import gradio as gr import spaces import os import shutil import json os.environ['TOKENIZERS_PARALLELISM'] = 'true' os.environ['SPCONV_ALGO'] = 'native' from typing import * import torch import numpy as np import imageio from easydict import EasyDict as edict from trellis.pipelines import TrellisTextTo3DPipeline from trellis.representations import Gaussian, MeshExtractResult from trellis.utils import render_utils, postprocessing_utils import traceback import sys # Add JSON encoder for NumPy arrays class NumpyEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.ndarray): return obj.tolist() return json.JSONEncoder.default(self, obj) 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): 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)) # Use shutil.rmtree with ignore_errors=True for robustness shutil.rmtree(user_dir, ignore_errors=True) 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'], ) # Ensure tensors are created on the correct device ('cuda') gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda', dtype=torch.float32) gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda', dtype=torch.float32) gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda', dtype=torch.float32) gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda', dtype=torch.float32) gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda', dtype=torch.float32) mesh = edict( vertices=torch.tensor(state['mesh']['vertices'], device='cuda', dtype=torch.float32), faces=torch.tensor(state['mesh']['faces'], device='cuda', dtype=torch.int64), # Faces are usually integers ) 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 text_to_3d( prompt: str, seed: int, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int, req: gr.Request, ) -> dict: # MODIFIED: Now returns only the state dict """ Convert a text prompt to a 3D model state object. Args: prompt (str): The text prompt. 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 JSON-serializable state object containing the generated 3D model info. """ # Ensure user directory exists (redundant if start_session is always called, but safe) user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) print(f"[{req.session_hash}] Running text_to_3d for prompt: {prompt}") # Add logging outputs = pipeline.run( prompt, seed=seed, formats=["gaussian", "mesh"], 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, }, ) # REMOVED: Video rendering logic moved to render_preview_video # 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) # Create the state object and ensure it's JSON serializable for API calls state = pack_state(outputs['gaussian'][0], outputs['mesh'][0]) # Convert to serializable format serializable_state = json.loads(json.dumps(state, cls=NumpyEncoder)) print(f"[{req.session_hash}] text_to_3d completed. Returning state.") # Add logging torch.cuda.empty_cache() return serializable_state # MODIFIED: Return only state # --- NEW FUNCTION --- @spaces.GPU def render_preview_video(state: dict, req: gr.Request) -> str: """ Renders a preview video from the provided state object. Args: state (dict): The state object containing Gaussian and mesh data. req (gr.Request): Gradio request object for session hash. Returns: str: The path to the rendered video file. """ if not state: print(f"[{req.session_hash}] render_preview_video called with empty state. Returning None.") # Consider returning a placeholder or raising an error if state is required return None user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) # Ensure directory exists print(f"[{req.session_hash}] Unpacking state for video rendering.") # Add logging gs, mesh = unpack_state(state) print(f"[{req.session_hash}] Rendering video...") # Add logging video = render_utils.render_video(gs, num_frames=120)['color'] video_geo = render_utils.render_video(mesh, 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, 'preview_sample.mp4') # Use a distinct name print(f"[{req.session_hash}] Saving video to {video_path}") # Add logging imageio.mimsave(video_path, video, fps=15) torch.cuda.empty_cache() return video_path # --- END NEW FUNCTION --- @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 state. 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 (for Model3D component). str: The path to the extracted GLB file (for DownloadButton). """ if not state: print(f"[{req.session_hash}] extract_glb called with empty state. Returning None.") return None, None # Return Nones if state is missing user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) print(f"[{req.session_hash}] Unpacking state for GLB extraction.") # Add logging gs, mesh = unpack_state(state) print(f"[{req.session_hash}] Extracting GLB (simplify={mesh_simplify}, texture={texture_size})...") # Add logging 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') print(f"[{req.session_hash}] Saving GLB to {glb_path}") # Add logging glb.export(glb_path) torch.cuda.empty_cache() # Return the same path for both Model3D and DownloadButton components return glb_path, glb_path @spaces.GPU def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]: """ Extract a Gaussian PLY file from the 3D model state. Args: state (dict): The state of the generated 3D model. Returns: str: The path to the extracted Gaussian file (for Model3D component). str: The path to the extracted Gaussian file (for DownloadButton). """ if not state: print(f"[{req.session_hash}] extract_gaussian called with empty state. Returning None.") return None, None # Return Nones if state is missing user_dir = os.path.join(TMP_DIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) print(f"[{req.session_hash}] Unpacking state for Gaussian extraction.") # Add logging gs, _ = unpack_state(state) gaussian_path = os.path.join(user_dir, 'sample.ply') print(f"[{req.session_hash}] Saving Gaussian PLY to {gaussian_path}") # Add logging gs.save_ply(gaussian_path) torch.cuda.empty_cache() # Return the same path for both Model3D and DownloadButton components return gaussian_path, gaussian_path # State object to hold the generated model info between steps output_buf = gr.State() # Video component placeholder (will be populated by render_preview_video) # video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300) # Defined later inside the Blocks with gr.Blocks(delete_cache=(600, 600)) as demo: gr.Markdown(""" ## Text to 3D Asset with [TRELLIS](https://trellis3d.github.io/) * Type a text prompt and click "Generate" to create a 3D asset. * The preview video will appear after generation. * If you find the generated 3D asset satisfactory, click "Extract GLB" or "Extract Gaussian" to extract the file and download it. """) with gr.Row(): with gr.Column(): text_prompt = gr.Textbox(label="Text Prompt", lines=5) 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=25, 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=7.5, step=0.1) slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, 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(): # Buttons start non-interactive, enabled after generation extract_glb_btn = gr.Button("Extract GLB", interactive=False) extract_gs_btn = gr.Button("Extract Gaussian", interactive=False) gr.Markdown(""" *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.* """) with gr.Column(): # Define UI components here video_output = gr.Video(label="Generated 3D Asset Preview", autoplay=True, loop=True, height=300) model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300) with gr.Row(): # Buttons start non-interactive, enabled after extraction download_glb = gr.DownloadButton(label="Download GLB", interactive=False) download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False) # Define the state buffer here, outside the component definitions but inside the Blocks scope output_buf = gr.State() # --- Handlers --- demo.load(start_session) demo.unload(end_session) # --- MODIFIED UI CHAIN --- # 1. Get Seed # 2. Run text_to_3d -> outputs state to output_buf # 3. Run render_preview_video (using state from output_buf) -> outputs video to video_output # 4. Enable extraction buttons generate_btn.click( get_seed, inputs=[randomize_seed, seed], outputs=[seed], queue=True # Use queue for potentially long-running steps ).then( text_to_3d, inputs=[text_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps], outputs=[output_buf], # text_to_3d now ONLY outputs state api_name="text_to_3d" # Keep API name consistent if needed ).then( render_preview_video, # NEW step: Render video from state inputs=[output_buf], outputs=[video_output], api_name="render_preview_video" # Assign API name if you want to call this separately ).then( lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]), # Enable extraction buttons outputs=[extract_glb_btn, extract_gs_btn], ) # Clear video and disable extraction buttons if prompt is cleared or generation restarted # (Consider adding logic to clear prompt on successful generation if desired) text_prompt.change( # Example: Clear video if prompt changes lambda: (None, gr.Button(interactive=False), gr.Button(interactive=False)), outputs=[video_output, extract_glb_btn, extract_gs_btn] ) video_output.clear( # This might be redundant if text_prompt.change handles it lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]), outputs=[extract_glb_btn, extract_gs_btn], ) # --- Extraction Handlers --- # GLB Extraction: Takes state from output_buf, outputs model and download path extract_glb_btn.click( extract_glb, inputs=[output_buf, mesh_simplify, texture_size], outputs=[model_output, download_glb], # Outputs to Model3D and DownloadButton path api_name="extract_glb" ).then( lambda: gr.Button(interactive=True), # Enable download button outputs=[download_glb], ) # Gaussian Extraction: Takes state from output_buf, outputs model and download path extract_gs_btn.click( extract_gaussian, inputs=[output_buf], outputs=[model_output, download_gs], # Outputs to Model3D and DownloadButton path api_name="extract_gaussian" ).then( lambda: gr.Button(interactive=True), # Enable download button outputs=[download_gs], ) # Clear model and disable download buttons if video/state is cleared model_output.clear( lambda: (gr.Button(interactive=False), gr.Button(interactive=False)), outputs=[download_glb, download_gs], # Disable both download buttons ) # --- Launch the Gradio app --- if __name__ == "__main__": print("Loading Trellis pipeline...") # Consider adding error handling for pipeline loading try: pipeline = TrellisTextTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-text-xlarge") pipeline.cuda() print("Pipeline loaded successfully.") except Exception as e: print(f"Error loading pipeline: {e}") # Optionally exit or provide a fallback UI sys.exit(1) print("Launching Gradio demo...") # Enable queue for handling multiple users/requests # Set share=True if you need a public link (requires login for private spaces) demo.queue().launch()