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ptp_utils.py
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ptp_utils.py
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# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import cv2
from typing import Optional, Union, Tuple, List, Callable, Dict
from IPython.display import display
from tqdm.notebook import tqdm
def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
h, w, c = image.shape
offset = int(h * .2)
img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
font = cv2.FONT_HERSHEY_SIMPLEX
# font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
img[:h] = image
textsize = cv2.getTextSize(text, font, 1, 2)[0]
text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
return img
def view_images(images, num_rows=1, offset_ratio=0.02):
if type(images) is list:
num_empty = len(images) % num_rows
elif images.ndim == 4:
num_empty = images.shape[0] % num_rows
else:
images = [images]
num_empty = 0
empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
num_items = len(images)
h, w, c = images[0].shape
offset = int(h * offset_ratio)
num_cols = num_items // num_rows
image_ = np.ones((h * num_rows + offset * (num_rows - 1),
w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
for i in range(num_rows):
for j in range(num_cols):
image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
i * num_cols + j]
pil_img = Image.fromarray(image_)
display(pil_img)
def diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False):
if low_resource:
noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
else:
latents_input = torch.cat([latents] * 2)
noise_pred = model.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 = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
latents = controller.step_callback(latents)
return latents
def latent2image(vae, latents):
latents = 1 / 0.18215 * latents
image = vae.decode(latents)['sample']
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
image = (image * 255).astype(np.uint8)
return image
def init_latent(latent, model, height, width, generator, batch_size):
if latent is None:
latent = torch.randn(
(1, model.unet.in_channels, height // 8, width // 8),
generator=generator,
)
latents = latent.expand(batch_size, model.unet.in_channels, height // 8, width // 8).to(model.device)
return latent, latents
@torch.no_grad()
def text2image_ldm(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: Optional[float] = 7.,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
):
register_attention_control(model, controller)
height = width = 256
batch_size = len(prompt)
uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
uncond_embeddings = model.bert(uncond_input.input_ids.to(model.device))[0]
text_input = model.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
text_embeddings = model.bert(text_input.input_ids.to(model.device))[0]
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
context = torch.cat([uncond_embeddings, text_embeddings])
model.scheduler.set_timesteps(num_inference_steps)
for t in tqdm(model.scheduler.timesteps):
latents = diffusion_step(model, controller, latents, context, t, guidance_scale)
image = latent2image(model.vqvae, latents)
return image, latent
@torch.no_grad()
def text2image_ldm_stable(
model,
prompt: List[str],
controller,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
generator: Optional[torch.Generator] = None,
latent: Optional[torch.FloatTensor] = None,
low_resource: bool = False,
):
register_attention_control(model, controller)
height = width = 512
batch_size = len(prompt)
text_input = model.tokenizer(
prompt,
padding="max_length",
max_length=model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = model.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
context = [uncond_embeddings, text_embeddings]
if not low_resource:
context = torch.cat(context)
latent, latents = init_latent(latent, model, height, width, generator, batch_size)
# set timesteps
extra_set_kwargs = {"offset": 1}
model.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
for t in tqdm(model.scheduler.timesteps):
latents = diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource)
image = latent2image(model.vae, latents)
return image, latent
def register_attention_control(model, controller):
def ca_forward(self, place_in_unet):
to_out = self.to_out
if type(to_out) is torch.nn.modules.container.ModuleList:
to_out = self.to_out[0]
else:
to_out = self.to_out
def forward(x, context=None, mask=None):
batch_size, sequence_length, dim = x.shape
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q = self.reshape_heads_to_batch_dim(q)
k = self.reshape_heads_to_batch_dim(k)
v = self.reshape_heads_to_batch_dim(v)
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
if mask is not None:
mask = mask.reshape(batch_size, -1)
max_neg_value = -torch.finfo(sim.dtype).max
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
attn = controller(attn, is_cross, place_in_unet)
out = torch.einsum("b i j, b j d -> b i d", attn, v)
out = self.reshape_batch_dim_to_heads(out)
return to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.unet.named_children()
for net in sub_nets:
if "down" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "up" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "mid" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
controller.num_att_layers = cross_att_count
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
word_inds: Optional[torch.Tensor]=None):
if type(bounds) is float:
bounds = 0, bounds
start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
if word_inds is None:
word_inds = torch.arange(alpha.shape[2])
alpha[: start, prompt_ind, word_inds] = 0
alpha[start: end, prompt_ind, word_inds] = 1
alpha[end:, prompt_ind, word_inds] = 0
return alpha
def get_time_words_attention_alpha(prompts, num_steps,
cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
tokenizer, max_num_words=77):
if type(cross_replace_steps) is not dict:
cross_replace_steps = {"default_": cross_replace_steps}
if "default_" not in cross_replace_steps:
cross_replace_steps["default_"] = (0., 1.)
alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
for i in range(len(prompts) - 1):
alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"],
i)
for key, item in cross_replace_steps.items():
if key != "default_":
inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
for i, ind in enumerate(inds):
if len(ind) > 0:
alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
return alpha_time_words