Score Jacobian Chaining (SJC) によるテキストからの3D生成を試す
「Score Jacobian Chaining」(SJC)によるテキストからの3D生成を試したのでまとめました。
前回
1. Score Jacobian Chaining (SJC)
「Score Jacobian Chaining」(SJC)は、テキストから3Dを生成する手法です。3Dデータへを必要とせずに、画像の事前学習済み2D 拡散生成モデルを放射輝度フィールドの3D生成モデルに変換します。
2. sjcのインストール
Colabへの「SJC」のインストール方法は、次のとおりです。公式ノートブックをベースに、Colabで実行できるよう手直ししてます。
(1) メニュー「編集→ノートブックの設定」で、「ハードウェアアクセラレータ」に「GPU」を選択。
自分は高速化のために「Colab Pro」の「プレミア」を使いました。
(2) GPUの確認。
# GPUの確認
!nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |
| N/A 42C P0 44W / 400W | 0MiB / 40536MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
(3) Googleドライブのマウント。
from google.colab import drive
drive.mount('/content/drive')
(3) 「sjc」パッケージをインストールして、「sjc」フォルダに移動。
import os
if not os.path.exists('/content/drive/MyDrive/sjc'):
print('Installing from scratch...')
%cd /content/drive/MyDrive/
!git clone https://github.com/pals-ttic/sjc/
%cd sjc
else:
print('repo already exists in Drive...')
%cd /content/drive/MyDrive/sjc
!pip install pytorch_lightning -U
!git clone --depth 1 https://github.com/CompVis/taming-transformers.git && pip install -e taming-transformers
!pip install -r requirements.txt
!pip install imageio-ffmpeg
print('Continue to next cell.')
(4) Stable Diffusionの依存関係のインストール。
# Stable Diffusionの依存関係のインストール
!pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
!pip install pytorch_lightning tensorboard==2.8 omegaconf einops taming-transformers==0.0.1 clip transformers kornia test-tube
!pip install diffusers invisible-watermark
!pip install pytorch-lightning==1.8.3.post0
(5) アセットのダウンロード。
# アセットのダウンロード
if not os.path.exists('/content/drive/MyDrive/sjc/release'):
print('Downloading checkpoints. This will take a while...')
!wget https://dl.ttic.edu/pals/sjc/release.tar
!tar -xvf /content/drive/MyDrive/sjc/release.tar
else:
print('checkpoints already downloaded')
(6) env.jsonのアップデート。
%%writefile env.json
{
"data_root": "/content/drive/MyDrive/sjc/release"
}
(7) adapt_sd.pyのアップデート。
%%writefile adapt_sd.py
import sys
from pathlib import Path
import torch
import numpy as np
from omegaconf import OmegaConf
from einops import rearrange
from contextlib import nullcontext
from math import sqrt
from adapt import ScoreAdapter
import warnings
from transformers import logging
warnings.filterwarnings("ignore", category=DeprecationWarning)
logging.set_verbosity_error()
device = torch.device("cuda")
def curr_dir():
return Path(__file__).resolve().parent
def add_import_path(dirname):
sys.path.append(str(
curr_dir() / str(dirname)
))
def load_model_from_config(config, ckpt, verbose=False):
from ldm.util import instantiate_from_config
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.to(device)
model.eval()
return model
def load_sd1_model(ckpt_root):
ckpt_fname = ckpt_root / "stable_diffusion" / "sd-v1-5.ckpt"
cfg_fname = curr_dir() / "sd1" / "configs" / "v1-inference.yaml"
H, W = 512, 512
config = OmegaConf.load(str(cfg_fname))
model = load_model_from_config(config, str(ckpt_fname))
return model, H, W
def load_sd2_model(ckpt_root, v2_highres):
if v2_highres:
ckpt_fname = ckpt_root / "sd2" / "768-v-ema.ckpt"
cfg_fname = curr_dir() / "sd2/configs/stable-diffusion/v2-inference-v.yaml"
H, W = 768, 768
else:
ckpt_fname = ckpt_root / "sd2" / "512-base-ema.ckpt"
cfg_fname = curr_dir() / "sd2/configs/stable-diffusion/v2-inference.yaml"
H, W = 512, 512
config = OmegaConf.load(f"{cfg_fname}")
model = load_model_from_config(config, str(ckpt_fname))
return model, H, W
def _sqrt(x):
if isinstance(x, float):
return sqrt(x)
else:
assert isinstance(x, torch.Tensor)
return torch.sqrt(x)
class StableDiffusion(ScoreAdapter):
def __init__(self, variant, v2_highres, prompt, scale, precision):
if variant == "v1":
add_import_path("sd1")
self.model, H, W = load_sd1_model(self.checkpoint_root())
elif variant == "v2":
add_import_path("sd2")
self.model, H, W = load_sd2_model(self.checkpoint_root(), v2_highres)
else:
raise ValueError(f"{variant}")
ae_resolution_f = 8
self._device = self.model._device
self.prompt = prompt
self.scale = scale
self.precision = precision
self.precision_scope = autocast if self.precision == True else nullcontext
self._data_shape = (4, H // ae_resolution_f, W // ae_resolution_f)
self.cond_func = self.model.get_learned_conditioning
self.M = 1000
noise_schedule = "linear"
self.noise_schedule = noise_schedule
self.us = self.linear_us(self.M)
def data_shape(self):
return self._data_shape
@property
def σ_max(self):
return self.us[0]
@property
def σ_min(self):
return self.us[-1]
@torch.no_grad()
def denoise(self, xs, σ, **model_kwargs):
with self.precision_scope("cuda"):
with self.model.ema_scope():
N = xs.shape[0]
c = model_kwargs.pop('c')
uc = model_kwargs.pop('uc')
cond_t, σ = self.time_cond_vec(N, σ)
unscaled_xs = xs
xs = xs / _sqrt(1 + σ**2)
if uc is None or self.scale == 1.:
output = self.model.apply_model(xs, cond_t, c)
else:
x_in = torch.cat([xs] * 2)
t_in = torch.cat([cond_t] * 2)
c_in = torch.cat([uc, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
output = e_t_uncond + self.scale * (e_t - e_t_uncond)
if self.model.parameterization == "v":
output = self.model.predict_eps_from_z_and_v(xs, cond_t, output)
else:
output = output
Ds = unscaled_xs - σ * output
return Ds
def cond_info(self, batch_size):
prompts = batch_size * [self.prompt]
return self.prompts_emb(prompts)
@torch.no_grad()
def prompts_emb(self, prompts):
assert isinstance(prompts, list)
batch_size = len(prompts)
with self.precision_scope("cuda"):
with self.model.ema_scope():
cond = {}
c = self.cond_func(prompts)
cond['c'] = c
uc = None
if self.scale != 1.0:
uc = self.cond_func(batch_size * [""])
cond['uc'] = uc
return cond
def unet_is_cond(self):
return True
def use_cls_guidance(self):
return False
def snap_t_to_nearest_tick(self, t):
j = np.abs(t - self.us).argmin()
return self.us[j], j
def time_cond_vec(self, N, σ):
if isinstance(σ, float):
σ, j = self.snap_t_to_nearest_tick(σ) # σ might change due to snapping
cond_t = (self.M - 1) - j
cond_t = torch.tensor([cond_t] * N, device=self.device)
return cond_t, σ
else:
assert isinstance(σ, torch.Tensor)
σ = σ.reshape(-1).cpu().numpy()
σs = []
js = []
for elem in σ:
_σ, _j = self.snap_t_to_nearest_tick(elem)
σs.append(_σ)
js.append((self.M - 1) - _j)
cond_t = torch.tensor(js, device=self.device)
σs = torch.tensor(σs, device=self.device, dtype=torch.float32).reshape(-1, 1, 1, 1)
return cond_t, σs
@staticmethod
def linear_us(M=1000):
assert M == 1000
β_start = 0.00085
β_end = 0.0120
βs = np.linspace(β_start**0.5, β_end**0.5, M, dtype=np.float64)**2
αs = np.cumprod(1 - βs)
us = np.sqrt((1 - αs) / αs)
us = us[::-1]
return us
@torch.no_grad()
def encode(self, xs):
model = self.model
with self.precision_scope("cuda"):
with self.model.ema_scope():
zs = model.get_first_stage_encoding(
model.encode_first_stage(xs)
)
return zs
@torch.no_grad()
def decode(self, xs):
with self.precision_scope("cuda"):
with self.model.ema_scope():
xs = self.model.decode_first_stage(xs)
return xs
def test():
sd = StableDiffusion("v2", True, "haha", 10.0, True)
print(sd)
if __name__ == "__main__":
test()
(8) 作業用にexpフォルダを作成し、expフォルダに移動。
# 作業フォルダの作成
if not os.path.exists('exp'):
!mkdir exp
%cd exp
3. 3Dモデルの生成の実行
テキストからの3D生成の実行手順は、次のとおりです。
(1) テキストからの3D生成を実行。
# テキストからの3D生成
!python ../run_sjc.py \
--sd.prompt "Biden figure" \
--n_steps 10000 \
--lr 0.05 \
--sd.scale 100.0 \
--emptiness_weight 10000 \
--emptiness_step 0.5 \
--emptiness_multiplier 20.0 \
--depth_weight 0
パラメータの説明は、次のとおり。
以下のような3Dモデルが生成されました。
この記事が気に入ったらサポートをしてみませんか?