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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)
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