Yeah, sometimes it does do that when the feet are very close together, in that it would detect both feet as one. Only thing I found that could help combat the inconsistency, is to increase the padding size, such that the other foot is visible in the frame. That, and/or using controlnets in conjunction with that option to keep the seeds the same in the ADetailer settings (never tried it myself, so I can't say how that would work).

On the other hand, and the reason why I didn't train it to always train pairs of feet as one, is because there are instances when that won't necessarily have a desired effect, as is the case with multiple people where advanced prompts might be used. Then again, I only trained it with one variable.

Perhaps down the line, I'll try training it with 2 variables: individual feet, and pairs of feet, such that it recognizes two feet belonging to one person, and individual feet if the person's other foot is out of frame or something.

@MysticDaedra I just realized something. We have the mask preprocessing setting for merging masks. While there's some limitations with that if multiple people are involved, since it'll do every detected feet in on go, if you activate the merge mode for it, it too might be a good solution for getting consistent footwear. I completely forgot about that setting lol

No clue why you're not able to download it; I was able to do so even when logged out. If it's still giving you problems, you can try the link on HuggingFace

As far as I'm aware, Adetailer models only come in .pt format, so I'm not sure how one would go about changing it to safetensors--or if Adetailer would accept that format. As for the training data, I believe it's obsolete; the original model was created because the older stable diffusion models were completely horrible for generating feet that wasn't close-up, thus my training data included bad generations that those models generated, as a means for it to detect (good and bad) feet to improve them.

These days, the XL/Pony models I use tend to be okay with feet in general, so a new dataset might be in order. On top of that, I've been pondering on how to make a segmented model--one that makes a form-fitting mask instead of a box, in order to mitigate the issue of visible mask residue.

In short, my old dataset isn't ideal; need a new one. Also trying to figure out how to make a segmented model (like how there's the person_yolov8n-seg model). I'm open to collaboration in getting that done, but for all intents and purposes I think I'm going to have to start from scratch again, as far as training images go.

@Sven111 No worries, Sven, been busy here too XD
Now, the reason why I say my old dataset is no good, is because I incorporated a lot of my old generations on SD1.5, some of which had eldritch abominations instead of feet--since at the time I made the first and second models, I was doing so to fix horrible feet in the first place. These days, and especially with the newer checkpoints I'm using, it's not so much bad feet as just refining them that I'm concerned about, so a new model to reflect that is definitely something I'm interested in doing. I believe shortly after I came out with the second model, that SD1.5 became much better in general when it came to feet (must have upgraded the diffusers or something, I don't know.)

As for the dataset images themselves, it was varied; full body, lower body, extreme closeups, and some images without feet at all were included, so as to get it to learn that not every image has feet in it. I'd included both anime and realistic images of men and women in different configurations and angles. Socks, high heels, sneakers, barefoot, boots, I included them all, even had some instances of people standing next to unworn shoes to get the model to try to only detect footwear when it was worn by a person. I even had images with no people at all, since it's a common occurrence with some of the hand models to detect things like trees as hands; didn't want that for the foot model.

Now, while I also included images of close-ups of feet, none of the two models seems to have picked up on that too well, though to be fair I believe it's a waste of time to train it on images where it's just a foot or two taking up the whole image, since if you tried to generate such an image with any checkpoint, the feet seems to be good on its own, and doesn't need ADetailer for further refinement. I believe it's the same issue with images with feet being the focus, as by default the feet generated in such images tends to be good by default, and doesn't necessarily warrant ADetailer, as it doesn't always fix things in the way you like; it'd be better to manually edit it if it was missing toes or something, and then manually inpaint it to make it seamlessly blend in. In other words, if the checkpoint generated a foot with 4 toes on an image where the foot is the focus, ADetailer on its own won't necessarily fix that, unless you try something with high denoising strength, but that could go south easily. I suppose the only real way that ADetailer could fix issues with missing toes is if it used a different checkpoint that happens to be very good with feet, though that could go south too if the styles between the two checkpoints are very different.

As for the actual detection stuff, I did it manually for all 1000+ images, since I couldn't figure out the automated detector (I believe it was detecting hands and calling them feet as well). So, I spent a good day just making boxes over feet lol

As stated before, I'm now interested to see if I can't make a segmented model, so that the mask is exactly around the feet and not a big box around it. But first I'm just going to need an updated dataset. I'm sure I can salvage most of my old dataset images, but they're all going to have to be redone in terms of mask labelling.

@Sven111 In short:

All images are useful, whether or not they have feet (or even people) in them. The feet ideally have to be in all different kinds of footwear--not just barefoot. High and low-quality images are welcome. Closeups are welcome, as well as full body images; upper and lower body images are welcome too.

I didn't use SAM because it made things difficult for me at the time, so I manually labelled each of the images myself. To be fair, quite a few images in the dataset had no feet at all, so those ones were left blank, to get the model to learn that sometimes there'll be images without feet.

I wrote a big reply and then I lost it by accidentally closing the tab lol.
Let me make a summary of what I can recall.

Regarding the labels, it works differently for ADetailer models, as the labels are simply the coordinates to the mask. In my case, since I used RectLabel to manually create a box over each foot in each image that contained feet, it automatically generated a .txt file with the coordinates of the vertices of each and every box for a given picture, so for instance a label could look something like this:
0 0.513021 0.953704 0.029948 0.092593

0 0.482422 0.962963 0.032552 0.074074

for one image. This would indicate the first target 0 (which is designated as "foot"), followed by their 4 corners. In the example above is a label for an image with 2 feet.

Getting images of feet is the easy part, and that's what I'm currently working on, to replace the old unusable images (see the image of Ishtar lying in bed in pink pajamas) which were normal to see in SD1.5 image generation back in the day. Since SD in general (and by extension Flux and whatever new model they got out there) do feet decently well, then those old images aren't necessary anymore; I can focus on a model for refining feet and not so much outright correcting abominations.

My main concern is whether or not making boxes over the new dataset with RectLabel or SAM would cut it for a segmented model which uses discreet masks conforming to the exact shape of the target. Since I'm not currently ready to train version 3, I'm not sure if it can still work. Never heard of JoyCaption before. If it's better than SAM at recognizing feet (or if SAM in general is better now than when I used it for feet detection) then I'm all aboard to use either tool to speed up the process--especially if they directly support discreet, form-fitting masks and not just box masks.

@Sven111 Yeah, I don't know anything about that; as far as I'm aware ADetailer currently only deals with .pt models, and I've never heard of a .safetensors ADetailer model before. If they end up changing formats to something else, I could use that I suppose. It's kind of the same way how upscaler models are only .pt/.pth models, it's just how it is currently.

@Sven111Also, for your information, the raw YOLO stuff is coming from here:

YOLO11 🚀 NEW - Ultralytics YOLO Docs
This is the stuff I download (I used version 8 at the time, since that was the latest version) to train on.

In my case, I used yolov8x to train both V1 and V2. I used the X version since it had the best detections (probably was overkill though lol) 

@Sven111 I see. That would explain some things I've seen over the past couple of days, in that all the old default versions of yolo models have been redownloaded automatically. Still, I'm not sure what this all means as far as my models go; if I need to remake them or have to discontinue them or something I'm in the dark about that.

I haven't dealt with ComfyUI in a long time, so I can't be certain why it's not working for you; there are many variables at play there. I have a feeling that what you're experiencing might be related to this post: Why is adetailer in comfyui so much worse than automatic1111? : r/comfyui

@IzayoiTsuki Letting you know, there's now a workflow in the form of the pinned image/image in the model details concerning ComfyUI. It's rudimentary, but it works. Be advised, that since I used my own checkpoint, LimestoneYen_v5, a Hyper model, I have the CFG lowered to 1.5, and the steps lowered to 8. Feel free to adjust the settings and see what works for you. Good hunting~

Honestly, it's not a situation I've encountered with the model at all. If it's a common occurrence, then there's a couple of suggestions I have:

1. Set the Mask Merge Mode to merge (it's set to None by default). By doing this, even if it picks up an unwanted target, it might yield to better consistency with feet in general, though you'll have to play around with other settings such as denoising and resolution to improve results (both the target and the extra stuff that sometimes gets detected) further.

2. Press the skip button when it's about to start on unwanted targets (assuming you haven't used option 1 and merged the masks). It's tedious for sure, but it'll stop it from doing unnecessary things. Also, best way to keep consistent is to go to the Adetailer settings, and under "Set bounding boxes by" check the "Position (left to right)". This ensures that when you see all the bounding boxes, you'll know the order it will take, so for instance if it detects 3 objects and it's a foot, an unwanted target, and another foot from left to right, then you'll know to skip when it starts on the second target.

3. Use manual inpainting in img2img. I know it sucks, but this model is just a tool to detect feet to be automatically inpainted. It cannot beat the human eye in detecting correct targets. Even I, the creator of this model, use manual inpainting more often than not--even with feet, as certain circumstances can and will happen.

Hope that helps~

@HaloSkull Ah, now I see how that could occur. If I ever get around to making v3, I'll be sure to incorporate such images so that it won't falsely detect it as feet. Thanks for the clarity!

@pornthulhu @HaloSkull Alright, I've figured out what I needed to work on a segment-based V3 model, and I've incorporated those positions into the new dataset. It will take some time to finish annotating the dataset, but once that's done, then I can start the training process.

Thank you for your patience~
And do let me know if you see any issue with V3 when it's done, and it'll just be a matter of adding to the existing dataset.

Not sure what you saw, but I'm still working on it. Going to see about preparing the new dataset for training.

Just an update for you, I've recently figured out how to get the SAM-based annotator working, and I've been annotating the new dataset with segmented polygons (as opposed to simple bounding boxes). Even though SAM helps a lot, I still have to edit each polygon in order to make it precise. It will take some time to go through all 600+ images in the dataset, but once that's done, all that's left will be simply training the new V3 model.
Thank you for your patience~

Thanks for the kind words! (^_^)v

I believe this is my most popular model, in terms of likes and downloads, but like you say, since you can't associate it with generated images (outside of directly posting to the model page), the images that people generate using this model won't normally show up here.

I'm actually in the process of annotating a new dataset for a segmented (finally figured it out) with the Segment Anything-based annotator. Once I get that finished, it's just a matter of training V3. This new model will use a mask that conforms to the target--as opposed to the bounding boxes from V1 and V2, so stay tuned! XD

@Mostima If by workflow, you're referring to ComfyUI stuff, then I'm not quite sure what to do there, since I don't deal with ComfyUI these days--in fact even though I have it installed, I can't seem to run it anymore. I believe another user mentioned something about ComfyUI settings to get it to work in the discussion here. I believe they said something around the lines of the threshold being too high by default on ComfyUI.

As far as A1111/Forge goes, I leave the detection threshold at the default value of 0.3, and it generally works. Be aware that version 2.0 is better at detecting feet in general than 1.0 due to a mixup in the training/validation folders.

I'm gonna see about reinstalling CUI from scratch and see what steps I can do to get this model to work there, if that is the issue, and I'll be sure to post such a workflow.

@Mostima Ok, I have created a rudimentary workflow, in the form of the pinned image you see in the gallery below. I hope that helps.

Glad you like it! (^_^)v

I'm actually working on annotating a new dataset now. It's taking longer than I'd like because I'm trying to make a segmented model, so instead of a simple box, it's a unique polygon that matches the target's shape. Additionally, the automatic Segment Anything Model does a bad job generating the polygons, so I have to edit them manually each time. I'm trying to not only create a version 3.0 foot model, but also a hand/face model as well.

Hopefully this new third model will improve on the deficiencies with regard to detection, as it has a completely different dataset. That being said, if you have example images of anything that the current/future model fails to detect, please be sure to submit them to this page's gallery below, and I'll try to include them and similar images into the dataset for future training.

My apologies, it looks like Civitai converted the image into a jpg. Use the Pinned image(s) in the gallery below; That still works.

You're absolutely correct. I'm currently working on a dataset for v3.0 to account for those non-regular poses. It's a bit more complicated in that, unlike the bounding box models of v1.0 and 2.0, v3.0 will be a segmented model, meaning that the mask will conform to the shape of the target. Once I've finished annotating them, then the training process can begin, and before you know it, you'll have an updated model.

That being said, if you have example images with feet that haven't been detected by either v1.0 or 2.0, please post those images to the model page here, so I may see it and add it to the dataset, for there may still be certain angles and poses that I might be overlooking. The more diverse the dataset images are, the better it will be for detecting those unconventional poses.

@eoa42 Thanks for the offer. Right now I'm in the annotation phase; I believe the dataset as it stands is sufficient--based on some very good recommendations from other users. It's taking longer than I'd like, because I'm refining each of the bounding masks manually, since the SAM model does a crude job. It's no longer simple boxes, so it's not something I can do in a single day like V1/2. On top of that, I'm annotating multiple targets--not just feet here, but hands and faces too, so the process is gonna be that much longer.

Once I'm done with the annotations, then the training can begin, which shouldn't be more than a day or two if V2 is a good baseline.

Nevertheless, I'm not a foot person, so there might be some poses/positions/angles I'm overlooking. If you have recommendations of certain poses, feel free to point them out to me. If you have a pose that's unrecognized by V1/V2, then please share that image to the gallery below, and I'll be sure to add it to the dataset (if there isn't already an example similar to it). Doesn't have to be barefoot stuff either; shoes, socks, and boots are very welcome here XD

Once I get the model trained successfully, I'll share how I got it done (got some notes already), so that a potential V4 can be outsourced via a bounty or something.

@reaper557 Thank you for the kind words! I'm quite committed to this particular model that I've halted all work on any new LoRA. I got a checkpoint I'm trying (and failing) to upload (I got really slow upload speeds), but after that, the next thing I intend to upload--as far as models go-- will be V3. Hopefully once I'm done annotating everything (got 1021 images) in this dataset, then the training will be a simple process as before. It's a time-consuming process, to say the very least, but I'm confident that all that work will pay off in the end.

@Karlmeister_AR I see, top view from behind, and side view from above... I don't think I had images like that in the original dataset for V1/V2. I am (slowly but surely) working on a 3rd version of this model; The next model I release will neither be a LoRA or a checkpoint, it will be version 3 of the foot detailer. As such, it needs to be better than V2 and V1. Would you be willing to provide example images to the model page here? I will then add it to the current dataset, and depending on how it is, add additional similar images that I can either find or generate. I am thrilled that V2/V1 has provided such utility to everyone, and wish to build upon it, though it will take some time. Thank you for your patience! (^_^)v

Hey Luxaria, thanks for your feedback! 😊

Yup, I’m still working on it—the next model I release won’t be a LoRA or a checkpoint, but V3.

Hopefully, V3 will improve foot detection from all conceivable angles and configurations. If you’ve got example images where V1/V2 fail—whether it's misidentifying hands, chair legs, tree branches, or anything else that isn't a foot;OR if it's not detecting actual feet at all—please post them on the model’s page. I believe the dataset for V3 is solid, but there might be overlooked cases, so additional images could help fine-tune the model. Thanks in advance for that! (^_^)v

Fixing Feet in ADetailer

Now, for improving foot results in V2, here’s what I do.

Settings Overview:

You'll find ADetailer under Uncategorized in the Settings tab. My current setup:

Max tabs: 4 (lets me handle multiple elements per image—person, hands, feet, face, etc.)

Sort bounding boxes by: Position (left to right) (helps with organizing specific targets, from left to right, within the context of the ADetailer prompt field, separated with the words "[SEP]")

Match inpainting size to bounding box size (if ‘Use separate width/height’ is not set): Strict (SDXL Only)

The Strict setting ensures inpainting matches the general aspect ratio of the detected bounding box, avoiding arbitrary resolutions (like locking everything to 1024x1024).

Advanced Prompting for Feet

If you have an image where one foot wears sneakers (left side) and the other is barefoot (right side), you can structure the foot ADetailer tab like this:

foot focus, closeup, depth of field, sneakers [SEP]

foot focus, closeup, depth of field, barefoot

Assuming both are detected correctly, this will apply sneaker-specific refinements to one, and barefoot-focused adjustments to the other.

Optimizing the Prompt for Foot Fixes

For general foot fixes, I use: foot focus, depth of field, closeup

(For hands, swap "foot" for "hand" in the hand tab.)

This minimizes unwanted artifacts—like faces appearing where they shouldn't—since the foot prompt stays separate from the main prompt.

If you're targeting specific footwear, expand the prompt accordingly: foot focus, depth of field, closeup, red high heels

Denoising & Checkpoints

Your checkpoint choice matters when refining feet.

My go-to is my very own SilenteMoney_Ill_V2—a hyper-based Illustrious model that requires only 8 steps for generation.

Denoising strength: 0.4 (default). I rarely go above 0.6.

Mask blur: 4 or 8 (higher values tend to soften rough edges).

Samplers

For Hyper/LCM-based models:

Best to stick to the LCM sampler.

For non-Hyper/LCM-based models:

Euler A: Best for fixing bad feet, though results depend on checkpoint quality, denoising strength, etc.

DDIM: Ideal for refining feet since it's an inpainting-focused sampler.

Last Resort: Manual Editing

If ADetailer isn’t cutting it, manual edits can save the image.

Using Clip Studio, Procreate, or any manual-edit tool to fix problem areas first can be effective.

You can use controlnet input images for inpainting refinements later.

Or directly touch up the image in img2img before applying ADetailer again—or even going straight to ADetailer to edit the manual edit directly, by skipping img2img.

I do this myself often—not just for feet but for hands, faces, and, sometimes, entire people. XD

Hope this helps! Let me know if anything’s unclear. I think I went overboard making this into a tutorial, but there it is lol

Hello there, and thanks for reaching out!

Would you be willing to share on this model page the image in question? V2 and V1 have difficulty detecting feet in certain positions, due to how it was trained, therefore I'm (still) working on annotating a new dataset for V3. It's a lot of images to go through, but all that work hopefully will pay off in the end. If you share any image you see that V1 and V2 cannot detect, I might add it to the dataset (assuming I don't already have sufficient images that are similar to it), and if it's something I've completely overlooked, I'd be sure to add more similar images in, so that the model recognizes such configurations better.

I have had images where even if the detection threshold was set all the way down to its lowest settings, that it would still fail to recognize certain configurations of feet. If this is happening to you, then other than sending me that image so I might add it to the new dataset, the best thing I could recommend is just doing a manual inpainting, using the similar default settings you would have otherwise used for ADetailer. It's tedious for sure, but that's a surefire way to get refined feet. Hopefully V3 will rectify that.

Take care~ (^_^)v

Glad you like it! (^_^)v

Hey Halo, rest assured I'm still very much working on it. (^_^)v

V1 and V2 is probably not good at sole detection, as it wasn't trained with that in mind. Though I think I've been surprised. It might detect it, but I wouldn't count on it.

@Bonticarius I'm (still) currently working on V3, which has far more images in the dataset than V1/2, and incorporates soles as well, since it was requested. Hopefully I'll get all this work over with promptly, but your patience will not go unrewarded (^_^)v

Haha
I'll double my efforts, but no promises that HL3 won't come first XD

I'm not sure what you mean. What kind of LoRA? I've also been out of the loop for some time, since I'm focused on V3, so I'm not even sure what this Z Image Turbo is, haha.

If you mean a foot detection model for Z Turbo Image, then as long as you're using ADetailer or its ComfyUI equivalent, then these models here should work, as these aren't LoRAs, but yolo8x BBOX-style detection models (at least V1 and V2 are, anyway); its function should be independent of the checkpoint you're using.

Hello there degurshaft, I do apologize for the late response. No exact date for V3, but rest assured I am actively working on it.