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import shlex |
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import subprocess |
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import gradio as gr |
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import numpy as np |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline |
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subprocess.run( |
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shlex.split( |
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"pip install https://huggingface.co/spaces/dylanebert/LGM-mini/resolve/main/wheel/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl" |
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) |
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) |
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pipeline = DiffusionPipeline.from_pretrained( |
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"dylanebert/LGM-full", |
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custom_pipeline="dylanebert/LGM-full", |
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torch_dtype=torch.float16, |
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trust_remote_code=True, |
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).to("cuda") |
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@spaces.GPU |
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def run(image): |
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input_image = np.array(image, dtype=np.float32) / 255.0 |
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splat = pipeline( |
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"", input_image, guidance_scale=5, num_inference_steps=30, elevation=0 |
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) |
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splat_file = "/tmp/output.ply" |
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pipeline.save_ply(splat, splat_file) |
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return splat_file |
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demo = gr.Interface( |
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fn=run, |
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inputs="image", |
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outputs=gr.Model3D(), |
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examples=[ |
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"https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/a_cat_statue.jpg" |
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], |
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) |
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demo.queue().launch() |
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