This commit does several things that improve the customizability of the CLI `outcrop` command:
1. When outcropping an image you can now add a `--new_prompt` option, to specify a new prompt to be applied to the outpainted region instead of the prompt used to generate the image.
2. Similarly you can provide a new seed using `--seed` (or `-S`). A seed less than zero will pick one randomly.
3. The metadata written into the outcropped file is now more informative about what was previously stored.
4. This PR also fixes the crash that happened when trying to outcrop an image that does not contain InvokeAI metadata.
Other changes:
- add error checking suggested by @Kyle0654
- add special case in invoke.py to allow -1 to be passed as seed.
This now only occurs for postprocessing commands. Previously, -1
caused previous seed to be used, and this still applies to generate
operations.
- Works best with runwayML inpainting model
- Numerous code changes required to propagate seed to final metadata.
Original code predicated on the image being generated within InvokeAI.
This was a difficult merge because both PR #1108 and #1243 made
changes to obscure parts of the diffusion code.
- prompt weighting, merging and cross-attention working
- cross-attention does not work with runwayML inpainting
model, but weighting and merging are tested and working
- CLI command parsing code rewritten in order to get embedded
quotes right
- --hires now works with runwayML inpainting
- --embiggen does not work with runwayML and will give an error
- Added an --invert option to invert masks applied to inpainting
- Updated documentation
- The plms sampler now works with custom inpainting model
- Quashed bug that was causing generation on normal models to fail (oops!)
- Can now generate non-square images with custom inpainting model
Credits for advice and assistance during porting:
@any-winter-4079 (http://github.com/any-winter-4079)
@db3000 (Danny Beer http://github.com/db3000)
This is still a work in progress but seems functional. It supports
inpainting, txt2img and img2img on the ddim and k* samplers (plms
still needs work, but I know what to do).
To test this, get the file `sd-v1-5-inpainting.ckpt' from
https://huggingface.co/runwayml/stable-diffusion-inpainting and place it
at `models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt`
Launch invoke.py with --model inpainting-1.5 and proceed as usual.
Caveats:
1. The inpainting model takes about 800 Mb more memory than the standard
1.5 model. This model will not work on 4 GB cards.
2. The inpainting model is temperamental. It wants you to describe the
entire scene and not just the masked area to replace. So if you want
to replace the parrot on a man's shoulder with a crow, the prompt
"crow" may fail. Try "man with a crow on shoulder" instead. The
symptom of a failed inpainting is that the area will be erased and
replaced with background.
3. This has not been tested well. Please report bugs.
- This is a merge of the final version of PR #1218 "Inpainting
Improvements"
Various merge conflicts made it easier to commit directly.
Author: Kyle0654
Co-Author: lstein
- This is a merge of the final version of PR #1218 "Inpainting
Improvements"
Various merge conflicts made it easier to commit directly.
Author: Kyle0654
Co-Author: lstein
Now you can activate the Hugging Face `diffusers` library safety check
for NSFW and other potentially disturbing imagery.
To turn on the safety check, pass --safety_checker at the command
line. For developers, the flag is `safety_checker=True` passed to
ldm.generate.Generate(). Once the safety checker is turned on, it
cannot be turned off unless you reinitialize a new Generate object.
When the safety checker is active, suspect images will be blurred and
a warning icon is added. There is also a warning message printed in
the CLI, but it can be a little hard to see because of its positioning
in the output stream.
There is a slight but noticeable delay when the safety checker runs.
Note that invisible watermarking is *not* currently implemented. The
watermark code distributed by the CompViz distribution uses a library
that does not seem to be able to retrieve the watermarks it creates,
and it does not appear that Hugging Face `diffusers` or other SD
distributions are doing any watermarking.
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.
- 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.
* 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>
