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main.py
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import sys
import torch
from open_clip import image_transform
from diffusers import DDIMScheduler
from pytorch_lightning import seed_everything
from tqdm import tqdm
from transformers import CLIPModel
import matplotlib.pyplot as plt
from SeedOptimizationStableDiffusion import SeedOptimizationStableDiffusion
import torch.nn.functional as F
import numpy as np
from encode_images import vae_encode, clip_encode
from nao.ddim_inversion import image_folder_to_ddim
from nao.norm_aware_optimization import norm_aware_centroid_optimization
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
""" helper function to spherically interpolate two arrays v1 v2 """
""" Code from: https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355"""
if not isinstance(v0, np.ndarray):
v0 = v0.cpu().numpy()
v1 = v1.cpu().numpy()
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
if np.abs(dot) > DOT_THRESHOLD:
v2 = (1 - t) * v0 + t * v1
else:
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
theta_t = theta_0 * t
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
v2 = s0 * v0 + s1 * v1
v2 = torch.from_numpy(v2).to("cuda")
return v2
def freeze_params(params):
for param in params:
param.requires_grad = False
def clip_img_transform(feature_extractor):
image_mean = feature_extractor.image_mean
image_std = feature_extractor.image_std
preprocess_val = image_transform(
feature_extractor.size["shortest_edge"],
is_train=False,
mean=image_mean,
std=image_std
)
return preprocess_val
def prepare_models():
clip = CLIPModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K", cache_dir="/inputs/huggingface_cache/")
clip.vision_model.to("cuda")
clip.visual_projection.to("cuda")
# freeze all models parameters
clip.eval()
freeze_params(clip.parameters())
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False, steps_offset=1)
model_id = "stabilityai/stable-diffusion-2-1-base"
sd_model = SeedOptimizationStableDiffusion.from_pretrained(model_id,
scheduler=scheduler,
cache_dir="/inputs/huggingface_cache/")
sd_model = sd_model.to("cuda")
clip_transform = clip_img_transform(sd_model.feature_extractor)
return sd_model, sd_model.vae, clip, clip_transform
def optimize_seed(init_seed, prompt, sd_model, clip_model, clip_transform, clip_centroid, vae_centroid,
n_iters=30, guidance_scale=7.5, lr=0.05, show_first_image=True):
img_seed = torch.nn.Parameter(init_seed.reshape((1, 4, 64, 64)), requires_grad=True)
optimizer = torch.optim.AdamW([img_seed], lr=lr)
weights = torch.tensor([0.05, 0.25, 0.7]).cuda().T
for i in tqdm(range(n_iters)):
if i == 3:
for g in optimizer.param_groups:
g['lr'] = lr/10
optimizer.zero_grad()
latents, losses, image_pil = sd_model.apply(prompt=prompt,
img_seed=img_seed,
clip_model=clip_model,
guidance_scale=guidance_scale,
clip_transform=clip_transform,
clip_img_centroid=clip_centroid)
if i != 0 or show_first_image == True:
plt.imshow(image_pil)
plt.axis("off")
plt.tight_layout()
plt.show()
semantic_loss = weights @ torch.stack(losses).squeeze(-1)
appearance_loss = F.mse_loss(latents, vae_centroid, reduction="none").mean([1, 2, 3]).mean()
loss = 10 * semantic_loss + appearance_loss
loss.backward()
optimizer.step()
if __name__ == "__main__":
prompt = sys.argv[2] # tail class
print(prompt)
img_folder = sys.argv[3] # path to folder with iamges of the rare concept
sd_model, vae, clip_model, clip_transform = prepare_models()
# Clip centroid from images
clip_centroid = clip_encode(clip_model, clip_transform, img_folder).mean(dim=0)
clip_centroid = clip_centroid / clip_centroid.norm(dim=-1, keepdim=True)
# Create latent centroid from images
vae_latents = vae_encode(vae, img_folder)
vae_centroid = vae_latents.mean(dim=0).unsqueeze(0)
height = sd_model.unet.config.sample_size * sd_model.vae_scale_factor
width = sd_model.unet.config.sample_size * sd_model.vae_scale_factor
shape = (1, sd_model.unet.in_channels, height // sd_model.vae_scale_factor, width // sd_model.vae_scale_factor)
seed_everything(22)
if sys.argv[1] == "StableDiffusion":
# Run raw stable diffusion
with torch.no_grad():
init_seed = torch.randn(shape, device=sd_model.device, dtype=clip_centroid.dtype).to(
"cuda") * sd_model.scheduler.init_noise_sigma
_, _, image_pil = sd_model.apply(prompt=prompt,
guidance_scale=7.5,
img_seed=init_seed,
run_raw_sd=True)
plt.imshow(image_pil)
plt.axis("off")
plt.tight_layout()
plt.show()
elif sys.argv[1] == "SeedSelect":
# run SeedSelect
init_seed = torch.randn(shape, device=sd_model.device, dtype=clip_centroid.dtype).to(
"cuda") * sd_model.scheduler.init_noise_sigma
### hyper-parameters
n_optimization_iters = 30
lr = 0.1
###
optimize_seed(init_seed, prompt, sd_model, clip_model, clip_transform.transforms, clip_centroid, vae_centroid,
n_iters=n_optimization_iters, guidance_scale=7.5, lr=lr)
elif sys.argv[1] == "NAO_SeedSelect":
# run SeedSelect initialized with NAO seeds
all_real_x_t = image_folder_to_ddim(img_folder, prompt, sd_model)
p1 = all_real_x_t[4].reshape((1, 4, 64, 64)).to("cuda")
p2 = all_real_x_t[3].reshape((1, 4, 64, 64)).to("cuda")
dim = 4 * 64 * 64
### hyper-parameters
n_points = 50 # number of points between a centroid to an inversion
n_optimization_iters = 5
lr = 0.05
p1 = p1.reshape(1, -1).T
p2 = p2.reshape(1, -1).T
eps = 2 * (torch.norm(p1 - p2) / n_points)
###
log_chi_dist = lambda x: ((dim - 1) * torch.log(x) - 0.5 * torch.pow(x, 2)) - (
(0.5 * dim - 1) * torch.log(torch.tensor(2.0)) + torch.lgamma(torch.tensor(dim / 2)))
c, paths_ls = norm_aware_centroid_optimization(log_chi_dist, [p1, p2], n_points, eps, init_c=slerp(0.5, p1, p2))
for path in paths_ls:
for seed_i in path:
optimize_seed(seed_i, prompt, sd_model, clip_model, clip_transform.transforms, clip_centroid,
vae_centroid, n_iters=n_optimization_iters, guidance_scale=1, lr=lr,
show_first_image=False)