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A10G
Running
on
A10G
import torch | |
import os | |
from tqdm import tqdm | |
from PIL import Image, ImageDraw ,ImageFont | |
from matplotlib import pyplot as plt | |
import torchvision.transforms as T | |
import os | |
import yaml | |
import numpy as np | |
import gradio as gr | |
torch.manual_seed(0) | |
def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None): | |
if type(image_path) is str: | |
image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3] | |
else: | |
image = image_path | |
h, w, c = image.shape | |
left = min(left, w-1) | |
right = min(right, w - left - 1) | |
top = min(top, h - left - 1) | |
bottom = min(bottom, h - top - 1) | |
image = image[top:h-bottom, left:w-right] | |
h, w, c = image.shape | |
if h < w: | |
offset = (w - h) // 2 | |
image = image[:, offset:offset + h] | |
elif w < h: | |
offset = (h - w) // 2 | |
image = image[offset:offset + w] | |
image = np.array(Image.fromarray(image).resize((512, 512))) | |
image = torch.from_numpy(image).float() / 127.5 - 1 | |
image = image.permute(2, 0, 1).unsqueeze(0).to(device) | |
return image | |
def load_real_image(folder = "data/", img_name = None, idx = 0, img_size=512, device='cuda'): | |
from PIL import Image | |
from glob import glob | |
if img_name is not None: | |
path = os.path.join(folder, img_name) | |
else: | |
path = glob(folder + "*")[idx] | |
img = Image.open(path).resize((img_size, | |
img_size)) | |
img = pil_to_tensor(img).to(device) | |
if img.shape[1]== 4: | |
img = img[:,:3,:,:] | |
return img | |
def mu_tilde(model, xt,x0, timestep): | |
"mu_tilde(x_t, x_0) DDPM paper eq. 7" | |
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps | |
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod | |
alpha_t = model.scheduler.alphas[timestep] | |
beta_t = 1 - alpha_t | |
alpha_bar = model.scheduler.alphas_cumprod[timestep] | |
return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt | |
def sample_xts_from_x0(model, x0, num_inference_steps=50): | |
""" | |
Samples from P(x_1:T|x_0) | |
""" | |
# torch.manual_seed(43256465436) | |
alpha_bar = model.scheduler.alphas_cumprod | |
sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5 | |
alphas = model.scheduler.alphas | |
betas = 1 - alphas | |
variance_noise_shape = ( | |
num_inference_steps, | |
model.unet.in_channels, | |
model.unet.sample_size, | |
model.unet.sample_size) | |
timesteps = model.scheduler.timesteps.to(model.device) | |
t_to_idx = {int(v):k for k,v in enumerate(timesteps)} | |
xts = torch.zeros(variance_noise_shape).to(x0.device) | |
for t in reversed(timesteps): | |
idx = t_to_idx[int(t)] | |
xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t] | |
xts = torch.cat([xts, x0 ],dim = 0) | |
return xts | |
def encode_text(model, prompts): | |
text_input = model.tokenizer( | |
prompts, | |
padding="max_length", | |
max_length=model.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
with torch.no_grad(): | |
text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0] | |
return text_encoding | |
def forward_step(model, model_output, timestep, sample): | |
next_timestep = min(model.scheduler.config.num_train_timesteps - 2, | |
timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps) | |
# 2. compute alphas, betas | |
alpha_prod_t = model.scheduler.alphas_cumprod[timestep] | |
# alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod | |
beta_prod_t = 1 - alpha_prod_t | |
# 3. compute predicted original sample from predicted noise also called | |
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) | |
# 5. TODO: simple noising implementatiom | |
next_sample = model.scheduler.add_noise(pred_original_sample, | |
model_output, | |
torch.LongTensor([next_timestep])) | |
return next_sample | |
def get_variance(model, timestep): #, prev_timestep): | |
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps | |
alpha_prod_t = model.scheduler.alphas_cumprod[timestep] | |
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod | |
beta_prod_t = 1 - alpha_prod_t | |
beta_prod_t_prev = 1 - alpha_prod_t_prev | |
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) | |
return variance | |
def inversion_forward_process(model, x0, | |
etas = None, | |
prog_bar = False, | |
prompt = "", | |
cfg_scale = 3.5, | |
num_inference_steps=50, eps = None | |
): | |
if not prompt=="": | |
text_embeddings = encode_text(model, prompt) | |
uncond_embedding = encode_text(model, "") | |
timesteps = model.scheduler.timesteps.to(model.device) | |
variance_noise_shape = ( | |
num_inference_steps, | |
model.unet.in_channels, | |
model.unet.sample_size, | |
model.unet.sample_size) | |
if etas is None or (type(etas) in [int, float] and etas == 0): | |
eta_is_zero = True | |
zs = None | |
else: | |
eta_is_zero = False | |
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps | |
xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps) | |
alpha_bar = model.scheduler.alphas_cumprod | |
zs = torch.zeros(size=variance_noise_shape, device=model.device) | |
t_to_idx = {int(v):k for k,v in enumerate(timesteps)} | |
xt = x0 | |
op = tqdm(reversed(timesteps)) if prog_bar else reversed(timesteps) | |
for t in op: | |
idx = t_to_idx[int(t)] | |
# 1. predict noise residual | |
if not eta_is_zero: | |
xt = xts[idx][None] | |
with torch.no_grad(): | |
out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding) | |
if not prompt=="": | |
cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings) | |
if not prompt=="": | |
## classifier free guidance | |
noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample) | |
else: | |
noise_pred = out.sample | |
if eta_is_zero: | |
# 2. compute more noisy image and set x_t -> x_t+1 | |
xt = forward_step(model, noise_pred, t, xt) | |
else: | |
xtm1 = xts[idx+1][None] | |
# pred of x0 | |
pred_original_sample = (xt - (1-alpha_bar[t]) ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5 | |
# direction to xt | |
prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps | |
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod | |
variance = get_variance(model, t) | |
pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred | |
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction | |
z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 ) | |
zs[idx] = z | |
# correction to avoid error accumulation | |
xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z | |
xts[idx+1] = xtm1 | |
if not zs is None: | |
zs[-1] = torch.zeros_like(zs[-1]) | |
return xt, zs, xts | |
def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None): | |
# 1. get previous step value (=t-1) | |
prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps | |
# 2. compute alphas, betas | |
alpha_prod_t = model.scheduler.alphas_cumprod[timestep] | |
alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod | |
beta_prod_t = 1 - alpha_prod_t | |
# 3. compute predicted original sample from predicted noise also called | |
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) | |
# 5. compute variance: "sigma_t(η)" -> see formula (16) | |
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) | |
# variance = self.scheduler._get_variance(timestep, prev_timestep) | |
variance = get_variance(model, timestep) #, prev_timestep) | |
std_dev_t = eta * variance ** (0.5) | |
# Take care of asymetric reverse process (asyrp) | |
model_output_direction = model_output | |
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction | |
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction | |
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction | |
# 8. Add noice if eta > 0 | |
if eta > 0: | |
if variance_noise is None: | |
variance_noise = torch.randn(model_output.shape, device=model.device) | |
sigma_z = eta * variance ** (0.5) * variance_noise | |
prev_sample = prev_sample + sigma_z | |
return prev_sample | |
def inversion_reverse_process(model, | |
xT, | |
etas = 0, | |
prompts = "", | |
cfg_scales = None, | |
prog_bar = False, | |
zs = None, | |
controller=None, | |
asyrp = False | |
): | |
batch_size = len(prompts) | |
cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device) | |
text_embeddings = encode_text(model, prompts) | |
uncond_embedding = encode_text(model, [""] * batch_size) | |
if etas is None: etas = 0 | |
if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps | |
assert len(etas) == model.scheduler.num_inference_steps | |
timesteps = model.scheduler.timesteps.to(model.device) | |
xt = xT.expand(batch_size, -1, -1, -1) | |
op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:] | |
t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])} | |
for t in op: | |
idx = t_to_idx[int(t)] | |
## Unconditional embedding | |
with torch.no_grad(): | |
uncond_out = model.unet.forward(xt, timestep = t, | |
encoder_hidden_states = uncond_embedding) | |
## Conditional embedding | |
if prompts: | |
with torch.no_grad(): | |
cond_out = model.unet.forward(xt, timestep = t, | |
encoder_hidden_states = text_embeddings) | |
z = zs[idx] if not zs is None else None | |
z = z.expand(batch_size, -1, -1, -1) | |
if prompts: | |
## classifier free guidance | |
noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample) | |
else: | |
noise_pred = uncond_out.sample | |
# 2. compute less noisy image and set x_t -> x_t-1 | |
xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z) | |
if controller is not None: | |
xt = controller.step_callback(xt) | |
return xt, zs | |