File size: 10,097 Bytes
1fae98d fc96ff2 1fae98d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
import os
import numpy as np
import torch
from contextlib import nullcontext
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from omegaconf import OmegaConf
from PIL import Image
from rich import print
from transformers import AutoFeatureExtractor
from torch import autocast
from torchvision import transforms
def load_model_from_config(config, ckpt, device, verbose=False):
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 init_model(device, ckpt):
import inspect
dir_path = os.path.dirname(os.path.abspath(
inspect.getfile(inspect.currentframe())))
config = os.path.join(dir_path, 'configs/sd-objaverse-finetune-c_concat-256.yaml')
config = OmegaConf.load(config)
# Instantiate all models beforehand for efficiency.
models = dict()
print('Instantiating LatentDiffusion...')
models['turncam'] = load_model_from_config(config, ckpt, device=device)
# print('Instantiating Carvekit HiInterface...')
# models['carvekit'] = create_carvekit_interface()
print('Instantiating StableDiffusionSafetyChecker...')
models['nsfw'] = StableDiffusionSafetyChecker.from_pretrained(
'CompVis/stable-diffusion-safety-checker').to(device)
print('Instantiating AutoFeatureExtractor...')
models['clip_fe'] = AutoFeatureExtractor.from_pretrained(
'CompVis/stable-diffusion-safety-checker')
# We multiply all by some factor > 1 to make them less likely to be triggered.
models['nsfw'].concept_embeds_weights *= 1.07
models['nsfw'].special_care_embeds_weights *= 1.07
return models
@torch.no_grad()
def sample_model_batch(model, sampler, input_im, xs, ys, n_samples=4, precision='fp32', ddim_eta=1.0, ddim_steps=75, scale=3.0, h=256, w=256):
precision_scope = autocast if precision == 'autocast' else nullcontext
with precision_scope(model.device):
with model.ema_scope():
c = model.get_learned_conditioning(input_im).tile(n_samples, 1, 1)
T = []
for x, y in zip(xs, ys):
T.append([np.radians(x), np.sin(np.radians(y)), np.cos(np.radians(y)), 0])
T = torch.tensor(np.array(T))[:, None, :].float().to(c.device)
c = torch.cat([c, T], dim=-1)
c = model.cc_projection(c)
print("debug c device", c.device)
cond = {}
cond['c_crossattn'] = [c]
# c_concat = model.encode_first_stage((input_im.to(c.device))).mode().detach()
cond['c_concat'] = [model.encode_first_stage((input_im.to(c.device))).mode().detach()
.repeat(n_samples, 1, 1, 1)]
if scale != 1.0:
uc = {}
uc['c_concat'] = [torch.zeros(n_samples, 4, h // 8, w // 8).to(c.device)]
uc['c_crossattn'] = [torch.zeros_like(c).to(c.device)]
else:
uc = None
shape = [4, h // 8, w // 8]
samples_ddim, _ = sampler.sample(S=ddim_steps,
conditioning=cond,
batch_size=n_samples,
shape=shape,
verbose=False,
unconditional_guidance_scale=scale,
unconditional_conditioning=uc,
eta=ddim_eta,
x_T=None)
print(samples_ddim.shape)
# samples_ddim = torch.nn.functional.interpolate(samples_ddim, 64, mode='nearest', antialias=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
def predict_stage1(model, sampler, input_img_path, save_path_8, adjust_set=[], device="cuda"):
raw_im = Image.open(input_img_path)
# raw_im = raw_im.resize([256, 256], Image.LANCZOS)
# input_im_init = preprocess_image(models, raw_im, preprocess=False)
input_im_init = np.asarray(raw_im, dtype=np.float32) / 255.0
input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
input_im = input_im * 2 - 1
# stage 1: 8
delta_x_1_8 = [0] * 4 + [30] * 4 + [-30] * 4
delta_y_1_8 = [0+90*(i%4) if i < 4 else 30+90*(i%4) for i in range(8)] + [30+90*(i%4) for i in range(4)]
x_samples_ddims_8 = sample_model_batch(model, sampler, input_im, delta_x_1_8, delta_y_1_8, n_samples=len(delta_x_1_8))
for stage1_idx in range(len(x_samples_ddims_8)):
if adjust_set != [] and stage1_idx not in adjust_set:
continue
x_sample = 255.0 * rearrange(x_samples_ddims_8[stage1_idx].cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(save_path_8, '%d.png'%(stage1_idx)))
del x_samples_ddims_8
del input_im
torch.cuda.empty_cache()
def predict_stage1_gradio(model, raw_im, save_path = "", adjust_set=[], device="cuda", ddim_steps=75, scale=3.0):
# raw_im = raw_im.resize([256, 256], Image.LANCZOS)
# input_im_init = preprocess_image(models, raw_im, preprocess=False)
input_im_init = np.asarray(raw_im, dtype=np.float32) / 255.0
input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
input_im = input_im * 2 - 1
# stage 1: 8
delta_x_1_8 = [0] * 4 + [30] * 4 + [-30] * 4
delta_y_1_8 = [0+90*(i%4) if i < 4 else 30+90*(i%4) for i in range(8)] + [30+90*(i%4) for i in range(4)]
ret_imgs = []
sampler = DDIMSampler(model)
# sampler.to(device)
if adjust_set != []:
x_samples_ddims_8 = sample_model_batch(model, sampler, input_im,
[delta_x_1_8[i] for i in adjust_set], [delta_y_1_8[i] for i in adjust_set],
n_samples=len(adjust_set), ddim_steps=ddim_steps, scale=scale)
else:
x_samples_ddims_8 = sample_model_batch(model, sampler, input_im, delta_x_1_8, delta_y_1_8, n_samples=len(delta_x_1_8), ddim_steps=ddim_steps, scale=scale)
sample_idx = 0
for stage1_idx in range(len(delta_x_1_8)):
if adjust_set != [] and stage1_idx not in adjust_set:
continue
x_sample = 255.0 * rearrange(x_samples_ddims_8[sample_idx].cpu().numpy(), 'c h w -> h w c')
out_image = Image.fromarray(x_sample.astype(np.uint8))
ret_imgs.append(out_image)
if save_path:
out_image.save(os.path.join(save_path, '%d.png'%(stage1_idx)))
sample_idx += 1
del x_samples_ddims_8
del input_im
del sampler
torch.cuda.empty_cache()
return ret_imgs
def infer_stage_2(model, save_path_stage1, save_path_stage2, delta_x_2, delta_y_2, indices, device, ddim_steps=75, scale=3.0):
for stage1_idx in indices:
# save stage 1 image
# x_sample = 255.0 * rearrange(x_samples_ddims[stage1_idx].cpu().numpy(), 'c h w -> h w c')
# Image.fromarray(x_sample.astype(np.uint8)).save()
stage1_image_path = os.path.join(save_path_stage1, '%d.png'%(stage1_idx))
raw_im = Image.open(stage1_image_path)
# input_im_init = preprocess_image(models, raw_im, preprocess=False)
input_im_init = np.asarray(raw_im, dtype=np.float32) #/ 255.0
input_im_init[input_im_init >= 253.0] = 255.0
input_im_init = input_im_init / 255.0
input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
input_im = input_im * 2 - 1
print("debug input device", input_im.device)
print("debug model device", model.device)
# infer stage 2
sampler = DDIMSampler(model)
print("debug sampler device", sampler.device)
# sampler.to(device)
# stage2_in = x_samples_ddims[stage1_idx][None, ...].to(device) * 2 - 1
x_samples_ddims_stage2 = sample_model_batch(model, sampler, input_im, delta_x_2, delta_y_2, n_samples=len(delta_x_2), ddim_steps=ddim_steps, scale=scale)
for stage2_idx in range(len(delta_x_2)):
x_sample_stage2 = 255.0 * rearrange(x_samples_ddims_stage2[stage2_idx].cpu().numpy(), 'c h w -> h w c')
Image.fromarray(x_sample_stage2.astype(np.uint8)).save(os.path.join(save_path_stage2, '%d_%d.png'%(stage1_idx, stage2_idx)))
del input_im
del sampler
del x_samples_ddims_stage2
torch.cuda.empty_cache()
def zero123_infer(model, input_dir_path, start_idx=0, end_idx=12, indices=None, device="cuda", ddim_steps=75, scale=3.0):
# input_dir_path = "/objaverse-processed/zero12345_img/eval/teddy_wild"
# input_img_path = os.path.join(input_dir_path, "input_256.png")
save_path_8 = os.path.join(input_dir_path, "stage1_8")
save_path_8_2 = os.path.join(input_dir_path, "stage2_8")
os.makedirs(save_path_8_2, exist_ok=True)
# raw_im = Image.open(input_img_path)
# # input_im_init = preprocess_image(models, raw_im, preprocess=False)
# input_im_init = np.asarray(raw_im, dtype=np.float32) / 255.0
# input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
# input_im = input_im * 2 - 1
# stage 2: 6*4 or 8*4
delta_x_2 = [-10, 10, 0, 0]
delta_y_2 = [0, 0, -10, 10]
infer_stage_2(model, save_path_8, save_path_8_2, delta_x_2, delta_y_2, indices=indices if indices else list(range(start_idx,end_idx)), device=device, ddim_steps=ddim_steps, scale=scale)
|