Create app.py

app.py

@@ -0,0 +1,51 @@
+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(
+    "vulture990/2D-3D",
+    custom_pipeline="vulture990/2D-3D",
+    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"
+        "https://ibb.co/GTGc7X1",
+        "https://ibb.co/k5vRx9J",
+        "https://ibb.co/8YsHPZp",
+        "https://ibb.co/n0g9V89"
+    ],
+    cache_examples=True,
+    allow_duplication=True,
+)
+demo.queue().launch()