Table of Contents for What is Stable Diffusion and How Does it Work?:
- What is Stable Diffusion?
- Step-by-step guide for Stable Diffusion
- Pros and Cons of the Stable Diffusion AI Image Generator
- Copyright in AI-Generated Content
- Alternatives to Stable Diffusion?
- Stable Diffusion vs. AI Midjourney
- Conclusion
- FAQ
What is Stable Diffusion?
Stable Diffusion is a family of AI models that generate images from text prompts and, depending on the workflow, modify existing images. The original version grew out of CompVis research at LMU Munich, with contributions from Stability AI and Runway. Later generations such as SD 3.5 differ in architecture, training, and licensing. LAION-5B is relevant to the original generation, but is not a shared training dataset for every version.
Read more: Overview of Stable Diffusion 3 - Stable Diffusion 3.5 (Models & Highlights) - CompVis (LMU) - GitHub - LAION-5B (Paper)
You can find model weights for several Stable Diffusion generations on the Hugging Face Hub. The Diffusers library provides programmatic access. Availability does not mean unrestricted use: check each model’s license. For SDXL, the documentation explains how to get started: Using SDXL with Diffusers.
For text-to-image generation, the model starts with noise in a compressed image representation called latent space. It gradually shapes this into an image guided by the prompt. The seed sets the random starting point; step count and guidance influence generation. It does not retrieve matching image pieces from a database. The official SD 3.5 Large model card explains the technical foundations and includes a usage example.
Models of Stable Diffusion 3.5
The 3.5 family addresses various use cases:
- 3.5 Large: for detailed images, designed around a typical resolution of one megapixel rather than a fixed maximum.
- 3.5 Large Turbo - significantly faster for sketches & variants, slight loss of quality possible.
- 3.5 Medium: a smaller model that balances computing requirements and image quality.
- Official overview: SD 3.5 Large, Large Turbo, and Medium. The Stability API offers additional variants such as SD 3.5 Flash; availability and settings depend on how you access the models.
Step-by-Step Guide to Stable Diffusion
How To Access Stable Diffusion?
Stable Diffusion can be accessed in several ways. You can open the tool as follows:
- Brand Studio, formerly DreamStudio: Stability AI’s web platform uses its own and third-party models. Check the model selection if you specifically want to test Stable Diffusion. See the provider for current plans and trial allowances; old DreamStudio credits are not a current offer.
- Hugging Face Hub: find Stability AI model weights and model cards. Some Spaces offer interactive demos. Availability and free access depend on the operator.
- Third-party providers: platforms such as Fireworks AI and DeepInfra maintain model catalogs. Check the specific Stable Diffusion version, billing, and data handling before choosing a service.
- API-based use: If you are familiar with programming, you can connect the Stable Diffusion API to a software or web service.
- Local installation: the official repository provides SD 3.5 reference code. ComfyUI offers a graphical alternative with a compatible model and workflow. Hardware needs depend on factors such as model size, precision, and image resolution.
How Does Stable Diffusion Work?
The following five steps explain how to get started locally with SD 3.5 in ComfyUI. The retained DreamStudio screenshots document the older interface. They show neither today’s ComfyUI interface nor current prices or trial allowances.
Step 1:
Choose a ComfyUI setup suitable for your computer and check its system requirements. Open the official SD 3.5 workflow examples, which connect the model, text encoders, and image output in a reproducible sequence.
Step 2:
Download the model files and text encoders specified in your chosen example and place them in the documented folders. Compatible checkpoints with integrated encoders are another option. Then load the example workflow into ComfyUI and check that it can find every selected file.
Historical DreamStudio Beta welcome screen, not the current ComfyUI interface
Step 3:
Start with the settings in the matching example. Choose an aspect ratio for your purpose, such as a square image for an initial material concept. Keep the workflow’s step count and guidance settings initially: Large, Medium, and Turbo do not use identical values.
Historical DreamStudio plan screen; the prices and credits shown are not a current offer
Step 4:
Describe the subject, material, setting, viewpoint, and lighting in your prompt. For example: “A light oak chair with a woven seat in a bright studio, three-quarter view, soft daylight.” This describes an image concept. For a specific real chair, compare the resulting shape, joints, and material details with the product references afterward.
Historical DreamStudio example of entering a text prompt
Step 5:
Run the generation and review the shape, material, lighting, and unwanted details. For a controlled comparison, keep the seed and change just one setting or part of the prompt at a time. Save useful images together with the model version and workflow so you can develop the visual direction further.
The SD 3.5 Large model card specifies English. A clear English prompt is therefore a useful starting point. Complete sentences work; the longest possible keyword list is not automatically better. Add details that clarify the intended image.
The interface or workflow determines how many variations you generate. The four dog images in the historical screenshot are an example, not a fixed property of Stable Diffusion.
Historical DreamStudio example: four variations of a dog image
AI-generated image by Danthree Studio
Want to dive even deeper? Check out our guide to Midjourney: How It Works. We explain many basic prompt principles that can be applied to SD. And if you’re interested in this career field: Prompt Engineer Explained.
Pros and Cons of the Stable Diffusion AI Image Generator
Stable Diffusion is useful for visual concepts, mood boards, and variations in lighting, color, or setting. You can explore a clearly described idea visually before planning a more involved production. Effort and costs differ between a local workflow and a hosted service.
The outputs are two-dimensional raster images, not editable 3D models. A convincing furniture concept in an image therefore does not provide reliable dimensions or construction data. For real product imagery, pay particular attention to proportions, hardware, seams, and material characteristics.
Stable Diffusion can generate new visual combinations. Training captures statistical relationships between images and descriptions, and generation uses those learned patterns. It is not a simple collage assembled from an image database. Outputs can still resemble training content, so review them for their intended use.
A key benefit is the choice between direct technical control and a more convenient web service. Locally, you can choose models and build specific workflows, while taking responsibility for setup, computing resources, and maintenance.
Benefits at a Glance:
- High control & openness: Can be used locally, fine-grained parameters, custom pipelines; ideal for integrations/automations.
- Good quality for many use cases; broad model/checkpoint ecosystem.
- Cost control: compare local hardware, electricity, and working time with a service’s usage charges. The more economical option depends on your workload.
Disadvantages at a Glance:
- Time required for tuning: Quality depends heavily on prompting, seeds, sampler & fine tuning.
- Prone to errors: Anatomy/details may be partially incorrect; requires revision.
- Data and rights: models can reflect biases in their training. Check the relevant model card and license, along with rights to your inputs and finished images.
For images of a specific product, a validated 3D model provides a controllable basis for proportions, material assignments, and additional camera angles. This is especially useful for image sets and close-ups. Learn more: 3D product visualization.
Copyright in AI-Generated Content
United States: the 2025 USCO report requires human authorship for protection. A prompt alone is generally insufficient. Creative selection, arrangement, or editing may qualify individually; this does not automatically protect the entire AI output.
Germany: copyright protects personal intellectual creations, and the author is the creator of the work (Section 2 and Section 7 UrhG). Pure AI output therefore does not automatically establish copyright protection. The EU AI Act’s transparency and provider obligations are a separate issue.
Commercial use: check the specific model’s license. For covered Stability Core Models, the Community License permits commercial use below one million US dollars in annual revenue under its conditions, including registration. For higher revenue, discuss enterprise licensing with Stability AI. Hosted services also have their own terms.
Practical tip: use AI to explore the image concept and keep product references separately. Your own 3D asset can provide a basis for verifiable product details. Check model and input rights, then review the finished advertising image. Manual editing alone does not resolve rights issues. Examples: 3D Render Studio.
Alternatives to Stable Diffusion
- OpenAI Images API: generate and edit images through an API.
- Adobe Firefly: image generation and editing. Adobe distinguishes its own Firefly models trained on licensed or public-domain content from partner models. Check the latter’s terms separately; Content Credentials document provenance but do not replace a rights review.
- Runway Gen-4 Image References: develop images using references for people, objects, or style.
- Ideogram: great for typography/text in images: Ideogram.
Stable Diffusion vs. AI Midjourney
Midjourney is a hosted image generator for the web and Discord. As of October 6, 2026, the official version overview lists V8.2 as the default. Its Edit Model replaces Omni Reference, Character Reference, and Retexture. Available features depend on the selected version.
Quick Comparison
- Control: local Stable Diffusion lets you choose models and customize workflows. Midjourney brings the tools together in a managed service. Which interface gets you there faster depends on the task and your experience.
- Confidential content: Stable Diffusion can run entirely locally if your workflow does not use cloud services. Midjourney’s Stealth controls visibility on its website. Posts in public Discord channels remain visible to others.
- Pricing and scale: local SD requires hardware, electricity, and working time; hosted access has separate pricing. Midjourney uses subscriptions with GPU time and plan-specific features, rather than a general credit system.
- Workflow: a controlled CGI foundation is useful for specific product and material details; generative tools can complement it with selected concepts or setting variations. We explain the differences here: AI vs. CGI: Differences.
Conclusion
Stable Diffusion gives you many ways to develop image concepts and build your own workflows. Choose the model and access method around the task, hardware, and terms of use. For furniture and interior projects, separate the roles: AI explores visual directions, while validated product references and a controlled CGI foundation make shapes, materials, and views traceable. This helps you compare ideas efficiently and review the final product imagery against concrete references.
If you need photorealistic, CI-clean product visuals/animations, talk to us: 3D animations for products - Contact us.
