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import torch
from diffusers import DiffusionPipeline
import trimesh
import numpy as np
from PIL import Image
from io import BytesIO
def load_pipeline():
"""
Load the LGM-full model pipeline from Hugging Face with remote code execution enabled.
"""
ckpt_id = "dylanebert/LGM-full"
pipe = DiffusionPipeline.from_pretrained(
ckpt_id,
torch_dtype=torch.float32,
trust_remote_code=True # Enable remote code execution for custom model code
).to("cpu")
return pipe
def generate_3d_model(pipe, prompt, output_path="output.obj", guidance_scale=7.5, num_inference_steps=32):
"""
Generate a 3D model from the prompt and save it in a Blender-compatible format (.obj).
"""
# Generate the model output
outputs = pipe(prompt=prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps)
# Extract mesh data if the output structure allows
vertices = outputs["vertices"][0].detach().cpu().numpy()
faces = outputs["faces"][0].detach().cpu().numpy()
# Create and save the mesh using trimesh
mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=True)
mesh.export(output_path)
return output_path
def convert_to_gif(images, gif_path="output.gif"):
"""
Convert a list of images into a GIF.
"""
images[0].save(
gif_path, save_all=True, append_images=images[1:], loop=0, duration=100
)
return gif_path
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