Ming Sun, Kun Yuan, Kai Zhao, Chao Zhou, Jinhua Hao, Yunpeng Qu, Qizhi Xie
We lifted 20 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| qyp2000/XPSR | canonical | 9 of 20 |
| Function | Status | Where it lives |
|---|---|---|
| attn_forward_new_pt2_0 | Ran | qyp2000/XPSR/myutils/vaehook.py pointer only (licence: NONE) · get_code("177c1cf8cd11be39") |
| convert_image_to_fn | Ran | qyp2000/XPSR/myutils/img_util.py pointer only (licence: NONE) · get_code("1b81186cfb3f8848") |
| exists | Ran | qyp2000/XPSR/dataloader/localdataset.py pointer only (licence: NONE) · get_code("608e364a9d2376a3") |
| identity | Ran | qyp2000/XPSR/myutils/misc.py pointer only (licence: NONE) · get_code("9910e2fc297f8665") |
| inplace_nonlinearity | Ran | qyp2000/XPSR/myutils/vaehook.py pointer only (licence: NONE) · get_code("76a017e88938061d") |
| rand_name | Ran | qyp2000/XPSR/myutils/misc.py pointer only (licence: NONE) · get_code("bdd74d6bfd709ddf") |
| save_videos_grid | Ran | qyp2000/XPSR/myutils/img_util.py pointer only (licence: NONE) · get_code("73408b539d7e8eca") |
| window_partition | Ran | qyp2000/XPSR/models/xpsr/unet_2d_blocks.py pointer only (licence: NONE) · get_code("bf260685de569a29") |
| window_reverse | Ran | qyp2000/XPSR/models/xpsr/unet_2d_blocks.py pointer only (licence: NONE) · get_code("184db8216d8a8a05") |
| attn_forward_new | Not yet run | qyp2000/XPSR/myutils/vaehook.py pointer only (licence: NONE) · get_code("08822a10826682a0") |
| cond_cast_float | Not yet run | qyp2000/XPSR/myutils/devices.py pointer only (licence: NONE) · get_code("a1316b96fc0dc988") |
| cond_cast_unet | Not yet run | qyp2000/XPSR/myutils/devices.py pointer only (licence: NONE) · get_code("6e1abd17fa1ce3b2") |
| convert_lora | Not yet run | qyp2000/XPSR/myutils/convert_lora_safetensor_to_diffusers.py pointer only (licence: NONE) · get_code("35fa1e7fbadd8f3d") |
| get_device_for | Not yet run | qyp2000/XPSR/myutils/devices.py pointer only (licence: NONE) · get_code("a861df384ac42eeb") |
| mesh_grid | Not yet run | qyp2000/XPSR/dataloader/degradations.py pointer only (licence: NONE) · get_code("794d4f7fc184e7e5") |
| opt_parse | Not yet run | qyp2000/XPSR/dataloader/realesrgan.py pointer only (licence: NONE) · get_code("fce64ea4638101fc") |
| pdf2 | Not yet run | qyp2000/XPSR/dataloader/degradations.py pointer only (licence: NONE) · get_code("321b9c73d589eb9e") |
| sigma_matrix2 | Not yet run | qyp2000/XPSR/dataloader/degradations.py pointer only (licence: NONE) · get_code("e3444ff2b9d24600") |
| window_process | Not yet run | qyp2000/XPSR/models/xpsr/unet_2d_blocks.py pointer only (licence: NONE) · get_code("d40a55c91fa32c4f") |
| zero_module | Not yet run | qyp2000/XPSR/models/xpsr/controlnet.py pointer only (licence: NONE) · get_code("da94debb8019ad46") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently. However, as low-resolution (LR) images often undergo severe degradation, it is challenging for ISR models to perceive the semantic and degradation information, resulting in restoration images with incorrect content or unrealistic artifacts. To address these issues, we propose a Cross-modal Priors for Super-Resolution (XPSR) framework. Within XPSR, to acquire precise and comprehensive semantic conditions for the diffusion model, cutting-edge Multimodal Large Language Models (MLLMs) are utilized. To facilitate better fusion of cross-modal priors, a Semantic-Fusion Attention is raised. To distill semantic-preserved information instead of undesired degradations, a Degradation-Free Constraint is attached between LR and its high-resolution (HR) counterpart. Quantitative and qualitative results show that XPSR is capable of generating high-fidelity and high-realism images across synthetic and real-world datasets. Codes are released at https://github.com/qyp2000/XPSR.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.05049")
get_code_for_paper("2403.05049")
have("2403.05049")
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