Spaces:
Runtime error
Runtime error
File size: 7,596 Bytes
bfd34e9 da1e12f bfd34e9 da1e12f bfd34e9 da1e12f bfd34e9 da1e12f bfd34e9 da1e12f bfd34e9 |
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 |
import importlib
from functools import partial
import cv2
import numpy as np
import safetensors
import safetensors.torch
import torch
import torch.nn as nn
from inspect import isfunction
from omegaconf import OmegaConf
from lib.smplfusion import DDIM, share, scheduler
from .common import *
DOWNLOAD_URL = 'https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.safetensors?download=true'
MODEL_PATH = f'{MODEL_FOLDER}/sd-2-0-upsample/x4-upscaler-ema.safetensors'
# pre-download
download_file(DOWNLOAD_URL, MODEL_PATH)
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def predict_eps_from_z_and_v(schedule, x_t, t, v):
return (
extract_into_tensor(schedule.sqrt_alphas.to(x_t.device), t, x_t.shape) * v +
extract_into_tensor(schedule.sqrt_one_minus_alphas.to(x_t.device), t, x_t.shape) * x_t
)
def predict_start_from_z_and_v(schedule, x_t, t, v):
return (
extract_into_tensor(schedule.sqrt_alphas.to(x_t.device), t, x_t.shape) * x_t -
extract_into_tensor(schedule.sqrt_one_minus_alphas.to(x_t.device), t, x_t.shape) * v
)
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
)
elif schedule == "cosine":
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
elif schedule == "sqrt":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class AbstractLowScaleModel(nn.Module):
# for concatenating a downsampled image to the latent representation
def __init__(self, noise_schedule_config=None):
super(AbstractLowScaleModel, self).__init__()
if noise_schedule_config is not None:
self.register_schedule(**noise_schedule_config)
def register_schedule(self, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def forward(self, x):
return x, None
def decode(self, x):
return x
class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
super().__init__(noise_schedule_config=noise_schedule_config)
self.max_noise_level = max_noise_level
def forward(self, x, noise_level=None):
if noise_level is None:
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
else:
assert isinstance(noise_level, torch.Tensor)
z = self.q_sample(x, noise_level)
return z, noise_level
def get_obj_from_str(string):
module, cls = string.rsplit(".", 1)
try:
return getattr(importlib.import_module(module, package=None), cls)
except:
return getattr(importlib.import_module('lib.' + module, package=None), cls)
def load_obj(path):
objyaml = OmegaConf.load(path)
return get_obj_from_str(objyaml['__class__'])(**objyaml.get("__init__", {}))
def load_model(dtype=torch.bfloat16, device='cuda:0'):
print ("Loading model: SD2 superresolution...")
download_file(DOWNLOAD_URL, MODEL_PATH)
state_dict = safetensors.torch.load_file(MODEL_PATH)
config = OmegaConf.load(f'{CONFIG_FOLDER}/ddpm/v2-upsample.yaml')
unet = load_obj(f'{CONFIG_FOLDER}/unet/upsample/v2.yaml').eval().cuda()
vae = load_obj(f'{CONFIG_FOLDER}/vae-upsample.yaml').eval().cuda()
encoder = load_obj(f'{CONFIG_FOLDER}/encoders/openclip.yaml').eval().cuda()
ddim = DDIM(config, vae, encoder, unet)
extract = lambda state_dict, model: {x[len(model)+1:]:y for x,y in state_dict.items() if model in x}
unet_state = extract(state_dict, 'model.diffusion_model')
encoder_state = extract(state_dict, 'cond_stage_model')
vae_state = extract(state_dict, 'first_stage_model')
unet.load_state_dict(unet_state)
encoder.load_state_dict(encoder_state)
vae.load_state_dict(vae_state)
unet = unet.requires_grad_(False)
encoder = encoder.requires_grad_(False)
vae = vae.requires_grad_(False)
unet.to(dtype=dtype, device=device)
vae.to(dtype=dtype, device=device)
encoder.to(dtype=dtype, device=device)
encoder.device = device
ddim = DDIM(config, vae, encoder, unet)
params = {
'noise_schedule_config': {
'linear_start': 0.0001,
'linear_end': 0.02
},
'max_noise_level': 350
}
low_scale_model = ImageConcatWithNoiseAugmentation(**params).eval().to('cuda')
low_scale_model.train = disabled_train
for param in low_scale_model.parameters():
param.requires_grad = False
low_scale_model = low_scale_model.to(dtype=dtype, device=device)
ddim.low_scale_model = low_scale_model
print('SD2 superresolution loaded')
return ddim
|