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import os | |
import cv2 | |
import numpy as np | |
import ffmpeg | |
from math import ceil | |
from segmindapi import SD2_1, Kadinsky | |
import gradio as gr | |
from .TextPreprocessor import TextPreprocessor | |
from .AVCombiner import AVCombiner | |
class Videobook: | |
def __init__(self): | |
self.preprocessor = TextPreprocessor() | |
self.combiner = AVCombiner() | |
def get_sentences(self, story): | |
return self.preprocessor(story) | |
def generate_imgs(self, sentences, steps): | |
imgs = [] | |
for sentence in sentences: | |
sentence['pos'] = self.style + ' of ' + str(sentence['pos']) + ', ' + self.tags | |
imgs.append(self.pipe.generate(prompt = sentence['pos'], negative_prompt = str(sentence['neg']) + " ,nude, disfigured, blurry", num_inference_steps = steps)) | |
return imgs | |
def generate(self, story, api_key, style, tags, model, steps): | |
self.style = style | |
self.tags = tags | |
if model == "Stable Diffusion v2.1": | |
self.pipe = SD2_1(api_key) | |
else: | |
self.pipe = Kadinsky(api_key) | |
processed_sentences, sentences = self.get_sentences(story) | |
return AVCombiner()(self.generate_imgs(processed_sentences, steps), sentences, os.getcwd()) |