






import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
  a_			=					pd.read_csv("""sample_data.csv""", header=None)
  a_			=					df.shape[:1][0]
  # If you're using some other dataset input the target column
  a_			=					df.iloc[:, 1:2]
  a_			=					actual_data.values.reshape(len_data, 1)
  a_			=					MinMaxScaler().fit_transform(actual_data)
  a_			=					10
  a_			=					5
  a_			=					20
  a_			=					len_data - periods * look_back
  a_			=					actual_data[:division]
  a_			=					actual_data[division - look_back :]
  a_			=					[], []
  a_			=					[], []

  for i in range(0, len(train_data) - forward_days - look_back + 1):
    train_x.append(train_data[i : i + look_back])
    train_y.append(train_data[i + look_back : i + look_back + forward_days])
  for i in range(0, len(test_data) - forward_days - look_back + 1):
    test_x.append(test_data[i : i + look_back])
    test_y.append(test_data[i + look_back : i + look_back + forward_days])
  a_			=					np.array(train_x)
  a_			=					np.array(test_x)
  a_			=					np.array([list(i.ravel()) for i in train_y])
  a_			=					np.array([list(i.ravel()) for i in test_y])

  a_			=					Sequential()
  model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(128, 1)))
  model.add(Dense(forward_days))
  model.compile(loss="""mean_squared_error""", optimizer="""adam""")
  a_			=					model.fit(
      x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
  )
  a_			=					model.predict(x_test)











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






