Comments (9)
I added the package to PyPI and now the entire setup process is pip install min-dalle
. The model parameters are downloaded as needed with just the requests
library. Thanks for the suggestions. I should probably also add a flag for verbose
to prevent the print statements
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The colab now runs purely in torch and downloads pre-converted pytorch parameters from the HF hub. Saves a lot of time.
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awesome thank you for doing this @kuprel
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This is great! Thank you for doing this. I also see the verbose flag now. I'll close this issue.
I've used your work for an example notebook that generates an image from a webpage summary. You can see it here. Thanks again for creating this library!
https://colab.research.google.com/github/neuml/txtai/blob/master/examples/35_Pictures_are_worth_a_thousand_words.ipynb
https://dev.to/neuml/pictures-are-a-worth-a-thousand-words-144k
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Wow that's really cool
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Thanks! I like your idea of uploading the pt files to the HF hub, doing that now. It will save a lot of time converting from flax and get rid of a lot of ugly conversion code. Will look into the PyPI install
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Sounds great!
I could see it where the default install_requires only needs torch and huggingface-hub. You could add optional dependencies for flax. That way if someone just wants to use the torch models, they only need torch installed.
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That's great! Definitely seems simpler now.
If you wanted to programmatically download models vs depending on running curl you could add the huggingface_hub library and do something like this:
from huggingface_hub import hf_hub_url, cached_download
config_file_url = hf_hub_url("kuprel/min-dalle", filename="vocab.json")
cached_download(config_file_url)
https://huggingface.co/docs/huggingface_hub/how-to-downstream
This would also open up option to parameterize the model loaded (defaulting to yours) and let someone load an alternative model.
Could also consider having two hf model projects, one for mini and one for mega.
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Thanks. Good luck with this project, looking forward to seeing it grow!
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Related Issues (20)
- Wrong Discord link
- GPU OOM if model ran in Python multithreading
- MPS backend not working
- huggingface 404not found HOT 1
- Training Pipeline
- RuntimeError: "LayerNormKernelImpl" not implemented for 'Half' torch HOT 2
- Could you put in your documentation what version of python you are using?
- How is this different from github.com/kakaobrain/minDALL-E ? HOT 1
- New Replicate Github sign-in required and pricing question HOT 3
- YouTube video walk-through of this codebase HOT 2
- TextTokenizer never initializes when is_reusable is false
- Should this work, even if slowly, on just a 2080Ti? HOT 7
- Any plans to supporting multi-GPU inferencing and training?
- encoder.embed_tokens.weight is smaller than the size of the TextTokenizer vocabulary
- Saving images didn't work for me without changes.
- Bfloat16 - precision loss
- Project dependencies may have API risk issues
- Mini-Dalle in OCaml
- Size HOT 1
- Cannot register hook? HOT 1
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