app.py

import spaces
import gradio as gr

import numpy as np
import torch
import rembg
from PIL import Image
from functools import partial

import logging
import os
import shlex
import subprocess
import tempfile
import time
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from einops import rearrange, repeat
from tqdm import tqdm
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler

import os
import imageio
import numpy as np
import torch
import rembg
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from einops import rearrange, repeat
from tqdm import tqdm
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler

import tempfile
from functools import partial

from huggingface_hub import hf_hub_download

from instantmesh.utils import get_render_cameras, find_cuda, check_input_image, generate_mvs, make3d
from instantmesh.src.utils.train_util import instantiate_from_config

# This was the code needed for TripoSR
"""
subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl'))

from tsr.system import TSR
from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
"""

HEADER = """
# Generate 3D Assets for Roblox

With this Space, you can generate 3D Assets using AI for your Roblox game for free.

Simply follow the 4 steps below.

1. Generate a 3D Mesh using an image model as input.
2. Simplify the Mesh to get lower polygon number
3. (Optional) make the Mesh more smooth
4. Get the Material

We wrote a tutorial here

"""

STEP1_HEADER = """
## Step 1: Generate the 3D Mesh

For this step, we use <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>InstantMesh</a>, an open-source model for **fast** feedforward 3D mesh generation from a single image.

During this step, you need to upload an image of what you want to generate a 3D Model from.


## 💡 Tips

-  If there's a background, ✅ Remove background.

- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
"""

STEP2_HEADER = """
## Step 2: Simplify the generated 3D Mesh

ADD ILLUSTRATION

The 3D Mesh Generated contains too much polygons, fortunately, we can use another AI model to help us optimize it.

The model we use is called [MeshAnythingV2]().


## 💡 Tips

- We don't click on Preprocess with marching Cubes, because in the last step the input mesh was produced by it.

- Limited by computational resources, MeshAnything is trained on meshes with fewer than 1600 faces and cannot generate meshes with more than 1600 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 1600 faces. Thus, feed-forward image-to-3D methods may often produce bad results due to insufficient shape quality.

"""

STEP3_HEADER = """
## Step 3 (optional): Shader Smooth

- The mesh simplified in step 2, looks low poly. One way to make it more smooth is to use Shader Smooth.
- You can usually do it in Blender, but we can do it directly here

ADD ILLUSTRATION

ADD SHADERSMOOTH
"""

STEP4_HEADER = """
## Step 4: Get the Mesh Material

"""

###############################################################################
# Configuration for InstantMesh
# All this code is from https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/app.py
###############################################################################
cuda_path = find_cuda()

if cuda_path:
    print(f"CUDA installation found at: {cuda_path}")
else:
    print("CUDA installation not found")

config_path = 'instantmesh/configs/instant-mesh-large.yaml'
config = OmegaConf.load(config_path)
config_name = os.path.basename(config_path).replace('.yaml', '')
model_config = config.model_config
infer_config = config.infer_config

IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False

device = torch.device('cuda')

# load diffusion model
print('Loading diffusion model ...')
pipeline = DiffusionPipeline.from_pretrained(
    "sudo-ai/zero123plus-v1.2", 
    custom_pipeline="zero123plus",
    torch_dtype=torch.float16,
)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
    pipeline.scheduler.config, timestep_spacing='trailing'
)

# load custom white-background UNet
unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
state_dict = torch.load(unet_ckpt_path, map_location='cpu')
pipeline.unet.load_state_dict(state_dict, strict=True)

pipeline = pipeline.to(device)

# load reconstruction model
print('Loading reconstruction model ...')
model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
model = instantiate_from_config(model_config)
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.') and 'source_camera' not in k}
model.load_state_dict(state_dict, strict=True)

model = model.to(device)

print('Loading Finished!')



with gr.Blocks() as demo:
	gr.Markdown(HEADER)
	gr.Markdown(STEP1_HEADER)
	with gr.Row(variant = "panel"):
		with gr.Column():
			with gr.Row():
				input_image = gr.Image(
					label = "Input Image",
					image_mode = "RGBA",
					sources = "upload",
					type="pil",
					elem_id="content_image"
				)
				processed_image = gr.Image(label="Processed Image",
					image_mode="RGBA",
					type="pil",
					interactive=False
				)
			with gr.Row():
				with gr.Group():
					do_remove_background = gr.Checkbox(
						label="Remove Background",
						value=True)
					sample_seed = gr.Number(
						value=42, 
						label="Seed Value", 
						precision=0
					)
					sample_steps = gr.Slider(
                        label="Sample Steps",
                        minimum=30,
                        maximum=75,
                        value=75,
                        step=5
                    )
			with gr.Row():
				step1_submit = gr.Button("Generate", elem_id="generate", variant="primary")
		with gr.Column():
			with gr.Row():
				with gr.Column():
					mv_show_images = gr.Image(
                        label="Generated Multi-views",
                        type="pil",
                        width=379,
                        interactive=False
                    )
		with gr.Column():
			with gr.Tab("OBJ"):
				output_model_obj = gr.Model3D(
					label = "Output Model (OBJ Format)",
					interactive = False,
				)
				gr.Markdown("Note: Downloaded object will be flipped in case of .obj export. Export .glb instead or manually flip it before usage.")
			with gr.Tab("GLB"):
				output_model_glb = gr.Model3D(
                    label="Output Model (GLB Format)",
                    interactive=False,
				)
				gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
			with gr.Row():
				gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
	
	mv_images = gr.State()

	step1_submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=preprocess,
        inputs=[input_image, do_remove_background],
        outputs=[processed_image],
    ).success(
        fn=generate_mvs,
        inputs=[processed_image, sample_steps, sample_seed],
        outputs=[mv_images, mv_show_images],
    ).success(
        fn=make3d,
        inputs=[mv_images],
        outputs=[output_model_obj, output_model_glb]
    )
	gr.Markdown(STEP2_HEADER)
	gr.Markdown(STEP3_HEADER)
	gr.Markdown(STEP4_HEADER)

demo.queue(max_size=10)
demo.launch()