Delete app1.py

app1.py

@@ -1,264 +0,0 @@
-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
-
-import logging
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    handlers=[
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger(__name__)
-
-# Log environment variables
-logger.info(f"ATTN_BACKEND: {os.environ.get('ATTN_BACKEND')}")
-logger.info(f"ATTN_DEBUG: {os.environ.get('ATTN_DEBUG')}")
-logger.info(f"SPARSE_BACKEND: {os.environ.get('SPARSE_BACKEND')}")
-logger.info(f"SPARSE_DEBUG: {os.environ.get('SPARSE_DEBUG')}")
-logger.info(f"SPARSE_ATTN_BACKEND: {os.environ.get('SPARSE_ATTN_BACKEND')}")
-
-MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = "/tmp/Trellis-demo"
-
-os.makedirs(TMP_DIR, exist_ok=True)
-
-
-def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
-    """
-    Preprocess the input image.
-    Args:
-        image (Image.Image): The input image.
-    Returns:
-        str: uuid of the trial.
-        Image.Image: The preprocessed 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) -> Tuple[dict, str]:
-    """
-    Convert an image to a 3D model.
-    Args:
-        trial_id (str): The uuid of the trial.
-        seed (int): The random seed.
-        randomize_seed (bool): Whether to randomize the 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.
-    """
-    if randomize_seed:
-        seed = np.random.randint(0, MAX_SEED)
-    outputs = pipeline.run(
-        Image.open(f"{TMP_DIR}/{trial_id}.png"),
-        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))]
-    trial_id = 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)
-    return state, video_path
-
-
-@spaces.GPU
-def extract_glb(state: dict, mesh_simplify: float, texture_size: int) -> 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.
-    """
-    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)
-
-
-with gr.Blocks() 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():
-            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)
-            
-    trial_id = gr.Textbox(visible=False)
-    output_buf = gr.State()
-
-    # Example images at the bottom of the page
-
-    # 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],
-    )
-    
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    if torch.cuda.is_available():
-        pipeline.cuda()
-        print("CUDA is available. Using GPU.")
-    else:
-        print("CUDA not available. Falling back to CPU.")
-    try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
-    except:
-        pass
-    print(f"CUDA Available: {torch.cuda.is_available()}")
-    print(f"CUDA Version: {torch.version.cuda}")
-    print(f"Number of GPUs: {torch.cuda.device_count()}")
-    demo.launch(debug=True)