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import shlex
import subprocess
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline
subprocess.run(
shlex.split(
"pip install https://huggingface.co/spaces/dylanebert/LGM-mini/resolve/main/wheel/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl"
)
)
pipeline = DiffusionPipeline.from_pretrained(
"dylanebert/LGM-full",
custom_pipeline="dylanebert/LGM-full",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
@spaces.GPU
def run(image):
input_image = np.array(image, dtype=np.float32) / 255.0
splat = pipeline(
"", input_image, guidance_scale=5, num_inference_steps=30, elevation=0
)
splat_file = "/tmp/output.ply"
pipeline.save_ply(splat, splat_file)
return splat_file
demo = gr.Interface(
fn=run,
title="LGM Tiny",
description="An extremely simplified version of [LGM](https://huggingface.co/ashawkey/LGM). Intended as resource for the [ML for 3D Course](https://huggingface.co/learn/ml-for-3d-course/unit0/introduction).",
inputs="image",
outputs=gr.Model3D(),
examples=[
"https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/a_cat_statue.jpg"
],
cache_examples=True,
)
demo.queue().launch()
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