Spaces:
Running
on
Zero
Running
on
Zero
| import os | |
| import shlex | |
| import subprocess | |
| import gradio as gr | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| from gradio_client import Client, file | |
| subprocess.run( | |
| shlex.split( | |
| "pip install wheel/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl" | |
| ) | |
| ) | |
| TMP_DIR = "/tmp" | |
| os.makedirs(TMP_DIR, exist_ok=True) | |
| image_pipeline = DiffusionPipeline.from_pretrained( | |
| "dylanebert/imagedream", | |
| custom_pipeline="dylanebert/multi-view-diffusion", | |
| torch_dtype=torch.float16, | |
| trust_remote_code=True, | |
| ).to("cuda") | |
| splat_pipeline = DiffusionPipeline.from_pretrained( | |
| "dylanebert/LGM", | |
| custom_pipeline="dylanebert/LGM", | |
| torch_dtype=torch.float16, | |
| trust_remote_code=True, | |
| ).to("cuda") | |
| def run(input_image): | |
| input_image = input_image.astype("float32") / 255.0 | |
| images = image_pipeline( | |
| "", input_image, guidance_scale=5, num_inference_steps=30, elevation=0 | |
| ) | |
| gaussians = splat_pipeline(images) | |
| output_ply_path = os.path.join(TMP_DIR, "output.ply") | |
| splat_pipeline.save_ply(gaussians, output_ply_path) | |
| return output_ply_path | |
| _TITLE = """LGM Mini""" | |
| _DESCRIPTION = """ | |
| <div> | |
| A lightweight version of <a href="https://huggingface.co/spaces/ashawkey/LGM">LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation</a>. | |
| To convert to mesh, download the output splat and visit [splat-to-mesh](https://huggingface.co/spaces/dylanebert/splat-to-mesh). | |
| </div> | |
| """ | |
| css = """ | |
| #duplicate-button { | |
| margin: auto; | |
| color: white; | |
| background: #1565c0; | |
| border-radius: 100vh; | |
| } | |
| """ | |
| block = gr.Blocks(title=_TITLE, css=css) | |
| with block: | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", elem_id="duplicate-button" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("# " + _TITLE) | |
| gr.Markdown(_DESCRIPTION) | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| input_image = gr.Image(label="image", type="numpy") | |
| button_gen = gr.Button("Generate") | |
| with gr.Column(scale=1): | |
| output_splat = gr.Model3D(label="3D Gaussians") | |
| button_gen.click( | |
| fn=run, inputs=[input_image], outputs=[output_splat] | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| "data_test/frog_sweater.jpg", | |
| "data_test/bird.jpg", | |
| "data_test/boy.jpg", | |
| "data_test/cat_statue.jpg", | |
| "data_test/dragontoy.jpg", | |
| "data_test/gso_rabbit.jpg", | |
| ], | |
| inputs=[input_image], | |
| outputs=[output_splat], | |
| fn=lambda x: run(input_image=x), | |
| cache_examples=True, | |
| label="Image-to-3D Examples", | |
| ) | |
| block.queue().launch(debug=True, share=True) | |