For each task, we selected the best fine-tuning learning rate (among 5e-5, 4e-5, 3e-5. We use a batch size of 32 and fine-tune for 3 epochs over the data for all GLUE tasks. Probably this is the reason why the BERT paper used 5e-5, 4e-5, 3e-5, and 2e-5 for fine-tuning. Image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5). With an aggressive learn rate of 4e-4, the training set fails to converge. Pipe = om_pretrained(model_id,torch_dtype=torch.float16).to(“cuda”) To generate images with your new fine tuned model, run below commandįrom diffusers import StableDiffusionPipeline –placeholder_token=”” –initializer_token=”toy” \ For many NLP applications involving Transformer models, you can simply take a pretrained model from the Hugging Face Hub and fine-tune it directly on Masked. –pretrained_model_name_or_path=$MODEL_NAME –use_auth_token \ Next, authenticate with your token by running below commandįine tuning can be started using below commandĮxport MODEL_NAME=”CompVis/stable-diffusion-v1-4″Įxport DATA_DIR=”path-to-dir-containing-images” Pip install diffusers accelerate transformers Next, configure the HuggingFace Accelerate environment by running the below commandĪccelerate config Advertisements Download Stable Diffusion weightsįirst, visit Stable Diffusion page on HuggingFace to accept the licenseįor the next part, you need HuggingFace access token You will also need to have Python 3 or higher installed and should know your way around the command line.īelow are the steps: Install Python dependencies by running this command Advertisementsįine tuning Stable Diffusion using textual Inversion locallyĪnother way to fine tune Stable Diffusion on your images is using your hardware.įor this, you either need a GPU-enabled machine locally or a GPU-enabled VM in the cloud. Next, Open the inference Notebook and Run all the cells.Open this Google Colab Notebook and follow the instructions in the notebook to run it.They can be run in your browser and you require any special hardware like GPUs. The easiest way of course is Google Colab Notebooks. Download Stable Diffusion weights Using Google Colab Notebooks to fine tune Stable Diffusion.Next, configure the HuggingFace Accelerate environment by running the below command We managed to successfully fine-tune a German BERT model using Transformers and Keras, without any heavy lifting or complex and unnecessary boilerplate code. You’ll find many tutorials for fine-tuning a pre-trained model with widely-used datasets, such as IMDB for sentiment analysis. Most online tutorials about fine-tuning models assume you already have a training dataset. Install Python dependencies by running this command This tutorial will show you how to fine-tune a sentiment classifier for your own domain, starting with no labeled data.Fine tuning Stable Diffusion using textual Inversion locally.Using Google Colab Notebooks to fine tune Stable Diffusion.
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