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Zero
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import logging
import os
import random
import time
import warnings
from dataclasses import dataclass, field
from shutil import copy, rmtree
import torch
import tyro
from huggingface_hub import snapshot_download
from packaging import version
# Suppress warnings
warnings.filterwarnings("ignore", category=FutureWarning)
logging.getLogger("transformers").setLevel(logging.ERROR)
logging.getLogger("diffusers").setLevel(logging.ERROR)
# TorchVision monkey patch for >0.16
if version.parse(torch.__version__) >= version.parse("0.16"):
import sys
import types
import torchvision.transforms.functional as TF
functional_tensor = types.ModuleType(
"torchvision.transforms.functional_tensor"
)
functional_tensor.rgb_to_grayscale = TF.rgb_to_grayscale
sys.modules["torchvision.transforms.functional_tensor"] = functional_tensor
from gsplat.distributed import cli
from txt2panoimg import Text2360PanoramaImagePipeline
from embodied_gen.trainer.gsplat_trainer import (
DefaultStrategy,
GsplatTrainConfig,
)
from embodied_gen.trainer.gsplat_trainer import entrypoint as gsplat_entrypoint
from embodied_gen.trainer.pono2mesh_trainer import Pano2MeshSRPipeline
from embodied_gen.utils.config import Pano2MeshSRConfig
from embodied_gen.utils.gaussian import restore_scene_scale_and_position
from embodied_gen.utils.gpt_clients import GPT_CLIENT
from embodied_gen.utils.log import logger
from embodied_gen.utils.process_media import is_image_file, parse_text_prompts
from embodied_gen.validators.quality_checkers import (
PanoHeightEstimator,
PanoImageOccChecker,
)
__all__ = [
"generate_pano_image",
"entrypoint",
]
@dataclass
class Scene3DGenConfig:
prompts: list[str] # Text desc of indoor room or style reference image.
output_dir: str
seed: int | None = None
real_height: float | None = None # The real height of the room in meters.
pano_image_only: bool = False
disable_pano_check: bool = False
keep_middle_result: bool = False
n_retry: int = 7
gs3d: GsplatTrainConfig = field(
default_factory=lambda: GsplatTrainConfig(
strategy=DefaultStrategy(verbose=True),
max_steps=4000,
init_opa=0.9,
opacity_reg=2e-3,
sh_degree=0,
means_lr=1e-4,
scales_lr=1e-3,
)
)
def generate_pano_image(
prompt: str,
output_path: str,
pipeline,
seed: int,
n_retry: int,
checker=None,
num_inference_steps: int = 40,
) -> None:
for i in range(n_retry):
logger.info(
f"GEN Panorama: Retry {i+1}/{n_retry} for prompt: {prompt}, seed: {seed}"
)
if is_image_file(prompt):
raise NotImplementedError("Image mode not implemented yet.")
else:
txt_prompt = f"{prompt}, spacious, empty, wide open, open floor, minimal furniture"
inputs = {
"prompt": txt_prompt,
"num_inference_steps": num_inference_steps,
"upscale": False,
"seed": seed,
}
pano_image = pipeline(inputs)
pano_image.save(output_path)
if checker is None:
break
flag, response = checker(pano_image)
logger.warning(f"{response}, image saved in {output_path}")
if flag is True or flag is None:
break
seed = random.randint(0, 100000)
return
def entrypoint(*args, **kwargs):
cfg = tyro.cli(Scene3DGenConfig)
# Init global models.
model_path = snapshot_download("archerfmy0831/sd-t2i-360panoimage")
IMG2PANO_PIPE = Text2360PanoramaImagePipeline(
model_path, torch_dtype=torch.float16, device="cuda"
)
PANOMESH_CFG = Pano2MeshSRConfig()
PANO2MESH_PIPE = Pano2MeshSRPipeline(PANOMESH_CFG)
PANO_CHECKER = PanoImageOccChecker(GPT_CLIENT, box_hw=[95, 1000])
PANOHEIGHT_ESTOR = PanoHeightEstimator(GPT_CLIENT)
prompts = parse_text_prompts(cfg.prompts)
for idx, prompt in enumerate(prompts):
start_time = time.time()
output_dir = os.path.join(cfg.output_dir, f"scene_{idx:04d}")
os.makedirs(output_dir, exist_ok=True)
pano_path = os.path.join(output_dir, "pano_image.png")
with open(f"{output_dir}/prompt.txt", "w") as f:
f.write(prompt)
generate_pano_image(
prompt,
pano_path,
IMG2PANO_PIPE,
cfg.seed if cfg.seed is not None else random.randint(0, 100000),
cfg.n_retry,
checker=None if cfg.disable_pano_check else PANO_CHECKER,
)
if cfg.pano_image_only:
continue
logger.info("GEN and REPAIR Mesh from Panorama...")
PANO2MESH_PIPE(pano_path, output_dir)
logger.info("TRAIN 3DGS from Mesh Init and Cube Image...")
cfg.gs3d.data_dir = output_dir
cfg.gs3d.result_dir = f"{output_dir}/gaussian"
cfg.gs3d.adjust_steps(cfg.gs3d.steps_scaler)
torch.set_default_device("cpu") # recover default setting.
cli(gsplat_entrypoint, cfg.gs3d, verbose=True)
# Clean up the middle results.
gs_path = (
f"{cfg.gs3d.result_dir}/ply/point_cloud_{cfg.gs3d.max_steps-1}.ply"
)
copy(gs_path, f"{output_dir}/gs_model.ply")
video_path = f"{cfg.gs3d.result_dir}/renders/video_step{cfg.gs3d.max_steps-1}.mp4"
copy(video_path, f"{output_dir}/video.mp4")
gs_cfg_path = f"{cfg.gs3d.result_dir}/cfg.yml"
copy(gs_cfg_path, f"{output_dir}/gsplat_cfg.yml")
if not cfg.keep_middle_result:
rmtree(cfg.gs3d.result_dir, ignore_errors=True)
os.remove(f"{output_dir}/{PANOMESH_CFG.gs_data_file}")
real_height = (
PANOHEIGHT_ESTOR(pano_path)
if cfg.real_height is None
else cfg.real_height
)
gs_path = os.path.join(output_dir, "gs_model.ply")
mesh_path = os.path.join(output_dir, "mesh_model.ply")
restore_scene_scale_and_position(real_height, mesh_path, gs_path)
elapsed_time = (time.time() - start_time) / 60
logger.info(
f"FINISHED 3D scene generation in {output_dir} in {elapsed_time:.2f} mins."
)
if __name__ == "__main__":
entrypoint()
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