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import os
import spaces
import subprocess
def install_cuda_toolkit():
CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_550.54.14_linux.run"
CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])
os.environ["CUDA_HOME"] = "/usr/local/cuda"
os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
os.environ["CUDA_HOME"],
"" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
)
os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
install_cuda_toolkit()
os.system('bash install.sh')
import time
import uuid
import torch
import trimesh
import argparse
import numpy as np
import gradio as gr
from step1x3d_geometry.models.pipelines.pipeline import Step1X3DGeometryPipeline
from step1x3d_texture.pipelines.step1x_3d_texture_synthesis_pipeline import (
Step1X3DTexturePipeline,
)
from step1x3d_texture.utils.shape_post_process import (
FaceReducer,
DegenerateFaceRemover,
)
@spaces.GPU(duration=240)
def generate_func(
input_image_path, guidance_scale, inference_steps, max_facenum, symmetry, edge_type
):
geometry_model = geometry_model.to("cuda")
if "Label" in args.geometry_model:
out = geometry_model(
input_image_path,
label={"symmetry": symmetry, "edge_type": edge_type},
guidance_scale=float(guidance_scale),
octree_resolution=384,
max_facenum=int(max_facenum),
num_inference_steps=int(inference_steps),
)
else:
out = geometry_model(
input_image_path,
guidance_scale=float(guidance_scale),
num_inference_steps=int(inference_steps),
max_facenum=int(max_facenum),
)
save_name = str(uuid.uuid4())
print(save_name)
geometry_save_path = f"{args.cache_dir}/{save_name}.glb"
geometry_mesh = out.mesh[0]
geometry_mesh.export(geometry_save_path)
geometry_mesh = DegenerateFaceRemover()(geometry_mesh)
geometry_mesh = FaceReducer()(geometry_mesh)
textured_mesh = texture_model(input_image_path, geometry_mesh)
textured_save_path = f"{args.cache_dir}/{save_name}-textured.glb"
textured_mesh.export(textured_save_path)
torch.cuda.empty_cache()
print("Generate finish")
return geometry_save_path, textured_save_path
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--geometry_model", type=str, default="Step1X-3D-Geometry-Label-1300m"
)
parser.add_argument(
"--texture_model", type=str, default="Step1X-3D-Texture"
)
parser.add_argument("--cache_dir", type=str, default="cache")
parser.add_argument("--port", type=int, default=7861)
parser.add_argument("--host", type=str, default="0.0.0.0")
args = parser.parse_args()
os.makedirs(args.cache_dir, exist_ok=True)
geometry_model = Step1X3DGeometryPipeline.from_pretrained(
"stepfun-ai/Step1X-3D", subfolder=args.geometry_model
)
texture_model = Step1X3DTexturePipeline.from_pretrained("stepfun-ai/Step1X-3D", subfolder=args.texture_model)
with gr.Blocks(title="Step1X-3D demo") as demo:
gr.Markdown("# Step1X-3D")
with gr.Row():
with gr.Column(scale=2):
input_image = gr.Image(
label="Image", type="filepath", image_mode="RGBA"
)
guidance_scale = gr.Number(label="Guidance Scale", value="7.5")
inference_steps = gr.Slider(
label="Inferece Steps", minimum=1, maximum=100, value=50
)
max_facenum = gr.Number(label="Max Face Num", value="400000")
symmetry = gr.Radio(
choices=["x", "asymmetry"],
label="Symmetry Type",
value="x",
type="value",
)
edge_type = gr.Radio(
choices=["sharp", "normal", "smooth"],
label="Edge Type",
value="sharp",
type="value",
)
btn = gr.Button("Start")
with gr.Column(scale=4):
textured_preview = gr.Model3D(label="Textured", height=380)
geometry_preview = gr.Model3D(label="Geometry", height=380)
with gr.Column(scale=1):
gr.Examples(
examples=[
["examples/images/000.png"],
["examples/images/001.png"],
["examples/images/004.png"],
["examples/images/008.png"],
["examples/images/028.png"],
["examples/images/032.png"],
["examples/images/061.png"],
["examples/images/107.png"],
],
inputs=[input_image],
cache_examples=False,
)
btn.click(
generate_func,
inputs=[
input_image,
guidance_scale,
inference_steps,
max_facenum,
symmetry,
edge_type,
],
outputs=[geometry_preview, textured_preview],
)
demo.launch(server_name=args.host, server_port=args.port)
demo.queue(concurrency_count=3)
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