script link

Author: johnowhitaker

unit2/README.md

@@ -47,12 +47,12 @@ There are a number of ways to pass in this conditioning information, such as
 
 | Chapter                                     | Colab                                                                                                                                                                                               | Kaggle                                                                                                                                                                                                   | Gradient                                                                                                                                                                               | Studio Lab                                                                                                                                                                                                   |
 |:--------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Fine-tuning and Guidance                                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit1/01_finetuning_and_guidance.ipynb)              | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit1/01_finetuning_and_guidance.ipynb)              | [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit1/01_finetuning_and_guidance.ipynb)              | [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit1/01_finetuning_and_guidance.ipynb)              |
-| Class-conditioned Diffusion Model Example                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit1/02_class_conditioned_diffusion_model_example.ipynb)              | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit1/02_class_conditioned_diffusion_model_example.ipynb)              | [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit1/02_class_conditioned_diffusion_model_example.ipynb)              | [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit1/02_class_conditioned_diffusion_model_example.ipynb)              |
+| Fine-tuning and Guidance                                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)              | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)              | [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)              | [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit2/01_finetuning_and_guidance.ipynb)              |
+| Class-conditioned Diffusion Model Example                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit2/02_class_conditioned_diffusion_model_example.ipynb)              | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/huggingface/diffusion-models-class/blob/main/unit2/02_class_conditioned_diffusion_model_example.ipynb)              | [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/huggingface/diffusion-models-class/blob/main/unit2/02_class_conditioned_diffusion_model_example.ipynb)              | [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/diffusion-models-class/blob/main/unit2/02_class_conditioned_diffusion_model_example.ipynb)              |
 
 At this point, you know enough to get started with the accompanying notebooks! Open them in your platform of choice using the links above. Fine-tuning is quite computationally intensive, so if you're using Kaggle or Google Colab make sure you set the runtime type to 'GPU' for best results.
 
-The bulk of the material is in _**Fine-tuning and Guidance**_, where we explore these two topics through worked examples. The notebook shows how you can fine-tune an existing model on new data, add guidance, and share the result as a Gradio demo. There is an accompanying script ([`finetune_model.py`](TODO link)) that makes it easy to experiment with different fine-tuning settings, and [an example space](https://huggingface.co/spaces/johnowhitaker/color-guided-wikiart-diffusion) which you can use as a template for sharing your own demo on 🤗 Spaces. 
+The bulk of the material is in _**Fine-tuning and Guidance**_, where we explore these two topics through worked examples. The notebook shows how you can fine-tune an existing model on new data, add guidance, and share the result as a Gradio demo. There is an accompanying script ([finetune_model.py](https://github.com/huggingface/diffusion-models-class/blob/main/unit2/finetune_model.py)) that makes it easy to experiment with different fine-tuning settings, and [an example space](https://huggingface.co/spaces/johnowhitaker/color-guided-wikiart-diffusion) which you can use as a template for sharing your own demo on 🤗 Spaces. 
 
 In _**Class-conditioned Diffusion Model Example**_ we show a brief worked example of creating a diffusion model conditioned on class labels using the MNIST dataset. The focus is on demonstrating the core idea as simply as possible: by giving the model extra information about what it is supposed to be denoising, we can later control what kinds of images are generated at inference time.