File size: 5,457 Bytes
57432d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
import tempfile
import numpy as np
import torch
import trimesh
from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
from shap_e.diffusion.sample import sample_latents
from shap_e.models.download import load_config, load_model
from shap_e.models.nn.camera import (DifferentiableCameraBatch,
DifferentiableProjectiveCamera)
from shap_e.models.transmitter.base import Transmitter, VectorDecoder
from shap_e.rendering.torch_mesh import TorchMesh
from shap_e.util.collections import AttrDict
from shap_e.util.image_util import load_image
# Copied from https://github.com/openai/shap-e/blob/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/util/notebooks.py#L15-L42
def create_pan_cameras(size: int,
device: torch.device) -> DifferentiableCameraBatch:
origins = []
xs = []
ys = []
zs = []
for theta in np.linspace(0, 2 * np.pi, num=20):
z = np.array([np.sin(theta), np.cos(theta), -0.5])
z /= np.sqrt(np.sum(z**2))
origin = -z * 4
x = np.array([np.cos(theta), -np.sin(theta), 0.0])
y = np.cross(z, x)
origins.append(origin)
xs.append(x)
ys.append(y)
zs.append(z)
return DifferentiableCameraBatch(
shape=(1, len(xs)),
flat_camera=DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(origins,
axis=0)).float().to(device),
x=torch.from_numpy(np.stack(xs, axis=0)).float().to(device),
y=torch.from_numpy(np.stack(ys, axis=0)).float().to(device),
z=torch.from_numpy(np.stack(zs, axis=0)).float().to(device),
width=size,
height=size,
x_fov=0.7,
y_fov=0.7,
),
)
# Copied from https://github.com/openai/shap-e/blob/8625e7c15526d8510a2292f92165979268d0e945/shap_e/util/notebooks.py#LL64C1-L76C33
@torch.no_grad()
def decode_latent_mesh(
xm: Transmitter | VectorDecoder,
latent: torch.Tensor,
) -> TorchMesh:
decoded = xm.renderer.render_views(
AttrDict(cameras=create_pan_cameras(
2, latent.device)), # lowest resolution possible
params=(xm.encoder if isinstance(xm, Transmitter) else
xm).bottleneck_to_params(latent[None]),
options=AttrDict(rendering_mode='stf', render_with_direction=False),
)
return decoded.raw_meshes[0]
class Model:
def __init__(self):
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.xm = load_model('transmitter', device=self.device)
self.diffusion = diffusion_from_config(load_config('diffusion'))
self.model_text = None
self.model_image = None
def load_model(self, model_name: str) -> None:
assert model_name in ['text300M', 'image300M']
if model_name == 'text300M' and self.model_text is None:
self.model_text = load_model(model_name, device=self.device)
elif model_name == 'image300M' and self.model_image is None:
self.model_image = load_model(model_name, device=self.device)
def to_glb(self, latent: torch.Tensor) -> str:
ply_path = tempfile.NamedTemporaryFile(suffix='.ply',
delete=False,
mode='w+b')
decode_latent_mesh(self.xm, latent).tri_mesh().write_ply(ply_path)
mesh = trimesh.load(ply_path.name)
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:
self.load_model('text300M')
torch.manual_seed(seed)
latents = sample_latents(
batch_size=1,
model=self.model_text,
diffusion=self.diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(texts=[prompt]),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=num_steps,
sigma_min=1e-3,
sigma_max=160,
s_churn=0,
)
return self.to_glb(latents[0])
def run_image(self,
image_path: str,
seed: int = 0,
guidance_scale: float = 3.0,
num_steps: int = 64) -> str:
self.load_model('image300M')
torch.manual_seed(seed)
image = load_image(image_path)
latents = sample_latents(
batch_size=1,
model=self.model_image,
diffusion=self.diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(images=[image]),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=num_steps,
sigma_min=1e-3,
sigma_max=160,
s_churn=0,
)
return self.to_glb(latents[0])
|