Update app.py
Browse files
app.py
CHANGED
@@ -8,45 +8,37 @@ def generate_3d_model(prompt, output_path="assistant_3d.obj"):
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Generate a 3D model using ShapE and export it in a Blender-compatible format
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"""
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try:
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# Load pipeline with
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pipe = ShapEPipeline.from_pretrained(
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"openai/shap-e",
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torch_dtype=torch.float32,
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low_cpu_mem_usage=True
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).to("cpu")
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# Generate with
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outputs = pipe(
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prompt,
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num_inference_steps=
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guidance_scale=
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# Using standard dimensions that work with the model
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frame_size=32, # Changed from 24 to standard size
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)
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# Extract
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vertices = outputs
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faces = outputs
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# Create trimesh object with
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mesh_obj = trimesh.Trimesh(
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vertices=vertices,
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faces=faces,
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process=True # Enable post-processing to fix any mesh issues
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)
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# Export with
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try:
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if output_path.endswith('.obj'):
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mesh_obj.export(output_path, include_normals=True)
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-
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mesh_obj.export(output_path)
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elif output_path.endswith('.stl'):
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mesh_obj.export(output_path)
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print(f"Successfully exported 3D model to: {output_path}")
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except Exception as export_error:
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print(f"Error during export: {export_error}")
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#
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backup_path = output_path.rsplit('.', 1)[0] + '.ply'
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mesh_obj.export(backup_path)
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print(f"Exported backup model to: {backup_path}")
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@@ -57,16 +49,11 @@ def generate_3d_model(prompt, output_path="assistant_3d.obj"):
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print(f"Error during generation: {e}")
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print(f"Error type: {type(e)}")
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print(f"Full error details: {str(e)}")
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-
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# Additional debug information
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if hasattr(outputs, 'shape'):
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print(f"Output shape: {outputs.shape}")
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raise
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if __name__ == "__main__":
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# Using a very specific prompt for better results
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prompt = "a simple 3D ring, perfect circle, clean geometry"
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try:
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generate_3d_model(prompt, "assistant_3d.obj")
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except Exception as e:
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print(f"Generation failed: {e}")
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Generate a 3D model using ShapE and export it in a Blender-compatible format
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"""
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try:
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+
# Load pipeline with memory constraints suitable for CPU-only usage
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pipe = ShapEPipeline.from_pretrained(
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"openai/shap-e",
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torch_dtype=torch.float32,
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low_cpu_mem_usage=True # Minimize CPU memory usage
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).to("cpu")
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# Generate with reduced inference steps and guidance scale for CPU
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outputs = pipe(
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prompt=prompt,
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num_inference_steps=8, # Reduced steps for CPU speed
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guidance_scale=5.0, # Lower guidance scale to save memory
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)
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# Extract vertices and faces for the 3D mesh generation
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vertices = outputs["vertices"][0].detach().cpu().numpy()
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faces = outputs["faces"][0].detach().cpu().numpy()
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# Create the trimesh object with error handling
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mesh_obj = trimesh.Trimesh(vertices=vertices, faces=faces, process=True)
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# Export model in desired format with robust handling for compatibility
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try:
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if output_path.endswith('.obj'):
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mesh_obj.export(output_path, include_normals=True)
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else:
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mesh_obj.export(output_path)
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print(f"Successfully exported 3D model to: {output_path}")
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except Exception as export_error:
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print(f"Error during export: {export_error}")
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# Use a fallback format in case of export failure
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backup_path = output_path.rsplit('.', 1)[0] + '.ply'
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mesh_obj.export(backup_path)
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print(f"Exported backup model to: {backup_path}")
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print(f"Error during generation: {e}")
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print(f"Error type: {type(e)}")
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print(f"Full error details: {str(e)}")
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raise
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if __name__ == "__main__":
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prompt = "a simple 3D ring, perfect circle, clean geometry"
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try:
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generate_3d_model(prompt, "assistant_3d.obj")
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except Exception as e:
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print(f"Generation failed: {e}")
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