-
Notifications
You must be signed in to change notification settings - Fork 34
/
tuning-free-control.py
190 lines (166 loc) · 7.83 KB
/
tuning-free-control.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import argparse
import copy
import logging
import inspect
import os
from typing import Dict, Optional
from omegaconf import OmegaConf
import torch
import torch.utils.checkpoint
import diffusers
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from glv.models_wota.unet import UNet3DConditionModel
from glv.models_wota.controlnet import ControlNetModel
from glv.data.dataset import GLVDataset
from glv.pipelines.pipeline_tuning_free_control import TuningFreeControlPipeline
from glv.util import ddim_inversion_long, save_videos_grid, ddim_inversion
from einops import rearrange
check_min_version("0.10.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def main(
pretrained_model_path: str,
output_dir: str,
train_data: Dict,
validation_data: Dict,
train_batch_size: int = 1,
mixed_precision: Optional[str] = "fp16",
enable_xformers_memory_efficient_attention: bool = True,
seed: Optional[int] = None,
controlnet_path = None,
controlnet_scale = 1.0,
):
*_, config = inspect.getargvalues(inspect.currentframe())
accelerator = Accelerator(
mixed_precision=mixed_precision,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if seed is not None:
set_seed(seed)
# Handle the output folder creation
if accelerator.is_main_process:
os.makedirs(output_dir, exist_ok=True)
os.makedirs(f"{output_dir}/samples", exist_ok=True)
os.makedirs(f"{output_dir}/inv_latents", exist_ok=True)
OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
# Load scheduler, tokenizer and models.
noise_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet")
if controlnet_path is not None:
controlnet = ControlNetModel.from_pretrained_2d(controlnet_path)
else:
controlnet = None
unet.controlnet = controlnet
unet.controlnet_scale = controlnet_scale
# Freeze vae and text_encoder
vae.requires_grad_(False)
vae.enable_slicing()
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
if controlnet is not None:
controlnet.requires_grad_(False)
if enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Get the training dataset
train_dataset = GLVDataset(**train_data)
# Preprocessing the dataset
train_dataset.prompt_ids = tokenizer(
train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
).input_ids[0]
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_batch_size
)
# Get the validation pipeline
validation_pipeline = TuningFreeControlPipeline(
vae=vae, text_encoder=text_encoder, unet=unet, tokenizer=tokenizer,
scheduler=noise_scheduler
)
validation_pipeline.enable_vae_slicing()
ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
# Prepare everything with our `accelerator`.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
unet.to(accelerator.device,dtype=weight_dtype)
if controlnet is not None:
controlnet.to(accelerator.device, dtype=weight_dtype)
# unet = accelerator.prepare(unet)
if accelerator.is_main_process:
accelerator.init_trackers("tuning-free t2v")
if accelerator.is_main_process:
for step, batch in enumerate(train_dataloader):
logger.info("inference pixel values")
pixel_values = batch["full_video"].to(accelerator.device,weight_dtype)[0].unsqueeze(0)
video_length = pixel_values.shape[1]
video_length = video_length - video_length % validation_data.video_length
pixel_values = pixel_values[:,:video_length]
pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
latents = [ ]
for i in range(0,video_length,validation_data.video_length):
latents.append( vae.encode(pixel_values[i:i+validation_data.video_length]).latent_dist.sample())
latents = torch.cat(latents,dim=0)
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
latents = latents * 0.18215
pixel_values = pixel_values
generator = torch.Generator(device=accelerator.device)
generator.manual_seed(seed)
clip_length = validation_data.video_length
samples = []
control = batch.get("full_control_video")
if control is not None:
control= rearrange(control, "b f c h w -> b c f h w")
control = control[:,:,:video_length]
control = control.to(accelerator.device,weight_dtype)
for idx, prompt in enumerate(validation_data.prompts):
with torch.autocast("cuda"):
validation_multidata = copy.deepcopy(validation_data)
validation_multidata.video_length = video_length
sample = validation_pipeline.gen_long(prompt,latents, generator=generator,window_size=validation_data.video_length,control=control,
**validation_multidata).videos
save_videos_grid(sample, f"{output_dir}/samples/sample/{idx}-{prompt[:32]}.gif")
samples.append(sample)
samples = torch.concat(samples)
save_path = f"{output_dir}/samples/sample.gif"
save_videos_grid(samples, save_path)
logger.info(f"Saved samples to {save_path}")
break
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/tuning-free-control/girl-glass.yaml")
args = parser.parse_args()
main(**OmegaConf.load(args.config))
max_memory_allocated = torch.cuda.max_memory_allocated() / (1024 ** 3)
print(f"max memory allocated: {max_memory_allocated:.3f} GB.")