File size: 24,100 Bytes
069c5f0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f63f07e
069c5f0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f63f07e
 
 
069c5f0
 
 
 
 
 
 
 
f63f07e
 
069c5f0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f63f07e
069c5f0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
104ba8e
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
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
import os
import torch
import random

import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from safetensors import safe_open

from diffusers import AutoencoderKL
from diffusers import EulerDiscreteScheduler, DDIMScheduler
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 AnimationFreeInitPipeline
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 diffusers.training_utils import set_seed

from animatediff.utils.freeinit_utils import get_freq_filter
from collections import namedtuple

pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"
inference_config_path = "configs/inference/inference-v1.yaml"

css = """
.toolbutton {
    margin-buttom: 0em 0em 0em 0em;
    max-width: 2.5em;
    min-width: 2.5em !important;
    height: 2.5em;
}
"""

examples = [
    # 1-ToonYou
    [
        "toonyou_beta3.safetensors", 
        "mm_sd_v14.ckpt", 
        "(best quality, masterpiece), close up, 1girl, red clothes, sitting, elf, pond, in water, deep forest, waterfall, looking away, blurry background",
        "worst quality, low quality, nsfw, logo",
        512, 512, "1566149281915957",
        "butterworth", 0.25, 0.25, 3,
        ["use_fp16"]
    ],
    # 2-Lyriel
    [
        "lyriel_v16.safetensors", 
        "mm_sd_v14.ckpt", 
        "hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space",
        "3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo",
        512, 512, "4954488479039740",
        "butterworth", 0.25, 0.25, 3,
        ["use_fp16"]
    ],
    # 3-RCNZ
    [
        "rcnzCartoon3d_v10.safetensors", 
        "mm_sd_v14.ckpt", 
        "A cute raccoon playing guitar in a boat on the ocean",
        "worst quality, low quality, nsfw, logo",
        512, 512, "2005563494988190",
        "butterworth", 0.25, 0.25, 3,
        ["use_fp16"]
    ],
    # 4-MajicMix
    [
        "majicmixRealistic_v5Preview.safetensors", 
        "mm_sd_v14.ckpt", 
        "1girl, reading book",
        "bad hand, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles",
        512, 512, "2005563494988190",
        "butterworth", 0.25, 0.25, 3,
        ["use_fp16"]
    ],
    # # 5-RealisticVision
    # [
    #     "realisticVisionV51_v20Novae.safetensors", 
    #     "mm_sd_v14.ckpt", 
    #     "A panda standing on a surfboard in the ocean in sunset.",
    #     "worst quality, low quality, nsfw, logo",
    #     512, 512, "2005563494988190",
    #     "butterworth", 0.25, 0.25, 3,
    #     ["use_fp16"]
    # ]
    # 5-RealisticVision
    [
        "realisticVisionV51_v20Novae.safetensors", 
        "mm_sd_v14.ckpt", 
        "b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
        "(semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
        512, 512, "1566149281915957",
        "butterworth", 0.25, 0.25, 3,
        ["use_fp16"]
    ]
]

# clean unrelated ckpts
# ckpts = [
#     "realisticVisionV40_v20Novae.safetensors",
#     "majicmixRealistic_v5Preview.safetensors",
#     "rcnzCartoon3d_v10.safetensors",
#     "lyriel_v16.safetensors",
#     "toonyou_beta3.safetensors"
# ]

# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
#     for ckpt in ckpts:
#         if path.endswith(ckpt): break
#     else:
#         print(f"### Cleaning {path} ...")
#         os.system(f"rm -rf {path}")

# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")

# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")

# clean Gradio cache
print(f"### Cleaning cached examples ...")
os.system(f"rm -rf gradio_cached_examples/")


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")
        os.makedirs(self.savedir, exist_ok=True)

        self.base_model_list    = []
        self.motion_module_list = []
        self.filter_type_list = [
            "butterworth",
            "gaussian",
            "box",
            "ideal"
        ]
        
        self.selected_base_model    = None
        self.selected_motion_module = None
        self.selected_filter_type = None
        self.set_width = None
        self.set_height = None
        self.set_d_s = None
        self.set_d_t = None
        
        self.refresh_motion_module()
        self.refresh_personalized_model()
        
        # config models
        self.inference_config      = OmegaConf.load(inference_config_path)

        self.tokenizer             = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
        self.text_encoder          = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
        self.vae                   = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
        self.unet                  = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()

        self.freq_filter = None

        self.update_base_model(self.base_model_list[-2])
        self.update_motion_module(self.motion_module_list[0])
        self.update_filter(512, 512, self.filter_type_list[0], 0.25, 0.25)
        
        
    def refresh_motion_module(self):
        motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
        self.motion_module_list = sorted([os.path.basename(p) for p in motion_module_list])

    def refresh_personalized_model(self):
        base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
        self.base_model_list = sorted([os.path.basename(p) for p in base_model_list])


    def update_base_model(self, base_model_dropdown):
        self.selected_base_model = base_model_dropdown
        
        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_motion_module(self, motion_module_dropdown):
        self.selected_motion_module = motion_module_dropdown
        
        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")
        _, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
        assert len(unexpected) == 0
        return gr.Dropdown.update()
    
    # def update_filter(self, shape, method, n, d_s, d_t):
    def update_filter(self, width_slider, height_slider, filter_type_dropdown, d_s_slider, d_t_slider):
        self.set_width = width_slider
        self.set_height = height_slider
        self.selected_filter_type = filter_type_dropdown
        self.set_d_s = d_s_slider
        self.set_d_t = d_t_slider

        vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

        shape = [1, 4, 16, self.set_width//vae_scale_factor, self.set_height//vae_scale_factor]
        self.freq_filter = get_freq_filter(
            shape, 
            device="cuda", 
            filter_type=self.selected_filter_type,
            n=4,
            d_s=self.set_d_s,
            d_t=self.set_d_t
        )

    def animate(
        self,
        base_model_dropdown,
        motion_module_dropdown,
        prompt_textbox,
        negative_prompt_textbox,
        width_slider,
        height_slider,
        seed_textbox,
        # freeinit params
        filter_type_dropdown,
        d_s_slider,
        d_t_slider,
        num_iters_slider,
        # speed up
        speed_up_options
    ):
        # set global seed
        set_seed(42)

        d_s = float(d_s_slider)
        d_t = float(d_t_slider)
        num_iters = int(num_iters_slider)


        if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
        if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)
        
        self.set_width = width_slider
        self.set_height = height_slider
        self.selected_filter_type = filter_type_dropdown
        self.set_d_s = d_s
        self.set_d_t = d_t
        if self.set_width != width_slider or self.set_height != height_slider or self.selected_filter_type != filter_type_dropdown or self.set_d_s != d_s or self.set_d_t != d_t:
            self.update_filter(width_slider, height_slider, filter_type_dropdown, d_s, d_t)
        
        if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()

        pipeline = AnimationFreeInitPipeline(
            vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
            scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
            ).to("cuda")
        
        # (freeinit) initialize frequency filter for noise reinitialization -------------
        pipeline.freq_filter = self.freq_filter
        # -------------------------------------------------------------------------------

        
        if int(seed_textbox) > 0: seed = int(seed_textbox)
        else: seed = random.randint(1, 1e16)
        torch.manual_seed(int(seed))
        
        assert seed == torch.initial_seed()
        print(f"### seed: {seed}")
        
        generator = torch.Generator(device="cuda")
        generator.manual_seed(seed)
               
        sample_output = pipeline(
            prompt_textbox,
            negative_prompt     = negative_prompt_textbox,
            num_inference_steps = 25,
            guidance_scale      = 7.5,
            width               = width_slider,
            height              = height_slider,
            video_length        = 16,
            num_iters           = num_iters,
            use_fast_sampling   = True if "use_coarse_to_fine_sampling" in speed_up_options else False,
            save_intermediate   = False,
            return_orig         = True,
            use_fp16            = True if "use_fp16" in speed_up_options else False
        )
        orig_sample = sample_output.orig_videos
        sample = sample_output.videos

        save_sample_path = os.path.join(self.savedir, f"sample.mp4")
        save_videos_grid(sample, save_sample_path)

        save_orig_sample_path = os.path.join(self.savedir, f"sample_orig.mp4")
        save_videos_grid(orig_sample, save_orig_sample_path)

        # save_compare_path = os.path.join(self.savedir, f"compare.mp4")
        # save_videos_grid(torch.concat([orig_sample, sample]), save_compare_path)
    
        json_config = {
            "prompt": prompt_textbox,
            "n_prompt": negative_prompt_textbox,
            "width": width_slider,
            "height": height_slider,
            "seed": seed,
            "base_model": base_model_dropdown,
            "motion_module": motion_module_dropdown,
            "filter_type": filter_type_dropdown,
            "d_s": d_s,
            "d_t": d_t,
            "num_iters": num_iters,
            "use_fp16": True if "use_fp16" in speed_up_options else False,
            "use_coarse_to_fine_sampling": True if "use_coarse_to_fine_sampling" in speed_up_options else False
        }

        # return gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config)
        # return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config)
        return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config)
        

controller = AnimateController()


def ui():
    with gr.Blocks(css=css) as demo:
        # gr.Markdown('# FreeInit')
        gr.Markdown(
            """
            <div align="center">
            <h1>FreeInit</h1>
            </div>
            """
        )
        gr.Markdown(
            """
            <p align="center">
                    <a title="Project Page" href="https://tianxingwu.github.io/pages/FreeInit/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                        <img src="https://img.shields.io/badge/Project-Website-5B7493?logo=googlechrome&logoColor=5B7493">
                    </a>
                    <a title="arXiv" href="https://arxiv.org/abs/2312.07537" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                        <img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=b31b1b">
                    </a>
                    <a title="GitHub" href="https://github.com/TianxingWu/FreeInit" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                        <img src="https://img.shields.io/github/stars/TianxingWu/FreeInit?label=GitHub%20%E2%98%85&&logo=github" alt="badge-github-stars">
                    </a>
                    <a title="Video" href="https://youtu.be/lS5IYbAqriI" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                        <img src="https://img.shields.io/badge/YouTube-Video-red?logo=youtube&logoColor=red">
                    </a>
                    <a title="Visitor" href="https://hits.seeyoufarm.com" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                        <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTianxingWu%2FFreeInit&count_bg=%23678F74&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false">
                    </a>
            </p>
            """
            # <a title="Visitor" href="https://hits.seeyoufarm.com" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
            #     <img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fhuggingface.co%2Fspaces%2FTianxingWu%2FFreeInit&count_bg=%23678F74&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false">
            # </a>
        )
        gr.Markdown(
            """
            Official Gradio Demo for ***FreeInit: Bridging Initialization Gap in Video Diffusion Models***.
            FreeInit improves time consistency of diffusion-based video generation at inference time. In this demo, we apply FreeInit on [AnimateDiff v1](https://github.com/guoyww/AnimateDiff) as an example.<br>
            """
        )

        with gr.Row():
            with gr.Column():
                # gr.Markdown(
                #     """
                #     ### Usage
                #     1. Select customized model and motion module in `Model Settings`.
                #     3. Set `FreeInit Settings`.
                #     3. Provide `Prompt` and `Negative Prompt` for your selected model. You can refer to each model's webpage on CivitAI to learn how to write prompts for them:
                #         - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)
                #         - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)
                #         - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)
                #         - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)
                #         - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)
                #     4. Click `Generate`.
                #     """
                # )
                prompt_textbox          = gr.Textbox( label="Prompt",          lines=3, placeholder="Enter your prompt here")
                negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo")

                gr.Markdown(
                    """
                    *Prompt Tips:*

                    For each personalized model in `Model Settings`, you can refer to their webpage on CivitAI to learn how to write good prompts for them:
                    - [`realisticVisionV51_v20Novae.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)
                    - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)
                    - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)
                    - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)
                    - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)   
                    """
                )
                
                with gr.Accordion("Model Settings", open=False):
                    gr.Markdown(
                        """
                        Select personalized model and motion module for AnimateDiff.
                        """
                        )
                    base_model_dropdown     = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list,    value=controller.base_model_list[-2],    interactive=True,
                                                          info="Select personalized text-to-image model from community")
                    motion_module_dropdown  = gr.Dropdown( label="Motion Module",  choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True,
                                                          info="Select motion module. Recommend mm_sd_v14.ckpt for larger movements.")
                
                base_model_dropdown.change(fn=controller.update_base_model,       inputs=[base_model_dropdown],    outputs=[base_model_dropdown])
                motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
                
                with gr.Accordion("FreeInit Params", open=False):
                    gr.Markdown(
                        """
                        Adjust to control the smoothness.
                        """
                    )
                    filter_type_dropdown    = gr.Dropdown( label="Filter Type",  choices=controller.filter_type_list, value=controller.filter_type_list[0], interactive=True, 
                                                          info="Default as Butterworth. To fix large inconsistencies, consider using Gaussian.")
                    d_s_slider             = gr.Slider( label="d_s",  value=0.25, minimum=0, maximum=1, step=0.125, 
                                                       info="Stop frequency for spatial dimensions (0.0-1.0)")
                    d_t_slider             = gr.Slider( label="d_t",  value=0.25, minimum=0, maximum=1, step=0.125, 
                                                       info="Stop frequency for temporal dimension (0.0-1.0)")
                    # num_iters_textbox       = gr.Textbox( label="FreeInit Iterations", value=3, info="Sould be integer >1, larger value leads to smoother results)")
                    num_iters_slider        = gr.Slider( label="FreeInit Iterations", value=3, minimum=2, maximum=5, step=1,
                                                        info="Larger value leads to smoother results & longer inference time.")

                with gr.Accordion("Advance", open=False):
                    with gr.Row():
                        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 )
                    with gr.Row():
                        seed_textbox = gr.Textbox( label="Seed",  value=1566149281915957)
                        seed_button  = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
                        seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox])
                    with gr.Row():
                        speed_up_options = gr.CheckboxGroup(
                            ["use_fp16", "use_coarse_to_fine_sampling"],
                            label="Speed-Up Options",
                            value=["use_fp16"]
                        )


                generate_button = gr.Button( value="Generate", variant='primary' )


            # with gr.Column():
            #     result_video = gr.Video( label="Generated Animation", interactive=False )
            #     json_config  = gr.Json( label="Config", value=None )
            with gr.Column():
                with gr.Row():
                    orig_video = gr.Video( label="AnimateDiff", interactive=False )
                    freeinit_video = gr.Video( label="AnimateDiff + FreeInit", interactive=False )
                # with gr.Row():
                #     compare_video = gr.Video( label="Compare", interactive=False )
                with gr.Row():
                    json_config  = gr.Json( label="Config", value=None )

            inputs  = [base_model_dropdown, motion_module_dropdown, 
                       prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox,
                       filter_type_dropdown, d_s_slider, d_t_slider, num_iters_slider,
                       speed_up_options
                       ]
            # outputs = [result_video, json_config]
            # outputs = [orig_video, freeinit_video, compare_video, json_config]
            outputs = [orig_video, freeinit_video, json_config]
            
            generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs )
                
        gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True)

    return demo


if __name__ == "__main__":
    demo = ui()
    demo.queue(max_size=20)
    demo.launch()