app-counterfeit-only.py

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(
            "<div align='center'><font size='7'> <img src=\"file/pia.png\" style=\"height: 72px;\"/ > Your Personalized Image Animator</font></div>"
            "<div align='center'><font size='7'>via Plug-and-Play Modules in Text-to-Image Models </font></div>"
        )
        with gr.Row():
            gr.Markdown(
                "<div align='center'><font size='5'><a href='https://pi-animator.github.io/'>Project Page</a> &ensp;"  # noqa
                "<a href='https://arxiv.org/abs/2312.13964/'>Paper</a> &ensp;"
                "<a href='https://github.com/open-mmlab/PIA'>Code</a> &ensp;"  # noqa
                # "Try More Style: <a href='https://openxlab.org.cn/apps/detail/zhangyiming/PiaPia'>Click Here!</a> </font></div>"  # noqa
                "Try More Style: <a href='https://openxlab.org.cn/apps/detail/zhangyiming/PiaPia'>Click here! </a></font></div>"  # 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'])