Dhiraj Salian

Software Engineer

5 minute read

In this step, we’ll explore how to fine-tune Stable Diffusion models on custom datasets, enhance the quality of generated images using upscalers, and how to apply ControlNet to gain more control over the image generation process. These techniques allow you to customize Stable Diffusion for specific tasks and improve the quality of outputs.

9.1 Fine-Tuning Stable Diffusion

Why Fine-Tune Stable Diffusion?

Fine-tuning allows you to adapt the pre-trained Stable Diffusion model to generate images for specific domains or styles. For example, you can fine-tune the model to generate images with a particular artistic style, for medical imaging, or for industry-specific applications.

Steps to Fine-Tune Stable Diffusion:

  1. Collect and Prepare Data:
    • Collect a dataset of images that represent the domain or style you want to fine-tune the model for.
    • Preprocess the images (resize, normalize, etc.) to ensure they match the input format required by the model.
  2. Modify the Pre-trained Model:
    • Use the pre-trained Stable Diffusion model as a base and modify it by introducing your custom dataset.
  3. Fine-Tuning Process:
    • Fine-tune the model by training it on your custom dataset using a lower learning rate to prevent overfitting.

Python Code:

Here’s an example of how to fine-tune the Stable Diffusion model on a custom dataset using Hugging Face.

from diffusers import StableDiffusionPipeline, StableDiffusionTrainer
from transformers import AdamW, get_scheduler

# Load pre-trained Stable Diffusion model
model_id = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPipeline.from_pretrained(model_id)

# Custom dataset for fine-tuning
train_dataset = ...  # Load your custom image dataset here

# Define training arguments
training_args = StableDiffusionTrainer.TrainingArguments(
    output_dir="./stable_diffusion_finetuned",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    learning_rate=5e-6,
)

# Initialize trainer
trainer = StableDiffusionTrainer(
    model=pipeline.unet,  # Use UNet from Stable Diffusion
    args=training_args,
    train_dataset=train_dataset,
    optimizers=(AdamW(pipeline.unet.parameters(), lr=5e-6), None),
)

# Fine-tune the model
trainer.train()

Key Points for Fine-Tuning:

  • Lower Learning Rate: Use a small learning rate to prevent catastrophic forgetting, where the model forgets the original knowledge it was pre-trained on.
  • Pre-trained Weights: Leverage pre-trained weights to minimize the amount of new training data required.
  • Specific Domain: Fine-tuning is especially useful when generating images in niche domains (e.g., medical images, artistic styles).

9.2 Using Upscalers for Image Enhancement

What are Upscalers?

Upscalers are models that enhance the resolution and quality of low-resolution images. They are commonly used with Stable Diffusion to further improve the quality of generated images, making them look more detailed and sharp.

  • ESRGAN (Enhanced Super-Resolution GAN) : ESRGAN is one of the most widely used upscalers for improving image resolution while preserving details.
  • Real-ESRGAN: A variant of ESRGAN, designed to handle real-world images with various levels of noise and distortion.

Installing realesrgan

pip install realesrgan

Downloading upscaler model

Download the upscaler model RealESRGAN_x4plus and use it in following python example.

Python Code:

import torch
import numpy as np
from PIL import Image
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet

# Set the path to the pre-trained model
model_path = 'weights/RealESRGAN_x4plus.pth'  # Adjust this if the path is different

# Initialize the RRDBNet model
model = RRDBNet(
    num_in_ch=3,
    num_out_ch=3,
    num_feat=64,
    num_block=23,
    num_grow_ch=32,
    scale=4
)

# Initialize the RealESRGANer class with the model
upscaler = RealESRGANer(
    scale=4,  # Upscale by 4x
    model_path=model_path,
    model=model,
    tile=0,  # Set tile size if you want to avoid memory overload (e.g., 128 or 256)
    tile_pad=10,
    pre_pad=0,
    half=True if torch.cuda.is_available() else False  # Use half precision if on GPU
)

# Load the image and convert it to a numpy array
input_image = Image.open("path/to/your/image.jpg").convert("RGB")
input_image_np = np.array(input_image)

# Perform the upscaling
with torch.no_grad():
    upscaled_image_np, _ = upscaler.enhance(input_image_np, outscale=4)

# Convert the result to a PIL image and save
upscaled_image = Image.fromarray(upscaled_image_np)
upscaled_image.save("upscaled_image_realesrgan.jpg")
upscaled_image.show()

9.3 ControlNet: Enhanced Control over Image Generation

What is ControlNet?

ControlNet is an extension for diffusion models that allows more control over the image generation process by conditioning the generation on external input, such as edge maps, poses, or semantic segmentation masks. This adds a level of precision and control to the generation process.

Key Applications of ControlNet:

  • Pose-guided Image Generation: You can guide the generation process using skeletons or pose landmarks.
  • Edge Detection: ControlNet can generate images based on edge maps, allowing you to influence the structure and form of the generated images.

Python Code:

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.utils.loading_utils import load_image
from controlnet_aux import OpenposeDetector

# Define a pose image
pose_image = load_image("person-pose.png")
# Define a text prompt
prompt = "A person dancing in a futuristic city"

# Load the pre-trained Stable Diffusion and ControlNet models
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet
).to("mps")

# Load or generate a pose skeleton
processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
control_image = processor(pose_image, hand_and_face=True)
control_image.save("control.png")

# Generate an image conditioned on the pose skeleton
image = pipe(prompt, image=control_image).images[0]

# Display the generated image
image.show()
image.save("controlnet_output.png")

How ControlNet Works:

  • Control Image: ControlNet takes an additional input image (e.g., a pose skeleton, edge map) and conditions the image generation process on this control image.
  • Guided Generation: This allows users to guide the generation process and maintain specific structure or forms in the final output.

9.4 Combining Techniques: Fine-Tuning + Upscaling + ControlNet

You can combine all of the above techniques to create powerful image generation pipelines. For example:

  • Fine-tune a pre-trained Stable Diffusion model on your custom dataset.
  • Use ControlNet to guide the generation process with specific conditions (e.g., pose maps).
  • Use Real-ESRGAN to upscale and enhance the final generated images for high-resolution outputs.

Python Code:

# Generate a controlled image with ControlNet
image = pipe(prompt, image=control_image).images[0]

# Upscale the generated image
upscaled_image = model.predict(image)

# Save the final high-resolution image
upscaled_image.save('final_image.png')
upscaled_image.show()

This approach allows for highly customized, high-quality image generation that is guided by specific external inputs (like poses) and enhanced with upscaling techniques for professional output.

Summary:

  • Fine-tuning Stable Diffusion: Adapt the pre-trained model to specific domains or styles using custom datasets.
  • Using Upscalers: Enhance the resolution and quality of generated images with models like Real-ESRGAN.
  • ControlNet: Gain more control over the image generation process by conditioning on external inputs such as poses or edge maps.
  • Combining Techniques: You can combine fine-tuning, ControlNet, and upscalers to create highly customized, high-quality images for various applications.

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