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* Removed duplicate fix_func for MPS * add support for loading VAE autoencoders 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! * ported code refactor changes from PR #1221 - pass a PIL.Image to img2img and inpaint rather than tensor - To support clipseg, inpaint needs to accept an "L" or "1" format mask. Made the appropriate change. * minor fixes to inpaint code 1. If tensors are passed to inpaint as init_image and/or init_mask, then the post-generation image fixup code will be skipped. 2. Post-generation image fixup will work with either a black and white "L" or "RGB" mask, or an "RGBA" mask. Co-authored-by: wfng92 <43742196+wfng92@users.noreply.github.com> |
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| data | ||
| invoke | ||
| models | ||
| modules | ||
| generate.py | ||
| lr_scheduler.py | ||
| simplet2i.py | ||
| util.py | ||