Welcome to the world of MistoLine, a versatile SDXL-ControlNet model designed specifically for adaptable line art conditioning. Whether you are an artist looking to refine your sketches, or a developer interested in exploring advanced image generation, this guide will walk you through the essentials of using MistoLine effectively.

What is MistoLine?

MistoLine is an innovative model that can intelligently interpret any kind of line art input. With MistoLine, you can achieve high-quality image outputs (with a short side greater than 1024px) from your user-provided line art, which can include:

• Hand-drawn sketches
• ControlNet line preprocessors
• Model-generated outlines

This model is powered by a novel line preprocessing algorithm known as Anyline, which improves detail restoration, prompt alignment, and stability.

How Does MistoLine Work?

Think of MistoLine as a skilled chef in a kitchen. Just as a chef takes various ingredients (line art) and expertly combines them to create a sumptuous dish (high-quality image), MistoLine processes different kinds of line inputs to produce stunning visual outputs, all while maintaining the essence of the original art style. Each ingredient must be carefully selected and combined in the right proportions to achieve delicious results. Here, the ingredients are your sketches and outlines, and the chef is the advanced neural network of MistoLine!

Getting Started with MistoLine

Follow these steps to integrate MistoLine into your projects:

1. Install Necessary Libraries

pip install accelerate transformers safetensors opencv-python diffusers

2. Initialize the MistoLine Model in Your Code

from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2

# Load and prepare image and model
prompt = "Aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = "low quality, bad quality, sketches"
image = load_image("https:huggingface.codatasetshf-internal-testingdiffusers-imagesresolvemainsd_controlnethf-logo.png")

# ControlNet setup
controlnet_conditioning_scale = 0.5
controlnet = ControlNetModel.from_pretrained("TheMistoAIMistoLine", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("madebyollinsdxl-vae-fp16-fix", torch_dtype=torch.float16)

# Pipeline setup
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityaistable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    vae=vae,
    torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()

# Process image
image_array = np.array(image)
image_array = cv2.Canny(image_array, 100, 200)
image_array = image_array[:, :, None]
image_array = np.concatenate([image_array, image_array, image_array], axis=2)
image = Image.fromarray(image_array)
images = pipe(prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale).images
images[0].save("hug_lab.png")

3. Visualize Your Results

You can now explore the generated outputs and save them for your projects!

ComfyUI Recommended Parameters

• Sampler Steps: 30
• CFG: 7.0
• Sampler Name: dpmpp_2m_sde
• Scheduler: karras
• Denoise: 0.93
• ControlNet Strength: 1.0
• Start Percent: 0.0
• End Percent: 0.9

Troubleshooting

If you run into any issues while using MistoLine, here are some troubleshooting tips:

• Ensure that all libraries are installed properly. You may want to reinstall if you encounter import errors.
• Double-check your image URL to ensure it is accessible and valid.
• Review the configurations to match the recommended parameters for optimal results.

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

Final Thoughts

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.

Conclusion

MistoLine represents a significant step forward in the realm of line art conditioning. With its robust capabilities, you can create beautiful, high-quality images that align with your artistic vision. Dive in, explore, and witness the magic unfold!