app.py

import gradio as gr
import os
import subprocess
import shlex
import spaces
import torch
access_token = os.getenv("HUGGINGFACE_TOKEN")
subprocess.run(
    shlex.split(
        "pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt210/download.html"
    )
)

subprocess.run(
    shlex.split(
        "pip install ./extension/nvdiffrast-0.3.1+torch-py3-none-any.whl --force-reinstall --no-deps"
    )
)

subprocess.run(
    shlex.split(
        "pip install ./extension/renderutils_plugin-1.0-cp310-cp310-linux_x86_64.whl --force-reinstall --no-deps"
    )
)
def install_cuda_toolkit():
    # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
    # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
    CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run"
    CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
    subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
    subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
    subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])

    os.environ["CUDA_HOME"] = "/usr/local/cuda"
    os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
    os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
        os.environ["CUDA_HOME"],
        "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
    )
    # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
    os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
    print("==> finfish install")
install_cuda_toolkit()
@spaces.GPU
def check_gpu():
    os.environ['CUDA_HOME'] = '/usr/local/cuda-12.1'
    os.environ['PATH'] += ':/usr/local/cuda-12.1/bin'
    # os.environ['LD_LIBRARY_PATH'] += ':/usr/local/cuda-12.1/lib64'
    os.environ['LD_LIBRARY_PATH'] = "/usr/local/cuda-12.1/lib64:" + os.environ.get('LD_LIBRARY_PATH', '')
    subprocess.run(['nvidia-smi'])  # 测试 CUDA 是否可用
    print(f"torch.cuda.is_available:{torch.cuda.is_available()}")
check_gpu()

from PIL import Image
from einops import rearrange
from diffusers import FluxPipeline
from models.lrm.utils.camera_util import get_flux_input_cameras
from models.lrm.utils.infer_util import save_video
from models.lrm.utils.mesh_util import save_obj, save_obj_with_mtl
from models.lrm.utils.render_utils import rotate_x, rotate_y
from models.lrm.utils.train_util import instantiate_from_config
from models.ISOMER.reconstruction_func import reconstruction
from models.ISOMER.projection_func import projection
import os
from einops import rearrange
from omegaconf import OmegaConf
import torch
import numpy as np
import trimesh
import torchvision
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from torchvision.transforms import v2
from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers import FluxPipeline
from pytorch_lightning import seed_everything
import os
from huggingface_hub import hf_hub_download


from utils.tool import NormalTransfer, get_background, get_render_cameras_video, load_mipmap, render_frames

device_0 = "cuda"
device_1 = "cuda"
resolution = 512
save_dir = "./outputs"
normal_transfer = NormalTransfer()
isomer_azimuths = torch.from_numpy(np.array([0, 90, 180, 270])).float().to(device_1)
isomer_elevations = torch.from_numpy(np.array([5, 5, 5, 5])).float().to(device_1)
isomer_radius = 4.5
isomer_geo_weights = torch.from_numpy(np.array([1, 0.9, 1, 0.9])).float().to(device_1)
isomer_color_weights = torch.from_numpy(np.array([1, 0.5, 1, 0.5])).float().to(device_1)

# model initialization and loading
# flux
# taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
# good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=access_token).to(device=device_0, dtype=torch.bfloat16)
# flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device_0)
flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model", token=access_token)
flux_pipe.load_lora_weights(flux_lora_ckpt_path)
flux_pipe.to(device=device_0, dtype=torch.bfloat16)
torch.cuda.empty_cache()
# flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)


# lrm
# config = OmegaConf.load("./models/lrm/config/PRM_inference.yaml")
# model_config = config.model_config
# infer_config = config.infer_config
# model = instantiate_from_config(model_config)
# model_ckpt_path = hf_hub_download(repo_id="LTT/PRM", filename="final_ckpt.ckpt", repo_type="model")
# state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
# state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.')}
# model.load_state_dict(state_dict, strict=True)
# model = model.to(device_1)
# torch.cuda.empty_cache()
@spaces.GPU
def lrm_reconstructions(image, input_cameras, save_path=None, name="temp", export_texmap=False, if_save_video=False):
    images = image.unsqueeze(0).to(device_1)
    images = v2.functional.resize(images, 512, interpolation=3, antialias=True).clamp(0, 1)
    # breakpoint()
    with torch.no_grad():
        # get triplane
        planes = model.forward_planes(images, input_cameras)
        
        mesh_path_idx = os.path.join(save_path, f'{name}.obj')

        mesh_out = model.extract_mesh(
            planes,
            use_texture_map=export_texmap,
            **infer_config,
        )
        if export_texmap:
            vertices, faces, uvs, mesh_tex_idx, tex_map = mesh_out
            save_obj_with_mtl(
                vertices.data.cpu().numpy(),
                uvs.data.cpu().numpy(),
                faces.data.cpu().numpy(),
                mesh_tex_idx.data.cpu().numpy(),
                tex_map.permute(1, 2, 0).data.cpu().numpy(),
                mesh_path_idx,
            )
        else:
            vertices, faces, vertex_colors = mesh_out
            save_obj(vertices, faces, vertex_colors, mesh_path_idx)
        print(f"Mesh saved to {mesh_path_idx}")

        render_size = 512
        if if_save_video:
            video_path_idx = os.path.join(save_path, f'{name}.mp4')
            render_size = infer_config.render_resolution
            ENV = load_mipmap("models/lrm/env_mipmap/6")
            materials = (0.0,0.9)
            
            all_mv, all_mvp, all_campos = get_render_cameras_video(
                batch_size=1, 
                M=240, 
                radius=4.5, 
                elevation=(90, 60.0),
                is_flexicubes=True,
                fov=30
            )
            
            frames, albedos, pbr_spec_lights, pbr_diffuse_lights, normals, alphas = render_frames(
                model, 
                planes, 
                render_cameras=all_mvp,
                camera_pos=all_campos,
                env=ENV,
                materials=materials,
                render_size=render_size, 
                chunk_size=20, 
                is_flexicubes=True,
            )
            normals = (torch.nn.functional.normalize(normals) + 1) / 2
            normals = normals * alphas + (1-alphas)
            all_frames = torch.cat([frames, albedos, pbr_spec_lights, pbr_diffuse_lights, normals], dim=3)
                
            save_video(
                all_frames,
                video_path_idx,
                fps=30,
            )
            print(f"Video saved to {video_path_idx}")

    return vertices, faces


def local_normal_global_transform(local_normal_images, azimuths_deg, elevations_deg):
    if local_normal_images.min() >= 0:
        local_normal = local_normal_images.float() * 2 - 1
    else:
        local_normal = local_normal_images.float()
    global_normal = normal_transfer.trans_local_2_global(local_normal, azimuths_deg, elevations_deg, radius=4.5, for_lotus=False)
    global_normal[...,0] *= -1
    global_normal = (global_normal + 1) / 2
    global_normal = global_normal.permute(0, 3, 1, 2)
    return global_normal

# 生成多视图图像
@spaces.GPU(duration=120)
def generate_multi_view_images(prompt, seed):
    # torch.cuda.empty_cache()
    # generator = torch.manual_seed(seed)
    generator = torch.Generator().manual_seed(seed)
    with torch.no_grad():
        img = flux_pipe(
            prompt=prompt,
            num_inference_steps=5,
            guidance_scale=3.5,
            num_images_per_prompt=1,
            width=resolution * 2,
            height=resolution * 1,
            output_type='np',
            generator=generator,
        ).images
        # for img in flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images(
        #     prompt=prompt,
        #     guidance_scale=3.5,
        #     num_inference_steps=4,
        #     width=resolution * 4,
        #     height=resolution * 2,
        #     generator=generator,
        #     output_type="np",
        #     good_vae=good_vae,
        # ):
        #     pass
    # 返回最终的图像和种子（通过外部调用处理）
    return img

# 重建 3D 模型
@spaces.GPU
def reconstruct_3d_model(images, prompt):
    global model
    model.init_flexicubes_geometry(device_1, fovy=50.0)
    model = model.eval()
    rgb_normal_grid = images
    save_dir_path = os.path.join(save_dir, prompt.replace(" ", "_"))
    os.makedirs(save_dir_path, exist_ok=True)

    images = torch.from_numpy(rgb_normal_grid).squeeze(0).permute(2, 0, 1).contiguous().float()     # (3, 1024, 2048)
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=2, m=4)        # (8, 3, 512, 512)
    rgb_multi_view = images[:4, :3, :, :]
    normal_multi_view = images[4:, :3, :, :]
    multi_view_mask = get_background(normal_multi_view)
    rgb_multi_view = rgb_multi_view * rgb_multi_view + (1-multi_view_mask)
    input_cameras = get_flux_input_cameras(batch_size=1, radius=4.2, fov=30).to(device_1)
    vertices, faces = lrm_reconstructions(rgb_multi_view, input_cameras, save_path=save_dir_path, name='lrm', export_texmap=False, if_save_video=False)
    # local normal to global normal

    global_normal = local_normal_global_transform(normal_multi_view.permute(0, 2, 3, 1), isomer_azimuths, isomer_elevations)
    global_normal = global_normal * multi_view_mask + (1-multi_view_mask)

    global_normal = global_normal.permute(0,2,3,1)
    rgb_multi_view = rgb_multi_view.permute(0,2,3,1)
    multi_view_mask = multi_view_mask.permute(0,2,3,1).squeeze(-1)
    vertices = torch.from_numpy(vertices).to(device_1)
    faces = torch.from_numpy(faces).to(device_1)
    vertices = vertices @ rotate_x(np.pi / 2, device=vertices.device)[:3, :3]
    vertices = vertices @ rotate_y(np.pi / 2, device=vertices.device)[:3, :3]

    # global_normal: B,H,W,3
    # multi_view_mask: B,H,W
    # rgb_multi_view: B,H,W,3
    
    meshes = reconstruction(
        normal_pils=global_normal, 
        masks=multi_view_mask, 
        weights=isomer_geo_weights, 
        fov=30, 
        radius=isomer_radius, 
        camera_angles_azi=isomer_azimuths, 
        camera_angles_ele=isomer_elevations, 
        expansion_weight_stage1=0.1,
        init_type="file",
        init_verts=vertices,
        init_faces=faces,
        stage1_steps=0,
        stage2_steps=50,
        start_edge_len_stage1=0.1,
        end_edge_len_stage1=0.02,
        start_edge_len_stage2=0.02,
        end_edge_len_stage2=0.005,
    )


    save_glb_addr = projection(
        meshes,
        masks=multi_view_mask,
        images=rgb_multi_view,
        azimuths=isomer_azimuths, 
        elevations=isomer_elevations, 
        weights=isomer_color_weights,
        fov=30,
        radius=isomer_radius,
        save_dir=f"{save_dir_path}/ISOMER/",
    )

    return save_glb_addr

# Gradio 接口函数
@spaces.GPU
def gradio_pipeline(prompt, seed):
    # 生成多视图图像
    rgb_normal_grid = generate_multi_view_images(prompt, seed)
    # rgb_normal_grid = np.load("rgb_normal_grid.npy")
    image_preview = Image.fromarray((rgb_normal_grid[0] * 255).astype(np.uint8))

    # 3d reconstruction


    # 重建 3D 模型并返回 glb 路径
    # save_glb_addr = reconstruct_3d_model(rgb_normal_grid, prompt)
    save_glb_addr = None
    return image_preview, save_glb_addr

# Gradio Blocks 应用
with gr.Blocks() as demo:
    with gr.Row(variant="panel"):
        # 左侧输入区域
        with gr.Column():
            with gr.Row():
                prompt_input = gr.Textbox(
                    label="Enter Prompt",
                    placeholder="Describe your 3D model...",
                    lines=2,
                    elem_id="prompt_input"
                )

            with gr.Row():
                sample_seed = gr.Number(value=42, label="Seed Value", precision=0)

            with gr.Row():
                submit = gr.Button("Generate", elem_id="generate", variant="primary")

            with gr.Row(variant="panel"):
                gr.Markdown("Examples:")
                gr.Examples(
                    examples=[
                        ["a castle on a hill"],
                        ["an owl wearing a hat"],
                        ["a futuristic car"]
                    ],
                    inputs=[prompt_input],
                    label="Prompt Examples"
                )

        # 右侧输出区域
        with gr.Column():
            with gr.Row():
                rgb_normal_grid_image = gr.Image(
                    label="RGB Normal Grid",
                    type="pil",
                    interactive=False
                )

            with gr.Row():
                with gr.Tab("GLB"):
                    output_glb_model = gr.Model3D(
                        label="Generated 3D Model (GLB Format)",
                        interactive=False
                    )
                    gr.Markdown("Download the model for proper visualization.")

    # 处理逻辑
    submit.click(
        fn=gradio_pipeline, inputs=[prompt_input, sample_seed],
        outputs=[rgb_normal_grid_image, output_glb_model]
    )

# 启动应用
# demo.queue(max_size=10)
demo.launch()