forked from guoyww/AnimateDiff
-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.py
328 lines (273 loc) · 14.6 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
import os
import json
import torch
import random
import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from datetime import datetime
from safetensors import safe_open
from diffusers import AutoencoderKL
from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
sample_idx = 0
scheduler_dict = {
"Euler": EulerDiscreteScheduler,
"PNDM": PNDMScheduler,
"DDIM": DDIMScheduler,
}
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(self.basedir, "samples", 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.stable_diffusion_list = []
self.motion_module_list = []
self.personalized_model_list = []
self.refresh_stable_diffusion()
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.tokenizer = None
self.text_encoder = None
self.vae = None
self.unet = None
self.pipeline = None
self.lora_model_state_dict = {}
self.inference_config = OmegaConf.load("configs/inference/inference.yaml")
def refresh_stable_diffusion(self):
self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/"))
def refresh_motion_module(self):
motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
self.motion_module_list = [os.path.basename(p) for p in motion_module_list]
def refresh_personalized_model(self):
personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
def update_stable_diffusion(self, stable_diffusion_dropdown):
self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda()
self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
return gr.Dropdown.update()
def update_motion_module(self, motion_module_dropdown):
if self.unet is None:
gr.Info(f"Please select a pretrained model path.")
return gr.Dropdown.update(value=None)
else:
motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
assert len(unexpected) == 0
return gr.Dropdown.update()
def update_base_model(self, base_model_dropdown):
if self.unet is None:
gr.Info(f"Please select a pretrained model path.")
return gr.Dropdown.update(value=None)
else:
base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
base_model_state_dict = {}
with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys():
base_model_state_dict[key] = f.get_tensor(key)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)
self.vae.load_state_dict(converted_vae_checkpoint)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
return gr.Dropdown.update()
def update_lora_model(self, lora_model_dropdown):
lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
self.lora_model_state_dict = {}
if lora_model_dropdown == "none": pass
else:
with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys():
self.lora_model_state_dict[key] = f.get_tensor(key)
return gr.Dropdown.update()
def animate(
self,
stable_diffusion_dropdown,
motion_module_dropdown,
base_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox
):
if self.unet is None:
raise gr.Error(f"Please select a pretrained model path.")
if motion_module_dropdown == "":
raise gr.Error(f"Please select a motion module.")
if base_model_dropdown == "":
raise gr.Error(f"Please select a base DreamBooth model.")
if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
pipeline = AnimationPipeline(
vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
).to("cuda")
if self.lora_model_state_dict != {}:
pipeline = convert_lora(pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider)
pipeline.to("cuda")
if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
else: torch.seed()
seed = torch.initial_seed()
sample = pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = sample_step_slider,
guidance_scale = cfg_scale_slider,
width = width_slider,
height = height_slider,
video_length = length_slider,
).videos
save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4")
save_videos_grid(sample, save_sample_path)
sample_config = {
"prompt": prompt_textbox,
"n_prompt": negative_prompt_textbox,
"sampler": sampler_dropdown,
"num_inference_steps": sample_step_slider,
"guidance_scale": cfg_scale_slider,
"width": width_slider,
"height": height_slider,
"video_length": length_slider,
"seed": seed
}
json_str = json.dumps(sample_config, indent=4)
with open(os.path.join(self.savedir, "logs.json"), "a") as f:
f.write(json_str)
f.write("\n\n")
return gr.Video.update(value=save_sample_path)
controller = AnimateController()
def ui():
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)
Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>
[Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
"""
)
with gr.Column(variant="panel"):
gr.Markdown(
"""
### 1. Model checkpoints (select pretrained model path first).
"""
)
with gr.Row():
stable_diffusion_dropdown = gr.Dropdown(
label="Pretrained Model Path",
choices=controller.stable_diffusion_list,
interactive=True,
)
stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown])
stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
def update_stable_diffusion():
controller.refresh_stable_diffusion()
return gr.Dropdown.update(choices=controller.stable_diffusion_list)
stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown])
with gr.Row():
motion_module_dropdown = gr.Dropdown(
label="Select motion module",
choices=controller.motion_module_list,
interactive=True,
)
motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
def update_motion_module():
controller.refresh_motion_module()
return gr.Dropdown.update(choices=controller.motion_module_list)
motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown])
base_model_dropdown = gr.Dropdown(
label="Select base Dreambooth model (required)",
choices=controller.personalized_model_list,
interactive=True,
)
base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])
lora_model_dropdown = gr.Dropdown(
label="Select LoRA model (optional)",
choices=["none"] + controller.personalized_model_list,
value="none",
interactive=True,
)
lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown])
lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.8, minimum=0, maximum=2, interactive=True)
personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
def update_personalized_model():
controller.refresh_personalized_model()
return [
gr.Dropdown.update(choices=controller.personalized_model_list),
gr.Dropdown.update(choices=["none"] + controller.personalized_model_list)
]
personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown])
with gr.Column(variant="panel"):
gr.Markdown(
"""
### 2. Configs for AnimateDiff.
"""
)
prompt_textbox = gr.Textbox(label="Prompt", lines=2)
negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)
with gr.Row().style(equal_height=False):
with gr.Column():
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)
width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64)
height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64)
length_slider = gr.Slider(label="Animation length", value=16, minimum=8, maximum=24, 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=-1)
seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])
generate_button = gr.Button(value="Generate", variant='primary')
result_video = gr.Video(label="Generated Animation", interactive=False)
generate_button.click(
fn=controller.animate,
inputs=[
stable_diffusion_dropdown,
motion_module_dropdown,
base_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox,
],
outputs=[result_video]
)
return demo
if __name__ == "__main__":
demo = ui()
demo.launch(share=True)