app.py

import gradio as gr
import spaces
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, ImageOps
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.representations import Gaussian, MeshExtractResult
from trellis.utils import render_utils, postprocessing_utils
import torch
import torchvision.transforms.functional as TF
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from pathlib import Path
style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
    },
    {
        "name": "3D Model",
        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
    {
        "name": "Anime",
        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
    },
]
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"
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 reset_canvas():
    return gr.update(value={"background":Image.new("RGB", (512, 512), (255, 255, 255)), "layers":[Image.new("RGB", (512, 512), (255, 255, 255))], "composite":Image.new("RGB", (512, 512), (255, 255, 255))})

def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive), n + negative

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)

@spaces.GPU
def preprocess_image(
    image: Image.Image,
    prompt: str = "",
    negative_prompt: str = "",
    style_name: str = "",
    num_steps: int = 25,
    guidance_scale: float = 5,
    controlnet_conditioning_scale: float = 1.0,
    req: gr.Request = None,
) -> Image.Image:
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    
    width, height = image['composite'].size
    ratio = np.sqrt(1024.0 * 1024.0 / (width * height))
    new_width, new_height = int(width * ratio), int(height * ratio)
    image = image['composite'].resize((new_width, new_height))
    image = ImageOps.invert(image)
    print("image:", type(image))
    
    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
    print("params:", prompt, negative_prompt, style_name, num_steps, guidance_scale, controlnet_conditioning_scale)
    
    output = pipe_control(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=image,
        num_inference_steps=num_steps,
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        guidance_scale=guidance_scale,
        width=new_width,
        height=new_height,
    ).images[0]
    
    processed_image_path = os.path.join(user_dir, 'processed_image.png')
    output.save(processed_image_path)

    processed_image = pipeline.preprocess_image(output)
    
    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:
    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]:
    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]:
    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

def reset_do_preprocess():
    return True

def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
    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

with gr.Blocks(delete_cache=(600, 600)) as demo:
    gr.Markdown("""
    # UTPL - Conversión de Boceto a objetos 3D usando IA
    ### Tesis: "Objetos tridimensionales creados por IA: Innovación en entornos virtuales"  
    **Autor:** Carlos Vargas  
    **Base técnica:** Adaptación de TRELLIS (herramienta de código abierto para generación 3D)  
    **Propósito educativo:** Demostraciones académicas e Investigación en modelado 3D automático  
    """)
    with gr.Row():
        with gr.Column():
            with gr.Column():    
                image_prompt = gr.ImageEditor(label="Input sketch", type="pil", image_mode="RGB", height=512, value={"background":Image.new("RGB", (512, 512), (255, 255, 255)), "layers":[Image.new("RGB", (512, 512), (255, 255, 255))], "composite":Image.new("RGB", (512, 512), (255, 255, 255))})
                with gr.Row():
                    sketch_btn = gr.Button("process sketch")
                    generate_btn = gr.Button("Generate 3D")
                with gr.Row():
                    prompt = gr.Textbox(label="Prompt")
                    style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
                with gr.Accordion(label="Generation Settings", open=False):
                    with gr.Tab(label="sketch-to-image generation"):
                        negative_prompt = gr.Textbox(label="Negative prompt")
                        num_steps = gr.Slider(1, 20, label="Number of steps", value=8, step=1)
                        guidance_scale = gr.Slider(0.1, 10.0, label="Guidance scale", value=5, step=0.1)
                        controlnet_conditioning_scale = gr.Slider(0.5, 5.0, label="controlnet conditioning scale", value=0.85, step=0.01)
                    with gr.Tab(label="3D generation"):
                        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)
            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)
            image_prompt_processed = gr.Image(label="processed sketch", interactive=False, type="pil", height=512)
            model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300)
            with gr.Row():
                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  

    do_preprocess = gr.State(True)
    output_buf = gr.State()

    demo.load(start_session)
    demo.unload(end_session)

    image_prompt.clear(
        fn=reset_canvas,
        outputs = [image_prompt]
    )

    sketch_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
    ).then(
        preprocess_image,
        inputs=[image_prompt, prompt, negative_prompt, style, num_steps, guidance_scale, controlnet_conditioning_scale],
        outputs=[image_prompt_processed],
    )

    generate_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
    ).then(
        image_to_3d,
        inputs=[image_prompt_processed, 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],
    )

if __name__ == "__main__":
    pipeline = TrellisImageTo3DPipeline.from_pretrained("cavargas10/TRELLIS")
    pipeline.cuda()
    device = "cuda" if torch.cuda.is_available() else "cpu"
    #scribble controlnet
    controlnet = ControlNetModel.from_pretrained(
    "xinsir/controlnet-scribble-sdxl-1.0",
    torch_dtype=torch.float16
)
    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
    pipe_control = StableDiffusionXLControlNetPipeline.from_pretrained(
        "sd-community/sdxl-flash",
        controlnet=controlnet,
        vae=vae,
        torch_dtype=torch.float16,
    )
    pipe_control.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_control.scheduler.config)
    pipe_control.to(device)
    try:
        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    except:
        pass
    demo.launch()