diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md index b8fd209dd..a3b675333 100644 --- a/examples/images/diffusion/README.md +++ b/examples/images/diffusion/README.md @@ -1,6 +1,6 @@ # Stable Diffusion with Colossal-AI -*[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and -fine-tuning for AIGC (AI-Generated Content) applications such as the model [stable-diffusion](https://github.com/CompVis/stable-diffusion) from [Stability AI](https://stability.ai/).* +*[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and +fine-tuning for AIGC (AI-Generated Content) applications such as the model [stable-diffusion](https://github.com/CompVis/stable-diffusion) from [Stability AI](https://stability.ai/).* We take advantage of [Colosssal-AI](https://github.com/hpcaitech/ColossalAI) to exploit multiple optimization strategies , e.g. data parallelism, tensor parallelism, mixed precision & ZeRO, to scale the training to multiple GPUs. @@ -8,8 +8,8 @@ We take advantage of [Colosssal-AI](https://github.com/hpcaitech/ColossalAI) to ## Stable Diffusion [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) is a latent text-to-image diffusion model. -Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. -Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), +Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. +Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.

@@ -37,24 +37,22 @@ You can also update an existing [latent diffusion](https://github.com/CompVis/la conda install pytorch torchvision -c pytorch pip install transformers==4.19.2 diffusers invisible-watermark pip install -e . -``` - -### Install Colossal-AI +``` +### Install [Colossal-AI v0.1.10](https://colossalai.org/download/) From Our Official Website ``` -git clone https://github.com/hpcaitech/ColossalAI.git -git checkout v0.1.10 -pip install . +pip install colossalai==0.1.10+torch1.11cu11.3 -f https://release.colossalai.org ``` -### Install Colossal-AI [Lightning](https://github.com/Lightning-AI/lightning) +### Install [Lightning](https://github.com/Lightning-AI/lightning) +We use the Sep. 2022 version with commit id as `b04a7aa`. ``` -git clone -b colossalai https://github.com/Fazziekey/lightning.git -pip install . +git clone https://github.com/Lightning-AI/lightning && cd lightning && git reset --hard b04a7aa +pip install -r requirements.txt && pip install . ``` ## Dataset -The DataSet is from [LAION-5B](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/), +The DataSet is from [LAION-5B](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/), you should the change the `data.file_path` in the `config/train_colossalai.yaml` ## Training @@ -63,7 +61,7 @@ we provide the script `train.sh` to run the training task , and three Stategy in for example, you can run the training from colossalai by ``` -python main.py --logdir /tmp -t --postfix test -b config/train_colossalai.yaml +python main.py --logdir /tmp -t --postfix test -b config/train_colossalai.yaml ``` - you can change the `--logdir` the save the log information and the last checkpoint @@ -71,22 +69,22 @@ python main.py --logdir /tmp -t --postfix test -b config/train_colossalai.yaml ### Training config you can change the trainging config in the yaml file -- accelerator: acceleratortype, default 'gpu' +- accelerator: acceleratortype, default 'gpu' - devices: device number used for training, default 4 - max_epochs: max training epochs - precision: usefp16 for training or not, default 16, you must use fp16 if you want to apply colossalai -## Comments +## Comments - Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion) , [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch), -[Stable Diffusion](https://github.com/CompVis/stable-diffusion) and [Hugging Face](https://huggingface.co/CompVis/stable-diffusion). +[Stable Diffusion](https://github.com/CompVis/stable-diffusion) and [Hugging Face](https://huggingface.co/CompVis/stable-diffusion). Thanks for open-sourcing! -- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). +- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). -- The implementation of [flash attention](https://github.com/HazyResearch/flash-attention) is from [HazyResearch](https://github.com/HazyResearch). +- The implementation of [flash attention](https://github.com/HazyResearch/flash-attention) is from [HazyResearch](https://github.com/HazyResearch). ## BibTeX @@ -98,7 +96,7 @@ Thanks for open-sourcing! year={2021} } @misc{rombach2021highresolution, - title={High-Resolution Image Synthesis with Latent Diffusion Models}, + title={High-Resolution Image Synthesis with Latent Diffusion Models}, author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer}, year={2021}, eprint={2112.10752}, @@ -112,5 +110,3 @@ Thanks for open-sourcing! year={2022} } ``` - -