Img2Img & Inpainting

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Introduction

So you want to become an effortgenner huh? Let's look at some basics again to understand what we are actually doing here.

A diffusion model is trained by adding noise to an image, then to reverse that process. Once a model lands in the grubby hands of a proompter, the model essentially generates a cloud of noise, then attempts to recreate an amalgam image according to whatever said proompter has put into their textbox. We are essentially working with an incredibly convoluted de-noising algorithm.

But here comes the issue, one that over the course of gennerations has had a couple of solutions: How can we steer the model further. Obviously checkpoints and loras exist, essentially focus-trained data to make the AI solve the cloud of noise into a pair of nice fat tits and not whatever Stability Ai considers attractive.
Now what if, what if we could make our own noise cloud? That's essentially the fundamental gimmick of Img2Img/Inpainting.

Img2Img: Understanding noise

Img2Img works by making you provide the initial noise cloud, and then making the AI solve it into an image. This is primarily influenced by the Denoising Strength variable. Doing an Img2Img process with denoise at 0.7 basically means "Hey keep at least 30% of it intact". Here comes the problem that many usually bump into, how does one find the right balance between a denoise value that lets the AI rock but not ruin the image. If you are doing a an Img2Img pass meant to enhance details, a too low value would barely change the image, a value too high would attempt to create something new.

Img2Img to steer a pose.

Meet Sexwolf, my new OC:

Sketch	0.3
0.8	1.0

Holy shiddd sexo. For this I imported a black blank background into the 'sketch' tab of Img2img, which is basically just a really shitty image editor in the WebUI to draw some solid blocks of color on. Alternatively you can (and should) just use a regular image editor to sketch.. As you can see, at low denoise values the AI does not feel confident enough to change anything. 0.8 keeps the general pose, whereas 1.0 ignores it (the prompt does the heavy lifting here, not the sketch). As you can see, Img2Img can be pretty damn useful for steering a general pose you might have. The AI needs shockingly little to understand it.
Take my example with a grain of salt, since it's very uhhh... simple... the AI interprets pretty heavily. Generally speaking if you have more time (and believe me you don't need skill or talent, legit use your mouse and solid colors), do try to make the posing a bit more clear, this was simple for tutorials sake.

Example, Img2Img to swap a species.

Meet Dork Cat, she will be Dork Wolf soon:

I will exchange the [siamese] for [wolf]:

Pretty straight forward results. The higher the denoise, the wolfier she gets as the AI has more power to deviate from the given image, but that power makes it steer from the given pose more and more. Inpainting might be more suited to something like this but it's very fun to play around with on occasion. The more you adjust the prompt the more subtle deviations can occur. We can somewhat relate this to the previous example as well, technically speaking there is no huge difference between providing an already "solved" image with a pose, vs one you have drawn.

Img2Img to upscale.

One fundamental problem that Stable Diffusion has is that it's trained on a base resolution, anything beyond that resolution tends to get garbled. In the Inpainting section I go over 'brain resolution' in more detail, here you just want to understand that models do not like being asked to generate much higher resolutions (or much lower) than they were trained on. This obviously sucks because we kinda want images to be big.

First we need to differentiate two different kinds of upscaling:
Simple Upscalers
This includes Extras Tab Upscalers and Hires.Fix Upscaling Models (these are the same thing). These do NOT use your actual AI model, they are more or less complex algorithms that are meant to stretch images in a smart manner. These do not use a prompt, and toasters can run them.
AI Upscaling
AI upscaling using your model. This is essentially what we want, since the AI can interpret and add meaningful details to images that were not there before. The trick essentially comes down to letting the AI run over a larger image at a lower denoise. Think of it as a "confidence level". At 0.3 denoise it does not feel "confident" enough to change regions drastically, instead just, well, "denoising" the stretched image.

Img2Img upscaling

Generate your image. At a normal standardized resolution, in my case it is a 1152x896 SDXL PonyXL image. Below your generated image click the 🖼 icon, which will send it to img2img. Change sampler, scheduler and steps to what you used in your base image. Now, above the resolution sliders you will find a tab simply named 'resize by', go to that and choose a reasonable resolution scale for now (like 1.5x). The important variable now is the denoising value, depending on how high you are upscaling this needs to be lower, if you are working with a simple image with flat shading you might get away with higher. Your mileage may vary. 0.3 is a very safe choice. Make sure your prompt is the same as your base image, hit generate, wait for it to finish. You should have a bigger image with hopefully higher detailing and no fucky limbs. If not, try lower denoise and make sure your other variables are correct. Sometimes fucky limbs are unavoidable, in those cases you need to inpaint after.

Hires.Fix

Chances are you have already used Img2Img to upscale:

Hires.Fix is a series of tasks that get chained into one function for convenience. The order of operation is as follows:
Basegen - Simple Upscaler - Img2Img upscaling

Generally speaking you would not want to have Hirex.fix enabled all the time. This is something you can enable on a good image candidate to generate a higher resolution version of it, similar to the img2img upscaling method above, just with a little more convenience. The big difference is that it also chains in a simple upscaler. The upscaling model I recommend for general purpose stuff is 4x_NMKD-Siax_200k (Installed in the models -> ESRGAN folder, if the folder does not exist, create it). Here's the kicker, the simple upscalers matter surprisingly little if you are doing AI upscaling after anyway. All those fine details get regenerated anyway. Does it hurt to do a simple upscaler instead of just letting img2img stretch it? Not really. It does add some non significant generation times on top however.
Tip: Hit the✨ below the finished base image and it will do hires.fix with your current settings, skipping the first step where it would generate your image (since you already have just done that. This button might be Forge exclusive, not sure.)

Examples and workflow

Using 0.3 Denoise and for Hires.fix the 4x_NMKD-Siax_200k upscaler.

Initial	Img2Img	Hires.Fix

I specifically chose an image that's a bit noisy and shows off some practical detailing differences. The fingernails are good examples, upscaling did improve those quite significantly. 0.3 Denoise is also a fairly safe value, higher values would add better and more intricate detailing but run the risk of extra limbs and that sorta stuff. I rarely go beyond 0.4 ever, mostly since I still inpaint for detailing. I also want to point out that there's not a huge difference between the img2img and hires.fix versions, and they are mostly differing through simple seed differences and by chance.
The general workflow for upscaling goes something like this:
Generate candidates - When you find one you tap the ✨to hires.fix - You now have a larger pic that you can jack off to or inpaint or whatever

Appendum

Latent Upscaling
Using Latent as your upscaling model works slightly different. Here you need significantly higher denoise values, around the 0.7 mark. I have somewhat of an idea on why and how it works but I'm not gonna attempt explaining it, I don't use this much. Just know it exists and is one of the popular "alternatives" to the other simple upscalers.

UltimateSDUpscale
An extension that lets you overcome VRAM bottlenecks by chunking parts of the image, and then automatically upscale those and piece them back together. There used to be a LARGE section on this in this guide, I removed it because it is very difficult to get consistent results from it. It's slow, it's clunky. Technically it can create very large very detailed images, but in 99% of cases it needs manual cleanup through inpainting and image editing, so for the sake of this guide I removed it for now.

Inpainting V2

Summary

Inpainting is perhaps the most powerful tool in a genners arsenal. Not only can you fix mistakes, you can also direct entire portions of an image to regenerate, and it will blend regions seamlessly (if done properly it can also enhance details quite drastically in certain prompts.)
There's many ways to inpainting. There's a Krita plugin that merges inpaint functionality into the popular Image editor/drawing software directly, which is convenient especially for people who already have a working ComfyUI workflow.
I will focus on my way, which is to just use the Forge WebUI inpainting. It's not the prettiest, sometimes it's a bit jank, but it does the job well, measured in real time and convenience.

On different models
PonyXL is pretty damn smart. Therefore it's also damn smart during inpainting.
If you are on CompassMix, switch to regular SeaArt during inpainting.
SD1.5 model mileage varies, I don't have enough experience.

The old inpaint guide can be found here InpaintV1

Dorkcat V2

For this version of the guide I used loras in the basegen. These are not required, I merely wanted higher quality examples.

Fundamental Terms

Context&Padding
The first thing to understand about inpainting is "Context". When you Inpaint you basically just do Img2Img but in a partial area of the image. In Img2Img the entire image becomes the "Context", in masked inpainting you specify an area to be re-generated by drawing a mask, then an area around it will additionally be presented to the AI depending on the "Padding" value. This is very important, as the AI needs to know what it's looking at, since it only gets presented with a partial version of the full image.

Let me illustrate:

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+ (5 fingers),
- Masked content: Original
- Inpaint Area: Only Masked
- Padding: 0/256
- Denoise: 0.65

In this example I want to change her hand to have 5 fingers instead of the mangled 4 she has. You can observe the total context area in the generation preview once you start generating.

Here's the generation at 0 "masked padding".

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- Padding: 0

To the AI, it just saw a brown vaguely hand shaped object. Now it has a prompt that talks about a siamese dorky librarian cat, and what do we get? A malignant furby growing out of her arm. The reason for this is the AI tries to 'solve' the image like it would any other.
So why don't we just reduce the prompt to just talk about (hand, 5 fingers)? Well, similar problems persist. The AI lacks context even here. Which way is the hand facing? Is that even an arm? What tends to happen is you may end up with the hand you asked for, but it doesn't go nearly the right way, or look feline like we wanted.

If you see the term Pseudolimb used, this is what is meant. I prefer the term over hallucination because it has a more specific meaning. Whenever the AI does not understand what it's looking at and instead tries to place "copies" of the main gen in there, that's a Pseudolimb.

Here's what happens if we put the "masked padding" value to the maximum of 256.

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- Padding: 256

As you can see in the preview, the AI can see part of her face, which is usually a very strong reference point for the AI to know what's going on. The more context is given the more confident the AI is in piecing things together, correctly angling the hand.

Looking much better already! It even placed a little heart awwww (this is because of the <3 prompt, definitely something that should get dropped from the prompt).
So why don't we just keep the padding maxed like this, always. Well,

Brain Resolution
For lack of a better term there is something I call 'Brain Resolution'. AI models are trained on specific resolutions. SD1.5 models around the 512-768 range, and SDXL model like PonyXL here on 1024. When we do masked inpainting we already saw how only a fraction of the total image gets presented to the AI, it then focuses all that brainpower on that section, and tries to solve it.
The reason why "AI is bad at hands" is partially related to this. Fine details that require a ton of 'brainpower' like correctly doing hands, (after all, this is a classic problem even for real artists) tend to not get enough love. When we now focus on only the hand (and the padded area around it) the AI can really 'concentrate' on doing things correctly... and detailed.
The reason we might not want to crank our padding up very high is because, if the AI hyperfocuses on an area, it also tends to enhance details. In the case of the hand, there might be a sweetspot padding value where the AI 'understands' what it's supposed to regenerate, making a better hand at high detail.
Depending on the actual image resolution you are working with this tends to also manifest in different padding values, and wildly different levels of detail.

Denoise
Denoise is the second value we mostly play around with. Denoise might be familiar to you if you used Hirezfix before, it works much the same way. The denoise value basically denotes how much the AI should keep the masked area intact. Ways you can look at it are, "0.5 denoise means I want at least 50% of my initial image to be similar!" or "My denoise of 0.75 means you should be confident in making something new!".
Generally speaking the denoise works in tandem with padding, this means that if you were to choose a lower denoise, you also often require less padding.
What value of denoise you choose is primarily dependant on what your change is. Want to blend in an area? 0.3. Want to enhance details? 0.45. Want to generate something new? 0.75. These values are some I use as baselines, as always you will have to go up and down depending on what you are working on.

Settings

Important Settings
Start by changing all the settings as per the image. If something is not mentioned either leave it as is or use common sense. Most of the options have hover tooltips.

Mask Blur: You can keep this at 4. Its like feathering in an image editor, it blends the masked area smoothly with the surroundings. Too little and you get strong seams, too much and it gets very blurry.

Masked Content: "Fill" will fill the masked area with a solid color like using a paintbucket tool in an image editor. Generally speaking you would use a high denoise with this of around 0.75 so the AI solves that solid chunk into something usable. This can be used to more forcefully tell the AI to regenerate an area, but I generally prefer to just nudge via an image editor like Gimp that way, as you get more control over rough drawings.
"Original" takes your current image, use this 99% of the time. You can ignore the other 2 options.

Inpaint Area: IMPORTANT!!! ALWAYS ON ONLY MASKED; I REPEAT; ALWAYS ONLY MASKED. This is the entire reason we are here. If you were to do 'whole picture' you would ignore the masked padding value and send the entire image as context. This is a mistake many still make. If you were to have a hires image thats 2000x2000 you would use your 1024x1024 brain resolution on the whole canvas, blurring the shit out of it.

Sampler: Short answer, use the same sampler you generated your image with. Long answer? DDIM is a sampler that's specifically tailored towards inpainting, making your inpaint behave closer to using a dedicated inpaint model. I like DDIM a lot, it uses the surrounding area smarter in influencing the masked it's solving. Some things make it more awkward, I recommend experimenting with it once you are a bit more comfortable with inpainting.

Soft Inpainting: Normally the masked area is a 1bit hard mask. Either it's masked or it's not. Soft inpainting gives the mask more nuance, being stronger in the center and softer on the edges. Some artstyles benefit more from this, some don't. I don't use it much because the gens take longer. Probably a good feature. Appendum 04/08/24: This was apparently a bit buggy in past Forge versions, it currently works as intended in the reForge branch. It's quite decent, I personally wouldn't use it all the time due to the generation overhead, but it's definitely something that can be enabled, for example to smooth out transitions between inpainted regions.

Resolution: IMPORTANT!!! ALWAYS ON MODEL BASERES; I REPEAT; ALWAYS ON MODEL BASE RES. For SDXL like in this example we use 1024x1024. If you are on SD1.5 then check your models base res, most good models work at 768x768. I talked about 'Brain Resolution' earlier. A common mistake people make is try to match the masked area with an approximation of it's real resolution here. The masked area + context gets treated as a 1024x1024 image, yes, but it then gets scaled and pasted onto the original image. The AI cannot work on higher resolutions, or lower for that matter, very well.

Preparing your prompt

At this point I want to give a few hints and tips as to how you want to structure your prompts during inpainting. As you might already know, the hard cap for Stable Diffusion is actually 75 tokens. You can view how many tokens are you are already using in the WebUI in the top right of the prompt window. Back in the day, anything above 75 would just be ignored, however as things developed, the 'hack' is to instead separate tokens into blocks, this happens automatically. For example, once you breach 76 tokens you will see it switches to 76/150, which means 2 blocks. The BREAK keyword forces the current block to end prematurely. This is also why you see more drastic changes after breaching a threshold. A lot of the finickiness of working around the blocks is done by just ordering and formatting your base prompt properly. Important tags go first.
Here's what you should do once you send your image to inpainting:

Examples

Detailing Parts: A better Adetailer

Adetailer is simply a facial recognition algorithm that runs at the end of your generation, then it draws a mask around the face, then goes over it at a low denoise. If you think "Damn, that sounds just like inpainting" then yes, that's all it is. Ditching Adetailer to instead manually touch up your faces is not only faster but it also gives you much more control about the quality of the end product.

We can use high-ish denoise coupled with low-ish padding on the face, because it's an area packed with 'information'. The AI has an easy time recognizing it even with a smaller amount of context.

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- Padding: 32
- Denoise: 0.55

Initial	Mask	Final

A good indicator of quality here are the teeth. What makes this more convenient over Adetailer too is that you can just gen again if you don't like the result. Especially at higher denoises when you want to change more than just add detailing, you can roll the gacha.
As always you can adjust your prompt to reflect changes here. Want her eyes to be open? Swap (eyes closed), with (green eyes, looking at viewer). For changes like that you are looking at the 0.55-0.65 denoise range at least.

Changing Parts: A better controlnet

Changing major parts requires a bit of practice, but isn't actually too hard.
First of all you, need at least around ~0.7 denoise for major regions to start changing. For major regions I also recommend starting at the maximum padding.
Importantly, you want to mask generously. In this example I want to make her spread her legs (😳) and show her panties (😳) because currently her foot is vanishing into the void. This means I not only need to mask where her leg currently is, but also where I want her leg to end up.

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+(spread legs, panties),
- Padding: 256
- Denoise: 0.75

Mask	Final

Using high denoise values tends to result in seams. You can kinda see a seam between the leg and tail, where the Ai "ran out of space".
I personally like to just go over on the result again, on a lower padding and denoise.

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+(spread legs, panties),
- Padding: 176
- Denoise: 0.3

Mask	Final

You can even use the mask you used prior for this, and extend it out a bit over the seams. In this example I just drew a new one over the leg.

Tricks

Use image editors

Using a regular image editor like Gimp or Krita helps A TON. The "fill" option for inpaint content is somewhat flawed, if I suddenly wanted a horsecock in my scene I would simple draw a fat black bar somewhere, then change the prompt for (horsecock:1.3), and mask over it at 0.75 denoise and maximum padding. You can also move bodyparts easily, then blend them over with a 0.3 denoise mask. It's INCREDIBLY convenient.
I'm not gonna go into detail on how to operate them, but the convenience of a regular ass image editor coupled with an inpaint pass to blend things together afterwards is VERY strong.

Direct context drawing: Fixing a hand far away from the main body

A very simple trick actually, but one that's incredibly useful. When setting the masked padding value the masked area's context expands around it in all 4 directions, yet some areas are more useful to include.

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+ (5 fingers),
- Padding: 16

In this example I drew the same mask over the hand, but with a low padding, then draw a tiny area mask under her ear. That dot can be smaller too if you wish, I recommend placing it in an area with a flat color. This will draw the context to be from your main mask, to the other region you specified. This makes it very easy to draw direct context areas around the key areas you actually need, ensuring you use JUST enough padding.

As you can see unlike our prior example we avoid all the water areas to the left which are irrelevant for the hand to 'know' where it has to go. You could technically do most of your context areas this way, these days I find myself doing it more and more because it significantly reduces the need to eyeball padding values. Other frontends also let you draw context boxes more directly. It's one area where the WebUI is unfortunately a bit jank.
This technique is especially useful for bodyparts that are far away from the main body

Prompt adjustment for the lazy

A trick I sometimes use to adjust my prompt, is to wrap the main body in a (:0.9) bracket, and then put whatever I'm working with at the top. That way the rest is still there for the AI to adjust towards, but it focuses directly on what I'm working on. Remember, a lot of tags have bleedover. For example, if you were to prompt (kimono), you are more likely to end up with Japanese backgrounds. Removing parts of your prompt that aren't "relevant" to your current mask isn't actually what you want to do in 99% of cases.

Plan ahead in your basegen

There are some prompts that create problems for inpainting down the line. (musk, steam) for some sniffclouds might look decent in your basegen, however they blend terribly during inpainting as they are essentially just noise. If you plan to inpaint anyway, consider dropping those prompts from your rawgen, then add them back in when you for example detail inpaint the crotch area.
You can also just leave certain specific detailing for later. (camel toe) could be added later during inpainting, unclogging your base prompt a little.

Lama inpainting: Easily remove watermarks and objects

Lama Inpainting adds an additional option to the (fill) and (original) content selection. You can read the documentation, in short it's a controlnet that is specialized in removing content. It works very very well, not just for removing watermarks, but also for redundant objects.
You use it the exact same as you would regular masked inpainting.

FAQ

What's a "Pseudolimb" or "Hallucination"? / "I am getting random limbs everywhere!"

• Pseudolimbs is what I call extra limbs, faces, or entire characters inside of an image. This can occur when A: You are generating an image at a much higher resolution than the model is trained for, or B: When you regenerate parts of the image at high denoising strengths (Inpainting/UltimateSDUpscale). Always make sure to match your models resolution during masked inpainting. Pseudolimbs are a side effect of your prompting and reducing the generation area. If the AI reads "Draw me a dorky cat" and gets a small quadrant to work with, it's gonna try to place one in there. The reason we don't just use blank prompts is that it still carries a lot of implicit data. If I fix a hand and remove everything but the prompts directly related to the cat and her hands, then it might forget other things. Could be gloves, it could also just imagine a different fur color. Plus it's also simply a convenience thing. Your mileage may vary. I mostly use the trick in "Prompt adjustment for the lazy" currently.

What about Kohya HRfix?

• I played with it a bunch, it tends to create artifacts, but of a different sort than the other alternatives. This is a good option if you are really strained for VRam, or your card gens super slowly. Try to just use the default values, occasionally you have to crank the block number to 4 if the image starts becoming noisy. I found that 1380x1280 or so was very safe for PDXL.

Does sampler choice matter?

• For SDXL I primarily use DPM++ 2M Karras these days. DDIM is a sampler that's specifically suited for inpainting, as it's more sensitive to the surrounding area. It's very good, and only rarely will run into issues. Say a black furred cat touches a white furred cat, if the context isn't sufficient it might take the black cats fur color and try to match your masked area over the white one. However it can usually be wrangled quite nicely if you do things correctly already.

My inpainted area looks stylistically different from the rest!

• Often a prompt issue. A lot of prompts carry more information than one might think. Dork cat wears round glasses in a medieval fantasy world. If I remove the glasses she is less likely to generate in cities that look like universities. If I attempt to refine a background bush and remove all of the character prompting then the AI might get lost. This is why I rarely actually adjust the prompt during Inpainting unless I have to, often opting to just give it a lower weight. If you are worried about seams, try another large mask and run 0.3 denoise over it.

My generated thing is cut off!

• Masked inpainting only lets the AI work in the given area. It will take the surrounding area into account, but it may not generate outside of it. This means that for example, if you try to correct a leg, the leg might cut off at the end when it reaches the mask edge. This can commonly happen with more aggressive inpaint settings such as high denoise or using "fill". You can usually fix this with a second inpaint pass, over the seam area, with large padding and low denoise.

My inpainted area has smudgy edges or doesn't blend in properly!

• Consider doing another pass, send it back to inpaint and mask generously around it and re-gen at 0.3 denoise. This is useful in general to smooth out edges. Preventing this is not easy, it's somewhat inevitable the higher you go on the denoise.

What about the "Refiner" option in the webUI?

• The refiner is literally just an Img2Img swap after a specific step. What makes it convenient is it loads models into your ram at the same time, so swapping and doing it's refining pass is fast and automatic. Unfortunately the vram/ram requirements to have 2 models loaded are too significant for most mortal men, so I never used it. But just running an Img2Img pass on a second model does give similar results.

Adetailer

• Adetailer is a facial recognition Algorithm that will do an Img2Img pass at the end of your base generation. Inpainting manually will do the same and often better. Unless you need the process automated for peak sloppa throughput you should be inpainting.

Some common mistakes.

• Forgetting to swap the resolution during masked inpainting back to the models default. Hitting "Send to Inpainting" below the preview also sets it to the image res, which is often higher. Consider setting "Send size when sending prompt or image to another interface [Off]" in the settings.
• Not using a proper prompt during Img2Img or Inpainting, or not using a prompt at all. I see the latter one a lot.
• Not being on the correct settings. Most commonly the masked area toggle.