Benjamin-paine

stable-diffusion-v1-5

benjamin-paine

48

Stable Diffusion is a latent text-to-image diffusion model developed by Robin Rombach and Patrick Esser that is capable of generating photo-realistic images from any text input. The Stable-Diffusion-v1-5 checkpoint was initialized from the Stable-Diffusion-v1-2 model and fine-tuned for 595k steps on the "laion-aesthetics v2 5+" dataset with 10% text-conditioning dropout to improve classifier-free guidance sampling. This model can be used with both the Diffusers library and the RunwayML GitHub repository. Model inputs and outputs Stable Diffusion is a diffusion-based text-to-image generation model. It takes a text prompt as input and outputs a corresponding image. Inputs Text prompt**: A natural language description of the desired image Outputs Image**: A synthesized image matching the input text prompt Capabilities Stable Diffusion can generate a wide variety of photo-realistic images from any text prompt, including scenes, objects, and even abstract concepts. For example, it can create images of "an astronaut riding a horse on Mars" or "a colorful abstract painting of a dream landscape". The model has been fine-tuned to improve image quality and handling of difficult prompts. What can I use it for? The primary intended use of Stable Diffusion is for research purposes, such as safely deploying models with potential to generate harmful content, understanding model biases, and exploring applications in areas like art and education. However, it could also be used to create custom images for design, illustration, or creative projects. The RunwayML repository provides more detailed instructions and examples for using the model. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism, even for complex or unusual prompts. You could try challenging the model with prompts that combine multiple concepts or elements, like "a robot unicorn flying over a futuristic city at night". Experimenting with different prompt styles, lengths, and keywords can also yield interesting and unexpected results.

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