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# Adapted from https://github.com/MichalGeyer/pnp-diffusers/blob/main/preprocess.py
from transformers import CLIPTextModel, CLIPTokenizer, logging
from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler
# suppress partial model loading warning
logging.set_verbosity_error()
import os
from PIL import Image
from tqdm import tqdm, trange
import torch
import torch.nn as nn
import argparse
from pathlib import Path
from pnp_utils import *
import torchvision.transforms as T
def get_timesteps(scheduler, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
class Preprocess(nn.Module):
def __init__(self, device, sd_version='2.0', hf_key=None):
super().__init__()
self.device = device
self.sd_version = sd_version
self.use_depth = False
print(f'[INFO] loading stable diffusion...')
if hf_key is not None:
print(f'[INFO] using hugging face custom model key: {hf_key}')
model_key = hf_key
elif self.sd_version == '2.1':
model_key = "stabilityai/stable-diffusion-2-1-base"
elif self.sd_version == '2.0':
model_key = "stabilityai/stable-diffusion-2-base"
elif self.sd_version == '1.5':
model_key = "runwayml/stable-diffusion-v1-5"
elif self.sd_version == 'depth':
model_key = "stabilityai/stable-diffusion-2-depth"
self.use_depth = True
elif self.sd_version == '1.4':
model_key = "CompVis/stable-diffusion-v1-4"
else:
raise ValueError(f'Stable-diffusion version {self.sd_version} not supported.')
# Create model
self.vae = AutoencoderKL.from_pretrained(model_key, subfolder="vae", revision="fp16",
torch_dtype=torch.float16).to(self.device)
self.tokenizer = CLIPTokenizer.from_pretrained(model_key, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(model_key, subfolder="text_encoder", revision="fp16",
torch_dtype=torch.float16).to(self.device)
self.unet = UNet2DConditionModel.from_pretrained(model_key, subfolder="unet", revision="fp16",
torch_dtype=torch.float16).to(self.device)
self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
print(f'[INFO] loaded stable diffusion!')
self.inversion_func = self.ddim_inversion
@torch.no_grad()
def get_text_embeds(self, prompt, negative_prompt, device="cuda"):
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
truncation=True, return_tensors='pt')
text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
return_tensors='pt')
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(device))[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
@torch.no_grad()
def decode_latents(self, latents):
with torch.autocast(device_type='cuda', dtype=torch.float32):
latents = 1 / 0.18215 * latents
imgs = self.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
def load_img(self, image_path):
image_pil = T.Resize(512)(Image.open(image_path).convert("RGB"))
image = T.ToTensor()(image_pil).unsqueeze(0).to(self.device)
return image
@torch.no_grad()
def encode_imgs(self, imgs):
with torch.autocast(device_type='cuda', dtype=torch.float32):
imgs = 2 * imgs - 1
posterior = self.vae.encode(imgs).latent_dist
latents = posterior.mean * 0.18215
return latents
@torch.no_grad()
def ddim_inversion(self, cond, latent, save_path, save_latents=True,
timesteps_to_save=None):
timesteps = reversed(self.scheduler.timesteps)
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(tqdm(timesteps)):
cond_batch = cond.repeat(latent.shape[0], 1, 1)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[timesteps[i - 1]]
if i > 0 else self.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = self.unet(latent, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (latent - sigma_prev * eps) / mu_prev
latent = mu * pred_x0 + sigma * eps
if save_latents:
torch.save(latent, os.path.join(save_path, f'noisy_latents_{t}.pt'))
torch.save(latent, os.path.join(save_path, f'noisy_latents_{t}.pt'))
return latent
@torch.no_grad()
def ddim_sample(self, x, cond, save_path, save_latents=False, timesteps_to_save=None):
timesteps = self.scheduler.timesteps
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(tqdm(timesteps)):
cond_batch = cond.repeat(x.shape[0], 1, 1)
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[timesteps[i + 1]]
if i < len(timesteps) - 1
else self.scheduler.final_alpha_cumprod
)
mu = alpha_prod_t ** 0.5
sigma = (1 - alpha_prod_t) ** 0.5
mu_prev = alpha_prod_t_prev ** 0.5
sigma_prev = (1 - alpha_prod_t_prev) ** 0.5
eps = self.unet(x, t, encoder_hidden_states=cond_batch).sample
pred_x0 = (x - sigma * eps) / mu
x = mu_prev * pred_x0 + sigma_prev * eps
if save_latents:
torch.save(x, os.path.join(save_path, f'noisy_latents_{t}.pt'))
return x
@torch.no_grad()
def extract_latents(self, num_steps, data_path, save_path, timesteps_to_save,
inversion_prompt='', extract_reverse=False):
self.scheduler.set_timesteps(num_steps)
cond = self.get_text_embeds(inversion_prompt, "")[1].unsqueeze(0)
image = self.load_img(data_path)
latent = self.encode_imgs(image)
inverted_x = self.inversion_func(cond, latent, save_path, save_latents=not extract_reverse,
timesteps_to_save=timesteps_to_save)
latent_reconstruction = self.ddim_sample(inverted_x, cond, save_path, save_latents=extract_reverse,
timesteps_to_save=timesteps_to_save)
rgb_reconstruction = self.decode_latents(latent_reconstruction)
return rgb_reconstruction # , latent_reconstruction
def run(opt):
device = 'cuda'
# timesteps to save
if opt.sd_version == '2.1':
model_key = "stabilityai/stable-diffusion-2-1-base"
elif opt.sd_version == '2.0':
model_key = "stabilityai/stable-diffusion-2-base"
elif opt.sd_version == '1.5':
model_key = "runwayml/stable-diffusion-v1-5"
elif opt.sd_version == 'depth':
model_key = "stabilityai/stable-diffusion-2-depth"
elif opt.sd_version == '1.4':
model_key = "CompVis/stable-diffusion-v1-4"
toy_scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
toy_scheduler.set_timesteps(opt.save_steps)
timesteps_to_save, num_inference_steps = get_timesteps(toy_scheduler, num_inference_steps=opt.save_steps,
strength=1.0,
device=device)
seed_everything(opt.seed)
extraction_path_prefix = "_reverse" if opt.extract_reverse else "_forward"
save_path = os.path.join(opt.save_dir + extraction_path_prefix, os.path.splitext(os.path.basename(opt.data_path))[0])
os.makedirs(save_path, exist_ok=True)
model = Preprocess(device, sd_version=opt.sd_version, hf_key=None)
recon_image = model.extract_latents(data_path=opt.data_path,
num_steps=opt.steps,
save_path=save_path,
timesteps_to_save=timesteps_to_save,
inversion_prompt=opt.inversion_prompt,
extract_reverse=opt.extract_reverse)
T.ToPILImage()(recon_image[0]).save(os.path.join(save_path, f'recon.jpg'))
if __name__ == "__main__":
device = 'cuda'
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', type=str,
default='data/source_2.png')
parser.add_argument('--save_dir', type=str, default='latents')
parser.add_argument('--sd_version', type=str, default='1.4', choices=['1.5', '2.0', '2.1', '1.4'],
help="stable diffusion version")
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--steps', type=int, default=50)
parser.add_argument('--save-steps', type=int, default=1000)
parser.add_argument('--inversion_prompt', type=str, default='')
parser.add_argument('--extract-reverse', default=False, action='store_true', help="extract features during the denoising process")
opt = parser.parse_args()
run(opt)
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