ControlNet in Stable Diffusion XL offers exceptional content generation features, but it often poses challenges for users, especially when it comes to training and fine-tuning these models. In this blog, we’ll walk you through how to set up and use ControlNet effectively, helping bring this powerful tool within reach of personal users.

Key Features of ControlNet for SDXL

This model aims to resolve the limitations associated with the ControlNet for SDXL, effectively lowering its requirements for personal GPU users. With the right setup, you can utilize SDXL’s capabilities without the need for high-end GPUs.

Environment Setup

To get started, you need to prepare your environment. The training script can be found in the official Diffuser library:

https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet_sdxl.py

You can find a comprehensive environment setup guide here.

Setting Up Your Training Script

To demonstrate how to complete the training setup, here’s an example of the Python code you will need:

from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image
import numpy as np
import torch
from PIL import Image

controlnet_conditioning_scale = 0.9

controlnet = ControlNetModel.from_pretrained("pathtothisdirectory", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("madebyollinsdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16)

pipe.enable_model_cpu_offload()

prompt = "Your prompt"
negative_prompt = "Your negative prompt"
line = Image.open("pathtoyourcontrolingimage")
image = pipe(prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=line).images[0]

Understanding the Code: An Analogy

Think of setting up ControlNet like preparing a recipe. Each step in the code is akin to an ingredient you need to gather for your dish. Just as you wouldn’t bake a cake without flour, your script won’t work correctly without the necessary imports and objects like StableDiffusionXLControlNetPipeline, which acts as your base batter. The prompts act as flavorings, specifying what you want your dish to taste like, while the control image is like your baking temperature, guiding the cooking process.

Training Setup Details

• Base Model: stabilityai/stable-diffusion-xl-base-1.0
• Dataset: cc12m with 1024 resolution and over 300k image pairs.
• Lineart: Utilizes LineartStandardDetector from controlnet_aux to extract controlling images.
• Total Batch Size: 16 (4 gradient accumulation steps * 4 GPUs in parallel)
• Steps: 50k

Results

This model significantly enhances line interpretation, even understanding depth relationships, as demonstrated in the example images:

• Example Image 1
• Example Image 2
• Example Image 3
• Example Image 4
• Example Image 5

Troubleshooting Tips

If you encounter issues loading custom datasets via HuggingFace, you might need to modify the script located in train_controlnet_sdxl.py. Look for line 650 and adapt it as follows:

if args.train_data_dir is not None:
    dataset = load_dataset(args.train_data_dir, cache_dir=args.cache_dir, trust_remote_code=True)

Ensure your dataset structure aligns with this example to ensure full automation.

Note that the ControlNet may not perform well with colorization tasks in the xl-base-1.0 setup. It excels at capturing lines, but if you encounter colorization issues, investigate the base model used. Continuous experimentation is key here!

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.