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To add a VAE autoencoder to an existing model: 1. Download the appropriate autoencoder and put it into models/ldm/stable-diffusion Note that you MUST use a VAE that was written for the original CompViz Stable Diffusion codebase. For v1.4, that would be the file named vae-ft-mse-840000-ema-pruned.ckpt that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original 2. Edit config/models.yaml to contain the following stanza, modifying `weights` and `vae` as required to match the weights and vae model file names. There is no requirement to rename the VAE file. ~~~ stable-diffusion-1.4: weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt description: Stable Diffusion v1.4 config: configs/stable-diffusion/v1-inference.yaml vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512 height: 512 ~~~ 3. Alternatively from within the `invoke.py` CLI, you may use the command `!editmodel stable-diffusion-1.4` to bring up a simple editor that will allow you to add the path to the VAE. 4. If you are just installing InvokeAI for the first time, you can also use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead to create the configuration from scratch. 5. That's it!
22 lines
809 B
YAML
22 lines
809 B
YAML
# This file describes the alternative machine learning models
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# available to the dream script.
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#
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# To add a new model, follow the examples below. Each
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# model requires a model config file, a weights file,
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# and the width and height of the images it
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# was trained on.
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stable-diffusion-1.4:
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config: configs/stable-diffusion/v1-inference.yaml
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weights: models/ldm/stable-diffusion-v1/model.ckpt
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vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
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description: Stable Diffusion inference model version 1.4
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width: 512
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height: 512
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stable-diffusion-1.5:
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config: configs/stable-diffusion/v1-inference.yaml
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weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
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description: Stable Diffusion inference model version 1.5
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width: 512
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height: 512
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