Support different image sizes in prefill in VLMs #2065
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When a batch contained images if different sizes during prefill, the server would fail (see e.g. #2056). Images were processed separately and then concatenated. However, this can fail for images with different sizes. Fix this by preprocessing all images in the batch together, so that the image processor can ensure that all image tensors have compatible sizes.
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drbh
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| response = await flash_pali_gemma.generate( | ||
| f"caption\n", | ||
| max_new_tokens=20, | ||
| ) | ||
| # Is PaliGemma not able to handle two separate images? At least we | ||
| # get output showing that both images are used. | ||
| assert ( | ||
| response.generated_text == "image result for chicken on the beach" | ||
| ), f"{repr(response.generated_text)}" |
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ha this is interesting!
I wonder if we should identify and throw an error for models that limit the number of images in the future?
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That sounds like a useful validation. Not sure which models are supposed to handle multiple images in one prompt.
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Thank you for this, @danieldk! Do you have a sense of when this should get published to the |
yuanwu2017
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When a batch contained images if different sizes during prefill, the server would fail (see e.g. huggingface#2056). Images were processed separately and then concatenated. However, this can fail for images with different sizes. Fix this by preprocessing all images in the batch together, so that the image processor can ensure that all image tensors have compatible sizes.
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What does this PR do?
When a batch contained images if different sizes during prefill, the server would fail (fixes #2056). Images were processed separately and then concatenated. However, this can fail for images with different sizes.
Fix this by preprocessing all images in the batch together, so that the image processor can ensure that all image tensors have compatible sizes.
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