Stable Diffusion is an AI art generator that easily transforms text into stunning visuals. It offers various plans, from a free tier with limited daily generations to paid Pro and Max plans with increased generation limits, faster processing, and more concurrent jobs. Both Stable Diffusion and its advanced version, SDXL, are available, focusing on high-resolution outputs for professional projects. The tool is open-source, allowing local installation and offline usage, or online access via the website. It features image editing capabilities (inpainting and outpainting) and supports the use of Loras and embeddings for enhanced style and detail control. Note that while it offers commercial licenses, some generated content might reflect biases in its training data.
• image generation
• commercial license
• image editing
• inpainting
• high-resolution image generation
• outpainting
• text to image
• customizable styles
'Stable difusion' and 'Stable difussion' are typographical errors of 'Stable Diffusion.' There are no separate platforms with these names. 'Stable Diffusion' is the correct term for the AI art generation tool known for transforming text into images. These misspellings are common but refer to the same technology.
Stability Diffusion XL is an advanced version of Stable Diffusion, specialized in creating high-resolution images. While Stable Diffusion focuses on AI-generated art, Stability Diffusion XL enhances this with greater detail and clarity, ideal for high-quality, professional projects.
Stable Diffusion is an open-source text-to-image generation tool based on diffusion models, developed by CompVis group at Ludwig Maximilian University of Munich and Runway ML, with compute support from Stability AI. It can generate high-quality images from text descriptions and can also perform image inpainting, outpainting and text-guided image-to-image translation. Stable Diffusion has open-sourced its code, pretrained models and license, allowing users to run it on a single GPU. This makes it the first open-source deep text-to-image model that can run locally on user devices.
Stable Diffusion uses a diffusion model architecture called Latent Diffusion Models (LDM). It consists of 3 components: a variational autoencoder (VAE), a U-Net and an optional text encoder. The VAE compresses the image from pixel space to a smaller latent space, capturing more fundamental semantic information. Gaussian noise is iteratively added to the compressed latent during forward diffusion. The U-Net block (consisting of a ResNet backbone) denoises the output from forward diffusion backwards to obtain a latent representation. Finally, the VAE decoder generates the final image by converting the representation back to pixel space. The text description is exposed to the denoising U-Nets via a cross-attention mechanism to guide image generation.
Stable Diffusion was trained on the LAION-5B dataset, which contains image-text pairs scraped from Common Crawl. The data was classified by language and filtered into subsets with higher resolution, lower chances of watermarks and higher predicted "aesthetic" scores. The last few rounds of training also dropped 10% of text conditioning to improve Classifier-Free Diffusion Guidance.
Stable Diffusion can generate new images from scratch based on text prompts, redraw existing images to incorporate new elements described in text, and modify existing images via inpainting and outpainting. It also supports using "ControlNet" to change image style and color while preserving geometric structure. Face swapping is also possible. All these provide great creative freedom to users.
Users can download the source code to set up Stable Diffusion locally, or access its API through the official Stable Diffusion website Dream Studio. Dream Studio provides a simple and intuitive interface and various setting tools. Users can also access Stable Diffusion API through third-party sites like Hugging Face and Civitai, which provide various Stable Diffusion models for different image styles.
A major limitation of Stable Diffusion is the bias in its training data, which is predominantly from English webpages. This leads to results biased towards Western culture. It also struggles with generating human limbs and faces. Some users also reported Stable Diffusion 2 performs worse than Stable Diffusion 1 series in depicting celebrities and artistic styles. However, users can expand model capabilities via fine-tuning. In summary, Stable Diffusion is a powerful and ever-improving open-source deep learning text-to-image model that provides great creative freedom to users. But we should also be mindful of potential biases from the training data and take responsibility for the content generated when using it.
• 10 generations per day (Valid for 7 days)
• Normal processing
• 1 running jobs at once
• No watermark
• Commercial license
• Images are private
• All free features
• 1000 fast generations per month
• unlimited normal processing generations
• 2 running jobs at once
• No watermark
• Commercial license
• Images are private
• All Pro features
• 3000 fast generations per month
• unlimited normal processing generations
• 5 running jobs at once
• No watermark
• Commercial license
• Images are private
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