add performance graph to readme
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README.md
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README.md
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@ -13,6 +13,10 @@ To generate a 4x4 grid of DALL·E Mega images it takes:
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- 48 sec with a P100 in Colab
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- 48 sec with a P100 in Colab
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- 13 sec with an A100 on Replicate
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- 13 sec with an A100 on Replicate
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Here's a more detailed breakdown on the A100 of total inference time vs number of generated images:
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<img src="https://github.com/kuprel/min-dalle/raw/main/performance_a100.png" alt="min-dalle" width="400"/>
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Credit to @technobird22 and his [NeoGen](https://github.com/technobird22/NeoGen) discord bot for the graph
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The flax model and code for converting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).
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The flax model and code for converting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).
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## Install
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## Install
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@ -186,7 +186,7 @@ class MinDalle:
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tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
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tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
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if len(tokens) > self.text_token_count:
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if len(tokens) > self.text_token_count:
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tokens = tokens[:self.text_token_count]
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tokens = tokens[:self.text_token_count]
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if is_verbose: print("text tokens", tokens)
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if is_verbose: print("{} text tokens".format(len(tokens)), tokens)
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text_tokens = numpy.ones((2, 64), dtype=numpy.int32)
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text_tokens = numpy.ones((2, 64), dtype=numpy.int32)
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text_tokens[0, :2] = [tokens[0], tokens[-1]]
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text_tokens[0, :2] = [tokens[0], tokens[-1]]
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text_tokens[1, :len(tokens)] = tokens
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text_tokens[1, :len(tokens)] = tokens
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performance_a100.png
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performance_a100.png
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