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26 changes: 0 additions & 26 deletions examples/tutorial/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -39,9 +39,6 @@ quickly deploy large AI model training and inference, reducing large AI model tr
- Try pre-trained OPT model weights with Colossal-AI
- Fine-tuning OPT with limited hardware using ZeRO, Gemini and parallelism
- Deploy the fine-tuned model to inference service
- Acceleration of Stable Diffusion
- Stable Diffusion with Lightning
- Try Lightning Colossal-AI strategy to optimize memory and accelerate speed


## Discussion
Expand Down Expand Up @@ -168,26 +165,3 @@ docker run -it --rm --gpus all --ipc host -p 7070:7070 hpcaitech/tutorial:opt-in
```bash
python opt_fastapi.py opt-125m --tp 2 --checkpoint /data/opt-125m
```

## 🖼️ Accelerate Stable Diffusion with Colossal-AI
1. Create a new environment for diffusion
```bash
conda env create -f environment.yaml
conda activate ldm
```
2. Install Colossal-AI from our official page
```bash
pip install colossalai==0.1.10+torch1.11cu11.3 -f https://release.colossalai.org
```
3. Install PyTorch Lightning compatible commit
```bash
git clone https://github.com/Lightning-AI/lightning && cd lightning && git reset --hard b04a7aa
pip install -r requirements.txt && pip install .
cd ..
```

4. Comment out the `from_pretrained` field in the `train_colossalai_cifar10.yaml`.
5. Run training with CIFAR10.
```bash
python main.py -logdir /tmp -t true -postfix test -b configs/train_colossalai_cifar10.yaml
```
82 changes: 0 additions & 82 deletions examples/tutorial/stable_diffusion/LICENSE

This file was deleted.

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