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app_text.py
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266
app_text.py
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import gradio as gr
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from gradio_litmodel3d import LitModel3D
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import os
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import shutil
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from typing import *
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import torch
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import numpy as np
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import imageio
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from easydict import EasyDict as edict
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from trellis.pipelines import TrellisTextTo3DPipeline
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from trellis.representations import Gaussian, MeshExtractResult
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from trellis.utils import render_utils, postprocessing_utils
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MAX_SEED = np.iinfo(np.int32).max
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TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
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os.makedirs(TMP_DIR, exist_ok=True)
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def start_session(req: gr.Request):
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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os.makedirs(user_dir, exist_ok=True)
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def end_session(req: gr.Request):
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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shutil.rmtree(user_dir)
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def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
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return {
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'gaussian': {
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**gs.init_params,
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'_xyz': gs._xyz.cpu().numpy(),
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'_features_dc': gs._features_dc.cpu().numpy(),
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'_scaling': gs._scaling.cpu().numpy(),
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'_rotation': gs._rotation.cpu().numpy(),
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'_opacity': gs._opacity.cpu().numpy(),
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},
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'mesh': {
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'vertices': mesh.vertices.cpu().numpy(),
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'faces': mesh.faces.cpu().numpy(),
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},
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}
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def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
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gs = Gaussian(
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aabb=state['gaussian']['aabb'],
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sh_degree=state['gaussian']['sh_degree'],
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mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
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scaling_bias=state['gaussian']['scaling_bias'],
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opacity_bias=state['gaussian']['opacity_bias'],
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scaling_activation=state['gaussian']['scaling_activation'],
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)
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gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
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gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
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gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
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gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
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gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
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mesh = edict(
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vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
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faces=torch.tensor(state['mesh']['faces'], device='cuda'),
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)
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return gs, mesh
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def get_seed(randomize_seed: bool, seed: int) -> int:
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"""
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Get the random seed.
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"""
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return np.random.randint(0, MAX_SEED) if randomize_seed else seed
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def text_to_3d(
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prompt: str,
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seed: int,
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ss_guidance_strength: float,
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ss_sampling_steps: int,
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slat_guidance_strength: float,
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slat_sampling_steps: int,
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req: gr.Request,
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) -> Tuple[dict, str]:
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"""
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Convert an text prompt to a 3D model.
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Args:
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prompt (str): The text prompt.
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seed (int): The random seed.
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ss_guidance_strength (float): The guidance strength for sparse structure generation.
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ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
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slat_guidance_strength (float): The guidance strength for structured latent generation.
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slat_sampling_steps (int): The number of sampling steps for structured latent generation.
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Returns:
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dict: The information of the generated 3D model.
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str: The path to the video of the 3D model.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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outputs = pipeline.run(
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prompt,
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seed=seed,
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formats=["gaussian", "mesh"],
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sparse_structure_sampler_params={
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"steps": ss_sampling_steps,
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"cfg_strength": ss_guidance_strength,
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},
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slat_sampler_params={
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"steps": slat_sampling_steps,
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"cfg_strength": slat_guidance_strength,
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},
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)
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video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
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video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
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video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
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video_path = os.path.join(user_dir, 'sample.mp4')
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imageio.mimsave(video_path, video, fps=15)
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state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
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torch.cuda.empty_cache()
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return state, video_path
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def extract_glb(
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state: dict,
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mesh_simplify: float,
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texture_size: int,
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req: gr.Request,
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) -> Tuple[str, str]:
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"""
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Extract a GLB file from the 3D model.
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Args:
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state (dict): The state of the generated 3D model.
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mesh_simplify (float): The mesh simplification factor.
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texture_size (int): The texture resolution.
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Returns:
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str: The path to the extracted GLB file.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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gs, mesh = unpack_state(state)
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glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
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glb_path = os.path.join(user_dir, 'sample.glb')
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glb.export(glb_path)
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torch.cuda.empty_cache()
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return glb_path, glb_path
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def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
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"""
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Extract a Gaussian file from the 3D model.
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Args:
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state (dict): The state of the generated 3D model.
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Returns:
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str: The path to the extracted Gaussian file.
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"""
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user_dir = os.path.join(TMP_DIR, str(req.session_hash))
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gs, _ = unpack_state(state)
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gaussian_path = os.path.join(user_dir, 'sample.ply')
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gs.save_ply(gaussian_path)
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torch.cuda.empty_cache()
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return gaussian_path, gaussian_path
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with gr.Blocks(delete_cache=(600, 600)) as demo:
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gr.Markdown("""
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## Text to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
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* Type a text prompt and click "Generate" to create a 3D asset.
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* If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
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""")
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with gr.Row():
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with gr.Column():
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text_prompt = gr.Textbox(label="Text Prompt", lines=5)
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with gr.Accordion(label="Generation Settings", open=False):
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seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
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randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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gr.Markdown("Stage 1: Sparse Structure Generation")
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with gr.Row():
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ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)
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gr.Markdown("Stage 2: Structured Latent Generation")
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with gr.Row():
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slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)
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generate_btn = gr.Button("Generate")
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with gr.Accordion(label="GLB Extraction Settings", open=False):
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mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
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texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
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with gr.Row():
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extract_glb_btn = gr.Button("Extract GLB", interactive=False)
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extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
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gr.Markdown("""
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*NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
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""")
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with gr.Column():
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video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
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model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
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with gr.Row():
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download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
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download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
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output_buf = gr.State()
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# Handlers
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demo.load(start_session)
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demo.unload(end_session)
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generate_btn.click(
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get_seed,
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inputs=[randomize_seed, seed],
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outputs=[seed],
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).then(
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text_to_3d,
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inputs=[text_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
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outputs=[output_buf, video_output],
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).then(
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lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
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outputs=[extract_glb_btn, extract_gs_btn],
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)
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video_output.clear(
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lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
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outputs=[extract_glb_btn, extract_gs_btn],
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)
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extract_glb_btn.click(
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extract_glb,
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inputs=[output_buf, mesh_simplify, texture_size],
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outputs=[model_output, download_glb],
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).then(
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lambda: gr.Button(interactive=True),
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outputs=[download_glb],
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)
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extract_gs_btn.click(
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extract_gaussian,
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inputs=[output_buf],
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outputs=[model_output, download_gs],
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).then(
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lambda: gr.Button(interactive=True),
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outputs=[download_gs],
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)
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model_output.clear(
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lambda: gr.Button(interactive=False),
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outputs=[download_glb],
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)
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# Launch the Gradio app
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if __name__ == "__main__":
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pipeline = TrellisTextTo3DPipeline.from_pretrained("microsoft/TRELLIS-text-xlarge")
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pipeline.cuda()
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demo.launch()
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