Fix #12116: preserve boolean dtype for attention masks in ChromaPipeline #12263
Fix #12116: preserve boolean dtype for attention masks in ChromaPipeline #12263DN6 merged 8 commits intohuggingface:mainfrom
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- Convert attention masks to bool and prevent dtype corruption - Fix both positive and negative mask handling in _get_t5_prompt_embeds - Remove float conversion in _prepare_attention_mask method Fixes huggingface#12116
akshay-babbar
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hello @DN6, just checking in to see if you’ve had a chance to look at the above PR. If you’re not the right person or are keeping busy, would you mind pointing me to someone who could review it? Thanks! |
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thanks @akshay-babbar |
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
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Thanks for the response! I'm new to diffusers, so still learning best practices through the docs and codebase. Do let me know if there are any issues with my changes. I used these these 3 prompts - [ negative prompt used - Below are the results! Please review and let me know your feedback and any next steps. Thanks! BeforeMan
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After Code ChangesMan
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DN6
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Thanks @akshay-babbar. Minor changes requested.
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| text_input_ids = text_inputs.input_ids | ||
| attention_mask = text_inputs.attention_mask.clone() | ||
| attention_mask = attention_mask.bool() |
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Don't think we need the type conversion here. We can just convert the final mask to bool.
| assert (output_height, output_width) == (expected_height, expected_width) | ||
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| class ChromaPipelineAttentionMaskTests(unittest.TestCase): |
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Dedicated tests aren't needed here. The existing tests should catch changes in numerical output if they are significant.
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Hello @DN6 , thanks for the review! I have made the changes, let me know your feedback and next steps. Thanks! |
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Sorry for not seeing this earlier, I was just notified by the commit. Are you sure that passing the attention mask as boolean... It seems to work I guess, but it's documented to require a FloatTensor: Textencoder: Float between 0 and 1 |
5 participants
Problem
Fixes #12116
Short prompts generate corrupted images due to attention mask dtype conversion bug.
Root Cause
Attention masks converted from bool → float16/bfloat16, but PyTorch's scaled_dot_product_attention requires boolean masks.
Solution
_get_t5_prompt_embeds_prepare_attention_maskTesting
✅ Added @slow unit tests for dtype preservation
✅ Verified fix with prompts: "man", "cat"
✅ All tests pass locally
Please review when you have a chance. Thank you for your time and consideration!