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") | |
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() | |