1. Curiously, I don't see a big difference after GAN apply (0. It will take about 1-2 hour. How to share XSeg Models: 1. 0146. 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. 5. It is used at 2 places. 000. Sydney Sweeney, HD, 18k images, 512x512. Business, Economics, and Finance. Src faceset should be xseg'ed and applied. 5) Train XSeg. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. DST and SRC face functions. 2. How to share SAEHD Models: 1. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. 0 using XSeg mask training (213. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. 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). CryptoHow to pretrain models for DeepFaceLab deepfakes. 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. Use the 5. 3. Introduction. 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. First one-cycle training with batch size 64. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. Training. With the first 30. 1 Dump XGBoost model with feature map using XGBClassifier. Tensorflow-gpu. ** Steps to reproduce **i tried to clean install windows , and follow all tips . Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. 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. 0 How to make XGBoost model to learn its mistakes. Remove filters by clicking the text underneath the dropdowns. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. 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. Step 5. 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. If it is successful, then the training preview window will open. learned-dst: uses masks learned during training. Differences from SAE: + new encoder produces more stable face and less scale jitter. com! 'X S Entertainment Group' is one option -- get in to view more @ The. The software will load all our images files and attempt to run the first iteration of our training. 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. Even though that. Where people create machine learning projects. Xseg遮罩模型的使用可以分为训练和使用两部分部分. I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. XSeg-prd: uses trained XSeg model to mask using data from source faces. Which GPU indexes to choose?: Select one or more GPU. Its a method of randomly warping the image as it trains so it is better at generalization. Link to that. 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. 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. Increased page file to 60 gigs, and it started. 2) extract images from video data_src. 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). Extract source video frame images to workspace/data_src. Python Version: The one that came with a fresh DFL Download yesterday. XSeg) data_dst mask - edit. 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. Where people create machine learning projects. You can apply Generic XSeg to src faceset. py","contentType":"file"},{"name. There were blowjob XSeg masked faces uploaded by someone before the links were removed by the mods. 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. 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。. 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. Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. And for SRC, what part is used as face for training. 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. 1. XSeg 蒙版还将帮助模型确定面部尺寸和特征，从而产生更逼真的眼睛和嘴巴运动。虽然默认蒙版可能对较小的面部类型有用，但较大的面部类型（例如全脸和头部）需要自定义 XSeg 蒙版才能获得. I've posted the result in a video. 0 XSeg Models and Datasets Sharing Thread. 建议萌. added 5. When SAEHD-training a head-model (res 288, batch 6, check full parameters below), I notice there is a huge difference between mentioned iteration time (581 to 590 ms) and the time it really takes (3 seconds per iteration). Model training is consumed, if prompts OOM. 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. The Xseg needs to be edited more or given more labels if I want a perfect mask. 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. 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. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. 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. Where people create machine learning projects. XSeg) train; Now it’s time to start training our XSeg model. I often get collapses if I turn on style power options too soon, or use too high of a value. Model training is consumed, if prompts OOM. S. . {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. MikeChan said: Dear all, I'm using DFL-colab 2. The software will load all our images files and attempt to run the first iteration of our training. 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. Read the FAQs and search the forum before posting a new topic. 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. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. Model training fails. 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. I have an Issue with Xseg training. 3. Describe the SAEHD model using SAEHD model template from rules thread. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. Basically whatever xseg images you put in the trainer will shell out. 00:00 Start00:21 What is pretraining?00:50 Why use i. 27 votes, 16 comments. == Model name: XSeg ==== Current iteration: 213522 ==== face_type: wf ==== p. Make a GAN folder: MODEL/GAN. The Xseg training on src ended up being at worst 5 pixels over. Consol logs. Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. ago. Copy link 1over137 commented Dec 24, 2020. even pixel loss can cause it if you turn it on too soon, I only use those. It will take about 1-2 hour. 0 to train my SAEHD 256 for over one month. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. Does model training takes into account applied trained xseg mask ? eg. 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. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. e, a neural network that performs better, in the same amount of training time, or less. BAT script, open the drawing tool, draw the Mask of the DST. npy . The Xseg training on src ended up being at worst 5 pixels over. Step 2: Faces Extraction. Instead of using a pretrained model. Notes, tests, experience, tools, study and explanations of the source code. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). #5726 opened on Sep 9 by damiano63it. Video created in DeepFaceLab 2. Xseg Training is a completely different training from Regular training or Pre - Training. Describe the XSeg model using XSeg model template from rules thread. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. X. DLF installation functions. 4. You can use pretrained model for head. However in order to get the face proportions correct, and a better likeness, the mask needs to be fit to the actual faces. 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. XSeg in general can require large amounts of virtual memory. Solution below - use Tensorflow 2. bat compiles all the xseg faces you’ve masked. Run 6) train SAEHD. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. 2. Post processing. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. 训练需要绘制训练素材，就是你得用deepfacelab自带的工具，手动给图片画上遮罩。. Double-click the file labeled ‘6) train Quick96. 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. 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. The images in question are the bottom right and the image two above that. #1. A skill in programs such as AfterEffects or Davinci Resolve is also desirable. Where people create machine learning projects. Xseg training functions. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. The exciting part begins! Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly. 0 XSeg Models and Datasets Sharing Thread. 1. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Train the fake with SAEHD and whole_face type. However, when I'm merging, around 40 % of the frames "do not have a face". But usually just taking it in stride and let the pieces fall where they may is much better for your mental health. 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. #4. Video created in DeepFaceLab 2. How to share AMP Models: 1. Just change it back to src Once you get the. What's more important is that the xseg mask is consistent and transitions smoothly across the frames. Setting Value Notes; iterations: 100000: Or until previews are sharp with eyes and teeth details. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. idk how the training handles jpeg artifacts so idk if it even matters, but iperov didn't really do. Describe the SAEHD model using SAEHD model template from rules thread. bat I don’t even know if this will apply without training masks. I'm facing the same problem. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. oneduality • 4 yr. DF Vagrant. I have now moved DFL to the Boot partition, the behavior remains the same. I turn random color transfer on for the first 10-20k iterations and then off for the rest. Where people create machine learning projects. xseg) Data_Dst Mask for Xseg Trainer - Edit. 192 it). For DST just include the part of the face you want to replace. + new decoder produces subpixel clear result. Oct 25, 2020. Mark your own mask only for 30-50 faces of dst video. In addition to posting in this thread or the general forum. py","contentType":"file"},{"name. - GitHub - Twenkid/DeepFaceLab-SAEHDBW: 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. RTX 3090 fails in training SAEHD or XSeg if CPU does not support AVX2 - "Illegal instruction, core dumped". ogt. BAT script, open the drawing tool, draw the Mask of the DST. It really is a excellent piece of software. Describe the XSeg model using XSeg model template from rules thread. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. pak file untill you did all the manuel xseg you wanted to do. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. It must work if it does for others, you must be doing something wrong. Complete the 4-day Level 1 Basic CPTED Course. 1256. 0 using XSeg mask training (213. 6) Apply trained XSeg mask for src and dst headsets. caro_kann; Dec 24, 2021; Replies 6 Views 3K. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Video created in DeepFaceLab 2. This forum is for reporting errors with the Extraction process. I have to lower the batch_size to 2, to have it even start. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. 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. #1. 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. . 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. 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. Apr 11, 2022. both data_src and data_dst. Double-click the file labeled ‘6) train Quick96. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. As you can see in the two screenshots there are problems. At last after a lot of training, you can merge. It really is a excellent piece of software. When the face is clear enough, you don't need. Train XSeg on these masks. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. Put those GAN files away; you will need them later. Final model config:===== Model Summary ==. Also it just stopped after 5 hours. I actually got a pretty good result after about 5 attempts (all in the same training session). 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. SRC Simpleware. How to share SAEHD Models: 1. 运行data_dst mask for XSeg trainer - edit. It depends on the shape, colour and size of the glasses frame, I guess. 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. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. 9794 and 0. XSeg) data_dst/data_src mask for XSeg trainer - remove. If it is successful, then the training preview window will open. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). 4 cases both for the SAEHD and Xseg, and with enough and not enough pagefile: SAEHD with Enough Pagefile:The DFL and FaceSwap developers have not been idle, for sure: it’s now possible to use larger input images for training deepfake models (see image below), though this requires more expensive video cards; masking out occlusions (such as hands in front of faces) in deepfakes has been semi-automated by innovations such as XSEG training;. 1) clear workspace. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Post in this thread or create a new thread in this section (Trained Models). Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 3. Also it just stopped after 5 hours. 4. The only available options are the three colors and the two "black and white" displays. Attempting to train XSeg by running 5. Blurs nearby area outside of applied face mask of training samples. SAEHD Training Failure · Issue #55 · chervonij/DFL-Colab · GitHub. 2. prof. Today, I train again without changing any setting, but the loss rate for src rised from 0. XSeg-prd: uses. Feb 14, 2023. It learns this to be able to. xseg train not working #5389. I do recommend che. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. Post in this thread or create a new thread in this section (Trained Models). Double-click the file labeled ‘6) train Quick96. 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. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 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. 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 overlay. npy","path. Several thermal modes to choose from. 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. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by nebelfuerst. 5. 000. You can then see the trained XSeg mask for each frame, and add manual masks where needed. npy","contentType":"file"},{"name":"3DFAN. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Get XSEG : Definition and Meaning. 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. #1. Step 3: XSeg Masks. Read the FAQs and search the forum before posting a new topic. 5. dump ( [train_x, train_y], f) #to load it with open ("train. . Step 4: Training. Already segmented faces can. Deep convolutional neural networks (DCNNs) have made great progress in recognizing face images under unconstrained environments [1]. 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. I mask a few faces, train with XSeg and results are pretty good. 3. XSeg Model Training. When it asks you for Face type, write “wf” and start the training session by pressing Enter. Yes, but a different partition. Repeat steps 3-5 until you have no incorrect masks on step 4. Manually labeling/fixing frames and training the face model takes the bulk of the time. Step 5: Training. Enter a name of a new model : new Model first run. But I have weak training. Run: 5. The dice, volumetric overlap error, relative volume difference. 1) except for some scenes where artefacts disappear. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. By modifying the deep network architectures [[2], [3], [4]] or designing novel loss functions [[5], [6], [7]] and training strategies, a model can learn highly discriminative facial features for face. 262K views 1 day ago. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. XSeg in general can require large amounts of virtual memory. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. You can use pretrained model for head. I have a model with quality 192 pretrained with 750. , gradient_accumulation_ste. 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. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . Pretrained models can save you a lot of time. Intel i7-6700K (4GHz) 32GB RAM (Already increased pagefile on SSD to 60 GB) 64 bit. . traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. 5. 2) Use “extract head” script. 0 using XSeg mask training (100. 2. Four iterations are made at the mentioned speed, followed by a pause of. . GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. 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. DeepFaceLab code and required packages. Lee - Dec 16, 2019 12:50 pm UTCForum rules. after that just use the command. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. 3X to 4. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. All reactions1. Manually mask these with XSeg. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. py","path":"models/Model_XSeg/Model. Manually fix any that are not masked properly and then add those to the training set. Xseg Training or Apply Mask First ? frankmiller92; Dec 13, 2022; Replies 5 Views 2K. 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 ". Looking for the definition of XSEG? Find out what is the full meaning of XSEG on Abbreviations. It should be able to use GPU for training. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd. . So we develop a high-efficiency face segmentation tool, XSeg, which allows everyone to customize to suit specific requirements by few-shot learning. )train xseg. 0 using XSeg mask training (100. 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. 3. Search for celebs by name and filter the results to find the ideal faceset! All facesets are released by members of the DFL community and are "Safe for Work". 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. Deletes all data in the workspace folder and rebuilds folder structure. Where people create machine learning projects. Step 6: Final Result. **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. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. Use XSeg for masking. 0rc3 Driver. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. npy","path":"facelib/2DFAN. 000 it) and SAEHD training (only 80. Choose the same as your deepfake model. I have an Issue with Xseg training. Normally at gaming temps reach high 85-90, and its confirmed by AMD that the Ryzen 5800H is made that way. Where people create machine learning projects. Sometimes, I still have to manually mask a good 50 or more faces, depending on. 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. workspace. learned-dst: uses masks learned during training. Hello, after this new updates, DFL is only worst. - Issues · nagadit/DeepFaceLab_Linux. . Then I apply the masks, to both src and dst. 000 it). 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. Video created in DeepFaceLab 2. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. From the project directory, run 6. Pass the in. 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. In the XSeg viewer there is a mask on all faces. . XSeg apply takes the trained XSeg masks and exports them to the data set.