Stable Diffusion 3.5: 30 seconds to generate Synthetic Data

I faced a key challenge when I developed my AI model for road scene detection. This model needed to help visually impaired people navigate city environments safely.

Recent research on AI-powered navigation devices describes similar assistive technology. The World Health Organization states that over 2.2 billion people worldwide experience vision impairment. This makes it an urgent problem to solve.

My model required thousands of diverse road scene images. These images needed to show various weather conditions, times of day, city layouts, and potential obstacles. However, I quickly encountered a problem.

Collecting and annotating this much real-world data would take months and cost thousands of dollars. Existing datasets focused mainly on vehicle navigation, not pedestrian pathways. This made them unsuitable for my specific needs.

I needed a solution that could:

Generate diverse, high-quality road scene images.
Create variations in weather, lighting, and obstacle types.
Produce enough training data in days, not months.

I discovered that Stable Diffusion 3.5 Large could solve my data generation problem. Stability AI released this powerful text-to-image model in late 2024. It represents a major step forward in synthetic image generation.

What is Stable Diffusion 3.5 Large?

What is Stable Diffusion 3.5 Large

Stability AI developed Stable Diffusion 3.5 Large, an 8.1 billion parameter Multimodal Diffusion Transformer (MMDiT) model. It includes several important improvements over older versions:

Superior Image Quality: It generates highly detailed images up to 1 megapixel resolution (1024×1024 pixels).
Enhanced Prompt Adherence: The model accurately follows text instructions. This is essential for generating specific road scenes.
Query-Key Normalization: This architectural improvement stabilizes the training process. It also simplifies further fine-tuning.
Multiple Text Encoders: The model uses three fixed, pretrained text encoders. This helps it understand text better.
Diverse Outputs: It creates images that show diverse people and scenarios without needing complex prompts.

The model runs efficiently on consumer hardware and still produces high-quality outputs. This makes it ideal for my synthetic data generation needs.

How Stable Diffusion 3.5 Makes My Images

When I started using Stable Diffusion 3.5 Large for my road detection project, I wanted to know how it actually created those pictures.

It's not just random; it's a smart system. I learned it uses a mix of ideas called Diffusion, Flow Matching, and a special way to sample time. Let me try to explain it simply.

How Stable Diffusion 3.5 Large Works

The specific version I used, Stable Diffusion 3.5 Large (with 8 billion parts, or parameters), uses an architecture called a Multimodal Diffusion Transformer (MMDiT).

Understanding My Words (Text Embeddings): To understand my road scene prompts, it uses three different text "brains" (encoders like CLIP and T5). This helps it really get what I'm asking for. I can even turn off the biggest one (T5 XXL) to save computer memory if I need to.
Chopping Up the Image (Patchify): It takes the image (or its small latent version) and breaks it into little squares or "patches."
The Main Engine (DiT Backbone): The MMDiT uses a powerful Transformer (like those in ChatGPT) to process both the image patches and my text instructions together. It has special layers that pay attention to both what the image looks like and what my words say.
Efficient Math (adaLN): It uses a smart kind of math (adaptive layer normalization) that works well and applies my text instructions evenly.
The Shrinker/Grower (VAE): And, of course, it uses the VAE to shrink images down to the latent space and grow them back up.

This whole system lets Stable Diffusion 3.5 Large create detailed, 1-megapixel images that closely follow my complex text prompts.

From LLM Prompts to Synthetic Road Images

I developed a two-stage pipeline. This pipeline combines Qwen3-8B (an LLM) to generate diverse prompts and Stable Diffusion 3.5 Large to create the actual images.

Step 1: Generating Diverse Prompts with Qwen3-8B

First, I used Qwen3-8B to generate varied, detailed prompts for road scenes:

# Load the LLM
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Qwen/Qwen3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", torch_dtype=torch.bfloat16
)

# Create a prompt seed that specifies what we need
prompt_seed = (
    "Generate 10 diverse and detailed text prompts for open world road scene images "
    "suitable for segmentation tasks. Include variations in weather conditions, "
    "time of day, vehicle density, and urban/rural settings."
)

# Generate the prompts
inputs = tokenizer(prompt_seed, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Parse the generated text into a list of prompts
prompts = [line.strip("- ").strip() 
           for line in generated_text.split("\n") 
           if line.strip()]

This code generated diverse prompts like:

"Urban intersection at rush hour with pedestrians crossing at crosswalks under clear blue skies"
"Rural road at sunset with light fog and a cyclist on the shoulder"
"Highway construction zone during heavy rainfall with reduced visibility and orange cones"

Step 2: Generating Images with Stable Diffusion 3.5 Large

Next, I fed these prompts to Stable Diffusion 3.5 Large. This step generated the actual images:

# Import the pipeline
from diffusers import StableDiffusion3Pipeline

# Load the model
pipe = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")

# Generate images from each prompt
for i, prompt in enumerate(prompts):
    image = pipe(
        prompt,
        num_inference_steps=40,
        guidance_scale=4.5,
    ).images[0]
    image.save(f"road_scene_{i+1}.png")

For faster generation, I implemented batch processing:

# Batch processing for maximum throughput
batch_size = 4  # Process multiple prompts simultaneously
for i in range(0, len(prompts), batch_size):
    batch_prompts = prompts[i:i+batch_size]
    images = pipe(
        batch_prompts,
        num_inference_steps=28,  # Optimized for speed/quality balance
        guidance_scale=3.5,
    ).images
    
    for j, image in enumerate(images):
        image.save(f"road_scene_batch_{i+j+1}.png")

This approach generated 10 high-quality road scene images in about 10 seconds on my RTX 4090 GPU. Within a week, I created over 50,000 diverse images. This was enough to train my road scene detection model effectively.

Output by Stable Diffusion 3.5 large

Other Powerful Use Cases for Stable Diffusion 3.5 Large

1. Concept Art & Storyboarding

Use Case: Visualize scenes for films, games, or comics during pre-production.

Prompt:
"A towering, ancient tree stands alone on a windswept plateau. Its gnarled branches twist from centuries of storms.

Prompt-1

Beneath its canopy, a group of glowing, ethereal spirits drift. Their translucent forms swirl like mist in the moonlight.

In the distance, a crumbling temple clings to a cliff edge. The distant howl of the wind echoes across the barren landscape. Below, a river of fog weaves between jagged peaks, lit by the pale glow of the spirits."

2. Product Design & Advertising

Use Case: Generate realistic product images for marketing materials or e-commerce platforms.

Prompt:
"A sleek holographic wristwatch floats in midair. It has a glowing blue interface, an ultra-modern design, and crisp reflections."

Prompt-2

3. Educational Illustrations

Use Case: Create detailed visuals for textbooks, presentations, or online courses.

Prompt:
"A detailed line drawing shows a dragon curled around a tree. The dragon's scales are intricately drawn. The tree's branches extend outward, twisting into a complex design. The drawing has clean, sharp lines with minimal shading."

Prompt-3

Stable Diffusion 3.5 Large vs. Flux Schnell: What I Found

Flux Schnell vs. Stable Diffusion 3.5

While I was looking into image generation, I also heard about another model called Flux Schnell. Here's a quick comparison from my perspective:

Size of the Model:

Flux Schnell: It's usually a bigger model, around 12 billion parameters.
Stable Diffusion 3.5 Large: This one is 8.1 billion parameters.

Inference Steps:

Flux Schnell: It's very fast. It often makes a picture in just 3 to 6 steps.
Stable Diffusion 3.5 Large Turbo: There's a "Turbo" version of SD 3.5 that's also super fast, designed to make images in only 4 steps. The standard SD 3.5 Large (which I used mostly) takes more steps, like 20 to 30, for top quality.

Guidance Scale:

Both models can work well with a guidance scale set to 0 (meaning they don't need extra pushing to follow the prompt).
Flux Schnell's system often doesn't even have a guidance scale setting.

Negative Prompts:

Flux Schnell: It's generally not set up to use negative prompts.
Stable Diffusion 3.5: I can give it negative prompts to tell it things to avoid in the image.

Resolution:

Flux Schnell: It can often make very large, high-resolution pictures.
Stable Diffusion 3.5 Large: It usually makes pictures around 1 megapixel (like 1024x1024 pixels).

Versatility:

Flux Schnell: People often say it's great for making very high-quality, detailed images quickly, especially for professional work where you need things to look just right. It might be less flexible if you want to try lots of different artistic styles.
Stable Diffusion 3.5 Large: I found this one to be more flexible for many artistic styles like 3D, photos, paintings, and line art. It's also known for anime styles. Because it's open source, people in the community can change and improve it.

Following Prompts & How Pictures Look:

Flux Schnell: It makes very good-looking, detailed pictures quickly.
Stable Diffusion 3.5 Large: This one is known for following prompts really well and making nice-looking images.

Any Downsides I Noticed:

Flux Schnell: Maybe not as good if you want to be super creative with prompts or try many different art styles.
Stable Diffusion 3.5 Large: Sometimes, it can have a little trouble with things like drawing hands perfectly or how objects interact.

Why I Chose Stable Diffusion 3.5 Large?

For my project making road scenes, Stable Diffusion 3.5 Large was a great fit. It followed my detailed text prompts very well, and I could get it to create all sorts of different road situations.

Flux Schnell is super fast and makes amazing high-resolution pictures, which would be great if I needed that. But for generating lots of different synthetic data based on specific text, SD 3.5 Large gave me the control and flexibility I needed.

Conclusion

Stable Diffusion 3.5 Large solved my data scarcity problem. It generated thousands of diverse, high-quality road scene images in days instead of months.

This approach dramatically cut the time and cost of creating my training dataset.

The combination of LLM-generated prompts and Stable Diffusion 3.5 Large offers several key advantages:

It produces diverse images with precise control over scene elements.
The generated images show remarkable realism and detail.
Batch processing allows rapid dataset creation.
The approach scales efficiently to create thousands or millions of images.

For AI developers who face data scarcity challenges, this synthetic data generation pipeline offers a powerful solution. As these models continue to improve, the gap between synthetic and real-world data will keep shrinking. This will open new possibilities for AI applications across many industries.

References

1. AI-Powered Real-Time Guidance for the Visually Impaired
2. Hugging Face Stable-Diffusion 3.5 Large
3. Hugging Face Qwen3-8b
4. Stable-Diffusoin Prompt Guide

FAQs

Q1: What is Stable Diffusion?
A: Stable Diffusion is an open-source text-to-image diffusion model capable of generating detailed images based on textual prompts.

Q2: How can Stable Diffusion be used for synthetic data generation?
A: By inputting descriptive prompts, Stable Diffusion can create diverse and realistic images, augmenting datasets for machine learning tasks.

Q3: What are the benefits of using synthetic data?
A: Synthetic data helps in scenarios with limited real data, enhances model robustness, and addresses privacy concerns by generating artificial yet realistic data.

Q4: How quickly can I generate synthetic data with Stable Diffusion?
A: With the right setup, you can generate high-quality synthetic images in as little as 30 seconds per image.

Q5: Are there tools to assist with Stable Diffusion image generation?
A: Yes, platforms like Hugging Face provide pipelines and interfaces to streamline the image generation process using Stable Diffusion.