app.py

import os
import random
import spaces
from datetime import datetime
import gradio as gr
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from einops import repeat
from huggingface_hub import snapshot_download
from omegaconf import OmegaConf
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from src.models.pose_guider import PoseGuider
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d import UNet3DConditionModel
from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
from src.utils.download_models import prepare_base_model, prepare_image_encoder
from src.utils.util import get_fps, read_frames, save_videos_grid

prepare_base_model()
prepare_image_encoder()
snapshot_download(repo_id="stabilityai/sd-vae-ft-mse", local_dir="./pretrained_weights/sd-vae-ft-mse")
snapshot_download(repo_id="patrolli/AnimateAnyone", local_dir="./pretrained_weights")

class AnimateController:
    def __init__(self, config_path="./configs/prompts/animation.yaml", weight_dtype=torch.float16):
        self.config = OmegaConf.load(config_path)
        self.pipeline = None
        self.weight_dtype = weight_dtype

    @spaces.GPU(duration=60)
    def animate(self, ref_image, pose_video_path, width=512, height=768, length=24, num_inference_steps=25, cfg=3.5, seed=123):
        generator = torch.manual_seed(seed)
        if isinstance(ref_image, np.ndarray):
            ref_image = Image.fromarray(ref_image)
        if self.pipeline is None:
            vae = AutoencoderKL.from_pretrained(self.config.pretrained_vae_path).to("cuda", dtype=self.weight_dtype)
            reference_unet = UNet2DConditionModel.from_pretrained(self.config.pretrained_base_model_path, subfolder="unet").to(dtype=self.weight_dtype, device="cuda")
            infer_config = OmegaConf.load(self.config.inference_config)
            denoising_unet = UNet3DConditionModel.from_pretrained_2d(
                self.config.pretrained_base_model_path,
                self.config.motion_module_path,
                subfolder="unet",
                unet_additional_kwargs=infer_config.unet_additional_kwargs,
            ).to(dtype=self.weight_dtype, device="cuda")
            pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(dtype=self.weight_dtype, device="cuda")
            image_enc = CLIPVisionModelWithProjection.from_pretrained(self.config.image_encoder_path).to(dtype=self.weight_dtype, device="cuda")
            sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
            scheduler = DDIMScheduler(**sched_kwargs)
            denoising_unet.load_state_dict(torch.load(self.config.denoising_unet_path, map_location="cpu"), strict=False)
            reference_unet.load_state_dict(torch.load(self.config.reference_unet_path, map_location="cpu"))
            pose_guider.load_state_dict(torch.load(self.config.pose_guider_path, map_location="cpu"))
            pipe = Pose2VideoPipeline(
                vae=vae,
                image_encoder=image_enc,
                reference_unet=reference_unet,
                denoising_unet=denoising_unet,
                pose_guider=pose_guider,
                scheduler=scheduler,
            )
            pipe = pipe.to("cuda", dtype=self.weight_dtype)
            self.pipeline = pipe

        pose_images = read_frames(pose_video_path)
        src_fps = get_fps(pose_video_path)
        pose_list = []
        total_length = min(length, len(pose_images))
        for pose_image_pil in pose_images[:total_length]:
            pose_list.append(pose_image_pil)
        video = self.pipeline(
            ref_image,
            pose_list,
            width=width,
            height=height,
            video_length=total_length,
            num_inference_steps=num_inference_steps,
            guidance_scale=cfg,
            generator=generator,
        ).videos

        new_h, new_w = video.shape[-2:]
        pose_transform = transforms.Compose([transforms.Resize((new_h, new_w)), transforms.ToTensor()])
        pose_tensor_list = []
        for pose_image_pil in pose_images[:total_length]:
            pose_tensor_list.append(pose_transform(pose_image_pil))

        ref_image_tensor = pose_transform(ref_image).unsqueeze(1).unsqueeze(0)
        ref_image_tensor = repeat(ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=total_length)
        pose_tensor = torch.stack(pose_tensor_list, dim=0).transpose(0, 1).unsqueeze(0)
        video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0)

        save_dir = "./output/gradio"
        if not os.path.exists(save_dir):
            os.makedirs(save_dir, exist_ok=True)
        date_str = datetime.now().strftime("%Y%m%d")
        time_str = datetime.now().strftime("%H%M")
        out_path = os.path.join(save_dir, f"{date_str}T{time_str}.mp4")
        save_videos_grid(video, out_path, n_rows=3, fps=src_fps)
        torch.cuda.empty_cache()
        return out_path

controller = AnimateController()

def ui():
    with gr.Blocks() as demo:
        gr.HTML(
            """
            <h1 style="color:#dc5b1c;text-align:center">
                Moore-AnimateAnyone Gradio Demo 
            </h1>
            <div style="text-align:center">
            <div style="display: inline-block; text-align: left;">
            <p>This is a quick preview demo of Moore-AnimateAnyone. We appreciate the assistance provided by the HuggingFace team in setting up this demo.</p> 
            <p>If you like this project, please consider giving a star on <a herf="https://github.com/MooreThreads/Moore-AnimateAnyone">our GitHub repo</a> 🤗.</p>
            </div>
            </div>
            """
        )
        animation = gr.Video(format="mp4", label="Animation Results", height=448, autoplay=True)
        with gr.Row():
            reference_image = gr.Image(label="Reference Image")
            motion_sequence = gr.Video(format="mp4", label="Motion Sequence", height=512)
            with gr.Column():
                width_slider = gr.Slider(label="Width", minimum=448, maximum=768, value=512, step=64)
                height_slider = gr.Slider(label="Height", minimum=512, maximum=960, value=768, step=64)
                length_slider = gr.Slider(label="Video Length", minimum=24, maximum=128, value=72, step=24)
                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])
                with gr.Row():
                    sampling_steps = gr.Slider(label="Sampling steps", value=15, info="default: 15", step=5, maximum=20, minimum=10)
                    guidance_scale = gr.Slider(label="Guidance scale", value=3.5, info="default: 3.5", step=0.5, maximum=6.5, minimum=2.0)
                submit = gr.Button("Animate")
        motion_sequence.upload(lambda x: x, motion_sequence, motion_sequence, queue=False)
        reference_image.upload(lambda x: Image.fromarray(x), reference_image, reference_image, queue=False)
        submit.click(
            controller.animate,
            [reference_image, motion_sequence, width_slider, height_slider, length_slider, sampling_steps, guidance_scale, seed_textbox],
            animation,
        )
        gr.Markdown("## Examples")
        gr.Examples(
            examples=[
                ["./configs/inference/ref_images/anyone-5.png", "./configs/inference/pose_videos/anyone-video-2_kps.mp4", 512, 768, 72],
                ["./configs/inference/ref_images/anyone-10.png", "./configs/inference/pose_videos/anyone-video-1_kps.mp4", 512, 768, 72],
                ["./configs/inference/ref_images/anyone-2.png", "./configs/inference/pose_videos/anyone-video-5_kps.mp4", 512, 768, 72],
            ],
            inputs=[reference_image, motion_sequence, width_slider, height_slider, length_slider],
            outputs=animation,
        )
    return demo

demo = ui()
demo.queue(max_size=10)
demo.launch(share=True, show_api=False)