We lifted 7 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sais-fuxi/evalalign | canonical | 4 of 6 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| divide_to_patches | Ran | sais-fuxi/evalalign/evalalign/mm_utils.py code served (permissive licence) · get_code("7e03b180fa317c9a") |
| image_parser | Ran | this paper's copy was not recorded; identical code first harvested from ictnlp/LLaVA-Mini pointer only · get_code("a7bee88c1c7fd6a3") |
| resize_and_pad_image | Ran | sais-fuxi/evalalign/evalalign/mm_utils.py code served (permissive licence) · get_code("468eedeba67f1b00") |
| select_best_resolution | Ran | sais-fuxi/evalalign/evalalign/mm_utils.py code served (permissive licence) · get_code("3999ff487573f32c") |
| unpad_image | Ran | sais-fuxi/evalalign/evalalign/model/llava_arch.py code served (permissive licence) · get_code("55c32993da87759b") |
| pretty_print_semaphore | Not yet run | sais-fuxi/evalalign/evalalign/utils.py code served (permissive licence) · get_code("37899f22fb191b37") |
| violates_moderation | Not yet run | sais-fuxi/evalalign/evalalign/utils.py code served (permissive licence) · get_code("f9939a84b9a65279") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The recent advancements in text-to-image generative models have been remarkable. Yet, the field suffers from a lack of evaluation metrics that accurately reflect the performance of these models, particularly lacking fine-grained metrics that can guide the optimization of the models. In this paper, we propose EvalAlign, a metric characterized by its accuracy, stability, and fine granularity. Our approach leverages the capabilities of Multimodal Large Language Models (MLLMs) pre-trained on extensive data. We develop evaluation protocols that focus on two key dimensions: image faithfulness and text-image alignment. Each protocol comprises a set of detailed, fine-grained instructions linked to specific scoring options, enabling precise manual scoring of the generated images. We supervised fine-tune (SFT) the MLLM to align with human evaluative judgments, resulting in a robust evaluation model. Our evaluation across 24 text-to-image generation models demonstrate that EvalAlign not only provides superior metric stability but also aligns more closely with human preferences than existing metrics, confirming its effectiveness and utility in model assessment.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.16562")
get_code_for_paper("2406.16562")
have("2406.16562")
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