gradio_app.py

import os
import torch
import fire
import gradio as gr
from PIL import Image
from functools import partial

import cv2
import time
import numpy as np
from rembg import remove
from segment_anything import sam_model_registry, SamPredictor

import os
import sys
import numpy
import torch
import rembg
import threading
import urllib.request
from PIL import Image
from typing import Dict, Optional, Tuple, List
from dataclasses import dataclass
import huggingface_hub
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel
from mvdiffusion.data.single_image_dataset import SingleImageDataset as MVDiffusionDataset
from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from einops import rearrange
import numpy as np





def save_image(tensor):
    ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
    # pdb.set_trace()
    im = Image.fromarray(ndarr)
    return ndarr

weight_dtype = torch.float16

_TITLE = '''Wonder3D: Single Image to 3D using Cross-Domain Diffusion'''
_DESCRIPTION = '''
<div>
Generate consistent multi-view normals maps and color images.
<a style="display:inline-block; margin-left: .5em" href='https://github.com/xxlong0/Wonder3D/'><img src='https://img.shields.io/github/stars/xxlong0/Wonder3D?style=social' /></a>
</div>
'''
_GPU_ID = 0


if not hasattr(Image, 'Resampling'):
    Image.Resampling = Image


def sam_init():
    sam_checkpoint = os.path.join(os.path.dirname(__file__), "sam_pt", "sam_vit_h_4b8939.pth")
    model_type = "vit_h"

    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{_GPU_ID}")
    predictor = SamPredictor(sam)
    return predictor

def sam_segment(predictor, input_image, *bbox_coords):
    bbox = np.array(bbox_coords)
    image = np.asarray(input_image)

    start_time = time.time()
    predictor.set_image(image)

    masks_bbox, scores_bbox, logits_bbox = predictor.predict(
        box=bbox,
        multimask_output=True
    )

    print(f"SAM Time: {time.time() - start_time:.3f}s")
    out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    out_image[:, :, :3] = image
    out_image_bbox = out_image.copy()
    out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
    torch.cuda.empty_cache()
    return Image.fromarray(out_image_bbox, mode='RGBA') 

def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result

def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=False):
    RES = 1024
    input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
    if chk_group is not None:
        segment = "Background Removal" in chk_group
        rescale = "Rescale" in chk_group
    if segment:
        image_rem = input_image.convert('RGBA')
        image_nobg = remove(image_rem, alpha_matting=True)
        arr = np.asarray(image_nobg)[:,:,-1]
        x_nonzero = np.nonzero(arr.sum(axis=0))
        y_nonzero = np.nonzero(arr.sum(axis=1))
        x_min = int(x_nonzero[0].min())
        y_min = int(y_nonzero[0].min())
        x_max = int(x_nonzero[0].max())
        y_max = int(y_nonzero[0].max())
        input_image = sam_segment(predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max)
    # Rescale and recenter
    if rescale:
        image_arr = np.array(input_image)
        in_w, in_h = image_arr.shape[:2]
        out_res = min(RES, max(in_w, in_h))
        ret, mask = cv2.threshold(np.array(input_image.split()[-1]), 0, 255, cv2.THRESH_BINARY)
        x, y, w, h = cv2.boundingRect(mask)
        max_size = max(w, h)
        ratio = 0.75
        side_len = int(max_size / ratio)
        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
        center = side_len//2
        padded_image[center-h//2:center-h//2+h, center-w//2:center-w//2+w] = image_arr[y:y+h, x:x+w]
        rgba = Image.fromarray(padded_image).resize((out_res, out_res), Image.LANCZOS)

        rgba_arr = np.array(rgba) / 255.0
        rgb = rgba_arr[...,:3] * rgba_arr[...,-1:] + (1 - rgba_arr[...,-1:])
        input_image = Image.fromarray((rgb * 255).astype(np.uint8))
    else:
        input_image = expand2square(input_image, (127, 127, 127, 0))
    return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)


def load_wonder3d_pipeline(cfg):
    # Load scheduler, tokenizer and models.
    # noise_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler")
    image_encoder = CLIPVisionModelWithProjection.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_encoder", revision=cfg.revision)
    feature_extractor = CLIPImageProcessor.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="feature_extractor", revision=cfg.revision)
    vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision)
    unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_unet_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
    unet.enable_xformers_memory_efficient_attention()

    # Move text_encode and vae to gpu and cast to weight_dtype
    image_encoder.to(dtype=weight_dtype)
    vae.to(dtype=weight_dtype)
    unet.to(dtype=weight_dtype)

    pipeline = MVDiffusionImagePipeline(
        image_encoder=image_encoder, feature_extractor=feature_extractor, vae=vae, unet=unet, safety_checker=None,
        scheduler=DDIMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
        **cfg.pipe_kwargs
    )

    if torch.cuda.is_available():
        pipeline.to('cuda:0')
    # sys.main_lock = threading.Lock()
    return pipeline

from mvdiffusion.data.single_image_dataset import SingleImageDataset
def prepare_data(single_image, crop_size):
    dataset = SingleImageDataset(
        root_dir = None,
        num_views = 6,
        img_wh=[256, 256],
        bg_color='white',
        crop_size=crop_size,
        single_image=single_image
    )
    return dataset[0]


def run_pipeline(pipeline, cfg, single_image, guidance_scale, steps, seed, crop_size):
    import pdb
    # pdb.set_trace()

    batch = prepare_data(single_image, crop_size)

    pipeline.set_progress_bar_config(disable=True)
    seed = int(seed)
    generator = torch.Generator(device=pipeline.unet.device).manual_seed(seed)

    # repeat  (2B, Nv, 3, H, W)
    imgs_in = torch.cat([batch['imgs_in']]*2, dim=0).to(weight_dtype)
    
    # (2B, Nv, Nce)
    camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0).to(weight_dtype)

    task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0).to(weight_dtype)

    camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1).to(weight_dtype)

    # (B*Nv, 3, H, W)
    imgs_in = rearrange(imgs_in, "Nv C H W -> (Nv) C H W")
    # (B*Nv, Nce)
    # camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce")

    out = pipeline(
        imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, 
        num_inference_steps=steps,
        output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
    ).images

    bsz = out.shape[0] // 2
    normals_pred = out[:bsz]
    images_pred = out[bsz:]

    normals_pred = [save_image(normals_pred[i]) for i in range(bsz)]
    images_pred = [save_image(images_pred[i]) for i in range(bsz)]

    out = images_pred + normals_pred
    return *out, images_pred, normals_pred


@dataclass
class TestConfig:
    pretrained_model_name_or_path: str
    pretrained_unet_path:str
    revision: Optional[str]
    validation_dataset: Dict
    save_dir: str
    seed: Optional[int]
    validation_batch_size: int
    dataloader_num_workers: int

    local_rank: int

    pipe_kwargs: Dict
    pipe_validation_kwargs: Dict
    unet_from_pretrained_kwargs: Dict
    validation_guidance_scales: List[float]
    validation_grid_nrow: int
    camera_embedding_lr_mult: float

    num_views: int
    camera_embedding_type: str

    pred_type: str  # joint, or ablation

    enable_xformers_memory_efficient_attention: bool

    cond_on_normals: bool
    cond_on_colors: bool


def run_demo():
    from utils.misc import load_config    
    from omegaconf import OmegaConf
    # parse YAML config to OmegaConf
    cfg = load_config("./configs/mvdiffusion-joint-ortho-6views.yaml")
    # print(cfg)
    schema = OmegaConf.structured(TestConfig)
    cfg = OmegaConf.merge(schema, cfg)

    pipeline = load_wonder3d_pipeline(cfg)
    torch.set_grad_enabled(False)
    pipeline.to(f'cuda:{_GPU_ID}')

    predictor = sam_init()

    custom_theme = gr.themes.Soft(primary_hue="blue").set(
                    button_secondary_background_fill="*neutral_100",
                    button_secondary_background_fill_hover="*neutral_200")
    custom_css = '''#disp_image {
        text-align: center; /* Horizontally center the content */
    }'''

    with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown('# ' + _TITLE)
        gr.Markdown(_DESCRIPTION)
        with gr.Row(variant='panel'):
            with gr.Column(scale=1):
                input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', tool=None)

                example_folder = os.path.join(os.path.dirname(__file__), "./example_images")
                example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
                gr.Examples(
                    examples=example_fns,
                    inputs=[input_image],
                    # outputs=[input_image],
                    cache_examples=False,
                    label='Examples (click one of the images below to start)',
                    examples_per_page=30
                )
            with gr.Column(scale=1):
                processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320, tool=None, image_mode='RGBA', elem_id="disp_image")
                processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False, tool=None)

                with gr.Accordion('Advanced options', open=True):
                    with gr.Row():
                        with gr.Column():
                            input_processing = gr.CheckboxGroup(['Background Removal'], 
                                                                label='Input Image Preprocessing',
                                                                 value=['Background Removal'],
                                                                 info='untick this, if masked image with alpha channel')
                        with gr.Column():
                            output_processing = gr.CheckboxGroup(['Background Removal'], label='Output Image Postprocessing', value=[]) 
                    with gr.Row():
                        with gr.Column():
                            scale_slider = gr.Slider(1, 5, value=3, step=1,
                                                        label='Classifier Free Guidance Scale')
                        with gr.Column():
                            steps_slider = gr.Slider(15, 100, value=50, step=1,
                                                        label='Number of Diffusion Inference Steps')
                    with gr.Row():
                        with gr.Column():
                            seed = gr.Number(42, label='Seed')
                        with gr.Column():
                            crop_size = gr.Number(210, label='Crop size')
                    # crop_size = 192
                run_btn = gr.Button('Generate', variant='primary', interactive=True)
        with gr.Row():
            view_1 = gr.Image(interactive=False, height=240, show_label=False)
            view_2 = gr.Image(interactive=False, height=240, show_label=False)
            view_3 = gr.Image(interactive=False, height=240, show_label=False)
            view_4 = gr.Image(interactive=False, height=240, show_label=False)
            view_5 = gr.Image(interactive=False, height=240, show_label=False)
            view_6 = gr.Image(interactive=False, height=240, show_label=False)
        with gr.Row():
            normal_1 = gr.Image(interactive=False, height=240, show_label=False)
            normal_2 = gr.Image(interactive=False, height=240, show_label=False)
            normal_3 = gr.Image(interactive=False, height=240, show_label=False)
            normal_4 = gr.Image(interactive=False, height=240, show_label=False)
            normal_5 = gr.Image(interactive=False, height=240, show_label=False)
            normal_6 = gr.Image(interactive=False, height=240, show_label=False)

        with gr.Row():
            view_gallery = gr.Gallery(interactive=False, show_label=False, container=True, preview=True,  allow_preview=False, height=1200)
            normal_gallery = gr.Gallery(interactive=False, show_label=False, container=True, preview=True, allow_preview=False, height=1200)
        

        run_btn.click(fn=partial(preprocess, predictor), 
                        inputs=[input_image, input_processing], 
                        outputs=[processed_image_highres, processed_image], queue=True
            ).success(fn=partial(run_pipeline, pipeline, cfg), 
                        inputs=[processed_image_highres, scale_slider, steps_slider, seed, crop_size],
                        outputs=[view_1, view_2, view_3, view_4, view_5, view_6, 
                                 normal_1, normal_2, normal_3, normal_4, normal_5, normal_6, 
                                 view_gallery, normal_gallery]
                        )
        demo.queue().launch(share=True, max_threads=80)


if __name__ == '__main__':
    fire.Fire(run_demo)