Stable Diffusion
- Shahab Nn
- Dec 8, 2024
- 5 min read
Updated: Apr 16
Stable Diffusion Overview
Stable Diffusion is a state-of-the-art generative model primarily used for creating high-quality images from text descriptions. However, over time, it has evolved into a multimodal generative model capable of handling a wide range of tasks, from image editing to video generation. It is based on a latent diffusion model (LDM), which operates efficiently in a compressed latent space, enabling faster image generation with high-quality results. Notably, it is an open-source model, allowing users to fine-tune and adapt it for specific needs.
How Stable Diffusion Works
Well I guess first I need to clarify that this is not really necessary to know this part as it includes lots of computing and technical knowledge , I spend months to understand that , In fact I suggest to Read Here to learn More as Andrew Explained it way better than I ever can,
however I try to simply explain the process ;
At its core, Stable Diffusion employs a diffusion process. It begins by injecting a certain amount of random noise into the image, which is then progressively denoised over several iterations. In this way, the final image is generated corresponding to the input text (e.g., text-to-image generation) or source image (e.g., image-to-image generation). Lastly, the generated image is aligned with the content described in the prompt using a CLIP model to make sure that the visual output does convey semantics carried within input text.
Noise Injection: Start with random noise.
Diffusion (Denoising): Gradually refine the image by reversing the noise process.
Text Conditioning: The CLIP model processes text prompts to guide the denoising process.
Final Image Generation: After several iterations, the noise is converted into a coherent image that matches the input description.
Source : Medium
Key Features of Stable Diffusion
Text-to-Image Generation: The original application of this model constitutes a text used to generate images (like “a castle in the mountains at sunset”).
Prompt:a "castle in the mountains at sunset" - Created using RealVisXL V5.0 (SD XL) Image-to-Image Generation: It generates another image based on a textual prompt or modifies an existing one. Applications include editing, style transfer, and image refinement (for example, turning sketches into detailed illustrations).
Image Credits: Stability AI Text-to-Video: A more recent development, where the model generates videos based on textual descriptions. This allows for dynamic scene creation and storytelling (e.g., “a spaceship flying through a galaxy at dusk”).
Prompt: The camera directly faces colorful buildings in Burano, Italy. An adorable dalmatian looks through a window on a building on the ground floor. Many people are walking and cycling along the canal streets in front of the buildings - Source: SORA.AI
Image-to-Video: From a given image or a sequence of images, it prepares animations for generating dynamic visual content based on static input.
Inpainting: To make image adjustments, create new prompts corresponding to selected areas of the input, allowing for object removal or background replacement operation.
Image Credit : ikomia.ai Outpainting: It expands the edges of an image to achieve a broader composition of what is already visible, which includes further parts of a scene or a panorama.
Image Credit : YakBen on Reddit ControlNet: An extension that provides additional control over the generation process by incorporating conditioning inputs like pose maps, depth maps, or edge detection, making it possible to control specific aspects of the generated image (e.g., precise character poses, object placement).
Image Credit :stable-diffusion-art.com
Versions of Stable Diffusion
Stable Diffusion v1: The initial version released in 2022, focused on text-to-image generation using latent diffusion.
Stable Diffusion v2: An updated version with improved image quality and additional features like inpainting.
Stable Diffusion v2.1: Refined version with further performance improvements and fine-tuned outputs.
Stable Diffusion XL: A larger and more powerful model offering higher image quality and faster inference.
There are Newer Models that have been Recently released :
Stable Diffusion v3: This version, released recently, comes with major advancements, including:
Improved coherence in images and more precise adherence to prompts.
Better fine-tuning capabilities for creating highly specific outputs, especially for detailed scenes or artistic styles.
Enhanced support for multimodal generation, making it more robust in image-to-image and text-to-video generation.
Stable Diffusion v3.5: A further update to v3, this version includes:
Even higher-quality images, with improvements in text-to-video generation and smoother transitions in image animations.
Improved user control via more intuitive input for pose control and depth mapping.
Optimization for higher resolution generation, allowing for clearer and more detailed outputs, especially for complex scenes.
Applications of Stable Diffusion
Creative Arts: Artists and designers use Stable Diffusion to generate new artwork, concept designs, or illustrations based on textual descriptions or reference images.
Advertising and Marketing: The model helps in creating customized promotional visuals and graphics for marketing campaigns.
Entertainment and Media: Filmmakers and game developers use it to generate concept art, characters, or environments.
Image Editing: Through inpainting and image-to-image generation, Stable Diffusion allows for advanced photo manipulation and style customization.
Video Production: With the addition of text-to-video and image-to-video, it can be used to create animated clips or short video sequences based on prompts, expanding its use in media and entertainment.
The Big Catch
well there is a big catch in regards to use of Stable Diffusion , Since Stable Diffusion is an open-source project, there are significant ethical concerns regarding its use for professional or commercial purposes. The source of the training datasets is often unclear, and models are frequently trained by AI enthusiasts or local artists, which makes it risky to use commercially.
Copyright and Ownership: Techniques like Stable Diffusion are based on the wide range of large datasets obtained from the internet, which may include copyrighted materials, such as works of other artists, photographs, and other artistic works. Any use of any part of the model applied to create derivative works without explicit permission may be a violation of copyright laws even if the model is open-source.
Attribution and Fair Use: Most artists/creators complain that their work was inputs into the training data sets without consultation and attribution. This raises concerns as to whether the AI-generated artwork is within the provisions of "fair use" or it infringes the intellectual property rights of the original creator. Others felt that the author's specific style and techniques were misrepresented.
Licensing Issues: Besides the availability of Stable Diffusion as a freely available open-source, other models and produced outputs could be subject to variations of licensing. Limitations may apply to what kind of terms the model or its results can be put to commercial use, or restrictions may be placed on attribution or payment.
Lack of Transparency and Accountability: Most of the AI models are designed by individuals and not large organizations; due to this reason, there is almost no transparency about the data and techniques used in the training process. There is never a guaranteed way of ensuring that ethical practices are followed. Even if the developers did not have any intentions of using the model for negative purposes, they might still have to bear the liability for any harmful misuse of the model.
Some companies are, therefore, beginning to work on developing guidelines for AI-generated art to make sure that models at least claim to be trained on ethically sourced data and allow artists to opt out. There is also work to produce directly commercially licensed versions with clearer attribution and licensing terms.
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