import os import gradio as gr import torch from diffusers import DiffusionPipeline try: import diff_gaussian_rasterization except ImportError: os.system("pip install ./diff-gaussian-rasterization") TMP_DIR = "/tmp" os.makedirs(TMP_DIR, exist_ok=True) image_pipeline = DiffusionPipeline.from_pretrained( "ashawkey/imagedream-ipmv-diffusers", 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 ) images = (images * 255).astype("uint8") 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 = """
A lightweight version of LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation.
""" 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 input_image = gr.Image(label="image", type="numpy", height=320) # gen button 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)