Deepfake native resolution progress. . Step 9 – Creating and Editing XSEG Masks (Sped Up) Step 10 – Setting Model Folder (And Inserting Pretrained XSEG Model) Step 11 – Embedding XSEG Masks into Faces Step 12 – Setting Model Folder in MVE Step 13 – Training XSEG from MVE Step 14 – Applying Trained XSEG Masks Step 15 – Importing Trained XSEG Masks to View in MVEMy joy is that after about 10 iterations, my Xseg training was pretty much done (I ran it for 2k just to catch anything I might have missed). 6) Apply trained XSeg mask for src and dst headsets. py by just changing the line 669 to. Just change it back to src Once you get the. Consol logs. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. It depends on the shape, colour and size of the glasses frame, I guess. Where people create machine learning projects. Post in this thread or create a new thread in this section (Trained Models) 2. XSeg) train. Fit training is a technique where you train your model on data that it wont see in the final swap then do a short "fit" train to with the actual video you're swapping out in order to get the best. 2. py","contentType":"file"},{"name. Step 5: Training. 262K views 1 day ago. 2) Use “extract head” script. #4. DeepFaceLab is an open-source deepfake system created by iperov for face swapping with more than 3,000 forks and 13,000 stars in Github: it provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of deep learning framework or with model implementation required, while remains a flexible and loose coupling. Model first run. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Container for all video, image, and model files used in the deepfake project. In this video I explain what they are and how to use them. Saved searches Use saved searches to filter your results more quicklySegX seems to go hand in hand with SAEHD --- meaning train with SegX first (mask training and initial training) then move on to SAEHD Training to further better the results. Consol logs. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. bat. It really is a excellent piece of software. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). Where people create machine learning projects. in xseg model the exclusions indeed are learned and fine, the issue new is in training preview, it doesn't show that , i haven't done yet, so now sure if its a preview bug what i have done so far: - re checked frames to see if. , train_step_batch_size), the gradient accumulation steps (a. even pixel loss can cause it if you turn it on too soon, I only use those. Where people create machine learning projects. The images in question are the bottom right and the image two above that. But doing so means redo extraction while the XSEG masks just save them with XSEG_fetch, redo the Xseg training, apply, check and launch the SAEHD training. The more the training progresses, the more holes in the SRC model (who has short hair) will open up where the hair disappears. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. you’ll have to reduce number of dims (in SAE settings) for your gpu (probably not powerful enough for the default values) train for 12 hrs and keep an eye on the preview and loss numbers. XSeg allows everyone to train their model for the segmentation of a spe-Jan 11, 2021. Enjoy it. Where people create machine learning projects. Mark your own mask only for 30-50 faces of dst video. How to share SAEHD Models: 1. Where people create machine learning projects. Several thermal modes to choose from. With a batch size 512, the training is nearly 4x faster compared to the batch size 64! Moreover, even though the batch size 512 took fewer steps, in the end it has better training loss and slightly worse validation loss. Dst face eybrow is visible. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by nebelfuerst. I have now moved DFL to the Boot partition, the behavior remains the same. Share. DeepFaceLab code and required packages. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Requesting Any Facial Xseg Data/Models Be Shared Here. I do recommend che. It is now time to begin training our deepfake model. Where people create machine learning projects. in xseg model the exclusions indeed are learned and fine, the issue new is in training preview, it doesn't show that , i haven't done yet, so now sure if its a preview bug what i have done so far: - re checked frames to see if. The full face type XSeg training will trim the masks to the the biggest area possible by full face (that's about half of the forehead although depending on the face angle the coverage might be even bigger and closer to WF, in other cases face might be cut off oat the bottom, in particular chin when mouth is wide open will often get cut off with. After that we’ll do a deep dive into XSeg editing, training the model,…. The Xseg needs to be edited more or given more labels if I want a perfect mask. If I train src xseg and dst xseg separately, vs training a single xseg model for both src and dst? Does this impact the quality in any way? 2. I'm not sure if you can turn off random warping for XSeg training and frankly I don't thing you should, it helps to make the mask training be able to generalize on new data sets. For this basic deepfake, we’ll use the Quick96 model since it has better support for low-end GPUs and is generally more beginner friendly. . Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. All you need to do is pop it in your model folder along with the other model files, use the option to apply the XSEG to the dst set, and as you train you will see the src face learn and adapt to the DST's mask. 000 it) and SAEHD training (only 80. In this DeepFaceLab XSeg tutorial I show you how to make better deepfakes and take your composition to the next level! I’ll go over what XSeg is and some important terminology,. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 0 XSeg Models and Datasets Sharing Thread. slow We can't buy new PC, and new cards, after you every new updates ))). load (f) If your dataset is huge, I would recommend check out hdf5 as @Lukasz Tracewski mentioned. Without manually editing masks of a bunch of pics, but just adding downloaded masked pics to the dst aligned folder for xseg training, I'm wondering how DFL learns to. 000 iterations many masks look like. Actually you can use different SAEHD and XSeg models but it has to be done correctly and one has to keep in mind few things. Model training is consumed, if prompts OOM. It works perfectly fine when i start Training with Xseg but after a few minutes it stops for a few seconds and then continues but slower. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. However, since some state-of-the-art face segmentation models fail to generate fine-grained masks in some partic-ular shots, the XSeg was introduced in DFL. Hello, after this new updates, DFL is only worst. Where people create machine learning projects. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety. #1. . Src faceset is celebrity. XSeg is just for masking, that's it, if you applied it to SRC and all masks are fine on SRC faces, you don't touch it anymore, all SRC faces are masked, you then did the same for DST (labeled, trained xseg, applied), now this DST is masked properly, if new DST looks overall similar (same lighting, similar angles) you probably won't need to add. DF Admirer. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. Where people create machine learning projects. 0 using XSeg mask training (213. Post in this thread or create a new thread in this section (Trained Models). After training starts, memory usage returns to normal (24/32). HEAD masks are not ideal since they cover hair, neck, ears (depending on how you mask it but in most cases with short haired males faces you do hair and ears) which aren't fully covered by WF and not at all by FF,. Which GPU indexes to choose?: Select one or more GPU. Xseg training functions. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. 3. Step 5: Merging. Extract source video frame images to workspace/data_src. If your facial is 900 frames and you have a good generic xseg model (trained with 5k to 10k segmented faces, with everything, facials included but not only) then you don't need to segment 900 faces : just apply your generic mask, go the facial section of your video, segment 15 to 80 frames where your generic mask did a poor job, then retrain. So we develop a high-efficiency face segmentation tool, XSeg, which allows everyone to customize to suit specific requirements by few-shot learning. 522 it) and SAEHD training (534. Easy Deepfake tutorial for beginners Xseg. XSeg training GPU unavailable #5214. Phase II: Training. 0 using XSeg mask training (100. Part 2 - This part has some less defined photos, but it's. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. 000 it), SAEHD pre-training (1. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Aug 7, 2022. From the project directory, run 6. Grayscale SAEHD model and mode for training deepfakes. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. )train xseg. Double-click the file labeled ‘6) train Quick96. I could have literally started merging after about 3-4 hours (on a somewhat slower AMD integrated GPU). Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. Timothy B. Step 5. Xseg Training is for training masks over Src or Dst faces ( Telling DFL what is the correct area of the face to include or exclude ). Sometimes, I still have to manually mask a good 50 or more faces, depending on material. . Increased page file to 60 gigs, and it started. And then bake them in. It really is a excellent piece of software. ProTip! Adding no:label will show everything without a label. . To conclude, and answer your question, a smaller mini-batch size (not too small) usually leads not only to a smaller number of iterations of a training algorithm, than a large batch size, but also to a higher accuracy overall, i. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. . The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Already segmented faces can. When the face is clear enough, you don't need. also make sure not to create a faceset. But before you can stat training you aso have to mask your datasets, both of them, STEP 8 - XSEG MODEL TRAINING, DATASET LABELING AND MASKING: [News Thee snow apretralned Genere WF X5eg model Included wth DF (nternamodel generic xs) fyou dont have time to label aces for your own WF XSeg model or urt needto quickly pely base Wh. 000 it). During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. This one is only at 3k iterations but the same problem presents itself even at like 80k and I can't seem to figure out what is causing it. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. 1. bat. 2) extract images from video data_src. #1. Use XSeg for masking. Pretrained XSEG is a model for masking the generated face, very helpful to automatically and intelligently mask away obstructions. Otherwise, if you insist on xseg, you'd mainly have to focus on using low resolutions as well as bare minimum for batch size. Manually labeling/fixing frames and training the face model takes the bulk of the time. Where people create machine learning projects. It haven't break 10k iterations yet, but the objects are already masked out. XSeg) data_dst trained mask - apply or 5. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Copy link 1over137 commented Dec 24, 2020. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. oneduality • 4 yr. 6) Apply trained XSeg mask for src and dst headsets. Sep 15, 2022. 5. In addition to posting in this thread or the general forum. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. How to share SAEHD Models: 1. When it asks you for Face type, write “wf” and start the training session by pressing Enter. Looking for the definition of XSEG? Find out what is the full meaning of XSEG on Abbreviations. bat train the model Check the faces of 'XSeg dst faces' preview. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Step 2: Faces Extraction. The Xseg training on src ended up being at worst 5 pixels over. I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. XSeg) train. bat. XSeg) data_src trained mask - apply. The images in question are the bottom right and the image two above that. XSeg in general can require large amounts of virtual memory. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. py","path":"models/Model_XSeg/Model. Deletes all data in the workspace folder and rebuilds folder structure. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. learned-prd*dst: combines both masks, smaller size of both. I'm not sure if you can turn off random warping for XSeg training and frankly I don't thing you should, it helps to make the mask training be able to generalize on new data sets. The dice, volumetric overlap error, relative volume difference. 1) clear workspace. First one-cycle training with batch size 64. Xseg pred is correct as training and shape, but is moved upwards and discovers the beard of the SRC. As I understand it, if you had a super-trained model (they say its 400-500 thousand iterations) for all face positions, then you wouldn’t have to start training every time. 1. You can use pretrained model for head. **I've tryied to run the 6)train SAEHD using my GPU and CPU When running on CPU, even with lower settings and resolutions I get this error** Running trainer. + pixel loss and dssim loss are merged together to achieve both training speed and pixel trueness. 5) Train XSeg. After the draw is completed, use 5. X. . Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 0 XSeg Models and Datasets Sharing Thread. 3. SRC Simpleware. I was less zealous when it came to dst, because it was longer and I didn't really understand the flow/missed some parts in the guide. How to share AMP Models: 1. #5727 opened on Sep 19 by WagnerFighter. 4. In this DeepFaceLab XSeg tutorial I show you how to make better deepfakes and take your composition to the next level! I’ll go over what XSeg is and some. All you need to do is pop it in your model folder along with the other model files, use the option to apply the XSEG to the dst set, and as you train you will see the src face learn and adapt to the DST's mask. SRC Simpleware. Get any video, extract frames as jpg and extract faces as whole face, don't change any names, folders, keep everything in one place, make sure you don't have any long paths or weird symbols in the path names and try it again. XSeg) train; Now it’s time to start training our XSeg model. Please read the general rules for Trained Models in case you are not sure where to post requests or are looking for. DST and SRC face functions. Download RTT V2 224;Same problem here when I try an XSeg train, with my rtx2080Ti (using the rtx2080Ti build released on the 01-04-2021, same issue with end-december builds, work only with the 12-12-2020 build). Attempting to train XSeg by running 5. Verified Video Creator. I'm facing the same problem. If it is successful, then the training preview window will open. Post_date. Include link to the model (avoid zips/rars) to a free file. I have an Issue with Xseg training. v4 (1,241,416 Iterations). Training. Training; Blog; About;Then I'll apply mask, edit material to fix up any learning issues, and I'll continue training without the xseg facepak from then on. 运行data_dst mask for XSeg trainer - edit. 这一步工作量巨大，要给每一个关键动作都画上遮罩，作为训练数据，数量大约在几十到几百张不等。. Link to that. Feb 14, 2023. I was less zealous when it came to dst, because it was longer and I didn't really understand the flow/missed some parts in the guide. XSeg) data_dst mask - edit. xseg train not working #5389. The result is the background near the face is smoothed and less noticeable on swapped face. Manually mask these with XSeg. this happend on both Xsrg and SAEHD training, during initializing phase after loadind in the sample, the prpgram erros and stops memory usege start climbing while loading the Xseg mask applyed facesets. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. xseg) Train. bat opened for me, from the XSEG editor to training with SAEHD (I reached 64 it, later I suspended it and continued training my model in quick96), I am with the folder "DeepFaceLab_NVIDIA_up_to_RTX2080Ti ". [Tooltip: Half / mid face / full face / whole face / head. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep fakes,d. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. tried on studio drivers and gameready ones. However, since some state-of-the-art face segmentation models fail to generate fine-grained masks in some partic-ular shots, the XSeg was introduced in DFL. Oct 25, 2020. Post in this thread or create a new thread in this section (Trained Models). I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. 3X to 4. PayPal Tip Jar:Lab:MEGA:. 0 using XSeg mask training (100. Lee - Dec 16, 2019 12:50 pm UTCForum rules. 1. The software will load all our images files and attempt to run the first iteration of our training. I've been trying to use Xseg for the first time, today, and everything looks "good", but after a little training, I'm going back to the editor to patch/remask some pictures, and I can't see the mask. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. ogt. Quick96 seems to be something you want to use if you're just trying to do a quick and dirty job for a proof of concept or if it's not important that the quality is top notch. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. That just looks like "Random Warp". During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Use the 5. Step 6: Final Result. 5. ago. Double-click the file labeled ‘6) train Quick96. traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. Today, I train again without changing any setting, but the loss rate for src rised from 0. Describe the SAEHD model using SAEHD model template from rules thread. 000 it), SAEHD pre-training (1. pkl", "r") as f: train_x, train_y = pkl. 3: XSeg Mask Labeling & XSeg Model Training Q1: XSeg is not mandatory because the faces have a default mask. 00:00 Start00:21 What is pretraining?00:50 Why use i. with XSeg model you can train your own mask segmentator of dst (and src) faces that will be used in merger for whole_face. Again, we will use the default settings. The Xseg training on src ended up being at worst 5 pixels over. == Model name: XSeg ==== Current iteration: 213522 ==== face_type: wf ==== p. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. npy","contentType":"file"},{"name":"3DFAN. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. For a 8gb card you can place on. I turn random color transfer on for the first 10-20k iterations and then off for the rest. Step 4: Training. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. Post in this thread or create a new thread in this section (Trained Models). GPU: Geforce 3080 10GB. 2. I often get collapses if I turn on style power options too soon, or use too high of a value. Increased page file to 60 gigs, and it started. It's doing this to figure out where the boundary of the sample masks are on the original image and what collections of pixels are being included and excluded within those boundaries. There were blowjob XSeg masked faces uploaded by someone before the links were removed by the mods. Post in this thread or create a new thread in this section (Trained Models) 2. pak” archive file for faster loading times 47:40 – Beginning training of our SAEHD model 51:00 – Color transfer. fenris17. This is fairly expected behavior to make training more robust, unless it is incorrectly masking your faces after it has been trained and applied to merged faces. Otherwise, you can always train xseg in collab and then download the models and apply it to your data srcs and dst then edit them locally and reupload to collabe for SAEHD training. run XSeg) train. k. You can use pretrained model for head. When the face is clear enough, you don't need. . XSeg Model Training. Mar 27, 2021 #2 Could be related to the virtual memory if you have small amount of ram or are running dfl on a nearly full drive. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. A skill in programs such as AfterEffects or Davinci Resolve is also desirable. In my own tests, I only have to mask 20 - 50 unique frames and the XSeg Training will do the rest of the job for you. on a 320 resolution it takes upto 13-19 seconds . Again, we will use the default settings. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. Describe the AMP model using AMP model template from rules thread. The next step is to train the XSeg model so that it can create a mask based on the labels you provided. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). Intel i7-6700K (4GHz) 32GB RAM (Already increased pagefile on SSD to 60 GB) 64 bit. . Where people create machine learning projects. But usually just taking it in stride and let the pieces fall where they may is much better for your mental health. Choose the same as your deepfake model. Video created in DeepFaceLab 2. Where people create machine learning projects. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. However, when I'm merging, around 40 % of the frames "do not have a face". Instead of the trainer continuing after loading samples, it sits idle doing nothing infinitely like this:With XSeg training for example the temps stabilize at 70 for CPU and 62 for GPU. You can then see the trained XSeg mask for each frame, and add manual masks where needed. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . soklmarle; Jan 29, 2023; Replies 2 Views 597. Then if we look at the second training cycle losses for each batch size :Leave both random warp and flip on the entire time while training face_style_power 0 We'll increase this later You want only the start of training to have styles on (about 10-20k interations then set both to 0), usually face style 10 to morph src to dst, and/or background style 10 to fit the background and dst face border better to the src faceDuring training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Python Version: The one that came with a fresh DFL Download yesterday. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by. Introduction. bat’. With a batch size 512, the training is nearly 4x faster compared to the batch size 64! Moreover, even though the batch size 512 took fewer steps, in the end it has better training loss and slightly worse validation loss. Change: 5. 1256. XSeg-dst: uses trained XSeg model to mask using data from destination faces. 3. Enter a name of a new model : new Model first run. Train XSeg on these masks. first aply xseg to the model. 1. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. In my own tests, I only have to mask 20 - 50 unique frames and the XSeg Training will do the rest of the job for you. You could also train two src files together just rename one of them to dst and train. Download Celebrity Facesets for DeepFaceLab deepfakes. 27 votes, 16 comments. 000 iterations, but the more you train it the better it gets EDIT: You can also pause the training and start it again, I don't know why people usually do it for multiple days straight, maybe it is to save time, but I'm not surenew DeepFaceLab build has been released. Hi everyone, I'm doing this deepfake, using the head previously for me pre -trained. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. Leave both random warp and flip on the entire time while training face_style_power 0 We'll increase this later You want only the start of training to have styles on (about 10-20k interations then set both to 0), usually face style 10 to morph src to dst, and/or background style 10 to fit the background and dst face border better to the src faceDuring training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. It is used at 2 places. 6) Apply trained XSeg mask for src and dst headsets. 3. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. e, a neural network that performs better, in the same amount of training time, or less. Does Xseg training affects the regular model training? eg. added XSeg model. Solution below - use Tensorflow 2. added 5. gili12345 opened this issue Aug 27, 2021 · 3 comments Comments. k. a. bat I don’t even know if this will apply without training masks. If you want to get tips, or better understand the Extract process, then. Already segmented faces can. 192 it). XSeg in general can require large amounts of virtual memory. I have an Issue with Xseg training. py","path":"models/Model_XSeg/Model. remember that your source videos will have the biggest effect on the outcome!Out of curiosity I saw you're using xseg - did you watch xseg train, and then when you see a spot like those shiny spots begin to form, stop training and go find several frames that are like the one with spots, mask them, rerun xseg and watch to see if the problem goes away, then if it doesn't mask more frames where the shiniest faces. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. Tensorflow-gpu 2. DFL 2. It's doing this to figure out where the boundary of the sample masks are on the original image and what collections of pixels are being included and excluded within those boundaries. 2. XSeg is just for masking, that's it, if you applied it to SRC and all masks are fine on SRC faces, you don't touch it anymore, all SRC faces are masked, you then did the same for DST (labeled, trained xseg, applied), now this DST is masked properly, if new DST looks overall similar (same lighting, similar angles) you probably won't need to add. Unfortunately, there is no "make everything ok" button in DeepFaceLab. XSeg in general can require large amounts of virtual memory. The software will load all our images files and attempt to run the first iteration of our training. All reactions1. If you have found a bug are having issues with the Training process not working, then you should post in the Training Support forum. 000 it). Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. DFL 2. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 023 at 170k iterations, but when I go to the editor and look at the mask, none of those faces have a hole where I have placed a exclusion polygon around. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Train the fake with SAEHD and whole_face type. 2. XSeg) data_src trained mask - apply the CMD returns this to me. bat after generating masks using the default generic XSeg model. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. #5726 opened on Sep 9 by damiano63it.