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on
Zero
Running
on
Zero
import logging | |
from PIL import Image | |
import torch | |
import numpy as np | |
def create_logger(logging_dir): | |
""" | |
Create a logger that writes to a log file and stdout. | |
""" | |
logging.basicConfig( | |
level=logging.INFO, | |
format='[\033[34m%(asctime)s\033[0m] %(message)s', | |
datefmt='%Y-%m-%d %H:%M:%S', | |
handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")] | |
) | |
logger = logging.getLogger(__name__) | |
return logger | |
def update_ema(ema_model, model, decay=0.9999): | |
""" | |
Step the EMA model towards the current model. | |
""" | |
ema_params = dict(ema_model.named_parameters()) | |
for name, param in model.named_parameters(): | |
# TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed | |
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay) | |
def requires_grad(model, flag=True): | |
""" | |
Set requires_grad flag for all parameters in a model. | |
""" | |
for p in model.parameters(): | |
p.requires_grad = flag | |
def center_crop_arr(pil_image, image_size): | |
""" | |
Center cropping implementation from ADM. | |
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126 | |
""" | |
while min(*pil_image.size) >= 2 * image_size: | |
pil_image = pil_image.resize( | |
tuple(x // 2 for x in pil_image.size), resample=Image.BOX | |
) | |
scale = image_size / min(*pil_image.size) | |
pil_image = pil_image.resize( | |
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC | |
) | |
arr = np.array(pil_image) | |
crop_y = (arr.shape[0] - image_size) // 2 | |
crop_x = (arr.shape[1] - image_size) // 2 | |
return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size]) | |
def crop_arr(pil_image, max_image_size): | |
while min(*pil_image.size) >= 2 * max_image_size: | |
pil_image = pil_image.resize( | |
tuple(x // 2 for x in pil_image.size), resample=Image.BOX | |
) | |
if max(*pil_image.size) > max_image_size: | |
scale = max_image_size / max(*pil_image.size) | |
pil_image = pil_image.resize( | |
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC | |
) | |
if min(*pil_image.size) < 16: | |
scale = 16 / min(*pil_image.size) | |
pil_image = pil_image.resize( | |
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC | |
) | |
arr = np.array(pil_image) | |
crop_y1 = (arr.shape[0] % 16) // 2 | |
crop_y2 = arr.shape[0] % 16 - crop_y1 | |
crop_x1 = (arr.shape[1] % 16) // 2 | |
crop_x2 = arr.shape[1] % 16 - crop_x1 | |
arr = arr[crop_y1:arr.shape[0]-crop_y2, crop_x1:arr.shape[1]-crop_x2] | |
return Image.fromarray(arr) | |
def vae_encode(vae, x, weight_dtype): | |
if x is not None: | |
if vae.config.shift_factor is not None: | |
x = vae.encode(x).latent_dist.sample() | |
x = (x - vae.config.shift_factor) * vae.config.scaling_factor | |
else: | |
x = vae.encode(x).latent_dist.sample().mul_(vae.config.scaling_factor) | |
x = x.to(weight_dtype) | |
return x | |
def vae_encode_list(vae, x, weight_dtype): | |
latents = [] | |
for img in x: | |
img = vae_encode(vae, img, weight_dtype) | |
latents.append(img) | |
return latents | |