| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from transformers import pipeline, set_seed | |
| import re | |
| import random | |
| gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2') | |
| def generate(starting_text): | |
| seed = random.randint(100, 1000000) | |
| set_seed(seed) | |
| response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4) | |
| response_list = [] | |
| for x in response: | |
| resp = x['generated_text'].strip() | |
| if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False: | |
| response_list.append(resp+'\n') | |
| response_end = "\n".join(response_list) | |
| response_end = re.sub('[^ ]+\.[^ ]+','', response_end) | |
| response_end = response_end.replace("<", "").replace(">", "") | |
| if response_end != "": | |
| return response_end | |