@@ -26,6 +26,16 @@ Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1]
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@@ -26,6 +26,16 @@ Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1]
More details can be found in our [blog of Stable Diffusion v1](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) and [blog of Stable Diffusion v2](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0).
More details can be found in our [blog of Stable Diffusion v1](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) and [blog of Stable Diffusion v2](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0).
## Roadmap
This project is in rapid development.
- [X] Train a stable diffusion model v1/v2 from scatch
- [X] Finetune a pretrained Stable diffusion v1 model
- [X] Inference a pretrained model using PyTorch
- [ ] Finetune a pretrained Stable diffusion v2 model
If you want to useed the Last [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) wiegh from runwayml
If you want to useed the Last [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) weight from runwayml
```
```
git lfs install
git lfs install
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@@ -156,7 +166,7 @@ You can change the trainging config in the yaml file
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@@ -156,7 +166,7 @@ You can change the trainging config in the yaml file
- precision: the precision type used in training, default 16 (fp16), you must use fp16 if you want to apply colossalai
- precision: the precision type used in training, default 16 (fp16), you must use fp16 if you want to apply colossalai
- more information about the configuration of ColossalAIStrategy can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/model_parallel.html#colossal-ai)
- more information about the configuration of ColossalAIStrategy can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/model_parallel.html#colossal-ai)
## Finetune Example
## Finetune Example (Work In Progress)
### Training on Teyvat Datasets
### Training on Teyvat Datasets
We provide the finetuning example on [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset, which is create by BLIP generated captions.
We provide the finetuning example on [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset, which is create by BLIP generated captions.