<jupyter_start><jupyter_text>Preparing<jupyter_code>!pip install deep-daze --upgrade
!wget http://www.blakearchive.org/images/bb85.a.6.ps.100.jpg -O image.jpg<jupyter_output>--2021-09-14 14:15:07--  http://www.blakearchive.org/images/bb85.a.6.ps.100.jpg
Resolving www.blakearchive.org (www.blakearchive.org)... 192.168.3.11
Connecting to www.blakearchive.org (www.blakearchive.org)|192.168.3.11|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 128890 (126K) [image/jpeg]
Saving to: ‘image.jpg’

image.jpg           100%[===================>] 125.87K   711KB/s    in 0.2s    

2021-09-14 14:15:09 (711 KB/s) - ‘image.jpg’ saved [128890/128890]<jupyter_text>Put your own text here<jupyter_code>text = "Hello, World!"
text = text.replace(' ','\ ')<jupyter_output><empty_output><jupyter_text>Train<jupyter_code>!imagine $text --start_image_path image.jpg<jupyter_output><empty_output><jupyter_text>Deprecated<jupyter_code># from tqdm.notebook import trange
# from IPython.display import Image, display

# from deep_daze import Imagine

# TEXT = 'an apple next to a fireplace' #@param {type:"string"}
# NUM_LAYERS = 32 #@param {type:"number"}
# SAVE_EVERY =  20#@param {type:"number"}
# IMAGE_WIDTH = 512 #@param {type:"number"}
# SAVE_PROGRESS = True #@param {type:"boolean"}
# LEARNING_RATE = 1e-5 #@param {type:"number"}
# ITERATIONS = 1050 #@param {type:"number"}

# model = Imagine(
#     text = TEXT,
#     num_layers = NUM_LAYERS,
#     save_every = SAVE_EVERY,
#     image_width = IMAGE_WIDTH,
#     lr = LEARNING_RATE,
#     iterations = ITERATIONS,
#     save_progress = SAVE_PROGRESS,
#     start_image_path = 'image.jpg',
# )

# for epoch in trange(20, desc = 'epochs'):
#     for i in trange(ITERATIONS, desc = 'iteration'):
#         model.train_step(epoch, i)

#         if i % model.save_every != 0:
#             continue

#         filename = TEXT.replace(' ', '_')
#         image = Image(f'./{filename}.jpg')
#         display(image)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16483516483516483, 'fraction_numerical': 0.057942057942057944, 'mean_word_length': 3.982587064676617, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 17, 'https://': 0, 'lorem ipsum': 0, 'www.': 6, 'xml': 0}, 'pii_count': 2, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '359024', 'license': 'MIT', 'n_tokens_mistral': 844, 'n_tokens_neox': 753, 'n_words': 145, 'path': 'Copy_of_Deep_Daze.ipynb', 'repo_name': 'hihunjin/my_deep_daze', 'size': '2002'}