app.py

import gradio as gr
import spaces
from gradio_litmodel3d import LitModel3D
import io
import os
import os
os.environ["OMP_NUM_THREADS"] = "3500"


import shutil
os.environ['SPCONV_ALGO'] = 'native'
from typing import *
import torch
import numpy as np
import imageio
from easydict import EasyDict as edict
from PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.representations import Gaussian, MeshExtractResult
from trellis.utils import render_utils, postprocessing_utils


MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
os.makedirs(TMP_DIR, exist_ok=True)

# pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
# pipeline.cuda()

def start_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    
    
def end_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    shutil.rmtree(user_dir)


def preprocess_image(image: Image.Image) -> Image.Image:
    """
    Preprocess the input image.

    Args:
        image (Image.Image): The input image.

    Returns:
        Image.Image: The preprocessed image.
    """
    processed_image = pipeline.preprocess_image(image)
    return processed_image


def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
    """
    Preprocess a list of input images.
    
    Args:
        images (List[Tuple[Image.Image, str]]): The input images.
        
    Returns:
        List[Image.Image]: The preprocessed images.
    """
    images = [image[0] for image in images]
    processed_images = [pipeline.preprocess_image(image) for image in images]
    return processed_images


def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
    return {
        'gaussian': {
            **gs.init_params,
            '_xyz': gs._xyz.cpu().numpy(),
            '_features_dc': gs._features_dc.cpu().numpy(),
            '_scaling': gs._scaling.cpu().numpy(),
            '_rotation': gs._rotation.cpu().numpy(),
            '_opacity': gs._opacity.cpu().numpy(),
        },
        'mesh': {
            'vertices': mesh.vertices.cpu().numpy(),
            'faces': mesh.faces.cpu().numpy(),
        },
    }
    
    
def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
    gs = Gaussian(
        aabb=state['gaussian']['aabb'],
        sh_degree=state['gaussian']['sh_degree'],
        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
        scaling_bias=state['gaussian']['scaling_bias'],
        opacity_bias=state['gaussian']['opacity_bias'],
        scaling_activation=state['gaussian']['scaling_activation'],
    )
    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
    
    mesh = edict(
        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
    )
    
    return gs, mesh


def get_seed(randomize_seed: bool, seed: int) -> int:
    """
    Get the random seed.
    """
    return np.random.randint(0, MAX_SEED) if randomize_seed else seed


@spaces.GPU
def image_to_3d(
    image: Image.Image,
    multiimages: List[Tuple[Image.Image, str]],
    is_multiimage: bool,
    seed: int,
    ss_guidance_strength: float,
    ss_sampling_steps: int,
    slat_guidance_strength: float,
    slat_sampling_steps: int,
    multiimage_algo: Literal["multidiffusion", "stochastic"],
    req: gr.Request,
) -> Tuple[dict, str]:
    """
    Convert an image to a 3D model.

    Args:
        image (Image.Image): The input image.
        multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
        is_multiimage (bool): Whether is in multi-image mode.
        seed (int): The random seed.
        ss_guidance_strength (float): The guidance strength for sparse structure generation.
        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
        slat_guidance_strength (float): The guidance strength for structured latent generation.
        slat_sampling_steps (int): The number of sampling steps for structured latent generation.
        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.

    Returns:
        dict: The information of the generated 3D model.
        str: The path to the video of the 3D model.
    """
    
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    if not is_multiimage:
        outputs = pipeline.run(
            image,
            seed=seed,
            formats=["gaussian", "mesh"],
            preprocess_image=False,
            sparse_structure_sampler_params={
                "steps": ss_sampling_steps,
                "cfg_strength": ss_guidance_strength,
            },
            slat_sampler_params={
                "steps": slat_sampling_steps,
                "cfg_strength": slat_guidance_strength,
            },
        )
    else:
        outputs = pipeline.run_multi_image(
            [image[0] for image in multiimages],
            seed=seed,
            formats=["gaussian", "mesh"],
            preprocess_image=False,
            sparse_structure_sampler_params={
                "steps": ss_sampling_steps,
                "cfg_strength": ss_guidance_strength,
            },
            slat_sampler_params={
                "steps": slat_sampling_steps,
                "cfg_strength": slat_guidance_strength,
            },
            mode=multiimage_algo,
        )
    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
    video_path = os.path.join(user_dir, 'sample.mp4')
    imageio.mimsave(video_path, video, fps=15)
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
    torch.cuda.empty_cache()
    return state, video_path


@spaces.GPU
def image_to_3d2(
    image: Image.Image,
    seed: int,
    ss_guidance_strength: float,
    ss_sampling_steps: int,
    slat_guidance_strength: float,
    slat_sampling_steps: int,
) -> Tuple[dict, str]:
    
    outputs = pipeline.run(
        image,
        seed=seed,
        formats=["gaussian", "mesh"],
        preprocess_image=False,
        sparse_structure_sampler_params={
            "steps": ss_sampling_steps,
            "cfg_strength": ss_guidance_strength,
        },
        slat_sampler_params={
            "steps": slat_sampling_steps,
            "cfg_strength": slat_guidance_strength,
        },
    )

    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
        video_path = temp_video.name
        imageio.mimsave(video_path, video, fps=15)
    
    torch.cuda.empty_cache()
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
    return state, video_path

import random
@spaces.GPU(duration=90)
def extract_glb(
    state: dict,
    mesh_simplify: float,
    texture_size: int,
    req: gr.Request,
) -> Tuple[str, str]:
    """
    Extract a GLB file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.
        mesh_simplify (float): The mesh simplification factor.
        texture_size (int): The texture resolution.

    Returns:
        str: The path to the extracted GLB file.
    """
    user_dir = TMP_DIR
    gs, mesh = unpack_state(state)
    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
    glb_path = os.path.join(user_dir, f"test_{random.random()}.glb")

    glb.export(glb_path)
    torch.cuda.empty_cache()
    return glb_path, glb_path


@spaces.GPU
def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
    """
    Extract a Gaussian file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.

    Returns:
        str: The path to the extracted Gaussian file.
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    gs, _ = unpack_state(state)
    gaussian_path = os.path.join(user_dir, 'sample.ply')
    gs.save_ply(gaussian_path)
    torch.cuda.empty_cache()
    return gaussian_path, gaussian_path


def prepare_multi_example() -> List[Image.Image]:
    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
    images = []
    for case in multi_case:
        _images = []
        for i in range(1, 4):
            img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
            W, H = img.size
            img = img.resize((int(W / H * 512), 512))
            _images.append(np.array(img))
        images.append(Image.fromarray(np.concatenate(_images, axis=1)))
    return images


def split_image(image: Image.Image) -> List[Image.Image]:
    """
    Split an image into multiple views.
    """
    image = np.array(image)
    alpha = image[..., 3]
    alpha = np.any(alpha>0, axis=0)
    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
    images = []
    for s, e in zip(start_pos, end_pos):
        images.append(Image.fromarray(image[:, s:e+1]))
    return [preprocess_image(image) for image in images]

from fastapi import FastAPI, File, UploadFile, HTTPException, Depends, Header, Response
from fastapi.responses import JSONResponse, FileResponse, StreamingResponse
import tempfile
import os

app = FastAPI()

def verify_token(authorization: str = Header(...)):
    if not authorization.startswith("Bearer "):
        raise HTTPException(status_code=403, detail="Invalid or missing token")
    
    token = authorization.split("Bearer ")[1]
    if token != os.getenv("AUTH_TOKEN"):
        raise HTTPException(status_code=403, detail="Invalid or missing token")

@app.post("/generate")
async def generate_3d(image: UploadFile = File(...), token: str = Depends(verify_token)):
    if not image:
        raise HTTPException(status_code=400, detail="No image provided")

    try:
        image_data = Image.open(image.file)
        image_data = image_data.convert("RGBA")
        image_data = preprocess_image(image_data)

        seed = 42
        ss_guidance_strength = 7.5
        ss_sampling_steps = 12
        slat_guidance_strength = 3.0
        slat_sampling_steps = 12

        state, _ = image_to_3d2(
            image_data,
            seed=seed,
            ss_guidance_strength=ss_guidance_strength,
            ss_sampling_steps=ss_sampling_steps,
            slat_guidance_strength=slat_guidance_strength,
            slat_sampling_steps=slat_sampling_steps,
        )
        mesh_simplify = 0.95
        texture_size = 1024
        glb_path, _ = extract_glb(
            state=state,
            mesh_simplify=mesh_simplify,
            texture_size=texture_size,
            req=None  # Assuming req is not needed here
        )
        return FileResponse(glb_path, media_type='application/octet-stream', filename='model.glb')

    except Exception as e:
        print("ERROR IN GENERATING 3D FILE : " , e)
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/remove-image-background")
async def remove_image_background(image: UploadFile = File(...), token: str = Depends(verify_token)):
    if not image:
        raise HTTPException(status_code=400, detail="No image provided")

    try:
        image_data = Image.open(image.file)
        image_data = image_data.convert("RGBA")
        image_data = preprocess_image(image_data)
        buffer = io.BytesIO()
        image_data.save(buffer, format="PNG")
        buffer.seek(0)

        return StreamingResponse(
            content=buffer,
            media_type='image/png',
            headers={
                "Content-Disposition": "inline; filename=image.png",
                "Content-Length": str(len(buffer.getvalue()))
            }
        )

    except Exception as e:
        print(e)
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
def root_route():
    return JSONResponse({"message": "Hi"})

import requests
# Launch the Gradio app
if __name__ == "__main__":
    print("APPLICATION IS RUNNING ...")
    import uvicorn
    
    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
    pipeline.cuda()
    
    print("STARTING SERVER ...")
    uvicorn.run(app, host="0.0.0.0", port=7860)
    print("SERVER STARTED!!!")
    
    #When ever server starts running again, it will send a request to the client application, to run the requests queues
    requests.get(os.getenv("REQUEST_QUEUE_URL"))