Update README.md

README.md

@@ -83,6 +83,65 @@ pipeline = StableDiffusionXLPipeline.from_pretrained(
            model, scheduler=dpmsolver, torch.dtype=torch.float16,
            ).to("cuda")
 ```
-## Variable Autoencoder (VAE) Installation 🖼
-<small><b>(</b>To be added, will include VAEs from HuggingFace and CivitAI.<b>)</b></small>
+## Variational Autoencoder (VAE) Installation 🖼
+There are two ways to get [Variational Autoencoder (VAE)](https://huggingface.co/learn/computer-vision-course/en/unit5/generative-models/variational_autoencoders) file into the model. The first one
+is to download the file manually or remotely use code. In this repository,
+I'll explain the method of using code as the efficient way. First step is to download the VAE file.
+You can download the file manually or remotely, but I recommend you to use the remote. Usually, VAE
+files are in .safetensors format. There are two websites you can visit to download VAE. Those are HuggingFace
+and CivitAI.
+#### From HuggingFace 😊
+This method is pretty straightforward. Pick any VAE's repository you like. Then, navigate to "Files" and
+the VAE's file. Make sure to click the file.
+
+Copy the "Copy Download Link" for the file, you'll need this.
+
+Next step is to load [AutoencoderKL](https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl) pipeline into the code.
+```py
+from diffusers import StableDiffusionXLPipeline, AutoencoderKL
+```
+Finally, load the VAE file into [AutoencoderKL](https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl).
+```py
+link = "your vae's link"
+model = "IDK-ab0ut/Yiffymix_v51"
+vae = AutoencoderKL.from_single_file(link).to("cuda")
+pipeline = StableDiffusionXLPipeline.from_pretrained(
+           model, vae=vae).to("cuda")
+```
+
+If you're using FP16 for the model, it's essential to also use FP16 for the VAE.
+```py
+link = "your vae's link"
+model = "IDK-ab0ut/Yiffymix_v51"
+vae = AutoencoderKL.from_single_file(
+      link, torch_dtype=torch.float16).to("cuda")
+pipeline = StableDiffusionXLPipeline.from_pretrained(
+           model, torch_dtype=torch.float16,
+           vae=vae).to("cuda")
+```
+
+In case if you're experiencing `HTTP404` error because
+the program can't resolve your link, here's a simple fix.
+
+First, download [huggingface_hub](https://huggingface.co/docs/huggingface_hub/en/index) using `pip`.
+```py
+!pip install --upgrade huggingface_hub
+```
+Import [hf_hub_download()](https://huggingface.co/docs/huggingface_hub/en/guides/download) from [huggingface_hub](https://huggingface.co/docs/huggingface_hub/en/index).
+```py
+from huggingface_hub import hf_hub_download
+```
+
+Next, instead of direct link to the file, you want to use the repository ID.
+```py
+repo = "username/model"
+file = "the vae's file.safetensors"
+vae = AutoencoderKL.from_single_file(hf_hub_download(repo_id=repo, filename=file)).to("cuda")
+# use 'torch_dtype=torch.float16' for FP16.
+# add 'subfolder="folder_name"' argument if the VAE is in specific folder.
+```
+#### From CivitAI 🇨
+It's more tricky if the VAE is in [CivitAI](civitai.com), because you can't use
+`from_single_file()` method. It only works for files inside HuggingFace. To solve this issue, you may
+use `wget` or `curl` command to get the file from outside HuggingFace. (To be continued)
 # That's all for this repository. Thank you for reading my silly note. Have a nice day!