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295 lines
12 KiB
Markdown
295 lines
12 KiB
Markdown
---
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title: Inpainting
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---
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# :octicons-paintbrush-16: Inpainting
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## **Creating Transparent Regions for Inpainting**
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Inpainting is really cool. To do it, you start with an initial image
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and use a photoeditor to make one or more regions transparent
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(i.e. they have a "hole" in them). You then provide the path to this
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image at the dream> command line using the `-I` switch. Stable
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Diffusion will only paint within the transparent region.
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There's a catch. In the current implementation, you have to prepare
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the initial image correctly so that the underlying colors are
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preserved under the transparent area. Many imaging editing
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applications will by default erase the color information under the
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transparent pixels and replace them with white or black, which will
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lead to suboptimal inpainting. It often helps to apply incomplete
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transparency, such as any value between 1 and 99%
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You also must take care to export the PNG file in such a way that the
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color information is preserved. There is often an option in the export
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dialog that lets you specify this.
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If your photoeditor is erasing the underlying color information,
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`dream.py` will give you a big fat warning. If you can't find a way to
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coax your photoeditor to retain color values under transparent areas,
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then you can combine the `-I` and `-M` switches to provide both the
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original unedited image and the masked (partially transparent) image:
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```bash
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invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
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```
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If you are using Photoshop to make your transparent masks, here is a
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protocol contributed by III_Communication36 (Discord name):
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Create your alpha channel for mask in photoshop, then run
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image/adjust/threshold on that channel. Export as Save a copy using
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superpng (3rd party free download plugin) making sure alpha channel
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is selected. Then masking works as it should for the img2img
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process 100%. Can feed just one image this way without needing to
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feed the -M mask behind it
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## **Masking using Text**
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You can also create a mask using a text prompt to select the part of
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the image you want to alter, using the <a
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href="https://github.com/timojl/clipseg">clipseg</a> algorithm. This
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works on any image, not just ones generated by InvokeAI.
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The `--text_mask` (short form `-tm`) option takes two arguments. The
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first argument is a text description of the part of the image you wish
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to mask (paint over). If the text description contains a space, you must
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surround it with quotation marks. The optional second argument is the
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minimum threshold for the mask classifier's confidence score, described
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in more detail below.
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To see how this works in practice, here's an image of a still life
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painting that I got off the web.
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<img src="../assets/still-life-scaled.jpg">
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You can selectively mask out the
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orange and replace it with a baseball in this way:
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~~~
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invoke> a baseball -I /path/to/still_life.png -tm orange
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~~~
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<img src="../assets/still-life-inpainted.png">
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The clipseg classifier produces a confidence score for each region it
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identifies. Generally regions that score above 0.5 are reliable, but
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if you are getting too much or too little masking you can adjust the
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threshold down (to get more mask), or up (to get less). In this
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example, by passing `-tm` a higher value, we are insisting on a tigher
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mask. However, if you make it too high, the orange may not be picked
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up at all!
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~~~
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invoke> a baseball -I /path/to/breakfast.png -tm orange 0.6
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~~~
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The `!mask` command may be useful for debugging problems with the
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text2mask feature. The syntax is `!mask /path/to/image.png -tm <text>
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<threshold>`
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It will generate three files:
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- The image with the selected area highlighted.
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- it will be named XXXXX.<imagename>.<prompt>.selected.png
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- The image with the un-selected area highlighted.
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- it will be named XXXXX.<imagename>.<prompt>.deselected.png
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- The image with the selected area converted into a black and white
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image according to the threshold level
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- it will be named XXXXX.<imagename>.<prompt>.masked.png
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The `.masked.png` file can then be directly passed to the `invoke>`
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prompt in the CLI via the `-M` argument. Do not attempt this with
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the `selected.png` or `deselected.png` files, as they contain some
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transparency throughout the image and will not produce the desired
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results.
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Here is an example of how `!mask` works:
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```
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invoke> !mask ./test-pictures/curly.png -tm hair 0.5
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>> generating masks from ./test-pictures/curly.png
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>> Initializing clipseg model for text to mask inference
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Outputs:
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[941.1] outputs/img-samples/000019.curly.hair.deselected.png: !mask ./test-pictures/curly.png -tm hair 0.5
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[941.2] outputs/img-samples/000019.curly.hair.selected.png: !mask ./test-pictures/curly.png -tm hair 0.5
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[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
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```
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**Original image "curly.png"**
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<img src="../assets/outpainting/curly.png">
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**000019.curly.hair.selected.png**
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<img src="../assets/inpainting/000019.curly.hair.selected.png">
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**000019.curly.hair.deselected.png**
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<img src="../assets/inpainting/000019.curly.hair.deselected.png">
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**000019.curly.hair.masked.png**
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<img src="../assets/inpainting/000019.curly.hair.masked.png">
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It looks like we selected the hair pretty well at the 0.5 threshold
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(which is the default, so we didn't actually have to specify it), so
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let's have some fun:
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```
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invoke> medusa with cobras -I ./test-pictures/curly.png -M 000019.curly.hair.masked.png -C20
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>> loaded input image of size 512x512 from ./test-pictures/curly.png
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...
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Outputs:
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[946] outputs/img-samples/000024.801380492.png: "medusa with cobras" -s 50 -S 801380492 -W 512 -H 512 -C 20.0 -I ./test-pictures/curly.png -A k_lms -f 0.75
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```
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<img src="../assets/inpainting/000024.801380492.png">
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You can also skip the `!mask` creation step and just select the masked
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region directly:
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```
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invoke> medusa with cobras -I ./test-pictures/curly.png -tm hair -C20
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```
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## Using the RunwayML inpainting model
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The [RunwayML Inpainting Model
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v1.5](https://huggingface.co/runwayml/stable-diffusion-inpainting) is
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a specialized version of [Stable Diffusion
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v1.5](https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5)
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that contains extra channels specifically designed to enhance
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inpainting and outpainting. While it can do regular `txt2img` and
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`img2img`, it really shines when filling in missing regions. It has an
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almost uncanny ability to blend the new regions with existing ones in
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a semantically coherent way.
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To install the inpainting model, follow the
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[instructions](INSTALLING-MODELS.md) for installing a new model. You
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may use either the CLI (`invoke.py` script) or directly edit the
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`configs/models.yaml` configuration file to do this. The main thing to
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watch out for is that the the model `config` option must be set up to
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use `v1-inpainting-inference.yaml` rather than the `v1-inference.yaml`
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file that is used by Stable Diffusion 1.4 and 1.5.
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After installation, your `models.yaml` should contain an entry that
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looks like this one:
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inpainting-1.5:
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weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
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description: SD inpainting v1.5
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config: configs/stable-diffusion/v1-inpainting-inference.yaml
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vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
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width: 512
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height: 512
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As shown in the example, you may include a VAE fine-tuning weights
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file as well. This is strongly recommended.
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To use the custom inpainting model, launch `invoke.py` with the
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argument `--model inpainting-1.5` or alternatively from within the
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script use the `!switch inpainting-1.5` command to load and switch to
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the inpainting model.
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You can now do inpainting and outpainting exactly as described above,
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but there will (likely) be a noticeable improvement in
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coherence. Txt2img and Img2img will work as well.
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There are a few caveats to be aware of:
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1. The inpainting model is larger than the standard model, and will
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use nearly 4 GB of GPU VRAM. This makes it unlikely to run on
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a 4 GB graphics card.
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2. When operating in Img2img mode, the inpainting model is much less
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steerable than the standard model. It is great for making small
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changes, such as changing the pattern of a fabric, or slightly
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changing a subject's expression or hair, but the model will
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resist making the dramatic alterations that the standard
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model lets you do.
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3. While the `--hires` option works fine with the inpainting model,
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some special features, such as `--embiggen` are disabled.
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4. Prompt weighting (`banana++ sushi`) and merging work well with
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the inpainting model, but prompt swapping (a ("fluffy cat").swap("smiling dog") eating a hotdog`)
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will not have any effect due to the way the model is set up.
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You may use text masking (with `-tm thing-to-mask`) as an
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effective replacement.
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5. The model tends to oversharpen image if you use high step or CFG
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values. If you need to do large steps, use the standard model.
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6. The `--strength` (`-f`) option has no effect on the inpainting
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model due to its fundamental differences with the standard
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model. It will always take the full number of steps you specify.
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## Troubleshooting
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Here are some troubleshooting tips for inpainting and outpainting.
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## Inpainting is not changing the masked region enough!
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One of the things to understand about how inpainting works is that it
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is equivalent to running img2img on just the masked (transparent)
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area. img2img builds on top of the existing image data, and therefore
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will attempt to preserve colors, shapes and textures to the best of
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its ability. Unfortunately this means that if you want to make a
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dramatic change in the inpainted region, for example replacing a red
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wall with a blue one, the algorithm will fight you.
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You have a couple of options. The first is to increase the values of
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the requested steps (`-sXXX`), strength (`-f0.XX`), and/or
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condition-free guidance (`-CXX.X`). If this is not working for you, a
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more extreme step is to provide the `--inpaint_replace 0.X` (`-r0.X`)
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option. This value ranges from 0.0 to 1.0. The higher it is the less
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attention the algorithm will pay to the data underneath the masked
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region. At high values this will enable you to replace colored regions
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entirely, but beware that the masked region mayl not blend in with the
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surrounding unmasked regions as well.
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---
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## Recipe for GIMP
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[GIMP](https://www.gimp.org/) is a popular Linux photoediting tool.
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1. Open image in GIMP.
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2. Layer->Transparency->Add Alpha Channel
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3. Use lasso tool to select region to mask
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4. Choose Select -> Float to create a floating selection
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5. Open the Layers toolbar (^L) and select "Floating Selection"
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6. Set opacity to a value between 0% and 99%
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7. Export as PNG
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8. In the export dialogue, Make sure the "Save colour values from
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transparent pixels" checkbox is selected.
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---
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## Recipe for Adobe Photoshop
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1. Open image in Photoshop
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<div align="center" markdown></div>
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2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
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<div align="center" markdown></div>
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3. Because we'll be applying a mask over the area we want to preserve, you should now select the inverse by using the ++shift+ctrl+i++ shortcut, or right clicking and using the "Select Inverse" option.
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4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the underlying image, or your inpainting results will be dramatically impacted.
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<div align="center" markdown></div>
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5. Make sure to hide any background layers that are present. You should see the mask applied to your image layer, and the image on your canvas should display the checkered background.
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<div align="center" markdown></div>
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6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
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<div align="center" markdown></div>
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7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively invoke. Lookin' good!
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<div align="center" markdown></div>
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8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.
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