File size: 7,913 Bytes
904ef7d |
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 |
from transformers import CLIPTextModel, CLIPTokenizer, logging
from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
# suppress partial model loading warning
logging.set_verbosity_error()
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
class StableDiffusion(nn.Module):
def __init__(self, device):
super().__init__()
try:
with open('./TOKEN', 'r') as f:
self.token = f.read()
print(f'[INFO] successfully loaded hugging face user token!')
except FileNotFoundError as e:
print(e)
print(f'[INFO] Please first create a file called TOKEN and copy your hugging face access token into it to download stable diffusion checkpoints.')
self.device = device
self.num_train_timesteps = 1000
self.min_step = int(self.num_train_timesteps * 0.02)
self.max_step = int(self.num_train_timesteps * 0.98)
print(f'[INFO] loading stable diffusion...')
# 1. Load the autoencoder model which will be used to decode the latents into image space.
self.vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", use_auth_token=self.token).to(self.device)
# 2. Load the tokenizer and text encoder to tokenize and encode the text.
self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
self.text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").to(self.device)
# 3. The UNet model for generating the latents.
self.unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet", use_auth_token=self.token).to(self.device)
# 4. Create a scheduler for inference
self.scheduler = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=self.num_train_timesteps)
print(f'[INFO] loaded stable diffusion!')
def get_text_embeds(self, prompt):
# Tokenize text and get embeddings
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')
with torch.no_grad():
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# Do the same for unconditional embeddings
uncond_input = self.tokenizer([''] * len(prompt), padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt')
with torch.no_grad():
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# Cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def train_step(self, text_embeddings, pred_rgb, guidance_scale=100):
# interp to 512x512 to be fed into vae.
# _t = time.time()
pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False)
# torch.cuda.synchronize(); print(f'[TIME] guiding: interp {time.time() - _t:.4f}s')
# timestep ~ U(0.02, 0.98) to avoid very high/low noise level
t = torch.randint(self.min_step, self.max_step + 1, [1], dtype=torch.long, device=self.device)
# encode image into latents with vae, requires grad!
# _t = time.time()
latents = self.encode_imgs(pred_rgb_512)
# torch.cuda.synchronize(); print(f'[TIME] guiding: vae enc {time.time() - _t:.4f}s')
# predict the noise residual with unet, NO grad!
# _t = time.time()
with torch.no_grad():
# add noise
noise = torch.randn_like(latents)
latents_noisy = self.scheduler.add_noise(latents, noise, t)
# pred noise
latent_model_input = torch.cat([latents_noisy] * 2)
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# torch.cuda.synchronize(); print(f'[TIME] guiding: unet {time.time() - _t:.4f}s')
# perform guidance (high scale from paper!)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# w(t), one_minus_alpha_prod, i.e., sigma^2
w = (1 - self.scheduler.alphas_cumprod[t]).to(self.device)
grad = w * (noise_pred - noise)
# clip grad for stable training?
# grad = grad.clamp(-1, 1)
# manually backward, since we omitted an item in grad and cannot simply autodiff.
# _t = time.time()
latents.backward(gradient=grad, retain_graph=True)
# torch.cuda.synchronize(); print(f'[TIME] guiding: backward {time.time() - _t:.4f}s')
return 0 # fake loss value
def produce_latents(self, text_embeddings, height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None):
if latents is None:
latents = torch.randn((text_embeddings.shape[0] // 2, self.unet.in_channels, height // 8, width // 8), device=self.device)
self.scheduler.set_timesteps(num_inference_steps)
with torch.autocast('cuda'):
for i, t in enumerate(self.scheduler.timesteps):
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = torch.cat([latents] * 2)
# predict the noise residual
with torch.no_grad():
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)['sample']
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']
return latents
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
with torch.no_grad():
imgs = self.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
def encode_imgs(self, imgs):
# imgs: [B, 3, H, W]
imgs = 2 * imgs - 1
posterior = self.vae.encode(imgs).latent_dist
latents = posterior.sample() * 0.18215
return latents
def prompt_to_img(self, prompts, height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None):
if isinstance(prompts, str):
prompts = [prompts]
# Prompts -> text embeds
text_embeds = self.get_text_embeds(prompts) # [2, 77, 768]
# Text embeds -> img latents
latents = self.produce_latents(text_embeds, height=height, width=width, latents=latents, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale) # [1, 4, 64, 64]
# Img latents -> imgs
imgs = self.decode_latents(latents) # [1, 3, 512, 512]
# Img to Numpy
imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy()
imgs = (imgs * 255).round().astype('uint8')
return imgs
if __name__ == '__main__':
import argparse
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('prompt', type=str)
parser.add_argument('-H', type=int, default=512)
parser.add_argument('-W', type=int, default=512)
parser.add_argument('--steps', type=int, default=50)
opt = parser.parse_args()
device = torch.device('cuda')
sd = StableDiffusion(device)
imgs = sd.prompt_to_img(opt.prompt, opt.H, opt.W, opt.steps)
# visualize image
plt.imshow(imgs[0])
plt.show()
|