| 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, |
| allow_duplication=True, |
| ) |
| demo.queue().launch() |
|
|
