_factor=2`
|
66 |
+
if output_size is None:
|
67 |
+
hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
|
68 |
+
else:
|
69 |
+
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
|
70 |
+
|
71 |
+
# If the input is bfloat16, we cast back to bfloat16
|
72 |
+
if dtype == torch.bfloat16:
|
73 |
+
hidden_states = hidden_states.to(dtype)
|
74 |
+
|
75 |
+
if self.use_conv:
|
76 |
+
if self.name == "conv":
|
77 |
+
hidden_states = self.conv(hidden_states)
|
78 |
+
else:
|
79 |
+
hidden_states = self.Conv2d_0(hidden_states)
|
80 |
+
|
81 |
+
return hidden_states
|
82 |
+
|
83 |
+
|
84 |
+
class Downsample3D(nn.Module):
|
85 |
+
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
|
86 |
+
super().__init__()
|
87 |
+
self.channels = channels
|
88 |
+
self.out_channels = out_channels or channels
|
89 |
+
self.use_conv = use_conv
|
90 |
+
self.padding = padding
|
91 |
+
stride = 2
|
92 |
+
self.name = name
|
93 |
+
|
94 |
+
if use_conv:
|
95 |
+
conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
|
96 |
+
else:
|
97 |
+
raise NotImplementedError
|
98 |
+
|
99 |
+
if name == "conv":
|
100 |
+
self.Conv2d_0 = conv
|
101 |
+
self.conv = conv
|
102 |
+
elif name == "Conv2d_0":
|
103 |
+
self.conv = conv
|
104 |
+
else:
|
105 |
+
self.conv = conv
|
106 |
+
|
107 |
+
def forward(self, hidden_states):
|
108 |
+
assert hidden_states.shape[1] == self.channels
|
109 |
+
if self.use_conv and self.padding == 0:
|
110 |
+
raise NotImplementedError
|
111 |
+
|
112 |
+
assert hidden_states.shape[1] == self.channels
|
113 |
+
hidden_states = self.conv(hidden_states)
|
114 |
+
|
115 |
+
return hidden_states
|
116 |
+
|
117 |
+
|
118 |
+
class ResnetBlock3D(nn.Module):
|
119 |
+
def __init__(
|
120 |
+
self,
|
121 |
+
*,
|
122 |
+
in_channels,
|
123 |
+
out_channels=None,
|
124 |
+
conv_shortcut=False,
|
125 |
+
dropout=0.0,
|
126 |
+
temb_channels=512,
|
127 |
+
groups=32,
|
128 |
+
groups_out=None,
|
129 |
+
pre_norm=True,
|
130 |
+
eps=1e-6,
|
131 |
+
non_linearity="swish",
|
132 |
+
time_embedding_norm="default",
|
133 |
+
output_scale_factor=1.0,
|
134 |
+
use_in_shortcut=None,
|
135 |
+
):
|
136 |
+
super().__init__()
|
137 |
+
self.pre_norm = pre_norm
|
138 |
+
self.pre_norm = True
|
139 |
+
self.in_channels = in_channels
|
140 |
+
out_channels = in_channels if out_channels is None else out_channels
|
141 |
+
self.out_channels = out_channels
|
142 |
+
self.use_conv_shortcut = conv_shortcut
|
143 |
+
self.time_embedding_norm = time_embedding_norm
|
144 |
+
self.output_scale_factor = output_scale_factor
|
145 |
+
|
146 |
+
if groups_out is None:
|
147 |
+
groups_out = groups
|
148 |
+
|
149 |
+
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
|
150 |
+
|
151 |
+
self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
152 |
+
|
153 |
+
if temb_channels is not None:
|
154 |
+
if self.time_embedding_norm == "default":
|
155 |
+
time_emb_proj_out_channels = out_channels
|
156 |
+
elif self.time_embedding_norm == "scale_shift":
|
157 |
+
time_emb_proj_out_channels = out_channels * 2
|
158 |
+
else:
|
159 |
+
raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
|
160 |
+
|
161 |
+
self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
|
162 |
+
else:
|
163 |
+
self.time_emb_proj = None
|
164 |
+
|
165 |
+
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
|
166 |
+
self.dropout = torch.nn.Dropout(dropout)
|
167 |
+
self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
168 |
+
|
169 |
+
if non_linearity == "swish":
|
170 |
+
self.nonlinearity = lambda x: F.silu(x)
|
171 |
+
elif non_linearity == "mish":
|
172 |
+
self.nonlinearity = Mish()
|
173 |
+
elif non_linearity == "silu":
|
174 |
+
self.nonlinearity = nn.SiLU()
|
175 |
+
|
176 |
+
self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
|
177 |
+
|
178 |
+
self.conv_shortcut = None
|
179 |
+
if self.use_in_shortcut:
|
180 |
+
self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
|
181 |
+
|
182 |
+
def forward(self, input_tensor, temb):
|
183 |
+
hidden_states = input_tensor
|
184 |
+
|
185 |
+
hidden_states = self.norm1(hidden_states)
|
186 |
+
hidden_states = self.nonlinearity(hidden_states)
|
187 |
+
|
188 |
+
hidden_states = self.conv1(hidden_states)
|
189 |
+
|
190 |
+
if temb is not None:
|
191 |
+
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
|
192 |
+
|
193 |
+
if temb is not None and self.time_embedding_norm == "default":
|
194 |
+
hidden_states = hidden_states + temb
|
195 |
+
|
196 |
+
hidden_states = self.norm2(hidden_states)
|
197 |
+
|
198 |
+
if temb is not None and self.time_embedding_norm == "scale_shift":
|
199 |
+
scale, shift = torch.chunk(temb, 2, dim=1)
|
200 |
+
hidden_states = hidden_states * (1 + scale) + shift
|
201 |
+
|
202 |
+
hidden_states = self.nonlinearity(hidden_states)
|
203 |
+
|
204 |
+
hidden_states = self.dropout(hidden_states)
|
205 |
+
hidden_states = self.conv2(hidden_states)
|
206 |
+
|
207 |
+
if self.conv_shortcut is not None:
|
208 |
+
input_tensor = self.conv_shortcut(input_tensor)
|
209 |
+
|
210 |
+
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
|
211 |
+
|
212 |
+
return output_tensor
|
213 |
+
|
214 |
+
|
215 |
+
class Mish(torch.nn.Module):
|
216 |
+
def forward(self, hidden_states):
|
217 |
+
return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
|
models/unet.py
ADDED
@@ -0,0 +1,472 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
|
2 |
+
|
3 |
+
from dataclasses import dataclass
|
4 |
+
from typing import List, Optional, Tuple, Union
|
5 |
+
|
6 |
+
import os
|
7 |
+
import json
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.nn as nn
|
11 |
+
import torch.utils.checkpoint
|
12 |
+
|
13 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
14 |
+
from diffusers import ModelMixin
|
15 |
+
from diffusers.utils import BaseOutput, logging
|
16 |
+
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
|
17 |
+
from .unet_blocks import (
|
18 |
+
CrossAttnDownBlock3D,
|
19 |
+
CrossAttnUpBlock3D,
|
20 |
+
DownBlock3D,
|
21 |
+
UNetMidBlock3DCrossAttn,
|
22 |
+
UpBlock3D,
|
23 |
+
get_down_block,
|
24 |
+
get_up_block,
|
25 |
+
)
|
26 |
+
from .resnet import InflatedConv3d
|
27 |
+
|
28 |
+
|
29 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
30 |
+
|
31 |
+
|
32 |
+
@dataclass
|
33 |
+
class UNet3DConditionOutput(BaseOutput):
|
34 |
+
sample: torch.FloatTensor
|
35 |
+
|
36 |
+
|
37 |
+
class UNet3DConditionModel(ModelMixin, ConfigMixin):
|
38 |
+
_supports_gradient_checkpointing = True
|
39 |
+
|
40 |
+
@register_to_config
|
41 |
+
def __init__(
|
42 |
+
self,
|
43 |
+
sample_size: Optional[int] = None,
|
44 |
+
in_channels: int = 4,
|
45 |
+
out_channels: int = 4,
|
46 |
+
center_input_sample: bool = False,
|
47 |
+
flip_sin_to_cos: bool = True,
|
48 |
+
freq_shift: int = 0,
|
49 |
+
down_block_types: Tuple[str] = (
|
50 |
+
"CrossAttnDownBlock3D",
|
51 |
+
"CrossAttnDownBlock3D",
|
52 |
+
"CrossAttnDownBlock3D",
|
53 |
+
"DownBlock3D",
|
54 |
+
),
|
55 |
+
mid_block_type: str = "UNetMidBlock3DCrossAttn",
|
56 |
+
up_block_types: Tuple[str] = (
|
57 |
+
"UpBlock3D",
|
58 |
+
"CrossAttnUpBlock3D",
|
59 |
+
"CrossAttnUpBlock3D",
|
60 |
+
"CrossAttnUpBlock3D"
|
61 |
+
),
|
62 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
63 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
64 |
+
layers_per_block: int = 2,
|
65 |
+
downsample_padding: int = 1,
|
66 |
+
mid_block_scale_factor: float = 1,
|
67 |
+
act_fn: str = "silu",
|
68 |
+
norm_num_groups: int = 32,
|
69 |
+
norm_eps: float = 1e-5,
|
70 |
+
cross_attention_dim: int = 1280,
|
71 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
72 |
+
dual_cross_attention: bool = False,
|
73 |
+
use_linear_projection: bool = False,
|
74 |
+
class_embed_type: Optional[str] = None,
|
75 |
+
num_class_embeds: Optional[int] = None,
|
76 |
+
upcast_attention: bool = False,
|
77 |
+
resnet_time_scale_shift: str = "default",
|
78 |
+
):
|
79 |
+
super().__init__()
|
80 |
+
|
81 |
+
self.sample_size = sample_size
|
82 |
+
time_embed_dim = block_out_channels[0] * 4
|
83 |
+
|
84 |
+
# input
|
85 |
+
self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
|
86 |
+
|
87 |
+
# time
|
88 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
89 |
+
timestep_input_dim = block_out_channels[0]
|
90 |
+
|
91 |
+
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
92 |
+
|
93 |
+
# class embedding
|
94 |
+
if class_embed_type is None and num_class_embeds is not None:
|
95 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
96 |
+
elif class_embed_type == "timestep":
|
97 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
98 |
+
elif class_embed_type == "identity":
|
99 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
100 |
+
else:
|
101 |
+
self.class_embedding = None
|
102 |
+
|
103 |
+
self.down_blocks = nn.ModuleList([])
|
104 |
+
self.mid_block = None
|
105 |
+
self.up_blocks = nn.ModuleList([])
|
106 |
+
|
107 |
+
if isinstance(only_cross_attention, bool):
|
108 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
109 |
+
|
110 |
+
if isinstance(attention_head_dim, int):
|
111 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
112 |
+
|
113 |
+
# down
|
114 |
+
output_channel = block_out_channels[0]
|
115 |
+
for i, down_block_type in enumerate(down_block_types):
|
116 |
+
input_channel = output_channel
|
117 |
+
output_channel = block_out_channels[i]
|
118 |
+
is_final_block = i == len(block_out_channels) - 1
|
119 |
+
|
120 |
+
down_block = get_down_block(
|
121 |
+
down_block_type,
|
122 |
+
num_layers=layers_per_block,
|
123 |
+
in_channels=input_channel,
|
124 |
+
out_channels=output_channel,
|
125 |
+
temb_channels=time_embed_dim,
|
126 |
+
add_downsample=not is_final_block,
|
127 |
+
resnet_eps=norm_eps,
|
128 |
+
resnet_act_fn=act_fn,
|
129 |
+
resnet_groups=norm_num_groups,
|
130 |
+
cross_attention_dim=cross_attention_dim,
|
131 |
+
attn_num_head_channels=attention_head_dim[i],
|
132 |
+
downsample_padding=downsample_padding,
|
133 |
+
dual_cross_attention=dual_cross_attention,
|
134 |
+
use_linear_projection=use_linear_projection,
|
135 |
+
only_cross_attention=only_cross_attention[i],
|
136 |
+
upcast_attention=upcast_attention,
|
137 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
138 |
+
)
|
139 |
+
self.down_blocks.append(down_block)
|
140 |
+
|
141 |
+
# mid
|
142 |
+
if mid_block_type == "UNetMidBlock3DCrossAttn":
|
143 |
+
self.mid_block = UNetMidBlock3DCrossAttn(
|
144 |
+
in_channels=block_out_channels[-1],
|
145 |
+
temb_channels=time_embed_dim,
|
146 |
+
resnet_eps=norm_eps,
|
147 |
+
resnet_act_fn=act_fn,
|
148 |
+
output_scale_factor=mid_block_scale_factor,
|
149 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
150 |
+
cross_attention_dim=cross_attention_dim,
|
151 |
+
attn_num_head_channels=attention_head_dim[-1],
|
152 |
+
resnet_groups=norm_num_groups,
|
153 |
+
dual_cross_attention=dual_cross_attention,
|
154 |
+
use_linear_projection=use_linear_projection,
|
155 |
+
upcast_attention=upcast_attention,
|
156 |
+
)
|
157 |
+
else:
|
158 |
+
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
159 |
+
|
160 |
+
# count how many layers upsample the videos
|
161 |
+
self.num_upsamplers = 0
|
162 |
+
|
163 |
+
# up
|
164 |
+
reversed_block_out_channels = list(reversed(block_out_channels))
|
165 |
+
reversed_attention_head_dim = list(reversed(attention_head_dim))
|
166 |
+
only_cross_attention = list(reversed(only_cross_attention))
|
167 |
+
output_channel = reversed_block_out_channels[0]
|
168 |
+
for i, up_block_type in enumerate(up_block_types):
|
169 |
+
is_final_block = i == len(block_out_channels) - 1
|
170 |
+
|
171 |
+
prev_output_channel = output_channel
|
172 |
+
output_channel = reversed_block_out_channels[i]
|
173 |
+
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
174 |
+
|
175 |
+
# add upsample block for all BUT final layer
|
176 |
+
if not is_final_block:
|
177 |
+
add_upsample = True
|
178 |
+
self.num_upsamplers += 1
|
179 |
+
else:
|
180 |
+
add_upsample = False
|
181 |
+
|
182 |
+
up_block = get_up_block(
|
183 |
+
up_block_type,
|
184 |
+
num_layers=layers_per_block + 1,
|
185 |
+
in_channels=input_channel,
|
186 |
+
out_channels=output_channel,
|
187 |
+
prev_output_channel=prev_output_channel,
|
188 |
+
temb_channels=time_embed_dim,
|
189 |
+
add_upsample=add_upsample,
|
190 |
+
resnet_eps=norm_eps,
|
191 |
+
resnet_act_fn=act_fn,
|
192 |
+
resnet_groups=norm_num_groups,
|
193 |
+
cross_attention_dim=cross_attention_dim,
|
194 |
+
attn_num_head_channels=reversed_attention_head_dim[i],
|
195 |
+
dual_cross_attention=dual_cross_attention,
|
196 |
+
use_linear_projection=use_linear_projection,
|
197 |
+
only_cross_attention=only_cross_attention[i],
|
198 |
+
upcast_attention=upcast_attention,
|
199 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
200 |
+
)
|
201 |
+
self.up_blocks.append(up_block)
|
202 |
+
prev_output_channel = output_channel
|
203 |
+
|
204 |
+
# out
|
205 |
+
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
|
206 |
+
self.conv_act = nn.SiLU()
|
207 |
+
self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
|
208 |
+
|
209 |
+
def set_attention_slice(self, slice_size):
|
210 |
+
r"""
|
211 |
+
Enable sliced attention computation.
|
212 |
+
|
213 |
+
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
214 |
+
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
215 |
+
|
216 |
+
Args:
|
217 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
218 |
+
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
219 |
+
`"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
|
220 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
221 |
+
must be a multiple of `slice_size`.
|
222 |
+
"""
|
223 |
+
sliceable_head_dims = []
|
224 |
+
|
225 |
+
def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
|
226 |
+
if hasattr(module, "set_attention_slice"):
|
227 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
228 |
+
|
229 |
+
for child in module.children():
|
230 |
+
fn_recursive_retrieve_slicable_dims(child)
|
231 |
+
|
232 |
+
# retrieve number of attention layers
|
233 |
+
for module in self.children():
|
234 |
+
fn_recursive_retrieve_slicable_dims(module)
|
235 |
+
|
236 |
+
num_slicable_layers = len(sliceable_head_dims)
|
237 |
+
|
238 |
+
if slice_size == "auto":
|
239 |
+
# half the attention head size is usually a good trade-off between
|
240 |
+
# speed and memory
|
241 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
242 |
+
elif slice_size == "max":
|
243 |
+
# make smallest slice possible
|
244 |
+
slice_size = num_slicable_layers * [1]
|
245 |
+
|
246 |
+
slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
247 |
+
|
248 |
+
if len(slice_size) != len(sliceable_head_dims):
|
249 |
+
raise ValueError(
|
250 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
251 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
252 |
+
)
|
253 |
+
|
254 |
+
for i in range(len(slice_size)):
|
255 |
+
size = slice_size[i]
|
256 |
+
dim = sliceable_head_dims[i]
|
257 |
+
if size is not None and size > dim:
|
258 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
259 |
+
|
260 |
+
# Recursively walk through all the children.
|
261 |
+
# Any children which exposes the set_attention_slice method
|
262 |
+
# gets the message
|
263 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
264 |
+
if hasattr(module, "set_attention_slice"):
|
265 |
+
module.set_attention_slice(slice_size.pop())
|
266 |
+
|
267 |
+
for child in module.children():
|
268 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
269 |
+
|
270 |
+
reversed_slice_size = list(reversed(slice_size))
|
271 |
+
for module in self.children():
|
272 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
273 |
+
|
274 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
275 |
+
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
|
276 |
+
module.gradient_checkpointing = value
|
277 |
+
|
278 |
+
def forward(
|
279 |
+
self,
|
280 |
+
sample: torch.FloatTensor,
|
281 |
+
timestep: Union[torch.Tensor, float, int],
|
282 |
+
encoder_hidden_states: torch.Tensor,
|
283 |
+
class_labels: Optional[torch.Tensor] = None,
|
284 |
+
attention_mask: Optional[torch.Tensor] = None,
|
285 |
+
return_dict: bool = True,
|
286 |
+
cross_attention_kwargs = None,
|
287 |
+
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
288 |
+
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
289 |
+
inter_frame = False,
|
290 |
+
) -> Union[UNet3DConditionOutput, Tuple]:
|
291 |
+
r"""
|
292 |
+
Args:
|
293 |
+
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
|
294 |
+
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
|
295 |
+
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
|
296 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
297 |
+
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
|
298 |
+
|
299 |
+
Returns:
|
300 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
301 |
+
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
302 |
+
returning a tuple, the first element is the sample tensor.
|
303 |
+
"""
|
304 |
+
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
305 |
+
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
|
306 |
+
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
307 |
+
# on the fly if necessary.
|
308 |
+
default_overall_up_factor = 2**self.num_upsamplers
|
309 |
+
|
310 |
+
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
311 |
+
forward_upsample_size = False
|
312 |
+
upsample_size = None
|
313 |
+
|
314 |
+
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
315 |
+
logger.info("Forward upsample size to force interpolation output size.")
|
316 |
+
forward_upsample_size = True
|
317 |
+
|
318 |
+
# prepare attention_mask
|
319 |
+
if attention_mask is not None:
|
320 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
321 |
+
attention_mask = attention_mask.unsqueeze(1)
|
322 |
+
|
323 |
+
# center input if necessary
|
324 |
+
if self.config.center_input_sample:
|
325 |
+
sample = 2 * sample - 1.0
|
326 |
+
|
327 |
+
# time
|
328 |
+
timesteps = timestep
|
329 |
+
if not torch.is_tensor(timesteps):
|
330 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
331 |
+
is_mps = sample.device.type == "mps"
|
332 |
+
if isinstance(timestep, float):
|
333 |
+
dtype = torch.float32 if is_mps else torch.float64
|
334 |
+
else:
|
335 |
+
dtype = torch.int32 if is_mps else torch.int64
|
336 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
337 |
+
elif len(timesteps.shape) == 0:
|
338 |
+
timesteps = timesteps[None].to(sample.device)
|
339 |
+
|
340 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
341 |
+
timesteps = timesteps.expand(sample.shape[0])
|
342 |
+
|
343 |
+
t_emb = self.time_proj(timesteps)
|
344 |
+
|
345 |
+
# timesteps does not contain any weights and will always return f32 tensors
|
346 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
347 |
+
# there might be better ways to encapsulate this.
|
348 |
+
t_emb = t_emb.to(dtype=self.dtype)
|
349 |
+
emb = self.time_embedding(t_emb)
|
350 |
+
|
351 |
+
if self.class_embedding is not None:
|
352 |
+
if class_labels is None:
|
353 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
354 |
+
|
355 |
+
if self.config.class_embed_type == "timestep":
|
356 |
+
class_labels = self.time_proj(class_labels)
|
357 |
+
|
358 |
+
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
359 |
+
emb = emb + class_emb
|
360 |
+
|
361 |
+
# pre-process
|
362 |
+
sample = self.conv_in(sample)
|
363 |
+
|
364 |
+
# down
|
365 |
+
down_block_res_samples = (sample,)
|
366 |
+
for downsample_block in self.down_blocks:
|
367 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
368 |
+
sample, res_samples = downsample_block(
|
369 |
+
hidden_states=sample,
|
370 |
+
temb=emb,
|
371 |
+
encoder_hidden_states=encoder_hidden_states,
|
372 |
+
attention_mask=attention_mask,
|
373 |
+
inter_frame=inter_frame
|
374 |
+
)
|
375 |
+
else:
|
376 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
377 |
+
|
378 |
+
down_block_res_samples += res_samples
|
379 |
+
|
380 |
+
if down_block_additional_residuals is not None:
|
381 |
+
new_down_block_res_samples = ()
|
382 |
+
|
383 |
+
for down_block_res_sample, down_block_additional_residual in zip(
|
384 |
+
down_block_res_samples, down_block_additional_residuals
|
385 |
+
):
|
386 |
+
down_block_res_sample += down_block_additional_residual
|
387 |
+
new_down_block_res_samples += (down_block_res_sample,)
|
388 |
+
|
389 |
+
down_block_res_samples = new_down_block_res_samples
|
390 |
+
|
391 |
+
# mid
|
392 |
+
sample = self.mid_block(
|
393 |
+
sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask,
|
394 |
+
inter_frame=inter_frame
|
395 |
+
|
396 |
+
)
|
397 |
+
|
398 |
+
if mid_block_additional_residual is not None:
|
399 |
+
sample += mid_block_additional_residual
|
400 |
+
|
401 |
+
# up
|
402 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
403 |
+
is_final_block = i == len(self.up_blocks) - 1
|
404 |
+
|
405 |
+
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
406 |
+
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
407 |
+
|
408 |
+
# if we have not reached the final block and need to forward the
|
409 |
+
# upsample size, we do it here
|
410 |
+
if not is_final_block and forward_upsample_size:
|
411 |
+
upsample_size = down_block_res_samples[-1].shape[2:]
|
412 |
+
|
413 |
+
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
414 |
+
sample = upsample_block(
|
415 |
+
hidden_states=sample,
|
416 |
+
temb=emb,
|
417 |
+
res_hidden_states_tuple=res_samples,
|
418 |
+
encoder_hidden_states=encoder_hidden_states,
|
419 |
+
upsample_size=upsample_size,
|
420 |
+
attention_mask=attention_mask,
|
421 |
+
inter_frame=inter_frame
|
422 |
+
)
|
423 |
+
else:
|
424 |
+
sample = upsample_block(
|
425 |
+
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
426 |
+
)
|
427 |
+
# post-process
|
428 |
+
sample = self.conv_norm_out(sample)
|
429 |
+
sample = self.conv_act(sample)
|
430 |
+
sample = self.conv_out(sample)
|
431 |
+
|
432 |
+
if not return_dict:
|
433 |
+
return (sample,)
|
434 |
+
|
435 |
+
return UNet3DConditionOutput(sample=sample)
|
436 |
+
|
437 |
+
@classmethod
|
438 |
+
def from_pretrained_2d(cls, pretrained_model_path, subfolder=None):
|
439 |
+
if subfolder is not None:
|
440 |
+
pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
|
441 |
+
|
442 |
+
config_file = os.path.join(pretrained_model_path, 'config.json')
|
443 |
+
if not os.path.isfile(config_file):
|
444 |
+
raise RuntimeError(f"{config_file} does not exist")
|
445 |
+
with open(config_file, "r") as f:
|
446 |
+
config = json.load(f)
|
447 |
+
config["_class_name"] = cls.__name__
|
448 |
+
config["down_block_types"] = [
|
449 |
+
"CrossAttnDownBlock3D",
|
450 |
+
"CrossAttnDownBlock3D",
|
451 |
+
"CrossAttnDownBlock3D",
|
452 |
+
"DownBlock3D"
|
453 |
+
]
|
454 |
+
config["up_block_types"] = [
|
455 |
+
"UpBlock3D",
|
456 |
+
"CrossAttnUpBlock3D",
|
457 |
+
"CrossAttnUpBlock3D",
|
458 |
+
"CrossAttnUpBlock3D"
|
459 |
+
]
|
460 |
+
|
461 |
+
from diffusers.utils import WEIGHTS_NAME
|
462 |
+
model = cls.from_config(config)
|
463 |
+
model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
|
464 |
+
if not os.path.isfile(model_file):
|
465 |
+
raise RuntimeError(f"{model_file} does not exist")
|
466 |
+
state_dict = torch.load(model_file, map_location="cpu")
|
467 |
+
# for k, v in model.state_dict().items():
|
468 |
+
# if '_temp.' in k:
|
469 |
+
# state_dict.update({k: v})
|
470 |
+
model.load_state_dict(state_dict, strict=False)
|
471 |
+
|
472 |
+
return model
|
models/unet_blocks.py
ADDED
@@ -0,0 +1,588 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
|
6 |
+
from .attention import Transformer3DModel
|
7 |
+
from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
|
8 |
+
|
9 |
+
|
10 |
+
def get_down_block(
|
11 |
+
down_block_type,
|
12 |
+
num_layers,
|
13 |
+
in_channels,
|
14 |
+
out_channels,
|
15 |
+
temb_channels,
|
16 |
+
add_downsample,
|
17 |
+
resnet_eps,
|
18 |
+
resnet_act_fn,
|
19 |
+
attn_num_head_channels,
|
20 |
+
resnet_groups=None,
|
21 |
+
cross_attention_dim=None,
|
22 |
+
downsample_padding=None,
|
23 |
+
dual_cross_attention=False,
|
24 |
+
use_linear_projection=False,
|
25 |
+
only_cross_attention=False,
|
26 |
+
upcast_attention=False,
|
27 |
+
resnet_time_scale_shift="default",
|
28 |
+
):
|
29 |
+
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
|
30 |
+
if down_block_type == "DownBlock3D":
|
31 |
+
return DownBlock3D(
|
32 |
+
num_layers=num_layers,
|
33 |
+
in_channels=in_channels,
|
34 |
+
out_channels=out_channels,
|
35 |
+
temb_channels=temb_channels,
|
36 |
+
add_downsample=add_downsample,
|
37 |
+
resnet_eps=resnet_eps,
|
38 |
+
resnet_act_fn=resnet_act_fn,
|
39 |
+
resnet_groups=resnet_groups,
|
40 |
+
downsample_padding=downsample_padding,
|
41 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
42 |
+
)
|
43 |
+
elif down_block_type == "CrossAttnDownBlock3D":
|
44 |
+
if cross_attention_dim is None:
|
45 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
|
46 |
+
return CrossAttnDownBlock3D(
|
47 |
+
num_layers=num_layers,
|
48 |
+
in_channels=in_channels,
|
49 |
+
out_channels=out_channels,
|
50 |
+
temb_channels=temb_channels,
|
51 |
+
add_downsample=add_downsample,
|
52 |
+
resnet_eps=resnet_eps,
|
53 |
+
resnet_act_fn=resnet_act_fn,
|
54 |
+
resnet_groups=resnet_groups,
|
55 |
+
downsample_padding=downsample_padding,
|
56 |
+
cross_attention_dim=cross_attention_dim,
|
57 |
+
attn_num_head_channels=attn_num_head_channels,
|
58 |
+
dual_cross_attention=dual_cross_attention,
|
59 |
+
use_linear_projection=use_linear_projection,
|
60 |
+
only_cross_attention=only_cross_attention,
|
61 |
+
upcast_attention=upcast_attention,
|
62 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
63 |
+
)
|
64 |
+
raise ValueError(f"{down_block_type} does not exist.")
|
65 |
+
|
66 |
+
|
67 |
+
def get_up_block(
|
68 |
+
up_block_type,
|
69 |
+
num_layers,
|
70 |
+
in_channels,
|
71 |
+
out_channels,
|
72 |
+
prev_output_channel,
|
73 |
+
temb_channels,
|
74 |
+
add_upsample,
|
75 |
+
resnet_eps,
|
76 |
+
resnet_act_fn,
|
77 |
+
attn_num_head_channels,
|
78 |
+
resnet_groups=None,
|
79 |
+
cross_attention_dim=None,
|
80 |
+
dual_cross_attention=False,
|
81 |
+
use_linear_projection=False,
|
82 |
+
only_cross_attention=False,
|
83 |
+
upcast_attention=False,
|
84 |
+
resnet_time_scale_shift="default",
|
85 |
+
):
|
86 |
+
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
87 |
+
if up_block_type == "UpBlock3D":
|
88 |
+
return UpBlock3D(
|
89 |
+
num_layers=num_layers,
|
90 |
+
in_channels=in_channels,
|
91 |
+
out_channels=out_channels,
|
92 |
+
prev_output_channel=prev_output_channel,
|
93 |
+
temb_channels=temb_channels,
|
94 |
+
add_upsample=add_upsample,
|
95 |
+
resnet_eps=resnet_eps,
|
96 |
+
resnet_act_fn=resnet_act_fn,
|
97 |
+
resnet_groups=resnet_groups,
|
98 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
99 |
+
)
|
100 |
+
elif up_block_type == "CrossAttnUpBlock3D":
|
101 |
+
if cross_attention_dim is None:
|
102 |
+
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
|
103 |
+
return CrossAttnUpBlock3D(
|
104 |
+
num_layers=num_layers,
|
105 |
+
in_channels=in_channels,
|
106 |
+
out_channels=out_channels,
|
107 |
+
prev_output_channel=prev_output_channel,
|
108 |
+
temb_channels=temb_channels,
|
109 |
+
add_upsample=add_upsample,
|
110 |
+
resnet_eps=resnet_eps,
|
111 |
+
resnet_act_fn=resnet_act_fn,
|
112 |
+
resnet_groups=resnet_groups,
|
113 |
+
cross_attention_dim=cross_attention_dim,
|
114 |
+
attn_num_head_channels=attn_num_head_channels,
|
115 |
+
dual_cross_attention=dual_cross_attention,
|
116 |
+
use_linear_projection=use_linear_projection,
|
117 |
+
only_cross_attention=only_cross_attention,
|
118 |
+
upcast_attention=upcast_attention,
|
119 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
120 |
+
)
|
121 |
+
raise ValueError(f"{up_block_type} does not exist.")
|
122 |
+
|
123 |
+
|
124 |
+
class UNetMidBlock3DCrossAttn(nn.Module):
|
125 |
+
def __init__(
|
126 |
+
self,
|
127 |
+
in_channels: int,
|
128 |
+
temb_channels: int,
|
129 |
+
dropout: float = 0.0,
|
130 |
+
num_layers: int = 1,
|
131 |
+
resnet_eps: float = 1e-6,
|
132 |
+
resnet_time_scale_shift: str = "default",
|
133 |
+
resnet_act_fn: str = "swish",
|
134 |
+
resnet_groups: int = 32,
|
135 |
+
resnet_pre_norm: bool = True,
|
136 |
+
attn_num_head_channels=1,
|
137 |
+
output_scale_factor=1.0,
|
138 |
+
cross_attention_dim=1280,
|
139 |
+
dual_cross_attention=False,
|
140 |
+
use_linear_projection=False,
|
141 |
+
upcast_attention=False,
|
142 |
+
):
|
143 |
+
super().__init__()
|
144 |
+
|
145 |
+
self.has_cross_attention = True
|
146 |
+
self.attn_num_head_channels = attn_num_head_channels
|
147 |
+
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
148 |
+
|
149 |
+
# there is always at least one resnet
|
150 |
+
resnets = [
|
151 |
+
ResnetBlock3D(
|
152 |
+
in_channels=in_channels,
|
153 |
+
out_channels=in_channels,
|
154 |
+
temb_channels=temb_channels,
|
155 |
+
eps=resnet_eps,
|
156 |
+
groups=resnet_groups,
|
157 |
+
dropout=dropout,
|
158 |
+
time_embedding_norm=resnet_time_scale_shift,
|
159 |
+
non_linearity=resnet_act_fn,
|
160 |
+
output_scale_factor=output_scale_factor,
|
161 |
+
pre_norm=resnet_pre_norm,
|
162 |
+
)
|
163 |
+
]
|
164 |
+
attentions = []
|
165 |
+
|
166 |
+
for _ in range(num_layers):
|
167 |
+
if dual_cross_attention:
|
168 |
+
raise NotImplementedError
|
169 |
+
attentions.append(
|
170 |
+
Transformer3DModel(
|
171 |
+
attn_num_head_channels,
|
172 |
+
in_channels // attn_num_head_channels,
|
173 |
+
in_channels=in_channels,
|
174 |
+
num_layers=1,
|
175 |
+
cross_attention_dim=cross_attention_dim,
|
176 |
+
norm_num_groups=resnet_groups,
|
177 |
+
use_linear_projection=use_linear_projection,
|
178 |
+
upcast_attention=upcast_attention,
|
179 |
+
)
|
180 |
+
)
|
181 |
+
resnets.append(
|
182 |
+
ResnetBlock3D(
|
183 |
+
in_channels=in_channels,
|
184 |
+
out_channels=in_channels,
|
185 |
+
temb_channels=temb_channels,
|
186 |
+
eps=resnet_eps,
|
187 |
+
groups=resnet_groups,
|
188 |
+
dropout=dropout,
|
189 |
+
time_embedding_norm=resnet_time_scale_shift,
|
190 |
+
non_linearity=resnet_act_fn,
|
191 |
+
output_scale_factor=output_scale_factor,
|
192 |
+
pre_norm=resnet_pre_norm,
|
193 |
+
)
|
194 |
+
)
|
195 |
+
|
196 |
+
self.attentions = nn.ModuleList(attentions)
|
197 |
+
self.resnets = nn.ModuleList(resnets)
|
198 |
+
|
199 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, inter_frame=False):
|
200 |
+
hidden_states = self.resnets[0](hidden_states, temb)
|
201 |
+
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
202 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, inter_frame=inter_frame).sample
|
203 |
+
hidden_states = resnet(hidden_states, temb)
|
204 |
+
|
205 |
+
return hidden_states
|
206 |
+
|
207 |
+
|
208 |
+
class CrossAttnDownBlock3D(nn.Module):
|
209 |
+
def __init__(
|
210 |
+
self,
|
211 |
+
in_channels: int,
|
212 |
+
out_channels: int,
|
213 |
+
temb_channels: int,
|
214 |
+
dropout: float = 0.0,
|
215 |
+
num_layers: int = 1,
|
216 |
+
resnet_eps: float = 1e-6,
|
217 |
+
resnet_time_scale_shift: str = "default",
|
218 |
+
resnet_act_fn: str = "swish",
|
219 |
+
resnet_groups: int = 32,
|
220 |
+
resnet_pre_norm: bool = True,
|
221 |
+
attn_num_head_channels=1,
|
222 |
+
cross_attention_dim=1280,
|
223 |
+
output_scale_factor=1.0,
|
224 |
+
downsample_padding=1,
|
225 |
+
add_downsample=True,
|
226 |
+
dual_cross_attention=False,
|
227 |
+
use_linear_projection=False,
|
228 |
+
only_cross_attention=False,
|
229 |
+
upcast_attention=False,
|
230 |
+
):
|
231 |
+
super().__init__()
|
232 |
+
resnets = []
|
233 |
+
attentions = []
|
234 |
+
|
235 |
+
self.has_cross_attention = True
|
236 |
+
self.attn_num_head_channels = attn_num_head_channels
|
237 |
+
|
238 |
+
for i in range(num_layers):
|
239 |
+
in_channels = in_channels if i == 0 else out_channels
|
240 |
+
resnets.append(
|
241 |
+
ResnetBlock3D(
|
242 |
+
in_channels=in_channels,
|
243 |
+
out_channels=out_channels,
|
244 |
+
temb_channels=temb_channels,
|
245 |
+
eps=resnet_eps,
|
246 |
+
groups=resnet_groups,
|
247 |
+
dropout=dropout,
|
248 |
+
time_embedding_norm=resnet_time_scale_shift,
|
249 |
+
non_linearity=resnet_act_fn,
|
250 |
+
output_scale_factor=output_scale_factor,
|
251 |
+
pre_norm=resnet_pre_norm,
|
252 |
+
)
|
253 |
+
)
|
254 |
+
if dual_cross_attention:
|
255 |
+
raise NotImplementedError
|
256 |
+
attentions.append(
|
257 |
+
Transformer3DModel(
|
258 |
+
attn_num_head_channels,
|
259 |
+
out_channels // attn_num_head_channels,
|
260 |
+
in_channels=out_channels,
|
261 |
+
num_layers=1,
|
262 |
+
cross_attention_dim=cross_attention_dim,
|
263 |
+
norm_num_groups=resnet_groups,
|
264 |
+
use_linear_projection=use_linear_projection,
|
265 |
+
only_cross_attention=only_cross_attention,
|
266 |
+
upcast_attention=upcast_attention,
|
267 |
+
)
|
268 |
+
)
|
269 |
+
self.attentions = nn.ModuleList(attentions)
|
270 |
+
self.resnets = nn.ModuleList(resnets)
|
271 |
+
|
272 |
+
if add_downsample:
|
273 |
+
self.downsamplers = nn.ModuleList(
|
274 |
+
[
|
275 |
+
Downsample3D(
|
276 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
277 |
+
)
|
278 |
+
]
|
279 |
+
)
|
280 |
+
else:
|
281 |
+
self.downsamplers = None
|
282 |
+
|
283 |
+
self.gradient_checkpointing = False
|
284 |
+
|
285 |
+
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, inter_frame=False):
|
286 |
+
output_states = ()
|
287 |
+
|
288 |
+
for resnet, attn in zip(self.resnets, self.attentions):
|
289 |
+
if self.training and self.gradient_checkpointing:
|
290 |
+
|
291 |
+
def create_custom_forward(module, return_dict=None, inter_frame=None):
|
292 |
+
def custom_forward(*inputs):
|
293 |
+
if return_dict is not None:
|
294 |
+
return module(*inputs, return_dict=return_dict, inter_frame=inter_frame)
|
295 |
+
else:
|
296 |
+
return module(*inputs)
|
297 |
+
|
298 |
+
return custom_forward
|
299 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
300 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
301 |
+
create_custom_forward(attn, return_dict=False, inter_frame=inter_frame),
|
302 |
+
hidden_states,
|
303 |
+
encoder_hidden_states,
|
304 |
+
)[0]
|
305 |
+
else:
|
306 |
+
hidden_states = resnet(hidden_states, temb)
|
307 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, inter_frame=inter_frame).sample
|
308 |
+
|
309 |
+
output_states += (hidden_states,)
|
310 |
+
|
311 |
+
if self.downsamplers is not None:
|
312 |
+
for downsampler in self.downsamplers:
|
313 |
+
hidden_states = downsampler(hidden_states)
|
314 |
+
|
315 |
+
output_states += (hidden_states,)
|
316 |
+
|
317 |
+
return hidden_states, output_states
|
318 |
+
|
319 |
+
|
320 |
+
class DownBlock3D(nn.Module):
|
321 |
+
def __init__(
|
322 |
+
self,
|
323 |
+
in_channels: int,
|
324 |
+
out_channels: int,
|
325 |
+
temb_channels: int,
|
326 |
+
dropout: float = 0.0,
|
327 |
+
num_layers: int = 1,
|
328 |
+
resnet_eps: float = 1e-6,
|
329 |
+
resnet_time_scale_shift: str = "default",
|
330 |
+
resnet_act_fn: str = "swish",
|
331 |
+
resnet_groups: int = 32,
|
332 |
+
resnet_pre_norm: bool = True,
|
333 |
+
output_scale_factor=1.0,
|
334 |
+
add_downsample=True,
|
335 |
+
downsample_padding=1,
|
336 |
+
):
|
337 |
+
super().__init__()
|
338 |
+
resnets = []
|
339 |
+
|
340 |
+
for i in range(num_layers):
|
341 |
+
in_channels = in_channels if i == 0 else out_channels
|
342 |
+
resnets.append(
|
343 |
+
ResnetBlock3D(
|
344 |
+
in_channels=in_channels,
|
345 |
+
out_channels=out_channels,
|
346 |
+
temb_channels=temb_channels,
|
347 |
+
eps=resnet_eps,
|
348 |
+
groups=resnet_groups,
|
349 |
+
dropout=dropout,
|
350 |
+
time_embedding_norm=resnet_time_scale_shift,
|
351 |
+
non_linearity=resnet_act_fn,
|
352 |
+
output_scale_factor=output_scale_factor,
|
353 |
+
pre_norm=resnet_pre_norm,
|
354 |
+
)
|
355 |
+
)
|
356 |
+
|
357 |
+
self.resnets = nn.ModuleList(resnets)
|
358 |
+
|
359 |
+
if add_downsample:
|
360 |
+
self.downsamplers = nn.ModuleList(
|
361 |
+
[
|
362 |
+
Downsample3D(
|
363 |
+
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
364 |
+
)
|
365 |
+
]
|
366 |
+
)
|
367 |
+
else:
|
368 |
+
self.downsamplers = None
|
369 |
+
|
370 |
+
self.gradient_checkpointing = False
|
371 |
+
|
372 |
+
def forward(self, hidden_states, temb=None):
|
373 |
+
output_states = ()
|
374 |
+
|
375 |
+
for resnet in self.resnets:
|
376 |
+
if self.training and self.gradient_checkpointing:
|
377 |
+
|
378 |
+
def create_custom_forward(module):
|
379 |
+
def custom_forward(*inputs):
|
380 |
+
return module(*inputs)
|
381 |
+
|
382 |
+
return custom_forward
|
383 |
+
|
384 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
385 |
+
else:
|
386 |
+
hidden_states = resnet(hidden_states, temb)
|
387 |
+
|
388 |
+
output_states += (hidden_states,)
|
389 |
+
|
390 |
+
if self.downsamplers is not None:
|
391 |
+
for downsampler in self.downsamplers:
|
392 |
+
hidden_states = downsampler(hidden_states)
|
393 |
+
|
394 |
+
output_states += (hidden_states,)
|
395 |
+
|
396 |
+
return hidden_states, output_states
|
397 |
+
|
398 |
+
|
399 |
+
class CrossAttnUpBlock3D(nn.Module):
|
400 |
+
def __init__(
|
401 |
+
self,
|
402 |
+
in_channels: int,
|
403 |
+
out_channels: int,
|
404 |
+
prev_output_channel: int,
|
405 |
+
temb_channels: int,
|
406 |
+
dropout: float = 0.0,
|
407 |
+
num_layers: int = 1,
|
408 |
+
resnet_eps: float = 1e-6,
|
409 |
+
resnet_time_scale_shift: str = "default",
|
410 |
+
resnet_act_fn: str = "swish",
|
411 |
+
resnet_groups: int = 32,
|
412 |
+
resnet_pre_norm: bool = True,
|
413 |
+
attn_num_head_channels=1,
|
414 |
+
cross_attention_dim=1280,
|
415 |
+
output_scale_factor=1.0,
|
416 |
+
add_upsample=True,
|
417 |
+
dual_cross_attention=False,
|
418 |
+
use_linear_projection=False,
|
419 |
+
only_cross_attention=False,
|
420 |
+
upcast_attention=False,
|
421 |
+
):
|
422 |
+
super().__init__()
|
423 |
+
resnets = []
|
424 |
+
attentions = []
|
425 |
+
|
426 |
+
self.has_cross_attention = True
|
427 |
+
self.attn_num_head_channels = attn_num_head_channels
|
428 |
+
|
429 |
+
for i in range(num_layers):
|
430 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
431 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
432 |
+
|
433 |
+
resnets.append(
|
434 |
+
ResnetBlock3D(
|
435 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
436 |
+
out_channels=out_channels,
|
437 |
+
temb_channels=temb_channels,
|
438 |
+
eps=resnet_eps,
|
439 |
+
groups=resnet_groups,
|
440 |
+
dropout=dropout,
|
441 |
+
time_embedding_norm=resnet_time_scale_shift,
|
442 |
+
non_linearity=resnet_act_fn,
|
443 |
+
output_scale_factor=output_scale_factor,
|
444 |
+
pre_norm=resnet_pre_norm,
|
445 |
+
)
|
446 |
+
)
|
447 |
+
if dual_cross_attention:
|
448 |
+
raise NotImplementedError
|
449 |
+
attentions.append(
|
450 |
+
Transformer3DModel(
|
451 |
+
attn_num_head_channels,
|
452 |
+
out_channels // attn_num_head_channels,
|
453 |
+
in_channels=out_channels,
|
454 |
+
num_layers=1,
|
455 |
+
cross_attention_dim=cross_attention_dim,
|
456 |
+
norm_num_groups=resnet_groups,
|
457 |
+
use_linear_projection=use_linear_projection,
|
458 |
+
only_cross_attention=only_cross_attention,
|
459 |
+
upcast_attention=upcast_attention,
|
460 |
+
)
|
461 |
+
)
|
462 |
+
|
463 |
+
self.attentions = nn.ModuleList(attentions)
|
464 |
+
self.resnets = nn.ModuleList(resnets)
|
465 |
+
|
466 |
+
if add_upsample:
|
467 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
468 |
+
else:
|
469 |
+
self.upsamplers = None
|
470 |
+
|
471 |
+
self.gradient_checkpointing = False
|
472 |
+
|
473 |
+
def forward(
|
474 |
+
self,
|
475 |
+
hidden_states,
|
476 |
+
res_hidden_states_tuple,
|
477 |
+
temb=None,
|
478 |
+
encoder_hidden_states=None,
|
479 |
+
upsample_size=None,
|
480 |
+
attention_mask=None,
|
481 |
+
inter_frame=False
|
482 |
+
):
|
483 |
+
for resnet, attn in zip(self.resnets, self.attentions):
|
484 |
+
# pop res hidden states
|
485 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
486 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
487 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
488 |
+
|
489 |
+
if self.training and self.gradient_checkpointing:
|
490 |
+
|
491 |
+
def create_custom_forward(module, return_dict=None, inter_frame=None):
|
492 |
+
def custom_forward(*inputs):
|
493 |
+
if return_dict is not None:
|
494 |
+
return module(*inputs, return_dict=return_dict, inter_frame=inter_frame)
|
495 |
+
else:
|
496 |
+
return module(*inputs)
|
497 |
+
|
498 |
+
return custom_forward
|
499 |
+
|
500 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
501 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
502 |
+
create_custom_forward(attn, return_dict=False, inter_frame=inter_frame),
|
503 |
+
hidden_states,
|
504 |
+
encoder_hidden_states,
|
505 |
+
)[0]
|
506 |
+
else:
|
507 |
+
hidden_states = resnet(hidden_states, temb)
|
508 |
+
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, inter_frame=inter_frame).sample
|
509 |
+
|
510 |
+
if self.upsamplers is not None:
|
511 |
+
for upsampler in self.upsamplers:
|
512 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
513 |
+
|
514 |
+
return hidden_states
|
515 |
+
|
516 |
+
|
517 |
+
class UpBlock3D(nn.Module):
|
518 |
+
def __init__(
|
519 |
+
self,
|
520 |
+
in_channels: int,
|
521 |
+
prev_output_channel: int,
|
522 |
+
out_channels: int,
|
523 |
+
temb_channels: int,
|
524 |
+
dropout: float = 0.0,
|
525 |
+
num_layers: int = 1,
|
526 |
+
resnet_eps: float = 1e-6,
|
527 |
+
resnet_time_scale_shift: str = "default",
|
528 |
+
resnet_act_fn: str = "swish",
|
529 |
+
resnet_groups: int = 32,
|
530 |
+
resnet_pre_norm: bool = True,
|
531 |
+
output_scale_factor=1.0,
|
532 |
+
add_upsample=True,
|
533 |
+
):
|
534 |
+
super().__init__()
|
535 |
+
resnets = []
|
536 |
+
|
537 |
+
for i in range(num_layers):
|
538 |
+
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
539 |
+
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
540 |
+
|
541 |
+
resnets.append(
|
542 |
+
ResnetBlock3D(
|
543 |
+
in_channels=resnet_in_channels + res_skip_channels,
|
544 |
+
out_channels=out_channels,
|
545 |
+
temb_channels=temb_channels,
|
546 |
+
eps=resnet_eps,
|
547 |
+
groups=resnet_groups,
|
548 |
+
dropout=dropout,
|
549 |
+
time_embedding_norm=resnet_time_scale_shift,
|
550 |
+
non_linearity=resnet_act_fn,
|
551 |
+
output_scale_factor=output_scale_factor,
|
552 |
+
pre_norm=resnet_pre_norm,
|
553 |
+
)
|
554 |
+
)
|
555 |
+
|
556 |
+
self.resnets = nn.ModuleList(resnets)
|
557 |
+
|
558 |
+
if add_upsample:
|
559 |
+
self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
|
560 |
+
else:
|
561 |
+
self.upsamplers = None
|
562 |
+
|
563 |
+
self.gradient_checkpointing = False
|
564 |
+
|
565 |
+
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
566 |
+
for resnet in self.resnets:
|
567 |
+
# pop res hidden states
|
568 |
+
res_hidden_states = res_hidden_states_tuple[-1]
|
569 |
+
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
570 |
+
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
571 |
+
|
572 |
+
if self.training and self.gradient_checkpointing:
|
573 |
+
|
574 |
+
def create_custom_forward(module):
|
575 |
+
def custom_forward(*inputs):
|
576 |
+
return module(*inputs)
|
577 |
+
|
578 |
+
return custom_forward
|
579 |
+
|
580 |
+
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
581 |
+
else:
|
582 |
+
hidden_states = resnet(hidden_states, temb)
|
583 |
+
|
584 |
+
if self.upsamplers is not None:
|
585 |
+
for upsampler in self.upsamplers:
|
586 |
+
hidden_states = upsampler(hidden_states, upsample_size)
|
587 |
+
|
588 |
+
return hidden_states
|
models/util.py
ADDED
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import imageio
|
3 |
+
import numpy as np
|
4 |
+
from typing import Union
|
5 |
+
import decord
|
6 |
+
decord.bridge.set_bridge('torch')
|
7 |
+
import torch
|
8 |
+
import torchvision
|
9 |
+
import PIL
|
10 |
+
from typing import List
|
11 |
+
from tqdm import tqdm
|
12 |
+
from einops import rearrange
|
13 |
+
|
14 |
+
from controlnet_aux import CannyDetector
|
15 |
+
|
16 |
+
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
|
17 |
+
videos = rearrange(videos, "b c t h w -> t b c h w")
|
18 |
+
outputs = []
|
19 |
+
for x in videos:
|
20 |
+
x = torchvision.utils.make_grid(x, nrow=n_rows)
|
21 |
+
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
|
22 |
+
if rescale:
|
23 |
+
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
|
24 |
+
x = (x * 255).numpy().astype(np.uint8)
|
25 |
+
outputs.append(x)
|
26 |
+
|
27 |
+
os.makedirs(os.path.dirname(path), exist_ok=True)
|
28 |
+
imageio.mimsave(path, outputs, fps=fps)
|
29 |
+
|
30 |
+
def save_videos_grid_pil(videos: List[PIL.Image.Image], path: str, rescale=False, n_rows=4, fps=8):
|
31 |
+
videos = rearrange(videos, "b c t h w -> t b c h w")
|
32 |
+
outputs = []
|
33 |
+
for x in videos:
|
34 |
+
x = torchvision.utils.make_grid(x, nrow=n_rows)
|
35 |
+
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
|
36 |
+
if rescale:
|
37 |
+
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
|
38 |
+
x = (x * 255).numpy().astype(np.uint8)
|
39 |
+
outputs.append(x)
|
40 |
+
|
41 |
+
os.makedirs(os.path.dirname(path), exist_ok=True)
|
42 |
+
imageio.mimsave(path, outputs, fps=fps)
|
43 |
+
|
44 |
+
def read_video(video_path, video_length, width=512, height=512, frame_rate=2):
|
45 |
+
vr = decord.VideoReader(video_path, width=width, height=height)
|
46 |
+
sample_index = list(range(0, len(vr), frame_rate))[:video_length]
|
47 |
+
video = vr.get_batch(sample_index)
|
48 |
+
video = rearrange(video, "f h w c -> f c h w")
|
49 |
+
video = (video / 127.5 - 1.0)
|
50 |
+
return video
|
51 |
+
|
52 |
+
|
53 |
+
def get_annotation(video, annotator):
|
54 |
+
t2i_transform = torchvision.transforms.ToPILImage()
|
55 |
+
annotation = []
|
56 |
+
for frame in video:
|
57 |
+
pil_frame = t2i_transform(frame)
|
58 |
+
if isinstance(annotator, CannyDetector):
|
59 |
+
annotation.append(annotator(pil_frame, low_threshold=100, high_threshold=200))
|
60 |
+
else:
|
61 |
+
annotation.append(annotator(pil_frame))
|
62 |
+
return annotation
|
63 |
+
|
64 |
+
# DDIM Inversion
|
65 |
+
@torch.no_grad()
|
66 |
+
def init_prompt(prompt, pipeline):
|
67 |
+
uncond_input = pipeline.tokenizer(
|
68 |
+
[""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
|
69 |
+
return_tensors="pt"
|
70 |
+
)
|
71 |
+
uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
|
72 |
+
text_input = pipeline.tokenizer(
|
73 |
+
[prompt],
|
74 |
+
padding="max_length",
|
75 |
+
max_length=pipeline.tokenizer.model_max_length,
|
76 |
+
truncation=True,
|
77 |
+
return_tensors="pt",
|
78 |
+
)
|
79 |
+
text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
|
80 |
+
context = torch.cat([uncond_embeddings, text_embeddings])
|
81 |
+
|
82 |
+
return context
|
83 |
+
|
84 |
+
|
85 |
+
def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
|
86 |
+
sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
|
87 |
+
timestep, next_timestep = min(
|
88 |
+
timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
|
89 |
+
alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
|
90 |
+
alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
|
91 |
+
beta_prod_t = 1 - alpha_prod_t
|
92 |
+
next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
|
93 |
+
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
|
94 |
+
next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
|
95 |
+
return next_sample
|
96 |
+
|
97 |
+
|
98 |
+
def get_noise_pred_single(latents, t, context, unet):
|
99 |
+
noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
|
100 |
+
return noise_pred
|
101 |
+
|
102 |
+
|
103 |
+
@torch.no_grad()
|
104 |
+
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
|
105 |
+
context = init_prompt(prompt, pipeline)
|
106 |
+
uncond_embeddings, cond_embeddings = context.chunk(2)
|
107 |
+
all_latent = [latent]
|
108 |
+
latent = latent.clone().detach()
|
109 |
+
for i in tqdm(range(num_inv_steps)):
|
110 |
+
t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
|
111 |
+
noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
|
112 |
+
latent = next_step(noise_pred, t, latent, ddim_scheduler)
|
113 |
+
all_latent.append(latent)
|
114 |
+
return all_latent
|
115 |
+
|
116 |
+
|
117 |
+
@torch.no_grad()
|
118 |
+
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
|
119 |
+
ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
|
120 |
+
return ddim_latents
|
predict.py
ADDED
@@ -0,0 +1,164 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Prediction interface for Cog ⚙️
|
2 |
+
# https://github.com/replicate/cog/blob/main/docs/python.md
|
3 |
+
import os
|
4 |
+
import numpy as np
|
5 |
+
import argparse
|
6 |
+
import imageio
|
7 |
+
import torch
|
8 |
+
|
9 |
+
from einops import rearrange
|
10 |
+
from diffusers import DDIMScheduler, AutoencoderKL
|
11 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
12 |
+
import controlnet_aux
|
13 |
+
from controlnet_aux import OpenposeDetector, CannyDetector, MidasDetector
|
14 |
+
|
15 |
+
from models.pipeline_controlvideo import ControlVideoPipeline
|
16 |
+
from models.util import save_videos_grid, read_video, get_annotation
|
17 |
+
from models.unet import UNet3DConditionModel
|
18 |
+
from models.controlnet import ControlNetModel3D
|
19 |
+
from models.RIFE.IFNet_HDv3 import IFNet
|
20 |
+
from cog import BasePredictor, Input, Path
|
21 |
+
|
22 |
+
|
23 |
+
sd_path = "checkpoints/stable-diffusion-v1-5"
|
24 |
+
inter_path = "checkpoints/flownet.pkl"
|
25 |
+
controlnet_dict = {
|
26 |
+
"pose": "checkpoints/sd-controlnet-openpose",
|
27 |
+
"depth": "checkpoints/sd-controlnet-depth",
|
28 |
+
"canny": "checkpoints/sd-controlnet-canny",
|
29 |
+
}
|
30 |
+
|
31 |
+
controlnet_parser_dict = {
|
32 |
+
"pose": OpenposeDetector,
|
33 |
+
"depth": MidasDetector,
|
34 |
+
"canny": CannyDetector,
|
35 |
+
}
|
36 |
+
|
37 |
+
POS_PROMPT = " ,best quality, extremely detailed, HD, ultra-realistic, 8K, HQ, masterpiece, trending on artstation, art, smooth"
|
38 |
+
NEG_PROMPT = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer difits, cropped, worst quality, low quality, deformed body, bloated, ugly, unrealistic"
|
39 |
+
|
40 |
+
|
41 |
+
class Predictor(BasePredictor):
|
42 |
+
def setup(self):
|
43 |
+
"""Load the model into memory to make running multiple predictions efficient"""
|
44 |
+
|
45 |
+
self.tokenizer = CLIPTokenizer.from_pretrained(sd_path, subfolder="tokenizer")
|
46 |
+
self.text_encoder = CLIPTextModel.from_pretrained(
|
47 |
+
sd_path, subfolder="text_encoder"
|
48 |
+
).to(dtype=torch.float16)
|
49 |
+
self.vae = AutoencoderKL.from_pretrained(sd_path, subfolder="vae").to(
|
50 |
+
dtype=torch.float16
|
51 |
+
)
|
52 |
+
self.unet = UNet3DConditionModel.from_pretrained_2d(
|
53 |
+
sd_path, subfolder="unet"
|
54 |
+
).to(dtype=torch.float16)
|
55 |
+
self.interpolater = IFNet(ckpt_path=inter_path).to(dtype=torch.float16)
|
56 |
+
self.scheduler = DDIMScheduler.from_pretrained(sd_path, subfolder="scheduler")
|
57 |
+
self.controlnet = {
|
58 |
+
k: ControlNetModel3D.from_pretrained_2d(controlnet_dict[k]).to(
|
59 |
+
dtype=torch.float16
|
60 |
+
)
|
61 |
+
for k in ["depth", "canny", "pose"]
|
62 |
+
}
|
63 |
+
self.annotator = {k: controlnet_parser_dict[k]() for k in ["depth", "canny"]}
|
64 |
+
self.annotator["pose"] = controlnet_parser_dict["pose"].from_pretrained(
|
65 |
+
"lllyasviel/ControlNet", cache_dir="checkpoints"
|
66 |
+
)
|
67 |
+
|
68 |
+
def predict(
|
69 |
+
self,
|
70 |
+
prompt: str = Input(
|
71 |
+
description="Text description of target video",
|
72 |
+
default="A striking mallard floats effortlessly on the sparkling pond.",
|
73 |
+
),
|
74 |
+
video_path: Path = Input(description="source video"),
|
75 |
+
condition: str = Input(
|
76 |
+
default="depth",
|
77 |
+
choices=["depth", "canny", "pose"],
|
78 |
+
description="Condition of structure sequence",
|
79 |
+
),
|
80 |
+
video_length: int = Input(
|
81 |
+
default=15, description="Length of synthesized video"
|
82 |
+
),
|
83 |
+
smoother_steps: str = Input(
|
84 |
+
default="19, 20",
|
85 |
+
description="Timesteps at which using interleaved-frame smoother, separate with comma",
|
86 |
+
),
|
87 |
+
is_long_video: bool = Input(
|
88 |
+
default=False,
|
89 |
+
description="Whether to use hierarchical sampler to produce long video",
|
90 |
+
),
|
91 |
+
num_inference_steps: int = Input(
|
92 |
+
description="Number of denoising steps", default=50
|
93 |
+
),
|
94 |
+
guidance_scale: float = Input(
|
95 |
+
description="Scale for classifier-free guidance", ge=1, le=20, default=12.5
|
96 |
+
),
|
97 |
+
seed: str = Input(
|
98 |
+
default=None, description="Random seed. Leave blank to randomize the seed"
|
99 |
+
),
|
100 |
+
) -> Path:
|
101 |
+
"""Run a single prediction on the model"""
|
102 |
+
if seed is None:
|
103 |
+
seed = int.from_bytes(os.urandom(2), "big")
|
104 |
+
else:
|
105 |
+
seed = int(seed)
|
106 |
+
print(f"Using seed: {seed}")
|
107 |
+
|
108 |
+
generator = torch.Generator(device="cuda")
|
109 |
+
generator.manual_seed(seed)
|
110 |
+
|
111 |
+
pipe = ControlVideoPipeline(
|
112 |
+
vae=self.vae,
|
113 |
+
text_encoder=self.text_encoder,
|
114 |
+
tokenizer=self.tokenizer,
|
115 |
+
unet=self.unet,
|
116 |
+
controlnet=self.controlnet[condition],
|
117 |
+
interpolater=self.interpolater,
|
118 |
+
scheduler=self.scheduler,
|
119 |
+
)
|
120 |
+
|
121 |
+
pipe.enable_vae_slicing()
|
122 |
+
pipe.enable_xformers_memory_efficient_attention()
|
123 |
+
pipe.to("cuda")
|
124 |
+
|
125 |
+
# Step 1. Read a video
|
126 |
+
video = read_video(video_path=str(video_path), video_length=video_length)
|
127 |
+
|
128 |
+
# Step 2. Parse a video to conditional frames
|
129 |
+
pil_annotation = get_annotation(video, self.annotator[condition])
|
130 |
+
|
131 |
+
# Step 3. inference
|
132 |
+
smoother_steps = [int(s) for s in smoother_steps.split(",")]
|
133 |
+
|
134 |
+
if is_long_video:
|
135 |
+
window_size = int(np.sqrt(video_length))
|
136 |
+
sample = pipe.generate_long_video(
|
137 |
+
prompt + POS_PROMPT,
|
138 |
+
video_length=video_length,
|
139 |
+
frames=pil_annotation,
|
140 |
+
num_inference_steps=num_inference_steps,
|
141 |
+
smooth_steps=smoother_steps,
|
142 |
+
window_size=window_size,
|
143 |
+
generator=generator,
|
144 |
+
guidance_scale=guidance_scale,
|
145 |
+
negative_prompt=NEG_PROMPT,
|
146 |
+
).videos
|
147 |
+
else:
|
148 |
+
sample = pipe(
|
149 |
+
prompt + POS_PROMPT,
|
150 |
+
video_length=video_length,
|
151 |
+
frames=pil_annotation,
|
152 |
+
num_inference_steps=num_inference_steps,
|
153 |
+
smooth_steps=smoother_steps,
|
154 |
+
generator=generator,
|
155 |
+
guidance_scale=guidance_scale,
|
156 |
+
negative_prompt=NEG_PROMPT,
|
157 |
+
).videos
|
158 |
+
|
159 |
+
out_path = "/tmp/out.mp4"
|
160 |
+
save_videos_grid(sample, out_path)
|
161 |
+
del pipe
|
162 |
+
torch.cuda.empty_cache()
|
163 |
+
|
164 |
+
return Path(out_path)
|
requirements.txt
ADDED
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
accelerate=0.17.1
|
2 |
+
addict=2.4.0
|
3 |
+
basicsr=1.4.2
|
4 |
+
bitsandbytes=0.35.4
|
5 |
+
clip=1.0
|
6 |
+
cmake=3.25.2
|
7 |
+
controlnet-aux=0.0.4
|
8 |
+
decord=0.6.0
|
9 |
+
deepspeed=0.8.0
|
10 |
+
diffusers=0.14.0
|
11 |
+
easydict=1.10
|
12 |
+
einops=0.6.0
|
13 |
+
ffmpy=0.3.0
|
14 |
+
ftfy=6.1.1
|
15 |
+
imageio=2.25.1
|
16 |
+
imageio-ffmpeg=0.4.8
|
17 |
+
moviepy=1.0.3
|
18 |
+
numpy=1.24.2
|
19 |
+
omegaconf=2.3.0
|
20 |
+
opencv-python=4.7.0.68
|
21 |
+
pandas=1.5.3
|
22 |
+
pillow=9.4.0
|
23 |
+
scikit-image=0.19.3
|
24 |
+
scipy=1.10.1
|
25 |
+
tensorboard=2.12.0
|
26 |
+
tensorboard-data-server=0.7.0
|
27 |
+
tensorboard-plugin-wit=1.8.1
|
28 |
+
termcolor=2.2.0
|
29 |
+
thinc=8.1.10
|
30 |
+
timm=0.6.12
|
31 |
+
tokenizers=0.13.2
|
32 |
+
torch=1.13.1+cu116
|
33 |
+
torchvision=0.14.1+cu116
|
34 |
+
tqdm=4.64.1
|
35 |
+
transformers=4.26.1
|
36 |
+
wandb=0.13.10
|
37 |
+
xformers=0.0.16
|
38 |
+
modelcards
|