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| import os | |
| import shutil | |
| from typing import * | |
| import spaces | |
| os.environ['SPCONV_ALGO'] = 'native' | |
| import gradio as gr | |
| import imageio | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from easydict import EasyDict as edict | |
| from gradio_litmodel3d import LitModel3D | |
| from trellis.pipelines import TrellisImageTo3DPipeline | |
| from trellis.representations import Gaussian, MeshExtractResult | |
| from trellis.utils import render_utils, postprocessing_utils | |
| from occluded_trellis import apply_occluded_patch | |
| 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)) | |
| shutil.rmtree(user_dir) | |
| 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 | |
| def image_to_3d( | |
| image: Image.Image, | |
| mask: 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, | |
| mask, | |
| seed=seed, | |
| formats=["gaussian", "mesh"], | |
| preprocess_image=True, | |
| 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 | |
| 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 | |
| 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 extract_mask(editor_content): | |
| bg = editor_content.get("background", None) | |
| layers = editor_content.get("layers", []) | |
| if bg is None and not layers: | |
| raise ValueError("No background or layers provided") | |
| mask = layers[0] | |
| mask = np.array(mask) | |
| mask = np.where(mask[..., 3] != 0, 0, 255).astype( | |
| np.uint8) | |
| mask = np.stack([mask] * 3, -1) | |
| mask = Image.fromarray(mask) | |
| return [bg, mask] | |
| with gr.Blocks(delete_cache=(600, 600)) as demo: | |
| gr.Markdown(""" | |
| ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/), [Occluded version](https://github.com/cortwave/OccludedTrellis) | |
| * NOTICE: This is not an official space, only an occlusion-friendly fork. For the original space go [here](https://huggingface.co/spaces/JeffreyXiang/TRELLIS). | |
| * Upload an image, draw a mask and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background. | |
| * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it. | |
| * If image is already corrupted even before masking it's highly recommended to upload with your own alpha mask (remove background) | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| im_ed = gr.ImageMask(label="Image Prompt", | |
| format="png", | |
| image_mode="RGBA", | |
| type="pil", | |
| sources=["upload", "clipboard"]) | |
| image_prompt = gr.Image(visible=False, type="pil", image_mode="RGBA", format="png") | |
| mask = gr.Image(visible=False, type="pil") | |
| 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) | |
| gr.Markdown(""" | |
| *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.* | |
| """) | |
| 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) | |
| generate_btn.click( | |
| get_seed, | |
| inputs=[randomize_seed, seed], | |
| outputs=[seed], | |
| ).then( | |
| extract_mask, | |
| inputs=im_ed, | |
| outputs=[image_prompt, mask] | |
| ).then( | |
| image_to_3d, | |
| inputs=[image_prompt, mask, 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 = apply_occluded_patch(pipeline) | |
| pipeline.cuda() | |
| demo.launch() | |