After trying out all the possible classes in corpora.XCorpus
(http://radimrehurek.com/gensim/apiref.html), I've tried loading using the BleiCorpus and seems like it generated the same output with less decimal figures as the saved model.
>>> from gensim import corpora, models
>>> import numpy.random
>>> numpy.random.seed(10)
>>>
>>> doc0 = [(0, 1), (1, 1)]
>>> doc1 = [(0,1)]
>>> doc2 = [(0, 1), (1, 1)]
>>> doc3 = [(0, 3), (1, 1)]
>>> corpus = [doc0,doc1,doc2,doc3]
>>> dictionary = corpora.Dictionary(corpus)
>>>
>>> tfidf = models.TfidfModel(corpus)
>>> corpus_tfidf = tfidf[corpus]
>>>
>>> lda = models.ldamodel.LdaModel(corpus_tfidf, id2word=dictionary, num_topics=3)
>>> corpus_lda = lda[corpus]
>>> corpus_lda.save('x.corpus_lda')
>>>
>>> for i,j in enumerate(corpus_lda):
... print j, corpus[i]
...
[(0, 0.15441373560695118), (1, 0.56498524668290762), (2, 0.28060101771014123)] [(0, 1), (1, 1)]
[(0, 0.59512220481946487), (1, 0.22817873367464175), (2, 0.17669906150589348)] [(0, 1)]
[(0, 0.52219543266162705), (1, 0.15449347037173339), (2, 0.32331109696663957)] [(0, 1), (1, 1)]
[(0, 0.83364632205849853), (1, 0.086514534997754619), (2, 0.079839142943746944)] [(0, 3), (1, 1)]
>>>
>>> lda_corpus = corpora.BleiCorpus.load('x.corpus_lda')
>>> for i,j in enumerate(lda_corpus):
... print j, corpus[i]
...
[(0, 0.154413735607), (1, 0.564985246683), (2, 0.280601017710)] [(0, 1), (1, 1)]
[(0, 0.595122204819), (1, 0.228178733675), (2, 0.176699061506)] [(0, 1)]
[(0, 0.522195432662), (1, 0.154493470372), (2, 0.323311096967)] [(0, 1), (1, 1)]
[(0, 0.833646322058), (1, 0.086514534998), (2, 0.079839142944)] [(0, 3), (1, 1)]