Instructions to use hugaagg/IR_expeiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hugaagg/IR_expeiment with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hugaagg/IR_expeiment", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 2,066 Bytes
2ecc7ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # :milky_way: Training Procedures
[English](train.md) **|** [简体中文](train_CN.md)
## Preparing Dataset
- Download training dataset: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
---
## Training
```
For PyTorch versions >= 1.10, please replace `python -m torch.distributed.launch` in the commands below with `torchrun`.
```
### 👾 Stage I - VQGAN
- Training VQGAN:
> python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4321 basicsr/train.py -opt options/VQGAN_512_ds32_nearest_stage1.yml --launcher pytorch
- After VQGAN training, you can pre-calculate code sequence for the training dataset to speed up the later training stages:
> python scripts/generate_latent_gt.py
- If you don't require training your own VQGAN, you can find pre-trained VQGAN (`vqgan_code1024.pth`) and the corresponding code sequence (`latent_gt_code1024.pth`) in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
### 🚀 Stage II - CodeFormer (w=0)
- Training Code Sequence Prediction Module:
> python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4322 basicsr/train.py -opt options/CodeFormer_stage2.yml --launcher pytorch
- Pre-trained CodeFormer of stage II (`codeformer_stage2.pth`) can be found in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
### 🛸 Stage III - CodeFormer (w=1)
- Training Controllable Module:
> python -m torch.distributed.launch --nproc_per_node=gpu_num --master_port=4323 basicsr/train.py -opt options/CodeFormer_stage3.yml --launcher pytorch
- Pre-trained CodeFormer (`codeformer.pth`) can be found in the folder of Releases v0.1.0: https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
---
:whale: The project was built using the framework [BasicSR](https://github.com/XPixelGroup/BasicSR). For detailed information on training, resuming, and other related topics, please refer to the documentation: https://github.com/XPixelGroup/BasicSR/blob/master/docs/TrainTest.md
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