import json import os import os.path as osp import random from argparse import ArgumentParser from datetime import datetime import gradio as gr import numpy as np import openxlab import torch from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler from omegaconf import OmegaConf from openxlab.model import download from PIL import Image from animatediff.pipelines import I2VPipeline from animatediff.utils.util import RANGE_LIST, save_videos_grid 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', action='store_true') parser.add_argument('--local-debug', action='store_true') parser.add_argument('--save-path', default='samples') args = parser.parse_args() LOCAL_DEBUG = args.local_debug BASE_CONFIG = 'example/config/base.yaml' STYLE_CONFIG_LIST = { 'anime': './example/openxlab/2-animation.yaml', } # download models PIA_PATH = './models/PIA' VAE_PATH = './models/VAE' DreamBooth_LoRA_PATH = './models/DreamBooth_LoRA' if not LOCAL_DEBUG: CACHE_PATH = '/home/xlab-app-center/.cache/model' PIA_PATH = osp.join(CACHE_PATH, 'PIA') VAE_PATH = osp.join(CACHE_PATH, 'VAE') DreamBooth_LoRA_PATH = osp.join(CACHE_PATH, 'DreamBooth_LoRA') STABLE_DIFFUSION_PATH = osp.join(CACHE_PATH, 'StableDiffusion') IP_ADAPTER_PATH = osp.join(CACHE_PATH, 'IP_Adapter') os.makedirs(PIA_PATH, exist_ok=True) os.makedirs(VAE_PATH, exist_ok=True) os.makedirs(DreamBooth_LoRA_PATH, exist_ok=True) os.makedirs(STABLE_DIFFUSION_PATH, exist_ok=True) openxlab.login(os.environ['OPENXLAB_AK'], os.environ['OPENXLAB_SK']) download(model_repo='zhangyiming/PIA-pruned', model_name='PIA', output=PIA_PATH) download(model_repo='zhangyiming/Counterfeit-V3.0', model_name='Counterfeit-V3.0_fp32_pruned', output=DreamBooth_LoRA_PATH) download(model_repo='zhangyiming/kl-f8-anime2_VAE', model_name='kl-f8-anime2', output=VAE_PATH) # ip_adapter download(model_repo='zhangyiming/IP-Adapter', model_name='clip_encoder', output=osp.join(IP_ADAPTER_PATH, 'image_encoder')) download(model_repo='zhangyiming/IP-Adapter', model_name='config', output=osp.join(IP_ADAPTER_PATH, 'image_encoder')) download(model_repo='zhangyiming/IP-Adapter', model_name='ip_adapter_sd15', output=IP_ADAPTER_PATH) # unet download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Unet', model_name='unet', output=osp.join(STABLE_DIFFUSION_PATH, 'unet')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Unet', model_name='config', output=osp.join(STABLE_DIFFUSION_PATH, 'unet')) # vae download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_VAE', model_name='vae', output=osp.join(STABLE_DIFFUSION_PATH, 'vae')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_VAE', model_name='config', output=osp.join(STABLE_DIFFUSION_PATH, 'vae')) # text encoder download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_TextEncod', model_name='text_encoder', output=osp.join(STABLE_DIFFUSION_PATH, 'text_encoder')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_TextEncod', model_name='config', output=osp.join(STABLE_DIFFUSION_PATH, 'text_encoder')) # tokenizer download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Tokenizer', model_name='merge', output=osp.join(STABLE_DIFFUSION_PATH, 'tokenizer')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Tokenizer', model_name='special_tokens_map', output=osp.join(STABLE_DIFFUSION_PATH, 'tokenizer')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Tokenizer', model_name='tokenizer_config', output=osp.join(STABLE_DIFFUSION_PATH, 'tokenizer')) download(model_repo='zhangyiming/runwayml_stable-diffusion-v1-5_Tokenizer', model_name='vocab', output=osp.join(STABLE_DIFFUSION_PATH, 'tokenizer')) # scheduler scheduler_dict = { "_class_name": "PNDMScheduler", "_diffusers_version": "0.6.0", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "num_train_timesteps": 1000, "set_alpha_to_one": False, "skip_prk_steps": True, "steps_offset": 1, "trained_betas": None, "clip_sample": False } os.makedirs(osp.join(STABLE_DIFFUSION_PATH, 'scheduler'), exist_ok=True) with open(osp.join(STABLE_DIFFUSION_PATH, 'scheduler', 'scheduler_config.json'), 'w') as file: json.dump(scheduler_dict, file) # model index model_index_dict = { "_class_name": "StableDiffusionPipeline", "_diffusers_version": "0.6.0", "feature_extractor": [ "transformers", "CLIPImageProcessor" ], "safety_checker": [ "stable_diffusion", "StableDiffusionSafetyChecker" ], "scheduler": [ "diffusers", "PNDMScheduler" ], "text_encoder": [ "transformers", "CLIPTextModel" ], "tokenizer": [ "transformers", "CLIPTokenizer" ], "unet": [ "diffusers", "UNet2DConditionModel" ], "vae": [ "diffusers", "AutoencoderKL" ] } with open(osp.join(STABLE_DIFFUSION_PATH, 'model_index.json'), 'w') as file: json.dump(model_index_dict, file) else: PIA_PATH = './models/PIA' VAE_PATH = './models/VAE' DreamBooth_LoRA_PATH = './models/DreamBooth_LoRA' STABLE_DIFFUSION_PATH = './models/StableDiffusion/sd15' def preprocess_img(img_np, max_size: int = 512): ori_image = Image.fromarray(img_np).convert('RGB') width, height = ori_image.size short_edge = max(width, height) if short_edge > max_size: scale_factor = max_size / short_edge else: scale_factor = 1 width = int(width * scale_factor) height = int(height * scale_factor) ori_image = ori_image.resize((width, height)) if (width % 8 != 0) or (height % 8 != 0): in_width = (width // 8) * 8 in_height = (height // 8) * 8 else: in_width = width in_height = height in_image = ori_image in_image = ori_image.resize((in_width, in_height)) in_image_np = np.array(in_image) return in_image_np, in_height, in_width class AnimateController: def __init__(self): # config dirs self.basedir = os.getcwd() 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.inference_config = OmegaConf.load(args.config) self.style_configs = {k: OmegaConf.load( v) for k, v in STYLE_CONFIG_LIST.items()} self.pipeline_dict = self.load_model_list() def load_model_list(self): pipeline_dict = dict() for style, cfg in self.style_configs.items(): dreambooth_path = cfg.get('dreambooth', 'none') if dreambooth_path and dreambooth_path.upper() != 'NONE': dreambooth_path = osp.join( DreamBooth_LoRA_PATH, dreambooth_path) lora_path = cfg.get('lora', None) if lora_path is not None: lora_path = osp.join(DreamBooth_LoRA_PATH, lora_path) lora_alpha = cfg.get('lora_alpha', 0.0) vae_path = cfg.get('vae', None) if vae_path is not None: vae_path = osp.join(VAE_PATH, vae_path) pipeline_dict[style] = I2VPipeline.build_pipeline( self.inference_config, STABLE_DIFFUSION_PATH, unet_path=osp.join(PIA_PATH, 'pia.ckpt'), dreambooth_path=dreambooth_path, lora_path=lora_path, lora_alpha=lora_alpha, vae_path=vae_path, ip_adapter_path='h94/IP-Adapter', ip_adapter_scale=0.1) return pipeline_dict def fetch_default_n_prompt(self, style: str): cfg = self.style_configs[style] n_prompt = cfg.get('n_prompt', '') ip_adapter_scale = cfg.get('real_ip_adapter_scale', 0) gr.Info('Set default negative prompt and ip_adapter_scale.') print('Set default negative prompt and ip_adapter_scale.') return n_prompt, ip_adapter_scale def animate( self, init_img, motion_scale, prompt_textbox, negative_prompt_textbox, sampler_dropdown, sample_step_slider, cfg_scale_slider, seed_textbox, ip_adapter_scale, style, progress=gr.Progress(), ): if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox)) else: torch.seed() seed = torch.initial_seed() pipeline = self.pipeline_dict[style] init_img, h, w = preprocess_img(init_img) sample = pipeline( image=init_img, prompt=prompt_textbox, negative_prompt=negative_prompt_textbox, num_inference_steps=sample_step_slider, guidance_scale=cfg_scale_slider, width=w, height=h, video_length=16, mask_sim_template_idx=motion_scale - 1, ip_adapter_scale=ip_adapter_scale, progress_fn=progress, ).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": w, "height": h, "seed": seed, "motion": motion_scale, } 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 save_sample_path controller = AnimateController() def ui(): with gr.Blocks(css=css) as demo: gr.HTML( "
Your Personalized Image Animator
" "
via Plug-and-Play Modules in Text-to-Image Models
" ) with gr.Row(): gr.Markdown( "
Project Page  " # noqa "Paper  " "Code  " # noqa # "Try More Style: Click Here!
" # noqa "Try More Style: Click here! " # noqa ) with gr.Row(equal_height=False): with gr.Column(): with gr.Row(): init_img = gr.Image(label='Input Image') style_dropdown = gr.Dropdown(label='Style', choices=list( STYLE_CONFIG_LIST.keys()), value=list(STYLE_CONFIG_LIST.keys())[0]) with gr.Row(): prompt_textbox = gr.Textbox(label="Prompt", lines=1) gift_button = gr.Button( value='🎁', elem_classes='toolbutton' ) def append_gift(prompt): rand = random.randint(0, 2) if rand == 1: prompt = prompt + 'wearing santa hats' elif rand == 2: prompt = prompt + 'lift a Christmas gift' else: prompt = prompt + 'in Christmas suit, lift a Christmas gift' gr.Info('Merry Christmas! Add magic to your prompt!') return prompt gift_button.click( fn=append_gift, inputs=[prompt_textbox], outputs=[prompt_textbox], ) prompt_textbox = gr.Textbox(label="Prompt", lines=1) motion_scale_silder = gr.Slider( label='Motion Scale (Larger value means larger motion but less identity consistency)', value=2, step=1, minimum=1, maximum=len(RANGE_LIST)) ip_adapter_scale = gr.Slider( label='IP-Apdater Scale', value=controller.fetch_default_n_prompt( list(STYLE_CONFIG_LIST.keys())[0])[1], minimum=0, maximum=1) with gr.Accordion('Advance Options', open=False): negative_prompt_textbox = gr.Textbox( value=controller.fetch_default_n_prompt( list(STYLE_CONFIG_LIST.keys())[0])[0], label="Negative prompt", lines=2) 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=20, 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=-1) 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') result_video = gr.Video( label="Generated Animation", interactive=False) style_dropdown.change(fn=controller.fetch_default_n_prompt, inputs=[style_dropdown], outputs=[negative_prompt_textbox, ip_adapter_scale], queue=False) generate_button.click( fn=controller.animate, inputs=[ init_img, motion_scale_silder, prompt_textbox, negative_prompt_textbox, sampler_dropdown, sample_step_slider, cfg_scale_slider, seed_textbox, ip_adapter_scale, style_dropdown, ], outputs=[result_video] ) return demo if __name__ == "__main__": demo = ui() demo.queue(max_size=10) demo.launch(server_name=args.server_name, server_port=args.port, share=args.share, max_threads=10, allowed_paths=['pia.png'])