Closed minimaxir closed 4 years ago
I'd like a google colab of it
Hey @minimaxir! Here's a colab which loads the weights into a subclass of GPT2LMHeadModel
and demonstrates unconditional image generation and conditional image completion.
Some differences I've found between Image-GPT and GPT2 which are reflected in the subclass.
1) Image-GPT layer normalization doesn't subtract off the mean
2) different activations used in the MLP
3) In Image-GPT, the input and output embeddings are not tied
4) Image-GPT has an extra learned "sos" token embedding which is concatenated at the beginning of the sequence
5) The GPT2 [n_embd, 3*n_embd]
dimensional linear layer, c_attn
, which produces queries, keys, and values is instead split into 3 separate linear layers each with dimension [n_head, n_embd/n_head, n_embd]
in Image-GPT (this only affects how to load the weights and not the actual model).
6) In Image-GPT, the conv1d
module doesn't have a bias term
So what's our next step to add this to the repo?
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@apeguero1 we have an "Adding a new model" checklist at https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model
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🌟 New model addition
Model description
OpenAI just announced Image GPT: https://openai.com/blog/image-gpt/
Although image rendering would be out of scope for Transformers, the RGB generation would still be in scope and it would be best to port the weights to a
GPT2LMModel
.However, it's not immediately clear here how the tokenization is implemented in the downloaded model. (no separate
vocab.json
)Open source status