import gradio as gr import spaces from gradio_litmodel3d import LitModel3D import os os.environ['SPCONV_ALGO'] = 'native' from typing import * import torch import numpy as np import imageio import uuid 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 from transformers import pipeline as translation_pipeline from diffusers import FluxPipeline MAX_SEED = np.iinfo(np.int32).max TMP_DIR = "/tmp/Trellis-demo" os.makedirs(TMP_DIR, exist_ok=True) def initialize_models(): global pipeline, translator, flux_pipe # Trellis 파이프라인 초기화 pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large") pipeline.cuda() # 번역기 초기화 translator = translation_pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") # Flux 파이프라인 초기화 flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) flux_pipe.load_lora_weights("gokaygokay/Flux-Game-Assets-LoRA-v2") flux_pipe.fuse_lora(lora_scale=1.0) flux_pipe.to(device="cuda", dtype=torch.bfloat16) def translate_if_korean(text): if any(ord('가') <= ord(char) <= ord('힣') for char in text): translated = translator(text)[0]['translation_text'] return translated return text def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]: trial_id = str(uuid.uuid4()) processed_image = pipeline.preprocess_image(image) processed_image.save(f"{TMP_DIR}/{trial_id}.png") return trial_id, processed_image def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> 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(), }, 'trial_id': trial_id, } 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, state['trial_id'] @spaces.GPU def image_to_3d(trial_id: str, seed: int, randomize_seed: bool, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int) -> Tuple[dict, str]: if randomize_seed: seed = np.random.randint(0, MAX_SEED) outputs = pipeline.run( Image.open(f"{TMP_DIR}/{trial_id}.png"), 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))] trial_id = uuid.uuid4() video_path = f"{TMP_DIR}/{trial_id}.mp4" os.makedirs(os.path.dirname(video_path), exist_ok=True) imageio.mimsave(video_path, video, fps=15) state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id) return state, video_path @spaces.GPU def generate_image_from_text(prompt, height, width, guidance_scale, num_steps): translated_prompt = translate_if_korean(prompt) with torch.inference_mode(): image = flux_pipe( prompt=[translated_prompt], height=height, width=width, guidance_scale=guidance_scale, num_inference_steps=num_steps ).images[0] return image @spaces.GPU def extract_glb(state: dict, mesh_simplify: float, texture_size: int) -> Tuple[str, str]: gs, mesh, trial_id = unpack_state(state) glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False) glb_path = f"{TMP_DIR}/{trial_id}.glb" glb.export(glb_path) return glb_path, glb_path def activate_button() -> gr.Button: return gr.Button(interactive=True) def deactivate_button() -> gr.Button: return gr.Button(interactive=False) with gr.Blocks() as demo: gr.Markdown(""" # 3D Asset Creation & Text-to-Image Generation """) with gr.Tabs(): with gr.TabItem("Image to 3D"): with gr.Row(): with gr.Column(): image_prompt = gr.Image(label="Image Prompt", 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) extract_glb_btn = gr.Button("Extract GLB", 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", exposure=20.0, height=300) download_glb = gr.DownloadButton(label="Download GLB", interactive=False) with gr.TabItem("Text to Image"): with gr.Row(): with gr.Column(): text_prompt = gr.Textbox( label="Text Prompt", placeholder="Enter your image description...", lines=3 ) with gr.Row(): txt2img_height = gr.Slider(256, 1024, value=512, step=64, label="Height") txt2img_width = gr.Slider(256, 1024, value=512, step=64, label="Width") with gr.Row(): guidance_scale = gr.Slider(1.0, 20.0, value=7.5, label="Guidance Scale") num_steps = gr.Slider(1, 50, value=20, label="Number of Steps") generate_txt2img_btn = gr.Button("Generate Image") with gr.Column(): txt2img_output = gr.Image(label="Generated Image") trial_id = gr.Textbox(visible=False) output_buf = gr.State() # Example images with gr.Row(): examples = gr.Examples( examples=[ f'assets/example_image/{image}' for image in os.listdir("assets/example_image") ], inputs=[image_prompt], fn=preprocess_image, outputs=[trial_id, image_prompt], run_on_click=True, examples_per_page=64, ) # Handlers image_prompt.upload( preprocess_image, inputs=[image_prompt], outputs=[trial_id, image_prompt], ) image_prompt.clear( lambda: '', outputs=[trial_id], ) generate_btn.click( image_to_3d, inputs=[trial_id, seed, randomize_seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps], outputs=[output_buf, video_output], ).then( activate_button, outputs=[extract_glb_btn], ) video_output.clear( deactivate_button, outputs=[extract_glb_btn], ) extract_glb_btn.click( extract_glb, inputs=[output_buf, mesh_simplify, texture_size], outputs=[model_output, download_glb], ).then( activate_button, outputs=[download_glb], ) model_output.clear( deactivate_button, outputs=[download_glb], ) # Text to Image 핸들러 generate_txt2img_btn.click( generate_image_from_text, inputs=[text_prompt, txt2img_height, txt2img_width, guidance_scale, num_steps], outputs=[txt2img_output] ) # Launch the Gradio app if __name__ == "__main__": initialize_models() # 모든 모델 초기화 try: pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))) # Preload rembg except: pass demo.launch()