app.py

import gradio as gr
import os
from PIL import Image
import subprocess
from gradio_model4dgs import Model4DGS
import numpy
import hashlib
import shlex

subprocess.run(shlex.split("pip install wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"))
# subprocess.run(shlex.split("pip install xformers==0.0.23 --no-deps --index-url https://download.pytorch.org/whl/cu118"))

import rembg
import glob
import cv2
import numpy as np
from diffusers import StableVideoDiffusionPipeline
from scripts.gen_vid import * 

import sys
sys.path.append('lgm')
from safetensors.torch import load_file
from kiui.cam import orbit_camera
from core.options import config_defaults, Options
from core.models import LGM
from mvdream.pipeline_mvdream import MVDreamPipeline
from infer_demo import process as process_lgm

from main_4d_demo import process as process_dg4d
 


import spaces




from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(repo_id="ashawkey/LGM", filename="model_fp16_fixrot.safetensors")

js_func = """
function refresh() {
    const url = new URL(window.location);

    if (url.searchParams.get('__theme') !== 'light') {
        url.searchParams.set('__theme', 'light');
        window.location.href = url.href;
    }
}
"""


device = torch.device('cuda')
# device = torch.device('cpu')

session = rembg.new_session(model_name='u2net')

pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid", torch_dtype=torch.float16, variant="fp16"
)
pipe.to(device)

opt = config_defaults['big']
opt.resume = ckpt_path
# model
model = LGM(opt)

# resume pretrained checkpoint
if opt.resume is not None:
    if opt.resume.endswith('safetensors'):
        ckpt = load_file(opt.resume, device='cpu')
    else:
        ckpt = torch.load(opt.resume, map_location='cpu')
    model.load_state_dict(ckpt, strict=False)
    print(f'[INFO] Loaded checkpoint from {opt.resume}')
else:
    print(f'[WARN] model randomly initialized, are you sure?')

# device
model = model.half().to(device)
model.eval()
rays_embeddings = model.prepare_default_rays(device)

# load image dream
pipe_mvdream = MVDreamPipeline.from_pretrained(
    "ashawkey/imagedream-ipmv-diffusers", # remote weights
    torch_dtype=torch.float16,
    trust_remote_code=True,
    # local_files_only=True,
)
pipe_mvdream = pipe_mvdream.to(device)

from guidance.zero123_utils import Zero123
guidance_zero123 = Zero123(device, model_key='ashawkey/stable-zero123-diffusers')

def preprocess(path, recenter=True, size=256, border_ratio=0.2):
    files = [path]
    out_dir = os.path.dirname(path)

    for file in files:

        out_base = os.path.basename(file).split('.')[0]
        out_rgba = os.path.join(out_dir, out_base + '_rgba.png')

        # load image
        print(f'[INFO] loading image {file}...')
        image = cv2.imread(file, cv2.IMREAD_UNCHANGED)
        
        # carve background
        print(f'[INFO] background removal...')
        carved_image = rembg.remove(image, session=session) # [H, W, 4]
        mask = carved_image[..., -1] > 0

        # recenter
        if recenter:
            print(f'[INFO] recenter...')
            final_rgba = np.zeros((size, size, 4), dtype=np.uint8)
            
            coords = np.nonzero(mask)
            x_min, x_max = coords[0].min(), coords[0].max()
            y_min, y_max = coords[1].min(), coords[1].max()
            h = x_max - x_min
            w = y_max - y_min
            desired_size = int(size * (1 - border_ratio))
            scale = desired_size / max(h, w)
            h2 = int(h * scale)
            w2 = int(w * scale)
            x2_min = (size - h2) // 2
            x2_max = x2_min + h2
            y2_min = (size - w2) // 2
            y2_max = y2_min + w2
            final_rgba[x2_min:x2_max, y2_min:y2_max] = cv2.resize(carved_image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
            
        else:
            final_rgba = carved_image
        
        # write image
        cv2.imwrite(out_rgba, final_rgba)

def gen_vid(input_path, seed, bg='white'):
    name = input_path.split('/')[-1].split('.')[0]
    input_dir = os.path.dirname(input_path)
    height, width = 512, 512

    image = load_image(input_path, width, height, bg)

    generator = torch.manual_seed(seed)
    # frames = pipe(image, height, width, decode_chunk_size=2, generator=generator).frames[0]
    frames = pipe(image, height, width, generator=generator).frames[0]

    imageio.mimwrite(f"{input_dir}/{name}_generated.mp4", frames, fps=7)
    os.makedirs(f"{input_dir}/{name}_frames", exist_ok=True)
    for idx, img in enumerate(frames):
        img.save(f"{input_dir}/{name}_frames/{idx:03}.png")

# check if there is a picture uploaded or selected
def check_img_input(control_image):
    if control_image is None:
        raise gr.Error("Please select or upload an input image")

# check if there is a picture uploaded or selected
def check_video_3d_input(image_block: Image.Image):
    img_hash = hashlib.sha256(image_block.tobytes()).hexdigest()
    if not os.path.exists(os.path.join('tmp_data', f'{img_hash}_rgba_generated.mp4')):
        raise gr.Error("Please generate a video first")
    if not os.path.exists(os.path.join('vis_data', f'{img_hash}_rgba_static.mp4')):
        raise gr.Error("Please generate a 3D first")



@spaces.GPU()
def optimize_stage_0(image_block: Image.Image, preprocess_chk: bool, seed_slider: int):
    if not os.path.exists('tmp_data'):
        os.makedirs('tmp_data')
    img_hash = hashlib.sha256(image_block.tobytes()).hexdigest()
    if not os.path.exists(os.path.join('tmp_data', f'{img_hash}_rgba.png')):
        if preprocess_chk:
            # save image to a designated path
            image_block.save(os.path.join('tmp_data', f'{img_hash}.png'))

            # preprocess image
            # print(f'python scripts/process.py {os.path.join("tmp_data", f"{img_hash}.png")}')
            # subprocess.run(f'python scripts/process.py {os.path.join("tmp_data", f"{img_hash}.png")}', shell=True)
            preprocess(os.path.join("tmp_data", f"{img_hash}.png"))
        else:
            image_block.save(os.path.join('tmp_data', f'{img_hash}_rgba.png'))

    # stage 1
    # subprocess.run(f'export MKL_THREADING_LAYER=GNU;export MKL_SERVICE_FORCE_INTEL=1;python scripts/gen_vid.py --path tmp_data/{img_hash}_rgba.png --seed {seed_slider} --bg white', shell=True)
    gen_vid(f'tmp_data/{img_hash}_rgba.png', seed_slider)
    # return [os.path.join('logs', 'tmp_rgba_model.ply')]
    return os.path.join('tmp_data', f'{img_hash}_rgba_generated.mp4')


@spaces.GPU()
def optimize_stage_1(image_block: Image.Image, preprocess_chk: bool, seed_slider: int):
    if not os.path.exists('tmp_data'):
        os.makedirs('tmp_data')
    img_hash = hashlib.sha256(image_block.tobytes()).hexdigest()
    if not os.path.exists(os.path.join('tmp_data', f'{img_hash}_rgba.png')):
        if preprocess_chk:
            # save image to a designated path
            image_block.save(os.path.join('tmp_data', f'{img_hash}.png'))

            # preprocess image
            # print(f'python scripts/process.py {os.path.join("tmp_data", f"{img_hash}.png")}')
            # subprocess.run(f'python scripts/process.py {os.path.join("tmp_data", f"{img_hash}.png")}', shell=True)
            preprocess(os.path.join("tmp_data", f"{img_hash}.png"))
        else:
            image_block.save(os.path.join('tmp_data', f'{img_hash}_rgba.png'))

    # stage 1
    # subprocess.run(f'python lgm/infer.py big --resume {ckpt_path} --test_path tmp_data/{img_hash}_rgba.png', shell=True)
    process_lgm(opt, f'tmp_data/{img_hash}_rgba.png', pipe_mvdream, model, rays_embeddings, seed_slider)
    # return [os.path.join('logs', 'tmp_rgba_model.ply')]
    return os.path.join('vis_data', f'{img_hash}_rgba_static.mp4')

@spaces.GPU(duration=120)
def optimize_stage_2(image_block: Image.Image, seed_slider: int):
    img_hash = hashlib.sha256(image_block.tobytes()).hexdigest()

    # stage 2
    # subprocess.run(f'python main_4d.py --config {os.path.join("configs", "4d_demo.yaml")} input={os.path.join("tmp_data", f"{img_hash}_rgba.png")}', shell=True)
    process_dg4d(os.path.join("configs", "4d_demo.yaml"), os.path.join("tmp_data", f"{img_hash}_rgba.png"), guidance_zero123)
    # os.rename(os.path.join('logs', f'{img_hash}_rgba_frames'), os.path.join('logs', f'{img_hash}_{seed_slider:03d}_rgba_frames'))
    image_dir = os.path.join('logs', f'{img_hash}_rgba_frames')
    return os.path.join('vis_data', f'{img_hash}_rgba.mp4'), [image_dir+f'/{t:03d}.ply' for t in range(28)]
    # return [image_dir+f'/{t:03d}.ply' for t in range(28)]


if __name__ == "__main__":
    _TITLE = '''DreamGaussian4D: Generative 4D Gaussian Splatting'''

    _DESCRIPTION = '''
    <div>
    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2312.17142-f9f7f7?logo=data:image/png;base64,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"></a>
    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
    </div>
    We present DreamGausssion4D, an efficient 4D generation framework that builds on Gaussian Splatting. 
    '''
    _IMG_USER_GUIDE = "Please upload an image in the block above (or choose an example above), click **Generate Video** and **Generate 3D**. Finally, click **Generate 4D**."

    # load images in 'data' folder as examples
    example_folder = os.path.join(os.path.dirname(__file__), 'data')
    example_fns = os.listdir(example_folder)
    example_fns.sort()
    examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')]

    # Compose demo layout & data flow
    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft(), js=js_func) as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown('# ' + _TITLE)
        gr.Markdown(_DESCRIPTION)

        # Image-to-3D
        with gr.Row(variant='panel'):
            with gr.Column(scale=5):
                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')

                # elevation_slider = gr.Slider(-90, 90, value=0, step=1, label='Estimated elevation angle')
                seed_slider = gr.Slider(0, 100000, value=0, step=1, label='Random Seed (Video)')
                seed_slider2 = gr.Slider(0, 100000, value=0, step=1, label='Random Seed (3D)')
                gr.Markdown(
                    "random seed for video generation.")

                preprocess_chk = gr.Checkbox(True,
                                             label='Preprocess image automatically (remove background and recenter object)')

                gr.Examples(
                    examples=examples_full,  # NOTE: elements must match inputs list!
                    inputs=[image_block],
                    outputs=[image_block],
                    cache_examples=False,
                    label='Examples (click one of the images below to start)',
                    examples_per_page=40
                )
                img_run_btn = gr.Button("Generate Video")
                threed_run_btn = gr.Button("Generate 3D")
                fourd_run_btn = gr.Button("Generate 4D")
                img_guide_text = gr.Markdown(_IMG_USER_GUIDE, visible=True)

            with gr.Column(scale=5):
                with gr.Row():
                    with gr.Column(scale=5):
                        dirving_video = gr.Video(label="video",height=290)
                    with gr.Column(scale=5):
                        obj3d = gr.Video(label="3D Model",height=290)
                video4d =  gr.Video(label="4D video",height=290)
                obj4d = Model4DGS(label="4D Model", height=500, fps=28)

            
            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(optimize_stage_0,
                                                                                          inputs=[image_block,
                                                                                                  preprocess_chk,
                                                                                                  seed_slider],
                                                                                          outputs=[
                                                                                              dirving_video])

            threed_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(optimize_stage_1,
                                                                                          inputs=[image_block,
                                                                                                  preprocess_chk,
                                                                                                  seed_slider2],
                                                                                          outputs=[
                                                                                              obj3d])
            fourd_run_btn.click(check_video_3d_input, inputs=[image_block], queue=False).success(optimize_stage_2, inputs=[image_block, seed_slider], outputs=[video4d, obj4d])

    # demo.queue().launch(share=True)
    demo.queue(max_size=10)  # <-- Sets up a queue with default parameters
    demo.launch()