181 lines
6.0 KiB
Markdown
181 lines
6.0 KiB
Markdown
# FM_nodes
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A collection of ComfyUI nodes.
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Click name to jump to workflow
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1. [**WFEN**](#wfen) Face Restore. Paper: [Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network](https://github.com/PRIS-CV/WFEN)
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2. [**RealViformer**](#realviformer) - Paper: [Investigating Attention for Real-World Video Super-Resolution](https://github.com/Yuehan717/RealViformer)
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3. [**ProPIH**](#propih). Paper: [Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles](https://github.com/bcmi/ProPIH-Painterly-Image-Harmonization)
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4. [**CoLIE**](#colie). Paper: [Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations](https://github.com/ctom2/colie)
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5. [**VFIMamba**](#vfimamba). Paper: [Video Frame Interpolation with State Space Models](https://github.com/MCG-NJU/VFIMamba)
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6. [**ConvIR**](#convir). Paper: [Revitalizing Convolutional Network for Image Restoration](https://github.com/c-yn/ConvIR)
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## Workflows
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### WFEN
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Download the model [here](https://github.com/PRIS-CV/WFEN?tab=readme-ov-file#getting-started) and place it in `models/wfen/WFEN.pth`.
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[workflow_wfen_facecrop.json](workflow/workflow_wfen_facecrop.json)
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### RealViformer
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Download the model [here](https://github.com/Yuehan717/RealViformer?tab=readme-ov-file#usage) and place it in `models/realviformer/weights.pth`.
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[workflow_realviformer.json](workflow/workflow_realviformer.json)
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(Not a workflow-embedded image)
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https://github.com/user-attachments/assets/e89003c0-7be5-4263-b281-fd609807cea1
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RealViFormer upscale example
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### ProPIH
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Download the `vgg_normalised.pth` model in the [Installation section](https://github.com/bcmi/ProPIH-Painterly-Image-Harmonization?tab=readme-ov-file#installation) and `latest_net_G.pth` in the [Train/Test section](https://github.com/bcmi/ProPIH-Painterly-Image-Harmonization?tab=readme-ov-file#propih-traintest)
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```
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models/propih/vgg_normalised.pth
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models/propih/latest_net_G.pth
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```
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[workflow_propih.json](workflow/workflow_realviformer.json)
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### CoLIE
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No model needed to be downloaded. Lower `loss_mean` seems to result in brighter images. Node works with image and batched/video.
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[workflow_colie_lowlight.json](workflow/workflow_colie_lowlight.json)
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### VFIMamba
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Download the models from the [huggingface page](https://huggingface.co/MCG-NJU/VFIMamba/tree/main/ckpt)
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```
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models/vfimamba/VFIMamba_S.pkl
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models/vfimamba/VFIMamba.pkl
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```
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You will need to install `mamba-ssm`, which does not have a prebuilt Windows binary. You will need:
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1. [triton](https://github.com/triton-lang/triton). Prebuilt for `Python 3.10 and 3.11` can be found here: https://github.com/triton-lang/triton/issues/2881 - https://huggingface.co/madbuda/triton-windows-builds/tree/main
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2. [causal-conv1d](https://github.com/Dao-AILab/causal-conv1d). Follow this post: https://github.com/NVlabs/MambaVision/issues/14#issuecomment-2232581078
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3. [mamba-ssm](https://github.com/state-spaces/mamba). Follow this tutorial: https://blog.csdn.net/yyywxk/article/details/140420538. Fork that followed all the steps: https://github.com/FuouM/mamba-windows-build
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I've built `mamba-ssm` for `Python 3.11, torch 2.3.0+cu121`, which can be obtained here: https://huggingface.co/FuouM/mamba-ssm-windows-builds/tree/main
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To install, `pip install [].whl`
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[workflow_vfi_mamba.json](workflow/workflow_vfi_mamba.json)
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(Not a workflow-embedded image)
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https://github.com/user-attachments/assets/be263cc3-a104-4262-899b-242e9802719e
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VFIMamba Example (top: Original, bottom: 5X, 20FPS)
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### ConvIR
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Download models in the [Pretrained models - gdrive](https://github.com/c-yn/ConvIR?tab=readme-ov-file#pretrained-models) section
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[workflow_convir.json](workflow/workflow_convir.json)
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```
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models\convir
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│ deraining.pkl
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│
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├─defocus
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│ dpdd-base.pkl
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│ dpdd-large.pkl
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│ dpdd-small.pkl
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│
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├─dehaze
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│ densehaze-base.pkl
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│ densehaze-small.pkl
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│ gta5-base.pkl
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│ gta5-small.pkl
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│ haze4k-base.pkl
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│ haze4k-large.pkl
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│ haze4k-small.pkl
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│ ihaze-base.pkl
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│ ihaze-small.pkl
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│ its-base.pkl
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│ its-small.pkl
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│ nhhaze-base.pkl
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│ nhhaze-small.pkl
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│ nhr-base.pkl
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│ nhr-small.pkl
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│ ohaze-base.pkl
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│ ohaze-small.pkl
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│ ots-base.pkl
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│ ots-small.pkl
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│
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├─desnow
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│ csd-base.pkl
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│ csd-small.pkl
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│ snow100k-base.pkl
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│ snow100k-small.pkl
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│ srrs-base.pkl
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│ srrs-small.pkl
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│
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└─modeblur
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convir_gopro.pkl
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convir_rsblur.pkl
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```
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## Credits
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```
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@misc{chobola2024fast,
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title={Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations},
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author={Tomáš Chobola and Yu Liu and Hanyi Zhang and Julia A. Schnabel and Tingying Peng},
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year={2024},
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eprint={2407.12511},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2407.12511},
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}
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```
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```
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@misc{zhang2024vfimambavideoframeinterpolation,
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title={VFIMamba: Video Frame Interpolation with State Space Models},
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author={Guozhen Zhang and Chunxu Liu and Yutao Cui and Xiaotong Zhao and Kai Ma and Limin Wang},
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year={2024},
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eprint={2407.02315},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2407.02315},
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}
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```
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```
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@article{cui2024revitalizing,
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title={Revitalizing Convolutional Network for Image Restoration},
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author={Cui, Yuning and Ren, Wenqi and Cao, Xiaochun and Knoll, Alois},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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year={2024},
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publisher={IEEE}
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}
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@inproceedings{cui2023irnext,
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title={IRNeXt: Rethinking Convolutional Network Design for Image Restoration},
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author={Cui, Yuning and Ren, Wenqi and Yang, Sining and Cao, Xiaochun and Knoll, Alois},
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booktitle={International Conference on Machine Learning},
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pages={6545--6564},
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year={2023},
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organization={PMLR}
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}
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``` |