import tempfile import numpy as np import PIL.Image import torch import trimesh from diffusers import ShapEImg2ImgPipeline, ShapEPipeline from diffusers.utils import export_to_ply class Model: def __init__(self): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.pipe = ShapEPipeline.from_pretrained("openai/shap-e", torch_dtype=torch.float16) self.pipe.to(self.device) self.pipe_img = ShapEImg2ImgPipeline.from_pretrained("openai/shap-e-img2img", torch_dtype=torch.float16) self.pipe_img.to(self.device) def to_glb(self, ply_path: str) -> str: mesh = trimesh.load(ply_path) rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0]) mesh = mesh.apply_transform(rot) rot = trimesh.transformations.rotation_matrix(np.pi, [0, 1, 0]) mesh = mesh.apply_transform(rot) mesh_path = tempfile.NamedTemporaryFile(suffix=".glb", delete=False) mesh.export(mesh_path.name, file_type="glb") return mesh_path.name def run_text(self, prompt: str, seed: int = 0, guidance_scale: float = 15.0, num_steps: int = 64) -> str: generator = torch.Generator(device=self.device).manual_seed(seed) images = self.pipe( prompt, generator=generator, guidance_scale=guidance_scale, num_inference_steps=num_steps, output_type="mesh", ).images ply_path = tempfile.NamedTemporaryFile(suffix=".ply", delete=False, mode="w+b") export_to_ply(images[0], ply_path.name) return self.to_glb(ply_path.name) def run_image( self, image: PIL.Image.Image, seed: int = 0, guidance_scale: float = 3.0, num_steps: int = 64 ) -> str: generator = torch.Generator(device=self.device).manual_seed(seed) images = self.pipe_img( image, generator=generator, guidance_scale=guidance_scale, num_inference_steps=num_steps, output_type="mesh", ).images ply_path = tempfile.NamedTemporaryFile(suffix=".ply", delete=False, mode="w+b") export_to_ply(images[0], ply_path.name) return self.to_glb(ply_path.name)