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| import os | |
| import random | |
| from datetime import datetime | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from diffusers import AutoencoderKL, DDIMScheduler | |
| from einops import repeat | |
| 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.util import get_fps, read_frames, save_videos_grid | |
| class AnimateController: | |
| def __init__( | |
| self, | |
| config_path="./configs/prompts/animation.yaml", | |
| weight_dtype=torch.float16, | |
| ): | |
| # Read pretrained weights path from config | |
| self.config = OmegaConf.load(config_path) | |
| self.pipeline = None | |
| self.weight_dtype = weight_dtype | |
| 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") | |
| inference_config_path = self.config.inference_config | |
| infer_config = OmegaConf.load(inference_config_path) | |
| 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) | |
| # load pretrained weights | |
| 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 = [] | |
| pose_tensor_list = [] | |
| pose_transform = transforms.Compose( | |
| [transforms.Resize((height, width)), transforms.ToTensor()] | |
| ) | |
| for pose_image_pil in pose_images[:length]: | |
| pose_list.append(pose_image_pil) | |
| pose_tensor_list.append(pose_transform(pose_image_pil)) | |
| video = self.pipeline( | |
| ref_image, | |
| pose_list, | |
| width=width, | |
| height=height, | |
| video_length=length, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=cfg, | |
| generator=generator, | |
| ).videos | |
| ref_image_tensor = pose_transform(ref_image) # (c, h, w) | |
| ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0) # (1, c, 1, h, w) | |
| ref_image_tensor = repeat( | |
| ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=length | |
| ) | |
| pose_tensor = torch.stack(pose_tensor_list, dim=0) # (f, c, h, w) | |
| pose_tensor = pose_tensor.transpose(0, 1) | |
| pose_tensor = pose_tensor.unsqueeze(0) | |
| video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0) | |
| save_dir = f"./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.Markdown( | |
| """ | |
| # Moore-AnimateAnyone Demo | |
| """ | |
| ) | |
| 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=1024, value=768, step=64 | |
| ) | |
| length_slider = gr.Slider( | |
| label="Video Length", minimum=24, maximum=128, value=24, 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=25, | |
| info="default: 25", | |
| step=5, | |
| maximum=30, | |
| minimum=10, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| value=3.5, | |
| info="default: 3.5", | |
| step=0.5, | |
| maximum=10, | |
| minimum=2.0, | |
| ) | |
| submit = gr.Button("Animate") | |
| def read_video(video): | |
| return video | |
| def read_image(image): | |
| return Image.fromarray(image) | |
| # when user uploads a new video | |
| motion_sequence.upload(read_video, motion_sequence, motion_sequence) | |
| # when `first_frame` is updated | |
| reference_image.upload(read_image, reference_image, reference_image) | |
| # when the `submit` button is clicked | |
| submit.click( | |
| controller.animate, | |
| [ | |
| reference_image, | |
| motion_sequence, | |
| width_slider, | |
| height_slider, | |
| length_slider, | |
| sampling_steps, | |
| guidance_scale, | |
| seed_textbox, | |
| ], | |
| animation, | |
| ) | |
| # Examples | |
| gr.Markdown("## Examples") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "./configs/inference/ref_images/anyone-5.png", | |
| "./configs/inference/pose_videos/anyone-video-2_kps.mp4", | |
| ], | |
| [ | |
| "./configs/inference/ref_images/anyone-10.png", | |
| "./configs/inference/pose_videos/anyone-video-1_kps.mp4", | |
| ], | |
| [ | |
| "./configs/inference/ref_images/anyone-2.png", | |
| "./configs/inference/pose_videos/anyone-video-5_kps.mp4", | |
| ], | |
| ], | |
| inputs=[reference_image, motion_sequence], | |
| outputs=animation, | |
| ) | |
| return demo | |
| demo = ui() | |
| demo.launch(share=True) | |