app.py

import gc
import os
import spaces
import gradio as gr
import random
import tempfile
import time

from easydict import EasyDict
import numpy as np
import torch
from dav.pipelines import DAVPipeline
from dav.models import UNetSpatioTemporalRopeConditionModel
from diffusers import AutoencoderKLTemporalDecoder, FlowMatchEulerDiscreteScheduler
from dav.utils import img_utils


def seed_all(seed: int = 0):
    """
    Set random seeds for reproducibility.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


examples = [
    ["demos/wooly_mammoth.mp4", 3, 28, 8, 20, 6, 768],
]


def load_models(model_base, device):
    vae = AutoencoderKLTemporalDecoder.from_pretrained(model_base, subfolder="vae")
    scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
        model_base, subfolder="scheduler"
    )
    unet = UNetSpatioTemporalRopeConditionModel.from_pretrained(
        model_base, subfolder="unet"
    )
    unet_interp = UNetSpatioTemporalRopeConditionModel.from_pretrained(
        model_base, subfolder="unet_interp"
    )
    pipe = DAVPipeline(
        vae=vae,
        unet=unet,
        unet_interp=unet_interp,
        scheduler=scheduler,
    )
    pipe = pipe.to(device)
    return pipe


model_base = "hhyangcs/depth-any-video"
device_type = "cuda"
device = torch.device(device_type)
pipe = load_models(model_base, device)


@spaces.GPU(duration=140)
def infer_depth(
    file: str,
    denoise_steps: int = 3,
    num_frames: int = 32,
    decode_chunk_size: int = 16,
    num_interp_frames: int = 16,
    num_overlap_frames: int = 6,
    max_resolution: int = 1024,
    seed: int = 66,
    output_dir: str = "./outputs",
):
    seed_all(seed)

    max_frames = (num_interp_frames + 2 - num_overlap_frames) * (num_frames // 2)
    image, fps = img_utils.read_video(file, max_frames=max_frames)

    image = img_utils.imresize_max(image, max_resolution)
    image = img_utils.imcrop_multi(image)
    image_tensor = np.ascontiguousarray(
        [_img.transpose(2, 0, 1) / 255.0 for _img in image]
    )
    image_tensor = torch.from_numpy(image_tensor).to(device)
    print(f"==> video name: {file}, frames shape: {image_tensor.shape}")

    with torch.no_grad(), torch.autocast(device_type=device_type, dtype=torch.float16):
        pipe_out = pipe(
            image_tensor,
            num_frames=num_frames,
            num_overlap_frames=num_overlap_frames,
            num_interp_frames=num_interp_frames,
            decode_chunk_size=decode_chunk_size,
            num_inference_steps=denoise_steps,
        )

    disparity = pipe_out.disparity
    disparity_colored = pipe_out.disparity_colored
    image = pipe_out.image
    # (N, H, 2 * W, 3)
    merged = np.concatenate(
        [
            image,
            disparity_colored,
        ],
        axis=2,
    )

    file_name = os.path.splitext(os.path.basename(file))[0]
    os.makedirs(output_dir, exist_ok=True)
    output_path = os.path.join(output_dir, f"{file_name}_depth.mp4")
    img_utils.write_video(
        output_path,
        merged,
        fps,
    )

    # clear the cache for the next video
    gc.collect()
    torch.cuda.empty_cache()

    return output_path


def construct_demo():
    with gr.Blocks(analytics_enabled=False) as depthanyvideo_iface:

        with gr.Row(equal_height=True):
            with gr.Column(scale=1):
                input_video = gr.Video(label="Input Video")

            with gr.Column(scale=2):
                with gr.Row(equal_height=True):
                    output_video = gr.Video(
                        label="Ouput Video & Depth",
                        interactive=False,
                        autoplay=True,
                        loop=True,
                        show_share_button=True,
                        scale=2,
                    )

        with gr.Row(equal_height=True):
            with gr.Column(scale=1):
                with gr.Row(equal_height=False):
                    with gr.Accordion("Advanced Settings", open=False):
                        denoise_steps = gr.Slider(
                            label="Denoise Steps",
                            minimum=1,
                            maximum=10,
                            value=3,
                            step=1,
                        )
                        num_frames = gr.Slider(
                            label="Number of Key Frames",
                            minimum=16,
                            maximum=32,
                            value=28,
                            step=2,
                        )
                        decode_chunk_size = gr.Slider(
                            label="Decode Chunk Size",
                            minimum=8,
                            maximum=32,
                            value=8,
                            step=1,
                        )
                        num_interp_frames = gr.Slider(
                            label="Number of Interpolation Frames",
                            minimum=8,
                            maximum=32,
                            value=20,
                            step=1,
                        )
                        num_overlap_frames = gr.Slider(
                            label="Number of Overlap Frames",
                            minimum=2,
                            maximum=10,
                            value=6,
                            step=1,
                        )
                        max_resolution = gr.Slider(
                            label="Maximum Resolution",
                            minimum=512,
                            maximum=2048,
                            value=768,
                            step=32,
                        )
                    generate_btn = gr.Button("Generate")
            with gr.Column(scale=2):
                pass

        gr.Examples(
            examples=examples,
            inputs=[
                input_video,
                denoise_steps,
                num_frames,
                decode_chunk_size,
                num_interp_frames,
                num_overlap_frames,
                max_resolution,
            ],
            outputs=output_video,
            fn=infer_depth,
            cache_examples="lazy",
        )

        generate_btn.click(
            fn=infer_depth,
            inputs=[
                input_video,
                denoise_steps,
                num_frames,
                decode_chunk_size,
                num_interp_frames,
                num_overlap_frames,
                max_resolution,
            ],
            outputs=output_video,
        )

    return depthanyvideo_iface


demo = construct_demo()

if __name__ == "__main__":
    demo.queue()
    demo.launch(share=True)