File size: 11,795 Bytes
0d4fff7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
import inspect
import os
import numpy as np
import torch
import torch.nn.functional as nnf
from PIL import Image
from torch.optim.adam import Adam
from tqdm import tqdm
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
def retrieve_timesteps(
scheduler,
num_inference_steps=None,
device=None,
timesteps=None,
**kwargs,
):
"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used,
`timesteps` must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class NullTextPipeline(StableDiffusionPipeline):
def get_noise_pred(self, latents, t, context):
latents_input = torch.cat([latents] * 2)
guidance_scale = 7.5
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context)["sample"]
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
latents = self.prev_step(noise_pred, t, latents)
return latents
def get_noise_pred_single(self, latents, t, context):
noise_pred = self.unet(latents, t, encoder_hidden_states=context)["sample"]
return noise_pred
@torch.no_grad()
def image2latent(self, image_path):
image = Image.open(image_path).convert("RGB")
image = np.array(image)
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(self.device)
latents = self.vae.encode(image)["latent_dist"].mean
latents = latents * 0.18215
return latents
@torch.no_grad()
def latent2image(self, latents):
latents = 1 / 0.18215 * latents.detach()
image = self.vae.decode(latents)["sample"].detach()
image = self.processor.postprocess(image, output_type="pil")[0]
return image
def prev_step(self, model_output, timestep, sample):
prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
)
beta_prod_t = 1 - alpha_prod_t
pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
prev_sample = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction
return prev_sample
def next_step(self, model_output, timestep, sample):
timestep, next_timestep = (
min(timestep - self.scheduler.config.num_train_timesteps // self.num_inference_steps, 999),
timestep,
)
alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
beta_prod_t = 1 - alpha_prod_t
next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction
return next_sample
def null_optimization(self, latents, context, num_inner_steps, epsilon):
uncond_embeddings, cond_embeddings = context.chunk(2)
uncond_embeddings_list = []
latent_cur = latents[-1]
bar = tqdm(total=num_inner_steps * self.num_inference_steps)
for i in range(self.num_inference_steps):
uncond_embeddings = uncond_embeddings.clone().detach()
uncond_embeddings.requires_grad = True
optimizer = Adam([uncond_embeddings], lr=1e-2 * (1.0 - i / 100.0))
latent_prev = latents[len(latents) - i - 2]
t = self.scheduler.timesteps[i]
with torch.no_grad():
noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)
for j in range(num_inner_steps):
noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)
noise_pred = noise_pred_uncond + 7.5 * (noise_pred_cond - noise_pred_uncond)
latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
loss = nnf.mse_loss(latents_prev_rec, latent_prev)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_item = loss.item()
bar.update()
if loss_item < epsilon + i * 2e-5:
break
for j in range(j + 1, num_inner_steps):
bar.update()
uncond_embeddings_list.append(uncond_embeddings[:1].detach())
with torch.no_grad():
context = torch.cat([uncond_embeddings, cond_embeddings])
latent_cur = self.get_noise_pred(latent_cur, t, context)
bar.close()
return uncond_embeddings_list
@torch.no_grad()
def ddim_inversion_loop(self, latent, context):
self.scheduler.set_timesteps(self.num_inference_steps)
_, cond_embeddings = context.chunk(2)
all_latent = [latent]
latent = latent.clone().detach()
with torch.no_grad():
for i in range(0, self.num_inference_steps):
t = self.scheduler.timesteps[len(self.scheduler.timesteps) - i - 1]
noise_pred = self.unet(latent, t, encoder_hidden_states=cond_embeddings)["sample"]
latent = self.next_step(noise_pred, t, latent)
all_latent.append(latent)
return all_latent
def get_context(self, prompt):
uncond_input = self.tokenizer(
[""], padding="max_length", max_length=self.tokenizer.model_max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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(self.device))[0]
context = torch.cat([uncond_embeddings, text_embeddings])
return context
def invert(
self, image_path: str, prompt: str, num_inner_steps=10, early_stop_epsilon=1e-6, num_inference_steps=50
):
self.num_inference_steps = num_inference_steps
context = self.get_context(prompt)
latent = self.image2latent(image_path)
ddim_latents = self.ddim_inversion_loop(latent, context)
if os.path.exists(image_path + ".pt"):
uncond_embeddings = torch.load(image_path + ".pt")
else:
uncond_embeddings = self.null_optimization(ddim_latents, context, num_inner_steps, early_stop_epsilon)
uncond_embeddings = torch.stack(uncond_embeddings, 0)
torch.save(uncond_embeddings, image_path + ".pt")
return ddim_latents[-1], uncond_embeddings
@torch.no_grad()
def __call__(
self,
prompt,
uncond_embeddings,
inverted_latent,
num_inference_steps: int = 50,
timesteps=None,
guidance_scale=7.5,
negative_prompt=None,
num_images_per_prompt=1,
generator=None,
latents=None,
prompt_embeds=None,
negative_prompt_embeds=None,
output_type="pil",
):
self._guidance_scale = guidance_scale
# 0. Default height and width to unet
height = self.unet.config.sample_size * self.vae_scale_factor
width = self.unet.config.sample_size * self.vae_scale_factor
# to deal with lora scaling and other possible forward hook
callback_steps = None
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameter
device = self._execution_device
# 3. Encode input prompt
prompt_embeds, _ = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
latents = inverted_latent
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings[i])["sample"]
noise_pred = self.unet(latents, t, encoder_hidden_states=prompt_embeds)["sample"]
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
progress_bar.update()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
else:
image = latents
image = self.image_processor.postprocess(
image, output_type=output_type, do_denormalize=[True] * image.shape[0]
)
# Offload all models
self.maybe_free_model_hooks()
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)
|