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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)
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