| import gradio as gr |
| from gradio_litmodel3d import LitModel3D |
|
|
| import os |
| os.environ['ATTN_BACKEND'] = 'xformers' |
| os.environ['ATTN_BACKEND'] = 'xformers' |
| import shutil |
| 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): |
| 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 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 preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]: |
| """ |
| Preprocess a list of input images. |
| |
| Args: |
| images (List[Tuple[Image.Image, str]]): The input images. |
| |
| Returns: |
| List[Image.Image]: The preprocessed images. |
| """ |
| images = [image[0] for image in images] |
| processed_images = [pipeline.preprocess_image(image) for image in images] |
| return processed_images |
|
|
|
|
| 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, |
| multiimages: List[Tuple[Image.Image, str]], |
| is_multiimage: bool, |
| seed: int, |
| ss_guidance_strength: float, |
| ss_sampling_steps: int, |
| slat_guidance_strength: float, |
| slat_sampling_steps: int, |
| multiimage_algo: Literal["multidiffusion", "stochastic"], |
| req: gr.Request, |
| ) -> Tuple[dict, str]: |
| """ |
| Convert an image to a 3D model. |
| |
| Args: |
| image (Image.Image): The input image. |
| multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode. |
| is_multiimage (bool): Whether is in multi-image mode. |
| 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. |
| multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image 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)) |
| if not is_multiimage: |
| 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, |
| }, |
| ) |
| else: |
| outputs = pipeline.run_multi_image( |
| [image[0] for image in multiimages], |
| 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, |
| }, |
| mode=multiimage_algo, |
| ) |
| 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 prepare_multi_example() -> List[Image.Image]: |
| multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")])) |
| images = [] |
| for case in multi_case: |
| _images = [] |
| for i in range(1, 4): |
| img = Image.open(f'assets/example_multi_image/{case}_{i}.png') |
| W, H = img.size |
| img = img.resize((int(W / H * 512), 512)) |
| _images.append(np.array(img)) |
| images.append(Image.fromarray(np.concatenate(_images, axis=1))) |
| return images |
|
|
|
|
| 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(""" |
| ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/) |
| * Upload an image 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. |
| """) |
| |
| with gr.Row(): |
| with gr.Column(): |
| with gr.Tabs() as input_tabs: |
| with gr.Tab(label="Single Image", id=0) as single_image_input_tab: |
| image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300) |
| with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab: |
| multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3) |
| gr.Markdown(""" |
| Input different views of the object in separate images. |
| |
| *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.* |
| """) |
| |
| 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) |
| multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic") |
|
|
| 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) |
| |
| is_multiimage = gr.State(False) |
| output_buf = gr.State() |
|
|
| |
| with gr.Row() as single_image_example: |
| examples = gr.Examples( |
| examples=[ |
| f'assets/example_image/{image}' |
| for image in os.listdir("assets/example_image") |
| ], |
| inputs=[image_prompt], |
| fn=preprocess_image, |
| outputs=[image_prompt], |
| run_on_click=True, |
| examples_per_page=64, |
| ) |
| with gr.Row(visible=False) as multiimage_example: |
| examples_multi = gr.Examples( |
| examples=prepare_multi_example(), |
| inputs=[image_prompt], |
| fn=split_image, |
| outputs=[multiimage_prompt], |
| run_on_click=True, |
| examples_per_page=8, |
| ) |
|
|
| |
| demo.load(start_session) |
| demo.unload(end_session) |
| |
| single_image_input_tab.select( |
| lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]), |
| outputs=[is_multiimage, single_image_example, multiimage_example] |
| ) |
| multiimage_input_tab.select( |
| lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]), |
| outputs=[is_multiimage, single_image_example, multiimage_example] |
| ) |
| |
| image_prompt.upload( |
| preprocess_image, |
| inputs=[image_prompt], |
| outputs=[image_prompt], |
| ) |
| multiimage_prompt.upload( |
| preprocess_images, |
| inputs=[multiimage_prompt], |
| outputs=[multiimage_prompt], |
| ) |
|
|
| generate_btn.click( |
| get_seed, |
| inputs=[randomize_seed, seed], |
| outputs=[seed], |
| ).then( |
| image_to_3d, |
| inputs=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo], |
| 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], |
| ) |
| |
|
|
| |
| if __name__ == "__main__": |
| pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large") |
| pipeline.cuda() |
| demo.launch(share=True) |
|
|
