Support for Lora is built-in into the Web UI, but there is an extension with original implementation by kohyaa-ss.Ĭurrently, Lora networks for Stable Diffusion 2.0+ models are not supported by Web UI. A good way to train Lora is to use kohya-ss.
Long explanation: Textual Inversion LoraĪ method to fine tune weights for CLIP and Unet, the language model and the actual image de-noiser used by Stable Diffusion, published in 2021. Extra networkĪ method to fine tune weights for a token in CLIP, the language model used by Stable Diffusion, from summer 2021.
Clicking the card adds the model to prompt, where it will affect generation. It unifies multiple extra ways to extend your generation into one UI.Įxtra networks provides a set of cards, each corresponding to a file with a part of model you either train or obtain from somewhere. To reproduce results of the original repo, use denoising of 1.0, Euler a sampler, and edit the config in configs/instruct-pix2pix.yaml to say:Ī single button with a picture of a card on it. Most of img2img implementation is by the same person. Previously an extension by a contributor was required to generate pictures: it's no longer required, but should still work. The checkpoint is fully supported in img2img tab. Normally you would do this with denoising strength set to 1.0, since you don't actually want the normal img2img behaviour to have any influence on the generated image.
It works in the same way as the current support for the SD2.0 depth model, in that you run it from the img2img tab, it extracts information from the input image (in this case, CLIP or OpenCLIP embeddings), and feeds those into the model in addition to the text prompt. Support for stable-diffusion-2-1-unclip checkpoints that are used for generating image variations. This is a feature showcase page for Stable Diffusion web UI.Īll examples are non-cherrypicked unless specified otherwise.
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