Fine-tuning Stable Diffusion XL with DreamBooth and LoRA

This tutorial explores Stable Diffusion XL (SDXL) and DreamBooth, demonstrating how to leverage the diffusers library for image generation and model fine-tuning. We'll fine-tune SDXL using personal photos and assess the results. AI newcomers are encouraged to begin with an AI fundamentals course.

Understanding Stable Diffusion XL

Stability AI's SDXL 1.0 represents a significant leap in AI text-to-image generation. Building upon the research-only SDXL 0.9, it's now the most powerful publicly available image creation model. Extensive testing confirms its superior image quality compared to other open-source alternatives.

Image from arxiv.org

This improved quality stems from an ensemble of two models: a 3.5-billion parameter base generator and a 6.6-billion parameter refiner. This dual approach optimizes image quality while maintaining efficiency for consumer GPUs. SDXL 1.0 simplifies image generation, producing intricate results from concise prompts. Custom dataset fine-tuning is also streamlined, offering granular control over image structure, style, and composition.

DreamBooth: Personalized Image Generation

Google's DreamBooth (2022) is a breakthrough in generative AI, particularly for text-to-image models like Stable Diffusion. As the Google researchers describe it: "It's like a photo booth but captures the subject in a way that allows it to be synthesized wherever your dreams take you."

Image from DreamBooth

DreamBooth injects custom subjects into the model, creating a specialized generator for specific people, objects, or scenes. Training requires only a few (3-5) images. The trained model then places the subject in diverse settings and poses, limited only by imagination.

DreamBooth Applications

DreamBooth's customizable image generation benefits various fields:

1. Creative Industries: Graphic design, advertising, and entertainment benefit from its unique visual content creation capabilities.
2. Personalization: Creates scenarios difficult or impossible to replicate in reality or purely fictional settings.
3. Education & Research: Generates personalized educational content and aids research requiring visual representation.

Accessing Stable Diffusion XL

SDXL can be accessed via the Hugging Face Spaces demo (generating four images from a prompt) or the diffusers Python library for custom prompt image generation.

Setup and Image Generation with diffusers

Ensure a CUDA-enabled GPU is available:

!nvidia-smi

Install diffusers:

%pip install --upgrade diffusers[torch] -q

Load the model (using fp16 for GPU memory efficiency):

from diffusers import DiffusionPipeline, AutoencoderKL
import torch

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.to("cuda");

Generate images:

prompt = "A man in a spacesuit is running a marathon in the jungle."
image = pipe(prompt=prompt, num_inference_steps=25, num_images_per_prompt=4)

Display images using a helper function (provided in the original):

# ... (image_grid function from original code) ...
image_grid(image.images, 2, 2)

Improving Results with the Refiner

For enhanced quality, utilize the SDXL refiner:

# ... (refiner loading and processing code from original) ...

Fine-tuning SDXL with AutoTrain Advanced

AutoTrain Advanced simplifies SDXL fine-tuning. Install it using:

%pip install -U autotrain-advanced

(Note: The original tutorial uses a now outdated Colab notebook for an alternative method; this is omitted for brevity.)

DreamBooth Fine-tuning (Abridged)

The tutorial then proceeds with a detailed example of fine-tuning SDXL using AutoTrain Advanced's DreamBooth script on a personal dataset of images. This section involves setting up variables, creating a Kaggle dataset, and running the AutoTrain script. The output shows the training process and the resulting LoRA weights uploaded to Hugging Face. Inference with the fine-tuned model is then demonstrated, showcasing generated images of the specified subject in various scenarios. Finally, the use of the refiner with the fine-tuned model is explored. Due to length constraints, this detailed section is significantly condensed here. Refer to the original for the complete code and explanation.

Conclusion

This tutorial provides a comprehensive overview of SDXL and DreamBooth, showcasing their capabilities and ease of use with the diffusers library and AutoTrain Advanced. The fine-tuning process demonstrates the power of personalized image generation, highlighting both successes and areas for further exploration (like the refiner's interaction with fine-tuned models). The tutorial concludes with recommendations for further learning in the field of AI.

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