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 try: # GPU 메모리 초기화 torch.cuda.empty_cache() # GPU 사용 가능 여부 확인 device = "cuda" if torch.cuda.is_available() else "cpu" # Trellis 파이프라인 초기화 pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large") pipeline.to(device) # 번역기 초기화 translator = translation_pipeline( "translation", model="Helsinki-NLP/opus-mt-ko-en", device=0 if device=="cuda" else -1 ) # Flux 파이프라인 초기화 flux_pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16 if device=="cuda" else torch.float32 ) if device == "cuda": flux_pipe.enable_model_cpu_offload() return True except Exception as e: print(f"Model initialization error: {str(e)}") torch.cuda.empty_cache() return False 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): try: torch.cuda.empty_cache() if randomize_seed: seed = np.random.randint(0, MAX_SEED) input_image = Image.open(f"{TMP_DIR}/{trial_id}.png") with torch.cuda.amp.autocast(enabled=torch.cuda.is_available()): with torch.no_grad(): outputs = pipeline.run( input_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, } ) # 비디오 렌더링 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 = str(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) if torch.cuda.is_available(): torch.cuda.empty_cache() return state, video_path except Exception as e: print(f"Error in image_to_3d: {str(e)}") torch.cuda.empty_cache() raise e @spaces.GPU def generate_image_from_text(prompt, height, width, guidance_scale, num_steps): # 기본 프롬프트를 추가 base_prompt = "wbgmsst, 3D, white background" # 사용자 프롬프트를 번역 (한국어인 경우) translated_prompt = translate_if_korean(prompt) # 최종 프롬프트 조합 final_prompt = f"{translated_prompt}, {base_prompt}" with torch.inference_mode(): image = flux_pipe( prompt=[final_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) css = """ footer { visibility: hidden; } """ with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo: gr.Markdown(""" # Craft3D : 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] ) if __name__ == "__main__": # 초기 GPU 메모리 정리 if torch.cuda.is_available(): torch.cuda.empty_cache() # 모델 초기화 확인 if not initialize_models(): print("Failed to initialize models") exit(1) try: # rembg 사전 로드 시도 test_image = Image.fromarray(np.zeros((256, 256, 3), dtype=np.uint8)) pipeline.preprocess_image(test_image) except Exception as e: print(f"Warning: Failed to preload rembg: {str(e)}") # Gradio 앱 실행 demo.queue(concurrency_count=1).launch( share=True, enable_queue=True, max_threads=1 )