The creativity of Studio Ghibli’s nostalgic art style is attracting audiences worldwide, inspiring many to recreate its magic in their own images. While premium AI models like ChatGPT are capable of producing Ghibli-style images, access to this feature is currently only available to premium members. However, as always, the open-source community has already launched its own compelling alternative – the “Flux-ChatGPT Ghibli LoRA” model, which has been trained on the FLUX.1-dev base model from Black Forest Labs. This powerful open-source image generation model empowers you to produce stunning Ghibli-inspired visuals free of cost.
In this article, we’ll guide you through the straightforward process of installing and running this model on your local machine or GPU-enabled environment. By the end of this article, you’ll be able to generate mesmerizing Ghibli-style artworks, all while maintaining complete control over the process.
Prerequisites
The minimum system requirements for running this model are:
- GPU: RTX A6000
- Storage: 100GB (preferable)
- Jupyter Notebook installed.
- VRAM: 16GB
Step-by-step process to install and run Flux-ChatGPT Ghibli LoRA locally
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Jupyter, where we’ll deploy and run the inference of our model using Diffusers.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
Step 7: Setting up Python Notebook
Start by creating a .ipynb notebook by clicking on Python 3 (ipykernel).
Next, If you want to check the GPU details, run the following command in the Jupyter Notebook cell:
!nvidia-smi
Output:
Step 8: Download and Set Up Model Dependencies.
- Install Python dependencies to run the model.
!pip install torch torchvision torchaudio einops timm pillow
!pip install git+https://github.com/huggingface/transformers
!pip install git+https://github.com/huggingface/accelerate
!pip install git+https://github.com/huggingface/diffusers
!pip install huggingface_hub
!pip install sentencepiece bitsandbytes protobuf decord numpy
!pip install peft
Output:
2. Install Hugging Face CLI.
!pip install -U "huggingface_hub[cli]"
Output:
3. Login to Hugging Face CLI using your HF READ Token.
!huggingface-cli login --token=<YOUR_HF_TOKEN>
Output:
Step 9: Download and Run the Model
- Finally, we’ll download the model files and load them for inference with the code snippet below:
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/flux-chatgpt-ghibli-lora', weight_name='flux-chatgpt-ghibli-lora.safetensors')
image = pipeline("ghibli style image of a boy and a girl looking out of a window with a cat perched on the window sill. There is a bicycle parked in front of them and a plant with flowers to the right side of the image. The wall behind them is visible in the background.").images[0]
image.save("my_image.png")
Output:
In the above code, we have asked the model to create our first Ghibli-style image, as mentioned in the given prompt.
2. Once it successfully downloads the model checkpoints and generates the image, you’ll be able to see the image inside the Jupyter Notebook folder as shown below:
Here’s the result for the above prompt:
Image – 2
"ghibli style image of a girl studying under a tree with sunset background"
Image-3
"ghibli style image of a girl in a cozy bakery slams her hands on the counter, flour flying everywhere. Her face is scrunched up in frustration as she glares at a stubborn loaf of bread that won’t rise."
Conclusion
By following this guide, you’ve learned how to install and run the Flux-ChatGPT Ghibli LoRA model to create captivating Ghibli-style images on your local machine or GPU-enabled environment. However, generating creative images and art like these demands sufficient compute power. Hence, NodeShift offers AI cloud platform for enhanced performance and scalability, providing affordable and secure GPU resources to simplify and power up your generative AI projects.
