Welcome to the world of image generation! In this guide, we will explore how to utilize the FLUX.1-dev ControlNet from Xlabs AI using the diffusers library. Whether you’re creating stunning visuals for your next project or delving into the domain of artificial intelligence artistry, follow along to make the most out of this powerful tool.

Getting Started with the Diffusers Library

To begin, ensure you have the required libraries installed. You will be working primarily with the diffusers library and other necessary packages such as PyTorch and PIL. Once you have that sorted, it’s time to dive into the code!

Setting Up the Code

The FLUX.1-dev ControlNet operates like a chef’s recipe aimed at creating vivid images by combining various inputs. Here is how you can execute this function:

import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
from PIL import Image
import numpy as np

# Preparing the generator with a specific seed
generator = torch.Generator(device='cuda').manual_seed(87544357)

# Loading the ControlNet model
controlnet = FluxControlNetModel.from_pretrained(
    'XLabs-AI/flux-controlnet-hed-diffusers',
    torch_dtype=torch.bfloat16,
    use_safetensors=True,
)

# Initializing the pipeline
pipe = FluxControlNetPipeline.from_pretrained(
    'black-forest-labs/FLUX.1-dev',
    controlnet=controlnet,
    torch_dtype=torch.bfloat16
)
pipe.to('cuda')

# Loading the control image from a URL
control_image = load_image('https://huggingface.co/XLabs-AI/flux-controlnet-hed-diffusers/resolve/main/hed_example.png')

# Prompting the system with a description of what you want
prompt = "photo of woman in the cyberpunk city"

# Generating the image
image = pipe(
    prompt,
    control_image=control_image,
    controlnet_conditioning_scale=0.7,
    num_inference_steps=25,
    guidance_scale=3.5,
    height=1376,
    width=1024,
    generator=generator,
    num_images_per_prompt=1,
).images[0]

# Saving the output image
image.save('output_test_controlnet.png')

Understanding the Code: An Analogy

Think of the above code as planning a grand dinner party:

• Chef (Your Code): The code you write acts as the chef responsible for executing every step to create a delicious meal (or image).
• Ingredients (Data and Libraries): You gather your ingredients (the libraries and model) — they must be fresh and prepared to contribute to the dish.
• Recipe (Function Calls): Each function is a crucial step in the recipe, guiding the chef (your code) on how to combine the ingredients meticulously.
• Cooking Process (Running the Code): Once everything is mixed and set, you execute the recipe (run the code) and patiently wait (adjust parameters) for the meal to be cooked perfectly.
• Serving the Dish (Output): Finally, you serve the end product (the generated image) to your guests (viewers or project stakeholders) ready to amaze them!

Troubleshooting Tips

While using the FLUX.1-dev ControlNet model, you might face some common hiccups. Here are some troubleshooting ideas:

• Issue: Running out of GPU memory
  • Solution: Try reducing the height and width in your parameters.
• Issue: Image generation not producing desired results
  • Solution: Adjust your prompt and play around with the controlnet_conditioning_scale and guidance_scale values.
• Issue: Library import errors
  • Solution: Ensure that all necessary libraries are correctly installed and that you’re using compatible versions.

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

License Information

This model’s weights are under a Non-Commercial License, which you can review for compliance.

Wrapping Up

Now you have the knowledge to wield the FLUX.1-dev ControlNet model effectively! The realm of image generation offers exciting possibilities for creativity. Don’t hesitate to experiment and explore further!

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.