{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. However in order to get the face proportions correct, and a better likeness, the mask needs to be fit to the actual faces. Xseg pred is correct as training and shape, but is moved upwards and discovers the beard of the SRC. 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. Please mark. Notes, tests, experience, tools, study and explanations of the source code. pak” archive file for faster loading times 47:40 – Beginning training of our SAEHD model 51:00 – Color transfer. Describe the XSeg model using XSeg model template from rules thread. 3. Src faceset should be xseg'ed and applied. 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. In this video I explain what they are and how to use them. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. bat训练遮罩，设置脸型和batch_size，训练个几十上百万，回车结束。 XSeg遮罩训练素材是不区分是src和dst。 2. . I wish there was a detailed XSeg tutorial and explanation video. py","path":"models/Model_XSeg/Model. Post in this thread or create a new thread in this section (Trained Models). 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. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 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. learned-dst: uses masks learned during training. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. Training speed. 522 it) and SAEHD training (534. #1. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. 4. XSeg in general can require large amounts of virtual memory. In a paper published in the Quarterly Journal of Experimental. v4 (1,241,416 Iterations). bat after generating masks using the default generic XSeg model. It is now time to begin training our deepfake model. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I turn random color transfer on for the first 10-20k iterations and then off for the rest. Four iterations are made at the mentioned speed, followed by a pause of. Xseg editor and overlays. Increased page file to 60 gigs, and it started. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. 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. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. Manually fix any that are not masked properly and then add those to the training set. Describe the XSeg model using XSeg model template from rules thread. 1 participant. It is now time to begin training our deepfake model. Include link to the model (avoid zips/rars) to a free file. I realized I might have incorrectly removed some of the undesirable frames from the dst aligned folder before I started training, I just deleted them to the. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. XSeg) train issue by. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. The software will load all our images files and attempt to run the first iteration of our training. then i reccomend you start by doing some manuel xseg. BAT script, open the drawing tool, draw the Mask of the DST. Easy Deepfake tutorial for beginners Xseg. Running trainer. Manually mask these with XSeg. also make sure not to create a faceset. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. 2 使用Xseg模型（推荐） 38:03 – Manually Xseg masking Jim/Ernest 41:43 – Results of training after manual Xseg’ing was added to Generically trained mask 43:03 – Applying Xseg training to SRC 43:45 – Archiving our SRC faces into a “faceset. py","contentType":"file"},{"name. 0rc3 Driver. 00:00 Start00:21 What is pretraining?00:50 Why use i. k. bat’. ago. Already segmented faces can. When it asks you for Face type, write “wf” and start the training session by pressing Enter. . I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. 9 XGBoost Best Iteration. Actual behavior XSeg trainer looks like this: (This is from the default Elon Musk video by the way) Steps to reproduce I deleted the labels, then labeled again. 3. ProTip! Adding no:label will show everything without a label. 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. S. 1. I have a model with quality 192 pretrained with 750. - GitHub - Twenkid/DeepFaceLab-SAEHDBW: Grayscale SAEHD model and mode for training deepfakes. 2. Where people create machine learning projects. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. Step 5: Training. The guide literally has explanation on when, why and how to use every option, read it again, maybe you missed the training part of the guide that contains detailed explanation of each option. - Issues · nagadit/DeepFaceLab_Linux. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 16 XGBoost produce prediction result and probability. , gradient_accumulation_ste. Lee - Dec 16, 2019 12:50 pm UTCForum rules. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. It will likely collapse again however, depends on your model settings quite usually. DeepFaceLab 2. On training I make sure I enable Mask Training (If I understand this is for the Xseg Masks) Am I missing something with the pretraining? Can you please explain #3 since I'm not sure if I should or shouldn't APPLY to pretrained Xseg before I. Keep shape of source faces. I often get collapses if I turn on style power options too soon, or use too high of a value. Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. The Xseg training on src ended up being at worst 5 pixels over. 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 ). Otherwise, if you insist on xseg, you'd mainly have to focus on using low resolutions as well as bare minimum for batch size. com XSEG Stands For : X S Entertainment GroupObtain the confidence needed to safely operate your Niton handheld XRF or LIBS analyzer. It haven't break 10k iterations yet, but the objects are already masked out. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. 5) Train XSeg. Phase II: Training. Post in this thread or create a new thread in this section (Trained Models). 1) clear workspace. 000 it). cpu_count = multiprocessing. 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. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Step 5: Training. prof. Actual behavior XSeg trainer looks like this: (This is from the default Elon Musk video by the way) Steps to reproduce I deleted the labels, then labeled again. bat. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. I guess you'd need enough source without glasses for them to disappear. Describe the SAEHD model using SAEHD model template from rules thread. Then I apply the masks, to both src and dst. Tensorflow-gpu 2. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. 5. Xseg Training is a completely different training from Regular training or Pre - Training. How to share SAEHD Models: 1. Hi everyone, I'm doing this deepfake, using the head previously for me pre -trained. slow We can't buy new PC, and new cards, after you every new updates ))). Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. 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. train untill you have some good on all the faces. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. XSeg) train. 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. In addition to posting in this thread or the general forum. 2. A lot of times I only label and train XSeg masks but forgot to apply them and that's how they looked like. I could have literally started merging after about 3-4 hours (on a somewhat slower AMD integrated GPU). after that just use the command. 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 XSeg model using XSeg model template from rules thread. Step 5. It will take about 1-2 hour. Sometimes, I still have to manually mask a good 50 or more faces, depending on. . with XSeg model you can train your own mask segmentator of dst (and src) faces that will be used in merger for whole_face. Step 5: Training. Enjoy it. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. XSeg) data_dst/data_src mask for XSeg trainer - remove. The training preview shows the hole clearly and I run on a loss of ~. Blurs nearby area outside of applied face mask of training samples. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). Intel i7-6700K (4GHz) 32GB RAM (Already increased pagefile on SSD to 60 GB) 64 bit. 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. Looking for the definition of XSEG? Find out what is the full meaning of XSEG on Abbreviations. Step 5. 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. soklmarle; Jan 29, 2023; Replies 2 Views 597. py","path":"models/Model_XSeg/Model. 5) Train XSeg. XSeg) data_dst/data_src mask for XSeg trainer - remove. #4. Training XSeg is a tiny part of the entire process. Usually a "Normal" Training takes around 150. 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. 0 using XSeg mask training (100. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Deletes all data in the workspace folder and rebuilds folder structure. 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。. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. 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. 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. As you can see in the two screenshots there are problems. bat’. Does model training takes into account applied trained xseg mask ? eg. For DST just include the part of the face you want to replace. Only deleted frames with obstructions or bad XSeg. Where people create machine learning projects. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. 6) Apply trained XSeg mask for src and dst headsets. Setting Value Notes; iterations: 100000: Or until previews are sharp with eyes and teeth details. Where people create machine learning projects. You can use pretrained model for head. . I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. It should be able to use GPU for training. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. With the help of. Expected behavior. Container for all video, image, and model files used in the deepfake project. Verified Video Creator. 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. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. 3. Video created in DeepFaceLab 2. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. First one-cycle training with batch size 64. Grayscale SAEHD model and mode for training deepfakes. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . workspace. Step 2: Faces Extraction. The fetch. The problem of face recognition in lateral and lower projections. Then restart training. Aug 7, 2022. MikeChan said: Dear all, I'm using DFL-colab 2. You can apply Generic XSeg to src faceset. 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. 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. 0 using XSeg mask training (213. #1. 1256. You can use pretrained model for head. Introduction. This seems to even out the colors, but not much more info I can give you on the training. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. PayPal Tip Jar:Lab:MEGA:. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. 000 it), SAEHD pre-training (1. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. 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. GPU: Geforce 3080 10GB. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. #5727 opened on Sep 19 by WagnerFighter. 000. 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. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. . 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. . 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. That just looks like "Random Warp". Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 5) Train XSeg. Where people create machine learning projects. 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. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. 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. Verified Video Creator. xseg) Data_Dst Mask for Xseg Trainer - Edit. It will take about 1-2 hour. 2. Where people create machine learning projects. 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. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. updated cuda and cnn and drivers. How to share XSeg Models: 1. 27 votes, 16 comments. Double-click the file labeled ‘6) train Quick96. Video created in DeepFaceLab 2. 2) Use “extract head” script. 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. bat’. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. Choose one or several GPU idxs (separated by comma). . idk how the training handles jpeg artifacts so idk if it even matters, but iperov didn't really do. Download this and put it into the model folder. If you have found a bug are having issues with the Training process not working, then you should post in the Training Support forum. 3. Where people create machine learning projects. 2. I mask a few faces, train with XSeg and results are pretty good. 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. After that we’ll do a deep dive into XSeg editing, training the model,…. 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. 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. I have to lower the batch_size to 2, to have it even start. Easy Deepfake tutorial for beginners Xseg. The only available options are the three colors and the two "black and white" displays. Again, we will use the default settings. 522 it) and SAEHD training (534. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. It depends on the shape, colour and size of the glasses frame, I guess. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. 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. You can use pretrained model for head. Where people create machine learning projects. Manually labeling/fixing frames and training the face model takes the bulk of the time. Get XSEG : Definition and Meaning. Step 1: Frame Extraction. Run: 5. The more the training progresses, the more holes in the SRC model (who has short hair) will open up where the hair disappears. py by just changing the line 669 to. traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. [new] No saved models found. Sep 15, 2022. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega) In addition to posting in this thread or. Describe the SAEHD model using SAEHD model template from rules thread. It is now time to begin training our deepfake model. 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. Xseg遮罩模型的使用可以分为训练和使用两部分部分. 5. . Post_date. DF Admirer. Where people create machine learning projects. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. At last after a lot of training, you can merge. Final model config:===== Model Summary ==. 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. when the rightmost preview column becomes sharper stop training and run a convert. Use the 5. SRC Simpleware. You can see one of my friend in Princess Leia ;-) I've put same scenes with different. Differences from SAE: + new encoder produces more stable face and less scale jitter. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. Where people create machine learning projects. 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. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. When the face is clear enough, you don't need. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. THE FILES the model files you still need to download xseg below. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. X. then copy pastE those to your xseg folder for future training. Copy link 1over137 commented Dec 24, 2020. both data_src and data_dst. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). 262K views 1 day ago. It must work if it does for others, you must be doing something wrong. . Repeat steps 3-5 until you have no incorrect masks on step 4. . 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. 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. If it is successful, then the training preview window will open. Describe the AMP model using AMP model template from rules thread. Apr 11, 2022. 1) except for some scenes where artefacts disappear. Attempting to train XSeg by running 5. Train the fake with SAEHD and whole_face type. Where people create machine learning projects. 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. How to Pretrain Deepfake Models for DeepFaceLab. Remove filters by clicking the text underneath the dropdowns. Post in this thread or create a new thread in this section (Trained Models) 2. 4. It really is a excellent piece of software. 2) Use “extract head” script. 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. Where people create machine learning projects. 6) Apply trained XSeg mask for src and dst headsets. I have now moved DFL to the Boot partition, the behavior remains the same. 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 ". The Xseg needs to be edited more or given more labels if I want a perfect mask. Model first run. Business, Economics, and Finance. 0 How to make XGBoost model to learn its mistakes. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. com! 'X S Entertainment Group' is one option -- get in to view more @ The. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega) In addition to posting in this thread or. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. (or increase) denoise_dst. 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. ]. bat train the model Check the faces of 'XSeg dst faces' preview. 1. resolution: 128: Increasing resolution requires significant VRAM increase: face_type: f: learn_mask: y: optimizer_mode: 2 or 3: Modes 2/3 place work on the gpu and system memory. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. This forum is for reporting errors with the Extraction process. 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. Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. Model training fails. xseg) Data_Dst Mask for Xseg Trainer - Edit. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 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.