Most “best AI image generator” roundups compare interfaces: pricing, ease of use, export options. That's useful if you're picking a tool for a one-off task, but it skips the part that actually explains why two generators produce visibly different results from the same prompt: the model architecture underneath the UI.
This breakdown looks at what's actually running behind the most commonly used free and freemium image generators, and where video-generation models fit into the same landscape.
The Two Architectures Behind Most Image Models
Latent diffusion. Most mainstream image generators, including the Stable Diffusion family and Adobe Firefly, are diffusion models. Training adds noise to real images step by step; generation reverses that process, starting from random noise and denoising it into a coherent image guided by a text encoder. “Latent” diffusion runs this process in a compressed representation of the image rather than on raw pixels, an approach first laid out in Rombach et al. 's 2022 paper on latent diffusion models, which is what makes it fast enough to run on consumer hardware.
Rectified-flow transformers. FLUX, introduced by Black Forest Labs in August 2024 (a team that includes former Stable Diffusion researchers), uses a newer approach that learns a more direct path from noise to image instead of the many small denoising steps classic diffusion relies on. In practice this tends to mean fewer sampling steps for comparable quality.
Multimodal transformers. Google's Nano Banana models inside Gemini and OpenAI's GPT Image take a different route: image generation is handled inside the same transformer architecture used for text and reasoning, rather than a separate diffusion pipeline bolted onto a language model. This is part of why these tools tend to follow multi-step, compositional prompts more reliably.
Free and Freemium Models, Compared
| Model | Architecture | Weights | API Access |
|---|---|---|---|
| Stable Diffusion (SDXL / SD3) | Latent diffusion | Open | Yes, self-hostable |
| FLUX.1 (Black Forest Labs) | Rectified-flow transformer | Open (schnell/dev), closed (pro) | Yes |
| Midjourney | Diffusion (proprietary) | Closed | No public API |
| DALL-E / GPT Image (OpenAI) | Diffusion + transformer prompt encoder | Closed | Yes |
| Nano Banana (Google Gemini) | Multimodal transformer | Closed | Yes |
| Adobe Firefly | Latent diffusion | Closed | Yes |
| Ideogram | Diffusion, text-rendering tuned | Closed | Yes |
A few things worth calling out from that table: open-weight models (Stable Diffusion, FLUX's schnell and dev variants) can be self-hosted or run through a third-party API host, which matters if you need to fine-tune on custom data or avoid sending prompts to a third party. Closed models are only accessible through the provider's own API or interface, and terms around commercial use and training data vary by provider.
Facy: A Workflow Layer, Not a Standalone Model
Facy doesn't publish a detailed model architecture the way Stability AI or Black Forest Labs do, so it's more accurate to describe it as a product layer than a new model family. Its AI image generator runs entirely in the browser and is aimed at reducing setup friction rather than competing on raw model benchmarks: no account for API keys, no separate hosting, no switching between a generator and an editor for basic touch-ups.
For developers evaluating tools by underlying model, that's a meaningful distinction: a workflow product built on top of one or more existing models optimizes for speed of use, while an open-weight model gives you architecture-level control at the cost of setup time.
Video Models Follow the Same Split
Image-to-video tools generally extend diffusion or diffusion-transformer architectures into the time dimension, generating a sequence of frames that stay consistent with each other rather than a single still.
• Sora and Veo are diffusion-transformer video models trained for scene-level generation, including camera movement and multi-shot coherence.
• Kling AI uses a comparable diffusion-transformer approach, with a free tier for shorter generations.
• Runway's Gen models are diffusion-based and exposed through both a web interface and an API, which makes them a common pick for teams building custom pipelines.
Facy AI image to video tool sits in the same product-layer category as its image generator: it takes a finished still and adds motion inside the same browser session, without requiring a separate account or export step to a different platform.
Open Weights vs. Closed API: What Actually Matters
• Need to fine-tune on your own data? Only open-weight models (Stable Diffusion, FLUX dev/schnell) support that directly. Closed models only offer this if the provider ships a fine-tuning API.
• Need to avoid sending prompts to a third-party server? Self-hosting an open-weight model is the only option that keeps inference fully local.
• Need the fastest path from prompt to usable asset? A closed, browser-based product usually wins here, since there's no environment setup involved.
• Building a production pipeline? Check whether the provider exposes a stable API with documented rate limits before committing, regardless of which architecture is underneath.
FAQ
What's the difference between diffusion and rectified-flow models?
Diffusion models generate an image through many small denoising steps starting from noise. Rectified-flow models, like FLUX, learn a more direct path from noise to image, which can reduce the number of steps needed for a comparable result.
Are any of these models open source?
Stable Diffusion's weights are open, and FLUX offers open-weight variants (schnell and dev) alongside a closed pro tier. Midjourney, Adobe Firefly, Nano Banana, and GPT Image are all closed.
Does Facy publish its underlying model architecture?
No. Facy is best understood as a browser-based product layer for image and video generation rather than a documented model family, so it doesn't fit neatly into an architecture-based comparison the way Stable Diffusion or FLUX do.
Can I self-host any of these for free?
Yes, Stable Diffusion and FLUX's open-weight variants can be run locally or on your own infrastructure, which avoids per-generation API costs but requires GPU hardware and setup.
Do video models use the same architecture as image models?
Generally an extended version of it. Most video generators are diffusion or diffusion-transformer models adapted to generate a coherent sequence of frames instead of a single image.
Where This Leaves Developers
If you're picking a model based on architecture, open-weight diffusion and rectified-flow models give you the most control, at the cost of setup and infrastructure. If you just need a finished asset without managing a pipeline, browser-based products like Facy trade that architectural control for speed. Neither approach is strictly better; it depends on whether the model itself or the output is the thing you're actually optimizing for.
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