Chieh-Yun Chen, Chiang Tseng, Li-Wu Tsao, Hong-Han Shuai
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 |
|---|---|---|
| basiclab/Unraveling-Information-Mix-ups | canonical | 4 of 6 |
| basiclab/unraveling-information-mix-ups | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| attn_map_tr2np | Ran | basiclab/Unraveling-Information-Mix-ups/utils/utils.py code served (permissive licence) · get_code("4f1fe3d1f6b89580") |
| check_mixture | Ran | basiclab/Unraveling-Information-Mix-ups/eval_metrics/owlv2.py code served (permissive licence) · get_code("99fc8c88c6520b92") |
| load_animals | Ran | basiclab/unraveling-information-mix-ups/data/gen_prompt.py code served (permissive licence) · get_code("5feb9e1dddda5062") |
| resize_net_attn_map | Ran | basiclab/Unraveling-Information-Mix-ups/utils/utils.py code served (permissive licence) · get_code("abb4ecbad6e0555f") |
| seed_everything | Ran | basiclab/Unraveling-Information-Mix-ups/utils/utils.py code served (permissive licence) · get_code("dad22709a45ad4ab") |
| owlv2_eval | Not yet run | basiclab/Unraveling-Information-Mix-ups/eval_metrics/owlv2.py code served (permissive licence) · get_code("346d093f84857a6b") |
| register_attention_control | Not yet run | basiclab/Unraveling-Information-Mix-ups/model/attentions_utils.py code served (permissive licence) · get_code("ff0f7c7f0e3e3086") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper analyzes the impact of causal manner in the text encoder of text-to-image (T2I) diffusion models, which can lead to information bias and loss. Previous works have focused on addressing the issues through the denoising process. However, there is no research discussing how text embedding contributes to T2I models, especially when generating more than one object. In this paper, we share a comprehensive analysis of text embedding: i) how text embedding contributes to the generated images and ii) why information gets lost and biases towards the first-mentioned object. Accordingly, we propose a simple but effective text embedding balance optimization method, which is training-free, with an improvement of 125.42% on information balance in stable diffusion. Furthermore, we propose a new automatic evaluation metric that quantifies information loss more accurately than existing methods, achieving 81% concordance with human assessments. This metric effectively measures the presence and accuracy of objects, addressing the limitations of current distribution scores like CLIP's text-image similarities. The code is available: https://github.com/basiclab/Unraveling-Information-Mix-ups. 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.00321")
get_code_for_paper("2410.00321")
have("2410.00321")
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