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# User Guide
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This is the user guide for ONNX web, a web GUI for running hardware-accelerated ONNX models.
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This is the user guide for ONNX web, a web GUI for running ONNX models with hardware acceleration on both AMD and Nvidia
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system, with a CPU software fallback.
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The API runs on both Linux and Windows and provides access to the major functionality of diffusers, along with metadata
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about the available models and accelerators, and the output of previous runs. Hardware acceleration is supported on both
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AMD and Nvidia for both Linux and Windows, with a CPU fallback capable of running on laptop-class machines.
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The GUI is hosted on Github Pages and runs in all major browsers, including on mobile devices. It allows you to select
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the model and accelerator being used for each image pipeline. Image parameters are shown for each of the major modes,
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and you can either upload or paint the mask for inpainting and outpainting. The last few output images are shown below
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the image controls, making it easy to refer back to previous parameters or save an image from earlier.
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## Contents
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### ONNX models
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The [ONNX runtime](https://onnxruntime.ai/) is a library for accelerating neural networks and machine learning models,
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using [the ONNX file format](https://onnx.ai/) to share them across different platforms. ONNX web is a server to run
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hardware-accelerated inference using those models and a web client to provide the parameters and view the results.
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Models are split up into three groups:
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1. Diffusion
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1. Stable Diffusion
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2. Knollingcase
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3. OpenJourney
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4. specialized models
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1. general models like [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5)
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2. specialized models like [Knollingcase](https://huggingface.co/Aybeeceedee/knollingcase) or [OpenJourney](https://huggingface.co/prompthero/openjourney)
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2. Upscaling
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1. Real ESRGAN
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1. [Real ESRGAN](https://github.com/xinntao/Real-ESRGAN)
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3. Correction
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1. GFPGAN
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1. [GFPGAN](https://github.com/TencentARC/GFPGAN)
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There are many other models available and specialized variations for anime, TV shows, and all sorts of other styles.
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This selects the scheduler algorithm used to resolve the latent noise into a coherent image.
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See [the scheduler comparison](#scheduler-comparison) for more details.
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#### CFG parameter
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Classifier free guidance. How strictly the model should follow the prompt. Anything from 5 to 15 usually works. More is
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longer to run.
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The Euler Ancestral scheduler can usually produce decent results in 30-45 steps, while some of the others need 80-100 or
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more. Inpainting may need more steps, up to 120 or 150 in some cases.
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more. Inpainting may need more steps, up to 120 or 150 in some cases. Using too many steps can increase the contrast
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of your image too much, almost like a posterize effect.
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#### Seed parameter
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The seed value used for the random number generators.
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The seed value used for the random number generators. This is a lot like the seed in a game like Minecraft and can be
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shared, but producing exactly the same image requires the same model, scheduler, and all of the other parameters as
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well.
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Using the same prompt and seed should produce similar images. Using the same prompt, seed, steps, and CFG should
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produce exactly the same image.
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You can use the same prompt and seed, while varying the steps and CFG, to produce similar images with small variations.
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Using -1 will generate a new seed on the server for each image.
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The input text for your image, things that should be included.
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The [OpenArt Stable Diffusion Prompt Book](https://cdn.openart.ai/assets/Stable%20Diffusion%20Prompt%20Book%20From%20OpenArt%2011-13.pdf)
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has a lot of useful tips on how to build a good prompt. You can include keywords to describe the subject, setting,
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style, and level of detail. Throwing a few extra keywords into the end of the prompt can help add specific details,
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like the color and intensity of the lighting.
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> TODO
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#### Negative prompt parameter
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The opposite of [the prompt parameter](#prompt-parameter), things that should _not_ be included.
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White pixels will be replaced with noise and then regenerated, black pixels will be kept as-is in the output.
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- Gray to black
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- Convert gray parts of the mask to black (keep them)
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- Fill with black
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- Keep all pixels
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- Fill with white
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- Replace all pixels
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- Invert
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- Replace black pixels with white and vice versa
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- If you accidentally painted a good mask in the wrong color, this can save it
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- Gray to black
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- Convert gray parts of the mask to black (keep them)
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- Gray to white
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- Convert gray parts of the mask to white (replace them)
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