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31f6f71 6c3b5ae 31f6f71 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import gradio as gr
import torch
import spaces
import json
import os
import subprocess
import sys
import uuid
from pathlib import Path
import numpy as np
os.environ.setdefault("SPCONV_ALGO", "native")
os.environ.setdefault("ATTN_BACKEND", "flash_attn")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("DVD_MODEL_REPO", "Zhengrui/dvd")
MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = Path(__file__).resolve().parent / "tmp" / "dvd_space_image"
TMP_DIR.mkdir(parents=True, exist_ok=True)
IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp"}
EXAMPLE_DIR = Path(__file__).resolve().parent / "assets" / "example_image"
GENERATION_IMAGE_EXAMPLES = [
str(path) for path in sorted(EXAMPLE_DIR.iterdir())
if path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS
]
def log_event(message: str):
print(f"[DVD Space Lean] {message}", flush=True)
def worker_path(name: str) -> str:
path = TMP_DIR / f"worker-{uuid.uuid4().hex}"
path.mkdir(parents=True, exist_ok=True)
return str(path / name)
def cfg_schedule(mode: str, constant: float, early: float, late: float, split: float):
if mode == "Constant":
return float(constant)
if mode == "Two-stage":
split = float(split)
early = float(early)
late = float(late)
return lambda t: early if t < split else late
return None
def save_voxel_coords(voxels, path: str) -> str:
coords = voxels.coords.detach().cpu().numpy().astype(np.int32)
np.save(path, coords)
return path
def get_seed(randomize_seed: bool, seed: int) -> int:
return int(np.random.randint(0, MAX_SEED)) if randomize_seed else int(seed)
@spaces.GPU(duration=30)
def zero_gpu_smoke_test():
log_event("zero_gpu_smoke_test start")
if not torch.cuda.is_available():
log_event("zero_gpu_smoke_test no cuda")
return "CUDA unavailable inside ZeroGPU worker"
value = torch.ones((1,), device="cuda").sum().item()
name = torch.cuda.get_device_name(0)
log_event(f"zero_gpu_smoke_test done device={name} value={value}")
return f"OK: {name}, value={value}"
@spaces.GPU(duration=300)
def generate_voxels(
image,
seed: int,
randomize_seed: bool,
preprocess_image: bool,
dvd_steps: int,
dvd_cfg_mode: str,
dvd_cfg_constant: float,
dvd_cfg_early: float,
dvd_cfg_late: float,
dvd_cfg_split: float,
progress=gr.Progress(track_tqdm=True),
):
progress(0.01, desc="Starting ZeroGPU callback")
log_event(f"generate_voxels start seed={seed} randomize={randomize_seed} steps={dvd_steps}")
if image is None:
raise gr.Error("Please provide an image.")
seed = get_seed(randomize_seed, seed)
image_path = worker_path("input.png")
mesh_path = worker_path("generated_voxels.glb")
npy_path = worker_path("generated_voxel64_coords.npy")
config_path = worker_path("generate_config.json")
image.save(image_path)
with open(config_path, "w") as f:
json.dump(
{
"image_path": image_path,
"mesh_path": mesh_path,
"npy_path": npy_path,
"seed": int(seed),
"preprocess_image": bool(preprocess_image),
"dvd_steps": int(dvd_steps),
"dvd_cfg_mode": dvd_cfg_mode,
"dvd_cfg_constant": float(dvd_cfg_constant),
"dvd_cfg_early": float(dvd_cfg_early),
"dvd_cfg_late": float(dvd_cfg_late),
"dvd_cfg_split": float(dvd_cfg_split),
},
f,
)
progress(0.08, desc="Running isolated DVD worker")
env = os.environ.copy()
env.setdefault("PYTHONUNBUFFERED", "1")
cmd = [sys.executable, str(Path(__file__).resolve().parent / "space_image_worker.py"), "generate", config_path]
log_event("starting isolated DVD worker")
proc = subprocess.run(cmd, env=env, text=True, capture_output=True)
if proc.stdout:
print(proc.stdout, flush=True)
if proc.stderr:
print(proc.stderr, flush=True)
if proc.returncode != 0:
raise gr.Error(f"DVD worker failed with exit code {proc.returncode}. Check Space logs.")
progress(0.98, desc="Done")
log_event(f"generate_voxels done seed={seed} npy={npy_path}")
return mesh_path, npy_path, seed, f"Done. seed={seed}"
with gr.Blocks(title="DVD Image Generation", fill_width=True) as demo:
gr.Markdown("## DVD Image Voxel Generation")
with gr.Row():
smoke_btn = gr.Button("ZeroGPU Smoke Test")
smoke_out = gr.Textbox(label="ZeroGPU Status", interactive=False)
smoke_btn.click(zero_gpu_smoke_test, outputs=smoke_out)
with gr.Row(equal_height=False):
with gr.Column(scale=1):
image = gr.Image(label="Input Image", format="png", image_mode="RGBA", type="pil", height=320)
gr.Examples(
examples=GENERATION_IMAGE_EXAMPLES[:12],
inputs=image,
examples_per_page=6,
)
with gr.Accordion("DVD Settings", open=False):
seed = gr.Slider(0, MAX_SEED, value=0, step=1, label="Seed")
randomize_seed = gr.Checkbox(value=True, label="Randomize seed")
preprocess_image = gr.Checkbox(value=True, label="DVD preprocess image")
dvd_steps = gr.Slider(1, 512, value=128, step=1, label="DVD steps")
dvd_cfg_mode = gr.Radio(["Default schedule", "Constant", "Two-stage"], value="Default schedule", label="DVD CFG mode")
dvd_cfg_constant = gr.Slider(0.0, 5.0, value=0.7, step=0.05, label="Constant CFG")
dvd_cfg_early = gr.Slider(0.0, 5.0, value=0.4, step=0.05, label="Early CFG")
dvd_cfg_late = gr.Slider(0.0, 5.0, value=0.7, step=0.05, label="Late CFG")
dvd_cfg_split = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="CFG switch time")
gen_btn = gr.Button("Generate DVD Voxels", variant="primary")
with gr.Column(scale=1):
voxel_view = gr.Model3D(label="Generated / Cubified Voxels", height=360, camera_position=(-180, 90, 3))
npy_download = gr.DownloadButton(label="Download Voxel Coords (.npy)", interactive=False)
status = gr.Textbox(label="Status", interactive=False)
gen_btn.click(
generate_voxels,
inputs=[
image,
seed,
randomize_seed,
preprocess_image,
dvd_steps,
dvd_cfg_mode,
dvd_cfg_constant,
dvd_cfg_early,
dvd_cfg_late,
dvd_cfg_split,
],
outputs=[voxel_view, npy_download, seed, status],
).then(lambda: gr.DownloadButton(interactive=True), outputs=[npy_download])
if __name__ == "__main__":
demo.queue().launch(show_api=False, show_error=True, ssr_mode=False)
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