app.py

import torch
import gradio as gr
import os
import numpy as np
import trimesh
import mcubes
from torchvision.utils import save_image
from PIL import Image
from transformers import AutoModel, AutoConfig
from rembg import remove, new_session
from functools import partial
from kiui.op import recenter
import kiui


# we load the pre-trained model from HF
class LRMGeneratorWrapper:
    def __init__(self):
        self.config = AutoConfig.from_pretrained("jadechoghari/custom-llrm", trust_remote_code=True)
        self.model = AutoModel.from_pretrained("jadechoghari/custom-llrm", trust_remote_code=True)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()

    def forward(self, image, camera):
        return self.model(image, camera)

model_wrapper = LRMGeneratorWrapper()


def preprocess_image(image, source_size):
    session = new_session("isnet-general-use")
    rembg_remove = partial(remove, session=session)
    image = np.array(image)
    image = rembg_remove(image)
    mask = rembg_remove(image, only_mask=True)
    image = recenter(image, mask, border_ratio=0.20)
    image = torch.tensor(image).permute(2, 0, 1).unsqueeze(0) / 255.0
    if image.shape[1] == 4:
        image = image[:, :3, ...] * image[:, 3:, ...] + (1 - image[:, 3:, ...])
    image = torch.nn.functional.interpolate(image, size=(source_size, source_size), mode='bicubic', align_corners=True)
    image = torch.clamp(image, 0, 1)
    return image

def get_normalized_camera_intrinsics(intrinsics: torch.Tensor):
    """
    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
    Return batched fx, fy, cx, cy
    """
    fx, fy = intrinsics[:, 0, 0], intrinsics[:, 0, 1]
    cx, cy = intrinsics[:, 1, 0], intrinsics[:, 1, 1]
    width, height = intrinsics[:, 2, 0], intrinsics[:, 2, 1]
    fx, fy = fx / width, fy / height
    cx, cy = cx / width, cy / height
    return fx, fy, cx, cy


def build_camera_principle(RT: torch.Tensor, intrinsics: torch.Tensor):
    """
    RT: (N, 3, 4)
    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
    """
    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
    return torch.cat([
        RT.reshape(-1, 12),
        fx.unsqueeze(-1), fy.unsqueeze(-1), cx.unsqueeze(-1), cy.unsqueeze(-1),
    ], dim=-1)


def _default_intrinsics():
        fx = fy = 384
        cx = cy = 256
        w = h = 512
        intrinsics = torch.tensor([
            [fx, fy],
            [cx, cy],
            [w, h],
        ], dtype=torch.float32)
        return intrinsics

def _default_source_camera(batch_size: int = 1):
        dist_to_center = 1.5
        canonical_camera_extrinsics = torch.tensor([[
            [0, 0, 1, 1],
            [1, 0, 0, 0],
            [0, 1, 0, 0],
        ]], dtype=torch.float32)
        canonical_camera_intrinsics = _default_intrinsics().unsqueeze(0)
        source_camera = build_camera_principle(canonical_camera_extrinsics, canonical_camera_intrinsics)
        return source_camera.repeat(batch_size, 1)


#Ref: https://github.com/jadechoghari/vfusion3d/blob/main/lrm/inferrer.py
def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export_mesh=True):
    image = preprocess_image(image, source_size).to(model_wrapper.device)
    source_camera = _default_source_camera(batch_size=1).to(model_wrapper.device)
    # TODO: export video we need render_camera
    # render_camera = _default_render_cameras(batch_size=1).to(model_wrapper.device)

    with torch.no_grad():
        planes = model_wrapper.forward(image, source_camera)
        
        if export_mesh:
            grid_out = model_wrapper.model.synthesizer.forward_grid(planes=planes, grid_size=mesh_size)
            vtx, faces = mcubes.marching_cubes(grid_out['sigma'].float().squeeze(0).squeeze(-1).cpu().numpy(), 1.0)
            vtx = vtx / (mesh_size - 1) * 2 - 1
            vtx_tensor = torch.tensor(vtx, dtype=torch.float32, device=model_wrapper.device).unsqueeze(0)
            vtx_colors = model_wrapper.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].float().squeeze(0).cpu().numpy()
            vtx_colors = (vtx_colors * 255).astype(np.uint8)
            mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
            
            mesh_path = "awesome_mesh.obj"
            mesh.export(mesh_path, 'obj')
            return mesh_path

# we will convert image to mesh
def step_1_generate_obj(image):
    mesh_path = generate_mesh(image)
    return mesh_path

# we will convert mesh to 3d-image
def step_2_display_3d_model(mesh_file):
    return mesh_file

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            img_input = gr.Image(type="pil", label="Input Image")
            generate_button = gr.Button("Generate and Visualize 3D Model")
            obj_file_output = gr.File(label="Download .obj File")
            
        with gr.Column():
            model_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model Visualization")
    
    def generate_and_visualize(image):
        mesh_path = step_1_generate_obj(image)
        return mesh_path, mesh_path
    
    generate_button.click(generate_and_visualize, inputs=img_input, outputs=[obj_file_output, model_output])

demo.launch()