Update README.md

README.md

@@ -73,7 +73,7 @@ Slower but more fluent generation:
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
 tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
 tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2', pad_token_id=tokenizer.eos_token_id)
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2')
 model.eval()
 
 prompt = r'a cat sitting'     # the beginning of the prompt
@@ -81,14 +81,12 @@ temperature = 0.9             # a higher temperature will produce more diverse r
 top_k = 8                     # the number of tokens to sample from at each step
 max_length = 80               # the maximum number of tokens for the output of the model
 repitition_penalty = 1.2      # the penalty value for each repetition of a token
-num_beams=10
-num_return_sequences=5        # the number of results with the highest probabilities out of num_beams
+num_return_sequences=5        # the number of results to generate
 
-# generate the result with contrastive search
+# generate the result with contrastive search. generate 5 results with the highest probability out of 10.
 input_ids = tokenizer(prompt, return_tensors='pt').input_ids
-output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, num_beams=num_beams, repetition_penalty=repitition_penalty, penalty_alpha=0.6, no_repeat_ngram_size=1, early_stopping=True)
+output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, repetition_penalty=repitition_penalty, penalty_alpha=0.6, no_repeat_ngram_size=1, early_stopping=True)
 
-# print results
 print('\nInput:\n' + 100 * '-')
 print('\033[96m' + prompt + '\033[0m')
 print('\nOutput:\n' + 100 * '-')