-
Notifications
You must be signed in to change notification settings - Fork 39
/
app.py
338 lines (292 loc) · 13.5 KB
/
app.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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
import os
import os.path as osp
import random
from argparse import ArgumentParser
from datetime import datetime
import gradio as gr
import soundfile as sf
import torch
import torchvision
from huggingface_hub import snapshot_download
from moviepy.editor import AudioFileClip, VideoFileClip
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler
from foleycrafter.models.onset import torch_utils
from foleycrafter.models.time_detector.model import VideoOnsetNet
from foleycrafter.pipelines.auffusion_pipeline import Generator, denormalize_spectrogram
from foleycrafter.utils.util import build_foleycrafter, read_frames_with_moviepy
os.environ["GRADIO_TEMP_DIR"] = "./tmp"
sample_idx = 0
scheduler_dict = {
"DDIM": DDIMScheduler,
"Euler": EulerDiscreteScheduler,
"PNDM": PNDMScheduler,
}
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
parser = ArgumentParser()
parser.add_argument("--config", type=str, default="example/config/base.yaml")
parser.add_argument("--server-name", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int, default=7860)
parser.add_argument("--share", type=bool, default=False)
parser.add_argument("--save-path", default="samples")
parser.add_argument("--ckpt", type=str, default="checkpoints/")
args = parser.parse_args()
N_PROMPT = ""
class FoleyController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.model_dir = os.path.join(self.basedir, args.ckpt)
self.savedir = os.path.join(self.basedir, args.save_path, datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
self.savedir_sample = os.path.join(self.savedir, "sample")
os.makedirs(self.savedir, exist_ok=True)
self.pipeline = None
self.loaded = False
self.load_model()
def load_model(self):
gr.Info("Start Load Models...")
print("Start Load Models...")
# download ckpt
pretrained_model_name_or_path = "auffusion/auffusion-full-no-adapter"
if not os.path.isdir(pretrained_model_name_or_path):
pretrained_model_name_or_path = snapshot_download(
pretrained_model_name_or_path, local_dir=osp.join(self.model_dir, "auffusion")
)
fc_ckpt = "ymzhang319/FoleyCrafter"
if not os.path.isdir(fc_ckpt):
fc_ckpt = snapshot_download(fc_ckpt, local_dir=self.model_dir)
# set model config
temporal_ckpt_path = osp.join(self.model_dir, "temporal_adapter.ckpt")
# load vocoder
vocoder_config_path = osp.join(self.model_dir, "auffusion")
self.vocoder = Generator.from_pretrained(vocoder_config_path, subfolder="vocoder")
# load time detector
time_detector_ckpt = osp.join(osp.join(self.model_dir, "timestamp_detector.pth.tar"))
time_detector = VideoOnsetNet(False)
self.time_detector, _ = torch_utils.load_model(time_detector_ckpt, time_detector, strict=True)
self.pipeline = build_foleycrafter()
ckpt = torch.load(temporal_ckpt_path)
# load temporal adapter
if "state_dict" in ckpt.keys():
ckpt = ckpt["state_dict"]
load_gligen_ckpt = {}
for key, value in ckpt.items():
if key.startswith("module."):
load_gligen_ckpt[key[len("module.") :]] = value
else:
load_gligen_ckpt[key] = value
m, u = self.pipeline.controlnet.load_state_dict(load_gligen_ckpt, strict=False)
print(f"### Control Net missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
self.image_processor = CLIPImageProcessor()
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter", subfolder="models/image_encoder"
)
self.pipeline.load_ip_adapter(
fc_ckpt, subfolder="semantic", weight_name="semantic_adapter.bin", image_encoder_folder=None
)
gr.Info("Load Finish!")
print("Load Finish!")
self.loaded = True
return "Load"
def foley(
self,
input_video,
prompt_textbox,
negative_prompt_textbox,
ip_adapter_scale,
temporal_scale,
sampler_dropdown,
sample_step_slider,
cfg_scale_slider,
seed_textbox,
):
device = "cuda"
# move to gpu
self.time_detector = controller.time_detector.to(device)
self.pipeline = controller.pipeline.to(device)
self.vocoder = controller.vocoder.to(device)
self.image_encoder = controller.image_encoder.to(device)
vision_transform_list = [
torchvision.transforms.Resize((128, 128)),
torchvision.transforms.CenterCrop((112, 112)),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
video_transform = torchvision.transforms.Compose(vision_transform_list)
# if not self.loaded:
# raise gr.Error("Error with loading model")
generator = torch.Generator()
if seed_textbox != "":
torch.manual_seed(int(seed_textbox))
generator.manual_seed(int(seed_textbox))
max_frame_nums = 150
frames, duration = read_frames_with_moviepy(input_video, max_frame_nums=max_frame_nums)
if duration >= 10:
duration = 10
time_frames = torch.FloatTensor(frames).permute(0, 3, 1, 2).to(device)
time_frames = video_transform(time_frames)
time_frames = {"frames": time_frames.unsqueeze(0).permute(0, 2, 1, 3, 4)}
preds = self.time_detector(time_frames)
preds = torch.sigmoid(preds)
# duration
time_condition = [
-1 if preds[0][int(i / (1024 / 10 * duration) * max_frame_nums)] < 0.5 else 1
for i in range(int(1024 / 10 * duration))
]
time_condition = time_condition + [-1] * (1024 - len(time_condition))
# w -> b c h w
time_condition = torch.FloatTensor(time_condition).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(1, 1, 256, 1)
# Note that clip need fewer frames
frames = frames[::10]
images = self.image_processor(images=frames, return_tensors="pt").to(device)
image_embeddings = self.image_encoder(**images).image_embeds
image_embeddings = torch.mean(image_embeddings, dim=0, keepdim=True).unsqueeze(0).unsqueeze(0)
neg_image_embeddings = torch.zeros_like(image_embeddings)
image_embeddings = torch.cat([neg_image_embeddings, image_embeddings], dim=1)
self.pipeline.set_ip_adapter_scale(ip_adapter_scale)
sample = self.pipeline(
prompt=prompt_textbox,
negative_prompt=negative_prompt_textbox,
ip_adapter_image_embeds=image_embeddings,
image=time_condition,
controlnet_conditioning_scale=float(temporal_scale),
num_inference_steps=sample_step_slider,
height=256,
width=1024,
output_type="pt",
generator=generator,
)
name = "output"
audio_img = sample.images[0]
audio = denormalize_spectrogram(audio_img)
audio = self.vocoder.inference(audio, lengths=160000)[0]
audio_save_path = osp.join(self.savedir_sample, "audio")
os.makedirs(audio_save_path, exist_ok=True)
audio = audio[: int(duration * 16000)]
save_path = osp.join(audio_save_path, f"{name}.wav")
sf.write(save_path, audio, 16000)
audio = AudioFileClip(osp.join(audio_save_path, f"{name}.wav"))
video = VideoFileClip(input_video)
audio = audio.subclip(0, duration)
video.audio = audio
video = video.subclip(0, duration)
video.write_videofile(osp.join(self.savedir_sample, f"{name}.mp4"))
save_sample_path = os.path.join(self.savedir_sample, f"{name}.mp4")
return save_sample_path
controller = FoleyController()
device = "cuda" if torch.cuda.is_available() else "cpu"
with gr.Blocks(css=css) as demo:
gr.HTML(
'<h1 style="height: 136px; display: flex; align-items: center; justify-content: space-around;"><span style="height: 100%; width:136px;"><img src="file/assets/foleycrafter.png" alt="logo" style="height: 100%; width:auto; object-fit: contain; margin: 0px 0px; padding: 0px 0px;"></span><strong style="font-size: 36px;">FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds</strong></h1>'
)
gr.HTML(
'<p id="authors" style="text-align:center; font-size:24px;"> \
<a href="https://github.com/ymzhang0319">Yiming Zhang</a><sup>1</sup>,  \
<a href="https://github.com/VocodexElysium">Yicheng Gu</a><sup>2</sup>,  \
<a href="https://zengyh1900.github.io/">Yanhong Zeng</a><sup>1 †</sup>,  \
<a href="https://github.com/LeoXing1996/">Zhening Xing</a><sup>1</sup>,  \
<a href="https://github.com/HeCheng0625">Yuancheng Wang</a><sup>2</sup>,  \
<a href="https://drwuz.com/">Zhizheng Wu</a><sup>2</sup>,  \
<a href="https://chenkai.site/">Kai Chen</a><sup>1 †</sup>\
<br>\
<span>\
<sup>1</sup>Shanghai AI Laboratory \
<sup>2</sup>Chinese University of Hong Kong, Shenzhen \
†Corresponding author\
</span>\
</p>'
)
with gr.Row():
gr.Markdown(
"<div align='center'><font size='5'><a href='https://foleycrafter.github.io/'>Project Page</a>  " # noqa
"<a href='https://arxiv.org/abs/2407.01494/'>Paper</a>  "
"<a href='https://github.com/open-mmlab/foleycrafter'>Code</a>  "
"<a href='https://huggingface.co/spaces/ymzhang319/FoleyCrafter'>Demo</a> </font></div>"
)
with gr.Column(variant="panel"):
with gr.Row(equal_height=False):
with gr.Column():
with gr.Row():
init_img = gr.Video(label="Input Video")
with gr.Row():
prompt_textbox = gr.Textbox(value="", label="Prompt", lines=1)
with gr.Row():
negative_prompt_textbox = gr.Textbox(value=N_PROMPT, label="Negative prompt", lines=1)
with gr.Row():
ip_adapter_scale = gr.Slider(label="Visual Content Scale", value=1.0, minimum=0, maximum=1)
temporal_scale = gr.Slider(label="Temporal Align Scale", value=0.2, minimum=0.0, maximum=1.0)
with gr.Accordion("Sampling Settings", open=False):
with gr.Row():
sampler_dropdown = gr.Dropdown(
label="Sampling method",
choices=list(scheduler_dict.keys()),
value=list(scheduler_dict.keys())[0],
)
sample_step_slider = gr.Slider(
label="Sampling steps", value=25, minimum=10, maximum=100, step=1
)
cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20)
with gr.Row():
seed_textbox = gr.Textbox(label="Seed", value=42)
seed_button = gr.Button(value="\U0001f3b2", elem_classes="toolbutton")
seed_button.click(fn=lambda x: random.randint(1, 1e8), outputs=[seed_textbox], queue=False)
generate_button = gr.Button(value="Generate", variant="primary")
with gr.Column():
result_video = gr.Video(label="Generated Audio", interactive=False)
with gr.Row():
gr.Markdown(
"<div style='word-spacing: 6px;'><font size='5'><b>Tips</b>: <br> \
1. With strong temporal visual cues in input video, you can scale up the <b>Temporal Align Scale</b>. <br>\
2. <b>Visual content scale</b> is the level of semantic alignment with visual content.</font></div> \
"
)
generate_button.click(
fn=controller.foley,
inputs=[
init_img,
prompt_textbox,
negative_prompt_textbox,
ip_adapter_scale,
temporal_scale,
sampler_dropdown,
sample_step_slider,
cfg_scale_slider,
seed_textbox,
],
outputs=[result_video],
)
gr.Examples(
examples=[
["examples/gen3/case1.mp4", "", "", 1.0, 0.2, "DDIM", 25, 7.5, 33817921],
["examples/gen3/case3.mp4", "", "", 1.0, 0.2, "DDIM", 25, 7.5, 94667578],
["examples/gen3/case5.mp4", "", "", 0.75, 0.2, "DDIM", 25, 7.5, 92890876],
["examples/gen3/case6.mp4", "", "", 1.0, 0.2, "DDIM", 25, 7.5, 77015909],
],
inputs=[
init_img,
prompt_textbox,
negative_prompt_textbox,
ip_adapter_scale,
temporal_scale,
sampler_dropdown,
sample_step_slider,
cfg_scale_slider,
seed_textbox,
],
cache_examples=True,
outputs=[result_video],
fn=controller.foley,
)
demo.queue(10)
demo.launch(
server_name=args.server_name,
server_port=args.port,
share=args.share,
allowed_paths=["./assets/foleycrafter.png"],
)