Cog wrapper for Realistic Vision v6.0

This is a cog wrapper for Realistic Vision v6.0 for photorealistic image generation. See the original repo, Civit AI model page and Replicate demo for details.

Basic Usage

You will need to have Cog and Docker installed to serve your model as an API. Follow the model pushing guide to push your own fork of the model to Replicate with Cog. To run a prediction:

cog predict -i prompt="An astronaut riding a rainbow unicorn"

To start your server and serve the model as an API:

cog run -p 5000 python -m cog.server.http

The API input arguments are as follows:

• prompt: The input prompt is a text description that guides the image generation process. It should be a detailed and specific description of the desired output image.
• negative_prompt: This parameter allows specifying negative prompts. Negative prompts are terms or descriptions that should be avoided in the generated image, helping to steer the output away from unwanted elements.
• width: This parameter sets the width of the output image.
• height: This parameter sets the height of the output image.
• num_outputs: Specifies the number of images to be generated for a given prompt. This allows for multiple variations of images based on the same input parameters.
• scheduler: The scheduler parameter determines the algorithm used for image generation. Different schedulers can affect the quality and characteristics of the output.
• num_steps: This parameter defines the number of denoising steps in the image generation process.
• guidance_scale: The guidance scale parameter adjusts the influence of the classifier-free guidance in the generation process. Higher values will make the model focus more on the prompt.
• seed: The seed parameter sets a random seed for image generation. A specific seed can be used to reproduce results, or left blank for random generation.

Model Details

Original Model: https://civitai.com/models/4201/realistic-vision-v60-b1

Some important usage tips from the original model page:

• Best performance comes with the scheduler “DPM++ SDE Karras” which is the default value in the API.
• Recommended number of denoising steps are: 10+ with DPM++ SDE Karras scheduler / 20+ with DPM++ 2M SDE scheduler.