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()