app.py

import gradio as gr
import spaces
from gradio_litmodel3d import LitModel3D

import os
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)


# def start_session(req: gr.Request):
#     gr.Warning('start start session')
#     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
#     os.makedirs(user_dir, exist_ok=True)
#     gr.Warning('end start session')
    
    
# 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 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,
#     seed: int,
#     ss_guidance_strength: float,
#     ss_sampling_steps: int,
#     slat_guidance_strength: float,
#     slat_sampling_steps: int,
#     req: gr.Request,
# ) -> Tuple[dict, str]:
#     """
#     Convert an image to a 3D model.

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

#     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))
#     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))]
#     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(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 = os.path.join(TMP_DIR, str(req.session_hash))
#     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, 'sample.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 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]


with gr.Blocks(delete_cache=(600, 600)) as demo:
    gr.Markdown("""
    ## Text to 3D Asset with Mistral + Flux + Trellis 
    * Upload an image and click "Generate" to create a 3D asset
    """)
    
    with gr.Row():
        with gr.Column():
            image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
            
            with gr.Accordion(label="Generation Settings", open=False):
                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                gr.Markdown("Stage 1: Sparse Structure Generation")
                with gr.Row():
                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
                gr.Markdown("Stage 2: Structured Latent Generation")
                with gr.Row():
                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)

            generate_btn = gr.Button("Generate")
            
            with gr.Accordion(label="GLB Extraction Settings", open=False):
                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
            
            with gr.Row():
                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
                extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)

        with gr.Column():
            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
            
            with gr.Row():
                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
    
    # output_buf = gr.State()


    # Handlers
    # demo.load(start_session)
    # demo.unload(end_session)
    
    
    # image_prompt.upload(
    #     preprocess_image,
    #     inputs=[image_prompt],
    #     outputs=[image_prompt],
    # )

    # generate_btn.click(
    #     get_seed,
    #     inputs=[randomize_seed, seed],
    #     outputs=[seed],
    # ).then(
    #     image_to_3d,
    #     inputs=[image_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
    #     outputs=[output_buf, video_output],
    # ).then(
    #     lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
    #     outputs=[extract_glb_btn, extract_gs_btn],
    # )

    # video_output.clear(
    #     lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
    #     outputs=[extract_glb_btn, extract_gs_btn],
    # )

    # extract_glb_btn.click(
    #     extract_glb,
    #     inputs=[output_buf, mesh_simplify, texture_size],
    #     outputs=[model_output, download_glb],
    # ).then(
    #     lambda: gr.Button(interactive=True),
    #     outputs=[download_glb],
    # )
    
    # extract_gs_btn.click(
    #     extract_gaussian,
    #     inputs=[output_buf],
    #     outputs=[model_output, download_gs],
    # ).then(
    #     lambda: gr.Button(interactive=True),
    #     outputs=[download_gs],
    # )

    # model_output.clear(
    #     lambda: gr.Button(interactive=False),
    #     outputs=[download_glb],
    # )
    

# Launch the Gradio app
if __name__ == "__main__":
    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
    pipeline.cuda()
    # try:
    #     pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    # except:
    #     pass
    demo.launch()