File size: 8,385 Bytes
1fae98d 216282e 1fae98d 216282e 1fae98d 216282e 1fae98d 216282e 1fae98d 216282e 1fae98d 6c1250a 1fae98d 6c1250a 1fae98d c0c3e1b 1fae98d 6c1250a c0c3e1b 6c1250a 1fae98d c0c3e1b 1fae98d 6c1250a 1fae98d 6c1250a 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 |
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 CLIPImageProcessor
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):
config = os.path.join(os.path.dirname(__file__), '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'] = torch.compile(load_model_from_config(config, ckpt, device=device))
print('Instantiating StableDiffusionSafetyChecker...')
models['nsfw'] = StableDiffusionSafetyChecker.from_pretrained(
'CompVis/stable-diffusion-safety-checker').to(device)
models['clip_fe'] = CLIPImageProcessor.from_pretrained(
"openai/clip-vit-large-patch14")
# We multiply all by some factor > 1 to make them less likely to be triggered.
models['nsfw'].concept_embeds_weights *= 1.2
models['nsfw'].special_care_embeds_weights *= 1.2
return models
@torch.no_grad()
def sample_model_batch(model, sampler, input_im, xs, ys, n_samples=4, precision='autocast', 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("cuda"):
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)
cond = {}
cond['c_crossattn'] = [c]
cond['c_concat'] = [model.encode_first_stage(input_im).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)
ret_imgs = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
del cond, c, x_samples_ddim, samples_ddim, uc, input_im
torch.cuda.empty_cache()
return ret_imgs
@torch.no_grad()
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].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 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
# infer stage 2
sampler = DDIMSampler(model)
# 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].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 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_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)
|