ptp_utils.py

# 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
import os

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


import datetime

def view_images(images, num_rows=1, offset_ratio=0.02, save=True, description='',folder=None):
    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)
    
    if save:
        # 获取当前时间
        now = datetime.datetime.now()
        # 将图片保存到./tmp 文件夹下
        if folder:  # 如果 folder 不为空
            # 判断 folder 文件夹是否存在，若不存在，则新建
            if not os.path.exists(f"./tmp/{folder}"):
                os.makedirs(f"./tmp/{folder}")
            # 使用 folder 文件夹保存图片
            pil_img.save(f"./tmp/{folder}/{description}+{now}.jpg")
        else:
            if not os.path.exists("./tmp"):
                os.makedirs("./tmp")
            pil_img.save(f"./tmp/{description}+{now}.jpg")



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