raaraya/AnimateDiff
0
1 2 3# import os4# import json5# import torch6# import random7# import copy8 9# import gradio as gr10# from glob import glob11# from omegaconf import OmegaConf12# from datetime import datetime13# from safetensors import safe_open14 15# from diffusers import AutoencoderKL16# from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler17# from diffusers.utils.import_utils import is_xformers_available18# from transformers import CLIPTextModel, CLIPTokenizer19 20# from animatediff.models.unet import UNet3DConditionModel21# from animatediff.pipelines.pipeline_animation import AnimationPipeline22# from animatediff.utils.util import save_videos_grid23# from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint24# from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora25 26 27# sample_idx = 028# scheduler_dict = {29# "Euler": EulerDiscreteScheduler,30# "PNDM": PNDMScheduler,31# "DDIM": DDIMScheduler,32# }33 34# css = """35# .toolbutton {36# margin-buttom: 0em 0em 0em 0em;37# max-width: 2.5em;38# min-width: 2.5em !important;39# height: 2.5em;40# }41# """42 43# class AnimateController:44# def __init__(self):45 46# # config dirs47# self.basedir = os.getcwd()48# self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")49# self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")50# self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")51# self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))52# self.savedir_sample = os.path.join(self.savedir, "sample")53# os.makedirs(self.savedir, exist_ok=True)54 55# self.stable_diffusion_list = []56# self.motion_module_list = []57# self.personalized_model_list = []58 59# self.refresh_stable_diffusion()60# self.refresh_motion_module()61# self.refresh_personalized_model()62 63# # config models64# self.tokenizer = None65# self.text_encoder = None66# self.vae = None67# self.unet = None68# self.pipeline = None69# self.lora_model_state_dict = {}70 71# self.inference_config = OmegaConf.load("configs/inference/inference.yaml")72 73# def refresh_stable_diffusion(self):74# self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/"))75 76# def refresh_motion_module(self):77# motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))78# self.motion_module_list = [os.path.basename(p) for p in motion_module_list]79 80# def refresh_personalized_model(self):81# personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))82# self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]83 84# def update_stable_diffusion(self, stable_diffusion_dropdown):85# self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer")86# self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda()87# self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda()88# self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()89# return gr.Dropdown.update()90 91# def update_motion_module(self, motion_module_dropdown):92# if self.unet is None:93# gr.Info(f"Please select a pretrained model path.")94# return gr.Dropdown.update(value=None)95# else:96# motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)97# motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")98# missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)99# assert len(unexpected) == 0100# return gr.Dropdown.update()101 102# def update_base_model(self, base_model_dropdown):103# if self.unet is None:104# gr.Info(f"Please select a pretrained model path.")105# return gr.Dropdown.update(value=None)106# else:107# base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)108# base_model_state_dict = {}109# with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:110# for key in f.keys():111# base_model_state_dict[key] = f.get_tensor(key)112 113# converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)114# self.vae.load_state_dict(converted_vae_checkpoint)115 116# converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)117# self.unet.load_state_dict(converted_unet_checkpoint, strict=False)118 119# self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)120# return gr.Dropdown.update()121 122# def update_lora_model(self, lora_model_dropdown):123# lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)124# self.lora_model_state_dict = {}125# if lora_model_dropdown == "none": pass126# else:127# with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f:128# for key in f.keys():129# self.lora_model_state_dict[key] = f.get_tensor(key)130# return gr.Dropdown.update()131 132# def animate(133# self,134# stable_diffusion_dropdown,135# motion_module_dropdown,136# base_model_dropdown,137# lora_alpha_slider,138# prompt_textbox, 139# negative_prompt_textbox, 140# sampler_dropdown, 141# sample_step_slider, 142# width_slider, 143# length_slider, 144# height_slider, 145# cfg_scale_slider, 146# seed_textbox147# ): 148# if self.unet is None:149# raise gr.Error(f"Please select a pretrained model path.")150# if motion_module_dropdown == "": 151# raise gr.Error(f"Please select a motion module.")152# # if base_model_dropdown == "":153# # raise gr.Error(f"Please select a base DreamBooth model.")154 155# if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()156 157# pipeline = AnimationPipeline(158# vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,159# scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))160# ).to("cuda")161 162# if self.lora_model_state_dict != {}:163# print(f"Lora alpha: {lora_alpha_slider}")164# pipeline = convert_lora(copy.deepcopy(pipeline), self.lora_model_state_dict, alpha=lora_alpha_slider)165# pipeline.to("cuda")166 167# torch.cuda.empty_cache()168 169# seed_textbox = int(seed_textbox)170# if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(seed_textbox)171# else: torch.seed()172# seed = torch.initial_seed()173 174# sample = pipeline(175# prompt_textbox,176# negative_prompt = negative_prompt_textbox,177# num_inference_steps = sample_step_slider,178# guidance_scale = cfg_scale_slider,179# width = width_slider,180# height = height_slider,181# video_length = length_slider,182# ).videos183 184# save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4")185# save_videos_grid(sample, save_sample_path)186 187# sample_config = {188# "prompt": prompt_textbox,189# "n_prompt": negative_prompt_textbox,190# "sampler": sampler_dropdown,191# "num_inference_steps": sample_step_slider,192# "guidance_scale": cfg_scale_slider,193# "width": width_slider,194# "height": height_slider,195# "video_length": length_slider,196# "seed": seed197# }198# json_str = json.dumps(sample_config, indent=4)199# with open(os.path.join(self.savedir, "logs.json"), "a") as f:200# f.write(json_str)201# f.write("\n\n")202 203# del pipeline204# torch.cuda.empty_cache()205 206# return gr.Video.update(value=save_sample_path)207 208 209# controller = AnimateController()210 211 212# def ui():213# with gr.Blocks(css=css) as demo:214# gr.Markdown(215# """216# # [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)217# Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>218# [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)219# """220# )221# with gr.Column(variant="panel"):222# gr.Markdown(223# """224# ### 1. Model checkpoints (select pretrained model path first).225# """226# )227# with gr.Row():228# stable_diffusion_dropdown = gr.Dropdown(229# label="Pretrained Model Path",230# choices=controller.stable_diffusion_list,231# interactive=True,232# )233# stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown])234 235# stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")236# def update_stable_diffusion():237# controller.refresh_stable_diffusion()238# return gr.Dropdown.update(choices=controller.stable_diffusion_list)239# stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown])240 241# with gr.Row():242# motion_module_dropdown = gr.Dropdown(243# label="Select motion module",244# choices=controller.motion_module_list,245# interactive=True,246# )247# motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])248 249# motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")250# def update_motion_module():251# controller.refresh_motion_module()252# return gr.Dropdown.update(choices=controller.motion_module_list)253# motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown])254 255# base_model_dropdown = gr.Dropdown(256# label="Select base Dreambooth model (required)",257# choices=controller.personalized_model_list,258# interactive=True,259# )260# base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])261 262# lora_model_dropdown = gr.Dropdown(263# label="Select LoRA model (optional)",264# choices=["none"] + controller.personalized_model_list,265# value="none",266# interactive=True,267# )268# lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown])269 270# lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.7, minimum=0, maximum=2, interactive=True)271 272# personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")273# def update_personalized_model():274# controller.refresh_personalized_model()275# return [276# gr.Dropdown.update(choices=controller.personalized_model_list),277# gr.Dropdown.update(choices=["none"] + controller.personalized_model_list)278# ]279# personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown])280 281# with gr.Column(variant="panel"):282# gr.Markdown(283# """284# ### 2. Configs for AnimateDiff.285# """286# )287 288# prompt_textbox = gr.Textbox(label="Prompt", lines=2)289# negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)290 291# with gr.Row().style(equal_height=False):292# with gr.Column():293# with gr.Row():294# sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])295# sample_step_slider = gr.Slider(label="Sampling steps", value=25, minimum=10, maximum=100, step=1)296 297# width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64)298# height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64)299# length_slider = gr.Slider(label="Animation length", value=16, minimum=8, maximum=24, step=1)300# cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20)301 302# with gr.Row():303# seed_textbox = gr.Textbox(label="Seed", value=-1)304# seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")305# seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])306 307# generate_button = gr.Button(value="Generate", variant='primary')308 309# result_video = gr.Video(label="Generated Animation", interactive=False)310 311# generate_button.click(312# fn=controller.animate,313# inputs=[314# stable_diffusion_dropdown,315# motion_module_dropdown,316# base_model_dropdown,317# lora_alpha_slider,318# prompt_textbox, 319# negative_prompt_textbox, 320# sampler_dropdown, 321# sample_step_slider, 322# width_slider, 323# length_slider, 324# height_slider, 325# cfg_scale_slider, 326# seed_textbox,327# ],328# outputs=[result_video]329# )330 331# return demo332 333 334# if __name__ == "__main__":335# demo = ui()336# demo.queue(max_size=20)337# demo.launch()338 339 340import os341import torch342import random343 344import gradio as gr345from glob import glob346from omegaconf import OmegaConf347from safetensors import safe_open348 349from diffusers import AutoencoderKL350from diffusers import EulerDiscreteScheduler, DDIMScheduler351from diffusers.utils.import_utils import is_xformers_available352from transformers import CLIPTextModel, CLIPTokenizer353 354from animatediff.models.unet import UNet3DConditionModel355from animatediff.pipelines.pipeline_animation import AnimationPipeline356from animatediff.utils.util import save_videos_grid357from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint358 359 360pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"361inference_config_path = "configs/inference/inference.yaml"362 363css = """364.toolbutton {365 margin-buttom: 0em 0em 0em 0em;366 max-width: 2.5em;367 min-width: 2.5em !important;368 height: 2.5em;369}370"""371 372examples = [373 # 1-ToonYou374 [375 "toonyou_beta3.safetensors", 376 "mm_sd_v14.ckpt", 377 "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes",378 "worst quality, low quality, nsfw, logo",379 512, 512, "13204175718326964000"380 ],381 # 2-Lyriel382 [383 "lyriel_v16.safetensors", 384 "mm_sd_v15.ckpt", 385 "A forbidden castle high up in the mountains, pixel art, intricate details2, hdr, intricate details, hyperdetailed5, natural skin texture, hyperrealism, soft light, sharp, game art, key visual, surreal",386 "3d, cartoon, anime, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, bad anatomy, girl, loli, young, large breasts, red eyes, muscular",387 512, 512, "6681501646976930000"388 ],389 # 3-RCNZ390 [391 "rcnzCartoon3d_v10.safetensors", 392 "mm_sd_v14.ckpt", 393 "Jane Eyre with headphones, natural skin texture,4mm,k textures, soft cinematic light, adobe lightroom, photolab, hdr, intricate, elegant, highly detailed, sharp focus, cinematic look, soothing tones, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, faded",394 "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",395 512, 512, "2416282124261060"396 ],397 # 4-MajicMix398 [399 "majicmixRealistic_v5Preview.safetensors", 400 "mm_sd_v14.ckpt", 401 "1girl, offshoulder, light smile, shiny skin best quality, masterpiece, photorealistic",402 "bad hand, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles",403 512, 512, "7132772652786303"404 ],405 # 5-RealisticVision406 [407 "realisticVisionV20_v20.safetensors", 408 "mm_sd_v15.ckpt", 409 "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",410 "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation",411 512, 512, "1490157606650685400"412 ]413]414 415# clean unrelated ckpts416# ckpts = [417# "realisticVisionV40_v20Novae.safetensors",418# "majicmixRealistic_v5Preview.safetensors",419# "rcnzCartoon3d_v10.safetensors",420# "lyriel_v16.safetensors",421# "toonyou_beta3.safetensors"422# ]423 424# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):425# for ckpt in ckpts:426# if path.endswith(ckpt): break427# else:428# print(f"### Cleaning {path} ...")429# os.system(f"rm -rf {path}")430 431# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")432 433# os.system(f"bash download_bashscripts/1-ToonYou.sh")434# os.system(f"bash download_bashscripts/2-Lyriel.sh")435# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")436# os.system(f"bash download_bashscripts/4-MajicMix.sh")437# os.system(f"bash download_bashscripts/5-RealisticVision.sh")438 439# clean Grdio cache440print(f"### Cleaning cached examples ...")441os.system(f"rm -rf gradio_cached_examples/")442 443 444class AnimateController:445 def __init__(self):446 447 # config dirs448 self.basedir = os.getcwd()449 self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")450 self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")451 self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")452 self.savedir = os.path.join(self.basedir, "samples")453 os.makedirs(self.savedir, exist_ok=True)454 455 self.base_model_list = []456 self.motion_module_list = []457 458 self.selected_base_model = None459 self.selected_motion_module = None460 461 self.refresh_motion_module()462 self.refresh_personalized_model()463 464 # config models465 self.inference_config = OmegaConf.load(inference_config_path)466 467 self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")468 self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()469 self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()470 self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()471 472 self.update_base_model(self.base_model_list[0])473 self.update_motion_module(self.motion_module_list[0])474 475 476 def refresh_motion_module(self):477 motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))478 self.motion_module_list = [os.path.basename(p) for p in motion_module_list]479 480 def refresh_personalized_model(self):481 base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))482 self.base_model_list = [os.path.basename(p) for p in base_model_list]483 484 485 def update_base_model(self, base_model_dropdown):486 self.selected_base_model = base_model_dropdown487 488 base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)489 base_model_state_dict = {}490 with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:491 for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key)492 493 converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)494 self.vae.load_state_dict(converted_vae_checkpoint)495 496 converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)497 self.unet.load_state_dict(converted_unet_checkpoint, strict=False)498 499 self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)500 return gr.Dropdown.update()501 502 def update_motion_module(self, motion_module_dropdown):503 self.selected_motion_module = motion_module_dropdown504 505 motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)506 motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")507 _, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)508 assert len(unexpected) == 0509 return gr.Dropdown.update()510 511 512 def animate(513 self,514 base_model_dropdown,515 motion_module_dropdown,516 prompt_textbox,517 negative_prompt_textbox,518 width_slider,519 height_slider,520 seed_textbox,521 ):522 if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)523 if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)524 525 if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()526 527 pipeline = AnimationPipeline(528 vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,529 scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))530 ).to("cuda")531 532 if int(seed_textbox) > 0: seed = int(seed_textbox)533 else: seed = random.randint(1, 1e16)534 torch.manual_seed(int(seed))535 536 assert seed == torch.initial_seed()537 print(f"### seed: {seed}")538 539 generator = torch.Generator(device="cuda")540 generator.manual_seed(seed)541 542 sample = pipeline(543 prompt_textbox,544 negative_prompt = negative_prompt_textbox,545 num_inference_steps = 25,546 guidance_scale = 8.,547 width = width_slider,548 height = height_slider,549 video_length = 16,550 generator = generator,551 ).videos552 553 save_sample_path = os.path.join(self.savedir, f"sample.mp4")554 save_videos_grid(sample, save_sample_path)555 556 json_config = {557 "prompt": prompt_textbox,558 "n_prompt": negative_prompt_textbox,559 "width": width_slider,560 "height": height_slider,561 "seed": seed,562 "base_model": base_model_dropdown,563 "motion_module": motion_module_dropdown,564 }565 return gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config)566 567 568controller = AnimateController()569 570 571def ui():572 with gr.Blocks(css=css) as demo:573 gr.Markdown(574 """575 # AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning576 Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>577 [Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)578 """579 )580 gr.Markdown(581 """582 ### Quick Start583 1. Select desired `Base DreamBooth Model`.584 2. Select `Motion Module` from `mm_sd_v14.ckpt` and `mm_sd_v15.ckpt`. We recommend trying both of them for the best results.585 3. Provide `Prompt` and `Negative Prompt` for each model. You are encouraged to refer to each model's webpage on CivitAI to learn how to write prompts for them. Below are the DreamBooth models in this demo. Click to visit their homepage.586 - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)587 - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)588 - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)589 - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)590 - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)591 4. Click `Generate`, wait for ~1 min, and enjoy.592 """593 )594 with gr.Row():595 with gr.Column():596 base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list, value=controller.base_model_list[0], interactive=True )597 motion_module_dropdown = gr.Dropdown( label="Motion Module", choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True )598 599 base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])600 motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])601 602 prompt_textbox = gr.Textbox( label="Prompt", lines=3 )603 negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo")604 605 with gr.Accordion("Advance", open=False):606 with gr.Row():607 width_slider = gr.Slider( label="Width", value=512, minimum=256, maximum=1024, step=64 )608 height_slider = gr.Slider( label="Height", value=512, minimum=256, maximum=1024, step=64 )609 with gr.Row():610 seed_textbox = gr.Textbox( label="Seed", value=-1)611 seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")612 seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox])613 614 generate_button = gr.Button( value="Generate", variant='primary' )615 616 with gr.Column():617 result_video = gr.Video( label="Generated Animation", interactive=False )618 json_config = gr.Json( label="Config", value=None )619 620 inputs = [base_model_dropdown, motion_module_dropdown, prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox]621 outputs = [result_video, json_config]622 623 generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs )624 625 gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True )626 627 return demo628 629 630if __name__ == "__main__":631 demo = ui()632 demo.queue(max_size=20)633 demo.launch()634