Adobe tools like Photoshop, even Lightroom have never been easy. It takes time and effort to get your image detailing right. AI image generation has completely rewritten the landscape. Images can now be generated via text prompts. Let that sink in - text prompt. You don’t need skilled artists anymore (sounds mean, I know) who can paint and illustrate paintings and portraits in hours. All you need now is a text prompt. They can be given specific artistic styles using different checkpoints. They can be further stylised by using specific LoRAs. It all started with open source projects like Stable Diffusion and made accessible by the community via Hugging Face and CivitAI platforms.
Stable Diffusion: The Open Source Champion
If there's one name that continues to dominate open source image generation, it's Stable Diffusion. This powerhouse has evolved significantly since its initial release.
SD1.5 vs. SDXL vs. SDXL 1.0
Let's compare these models:
SD1.5 remains popular for its speed and efficiency. It might be older, but many creators still prefer it for certain styles, especially when working with custom models.
SDXL introduced a significant leap in quality, with better understanding of prompts and more coherent image generation. The detail level and composition improved dramatically.
SDXL 1.0 built upon these foundations, further refining the image quality and fixing some inconsistencies in the previous versions.
In 2025, we're seeing specialized versions for different use cases, but having all three in your toolkit gives you maximum flexibility.
Getting Started: The Interfaces
There are largely 2 ways to interface
A1111 (Automatic1111)
This legendary interface remains the Swiss Army knife of Stable Diffusion. It might not be the prettiest, but it offers incredible depth and flexibility. The active community means there's a plugin for almost anything you can imagine.
For beginners, the learning curve can be steep, but once you get the hang of it, you'll appreciate the granular control it offers.
ComfyUI: The Power User's Choice
ComfyUI has gained massive popularity for its node-based workflow approach. Think of it as visual programming for image generation. You connect different components together to create your generation pipeline.
What makes ComfyUI shine is how it exposes the underlying mechanisms of the diffusion process. This gives technical folks the ability to experiment and optimize in ways that aren't possible with more simplified interfaces.
Enhancing Your Models
Getting good results isn't just about having the base models - it's about customization.
LORA: Low-Rank Adaptation Magic
LORA (Low-Rank Adaptation) has been a game-changer in the fine-tuning space. These small, specialized adaptations can dramatically change the output of your base models.
Want your images to have a specific artist's style? There's a LORA for that. Need consistent characters or objects? LORA has you covered.
The beauty of LORA is the file size - typically just a few hundred MB compared to several GB for full models. This makes them perfect for specific adjustments without the resource overhead.
Fine-tuning Your Models
If you need deeper customization than what LORAs offer, fine-tuning is your next step. By training the model on your specific dataset, you can create truly unique results.
The process has become much more accessible in 2025. Tools for dataset preparation, training scripts, and even specialized platforms make it possible for engineers without ML backgrounds to create custom models.
The Community: Hugging Face and Civitai
Two platforms stand out as the heart of the AI image generation community:
Hugging Face remains the go-to repository for models, code, and research. The collaborative aspect makes it invaluable for staying on top of the latest developments. Their inference APIs also make it easy to test models before committing to downloading them.
Civitai has evolved into the central marketplace for creative assets in the AI image space. From base models to LORAs and prompts, it's where creators share their work. The rating system helps you find the best resources, and the examples give you a clear idea of what each model can do.
Technical Deep Dive: Samplers
For the more technically inclined, let's talk about samplers - the algorithms that guide the noise-to-image process.
Different samplers offer trade-offs between speed and quality:
Euler a remains popular for its balance of speed and quality.
DPM++ 2M Karras gives excellent results for complex images but takes longer.
DDIM offers faster generation but sometimes with less detail.
Flux samplers (newer to the scene) have gained popularity for their efficiency in preserving fine details while using fewer steps.
The right sampler depends on your specific needs - are you prototyping (where speed matters) or creating final images (where quality is paramount)?
Hardware Considerations for Inference
Running these models requires some horsepower. While you can use CPU for inference, you'll want GPU acceleration for any serious work.
NVIDIA cards remain the most widely supported, with the RTX 4000 series offering excellent performance. However, the AMD support has improved significantly, and we're seeing more optimization for Apple's Metal framework.
Cloud inference is also an option if you don't want to invest in hardware. Many services now offer pay-as-you-go access to optimized inference endpoints.
Open Source vs. Proprietary Solutions
Let's talk about the elephant in the room: open source models have absolutely transformed the landscape. While commercial options still have their place, the community-driven innovation around open source image generation has been nothing short of revolutionary.
The beauty of open source models is that they give you complete control over the creation process. You can run them locally, fine-tune them to your specific needs, and even contribute back to the community. Plus, you're not tied to any monthly subscription or API call limits!
The Future of AI Image Generation
As we look ahead, a few trends are clear:
- Increased photorealism without the uncanny valley issues
- Better text handling (finally!)
- More specialized models for specific domains
- Improved animation capabilities bridging still images and video
- Ethical frameworks that help navigate the complex implications of synthetic media
Ethical Considerations
The recent Studio Ghibli trend reignited debate on ethical and privacy concerns. Many artists argue that AI generated images damage their work by using vast datasets that may contain copyrighted content without explicit permission. The concern is that as AI continues to grow and upgrade, human artists could be displaced, leading to a significant shift in the creative industry. Artist Hayao Miyazaki, the founder of Studio Ghibli himself has expressed skepticism regarding AI’s role in animation stating that AI-generated art is “an insult to life itself”. A key concern in this dispute is whether AI tools are trained on copyrighted works without permission. If AI generated images reflect the essence of a copyrighted style, it raises questions of whether the AI is merely reproducing original elements or creating new transformative works.
Conclusion
The AI image generation landscape in 2025 is rich with possibilities. Whether you're using Stable Diffusion variants through A1111 or ComfyUI, enhancing models with LORAa, or discovering new assets on Hugging Face and Civitai, there's never been a more exciting time to explore this technology.
The best approach is to just dive in, experiment, and find the workflow that suits your specific needs. The tools will continue to evolve, but understanding these fundamentals will serve you well no matter what comes next.
About Rohit Diwakar
Coder. Developer. Blogger. I'm an AI Agentic Developer Consultant, with 15+ years as a Full Stack Engineer and Cloud Architect for companies like Teradata and JPMorgan Chase. I have expertise in building scalable systems with recent focus on agentic AI solutions using Python, LLMs, and cloud platforms. You can find me on LinkedIn.