Again, we will use the default settings. ** Steps to reproduce **i tried to clean install windows , and follow all tips . bat. 000 iterations many masks look like. 522 it) and SAEHD training (534. Step 5. Face type ( h / mf / f / wf / head ): Select the face type for XSeg training. Then restart training. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. Final model. 3. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. py","path":"models/Model_XSeg/Model. XSeg-dst: uses trained XSeg model to mask using data from destination faces. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. 1) clear workspace. 000 it), SAEHD pre-training (1. It is now time to begin training our deepfake model. 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. Apr 11, 2022. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. 5. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Where people create machine learning projects. py","contentType":"file"},{"name. 192 it). For a 8gb card you can place on. Pretrained XSEG is a model for masking the generated face, very helpful to automatically and intelligently mask away obstructions. XSeg in general can require large amounts of virtual memory. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. py","contentType":"file"},{"name. learned-prd*dst: combines both masks, smaller size of both. 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. XSeg allows everyone to train their model for the segmentation of a spe- Pretrained XSEG is a model for masking the generated face, very helpful to automatically and intelligently mask away obstructions. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. Does model training takes into account applied trained xseg mask ? eg. Otherwise, if you insist on xseg, you'd mainly have to focus on using low resolutions as well as bare minimum for batch size. 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. updated cuda and cnn and drivers. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. (or increase) denoise_dst. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. Enter a name of a new model : new Model first run. Download this and put it into the model folder. Read the FAQs and search the forum before posting a new topic. 0 How to make XGBoost model to learn its mistakes. Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. 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. Where people create machine learning projects. The Xseg training on src ended up being at worst 5 pixels over. Deletes all data in the workspace folder and rebuilds folder structure. 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). 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. Expected behavior. 建议萌. Sometimes, I still have to manually mask a good 50 or more faces, depending on. 5. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. 5. 0146. 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。. As you can see in the two screenshots there are problems. XSeg) data_dst mask - edit. 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. Video created in DeepFaceLab 2. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. 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. Training; Blog; About; You can’t perform that action at this time. Train XSeg on these masks. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). Choose one or several GPU idxs (separated by comma). Feb 14, 2023. The next step is to train the XSeg model so that it can create a mask based on the labels you provided. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Xseg Training or Apply Mask First ? frankmiller92; Dec 13, 2022; Replies 5 Views 2K. 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. 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. added 5. dump ( [train_x, train_y], f) #to load it with open ("train. A lot of times I only label and train XSeg masks but forgot to apply them and that's how they looked like. 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. 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. both data_src and data_dst. . DF Admirer. 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. 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. thisdudethe7th Guest. 1. #4. [Tooltip: Half / mid face / full face / whole face / head. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. Put those GAN files away; you will need them later. I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. I have now moved DFL to the Boot partition, the behavior remains the same. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). XSeg in general can require large amounts of virtual memory. Verified Video Creator. If it is successful, then the training preview window will open. xseg) Data_Dst Mask for Xseg Trainer - Edit. . ProTip! Adding no:label will show everything without a label. #1. It learns this to be able to. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. then i reccomend you start by doing some manuel xseg. Plus, you have to apply the mask after XSeg labeling & training, then go for SAEHD training. The only available options are the three colors and the two "black and white" displays. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. Differences from SAE: + new encoder produces more stable face and less scale jitter. Post in this thread or create a new thread in this section (Trained Models) 2. Sep 15, 2022. Step 4: Training. In addition to posting in this thread or the general forum. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. SRC Simpleware. The dice, volumetric overlap error, relative volume difference. Describe the XSeg model using XSeg model template from rules thread. ago. npy","path":"facelib/2DFAN. Use Fit Training. Phase II: Training. Double-click the file labeled ‘6) train Quick96. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. DF Vagrant. 0 XSeg Models and Datasets Sharing Thread. tried on studio drivers and gameready ones. 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. The Xseg needs to be edited more or given more labels if I want a perfect mask. cpu_count() // 2. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. e, a neural network that performs better, in the same amount of training time, or less. 0 to train my SAEHD 256 for over one month. Even though that. Use XSeg for masking. first aply xseg to the model. 0 instead. If your model is collapsed, you can only revert to a backup. 3. Which GPU indexes to choose?: Select one or more GPU. Describe the XSeg model using XSeg model template from rules thread. You can apply Generic XSeg to src faceset. In the XSeg viewer there is a mask on all faces. 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. Several thermal modes to choose from. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. Copy link 1over137 commented Dec 24, 2020. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. even pixel loss can cause it if you turn it on too soon, I only use those. a. Do not post RTM, RTT, AMP or XSeg models here, they all have their own dedicated threads: RTT MODELS SHARING RTM MODELS SHARING AMP MODELS SHARING XSEG MODELS AND DATASETS SHARING 4. In addition to posting in this thread or the general forum. learned-prd+dst: combines both masks, bigger size of both. 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 ). 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. bat’. bat’. You should spend time studying the workflow and growing your skills. 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. Increased page file to 60 gigs, and it started. also make sure not to create a faceset. Requires an exact XSeg mask in both src and dst facesets. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/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). Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I do recommend che. Grayscale SAEHD model and mode for training deepfakes. But I have weak training. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. The Xseg needs to be edited more or given more labels if I want a perfect mask. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. DFL 2. Windows 10 V 1909 Build 18363. Step 5. DeepFaceLab is the leading software for creating deepfakes. 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. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. Training XSeg is a tiny part of the entire process. added 5. Extra trained by Rumateus. Verified Video Creator. 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. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. I mask a few faces, train with XSeg and results are pretty good. 2) Use “extract head” script. 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. Business, Economics, and Finance. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. After training starts, memory usage returns to normal (24/32). Already segmented faces can. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. DLF installation functions. , train_step_batch_size), the gradient accumulation steps (a. XSeg) train; Now it’s time to start training our XSeg model. Double-click the file labeled ‘6) train Quick96. At last after a lot of training, you can merge. 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. 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. 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. npy . 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. 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) data_src trained mask - apply. BAT script, open the drawing tool, draw the Mask of the DST. I have to lower the batch_size to 2, to have it even start. XSeg) data_src trained mask - apply the CMD returns this to me. It really is a excellent piece of software. 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. com! 'X S Entertainment Group' is one option -- get in to view more @ The. 16 XGBoost produce prediction result and probability. How to Pretrain Deepfake Models for DeepFaceLab. Easy Deepfake tutorial for beginners Xseg. pkl", "r") as f: train_x, train_y = pkl. After the XSeg trainer has loaded samples, it should continue on to the filtering stage and then begin training. 18K subscribers in the SFWdeepfakes community. . 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. Aug 7, 2022. 2) Use “extract head” script. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 3. 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. Increased page file to 60 gigs, and it started. python xgboost continue training on existing model. #1. Step 1: Frame Extraction. #5726 opened on Sep 9 by damiano63it. 6) Apply trained XSeg mask for src and dst headsets. 2. I'll try. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. 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. 1. then copy pastE those to your xseg folder for future training. First one-cycle training with batch size 64. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. with XSeg model you can train your own mask segmentator of dst (and src) faces that will be used in merger for whole_face. Part 1. With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. Training XSeg is a tiny part of the entire process. Consol logs. Describe the XSeg model using XSeg model template from rules thread. ogt. Again, we will use the default settings. 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. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. Thread starter thisdudethe7th; Start date Mar 27, 2021; T. Contribute to idorg/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. xseg) Train. #5732 opened on Oct 1 by gauravlokha. Also it just stopped after 5 hours. )train xseg. 3. Requesting Any Facial Xseg Data/Models Be Shared Here. 000 it) and SAEHD training (only 80. Where people create machine learning projects. The exciting part begins! Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. Run 6) train SAEHD. The result is the background near the face is smoothed and less noticeable on swapped face. In this video I explain what they are and how to use them. How to share SAEHD Models: 1. 运行data_dst mask for XSeg trainer - edit. . Get XSEG : Definition and Meaning. 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. It will take about 1-2 hour. fenris17. I have an Issue with Xseg training. Only deleted frames with obstructions or bad XSeg. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. 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). It is used at 2 places. #5727 opened on Sep 19 by WagnerFighter. Does Xseg training affects the regular model training? eg. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. Share. 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. Keep shape of source faces. I turn random color transfer on for the first 10-20k iterations and then off for the rest. Basically whatever xseg images you put in the trainer will shell out. Everything is fast. Then I apply the masks, to both src and dst. It is normal until yesterday. 1. . Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. Video created in DeepFaceLab 2. But usually just taking it in stride and let the pieces fall where they may is much better for your mental health. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. RTX 3090 fails in training SAEHD or XSeg if CPU does not support AVX2 - "Illegal instruction, core dumped". load (f) If your dataset is huge, I would recommend check out hdf5 as @Lukasz Tracewski mentioned. Train the fake with SAEHD and whole_face type. 1. 2) Use “extract head” script. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . Definitely one of the harder parts. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. . I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. 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. learned-dst: uses masks learned during training. 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 XSeg Models and Datasets Sharing Thread. 262K views 1 day ago. GameStop Moderna Pfizer Johnson & Johnson AstraZeneca Walgreens Best Buy Novavax SpaceX Tesla. 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. npy","contentType":"file"},{"name":"3DFAN. I have to lower the batch_size to 2, to have it even start. XSeg training GPU unavailable #5214. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. run XSeg) train. XSeg) data_dst trained mask - apply or 5. Oct 25, 2020. 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. Extract source video frame images to workspace/data_src. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. 0 using XSeg mask training (213. 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. Attempting to train XSeg by running 5. Deep convolutional neural networks (DCNNs) have made great progress in recognizing face images under unconstrained environments [1]. I could have literally started merging after about 3-4 hours (on a somewhat slower AMD integrated GPU). I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. Doing a rough project, I’ve run generic XSeg, going through the frames in edit on the destination, several frames have picked up the background as part of the face, may be a silly question, but if I manually add the mask boundary in edit view do I have to do anything else to apply the new mask area or will that not work, it. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. 6) Apply trained XSeg mask for src and dst headsets. The more the training progresses, the more holes in the SRC model (who has short hair) will open up where the hair disappears. Just let XSeg run a little longer. 3. learned-prd*dst: combines both masks, smaller size of both. 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. pak file untill you did all the manuel xseg you wanted to do. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. XSeg in general can require large amounts of virtual memory. train untill you have some good on all the faces. Mark your own mask only for 30-50 faces of dst video. pkl", "w") as f: pkl. 2. bat train the model Check the faces of 'XSeg dst faces' preview. It haven't break 10k iterations yet, but the objects are already masked out. 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 didn't try it. And the 2nd column and 5th column of preview photo change from clear face to yellow. . Describe the AMP model using AMP model template from rules thread. I wish there was a detailed XSeg tutorial and explanation video. After that we’ll do a deep dive into XSeg editing, training the model,…. 1 Dump XGBoost model with feature map using XGBClassifier. Pickle is a good way to go: import pickle as pkl #to save it with open ("train. 192 it). Link to that. XSeg-prd: uses trained XSeg model to mask using data from source faces. bat，会跳出界面绘制dst遮罩，就是框框抠抠，这是个细活儿，挺累的。 运行train. . learned-dst: uses masks learned during training. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. 000 it) and SAEHD training (only 80. 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. Post in this thread or create a new thread in this section (Trained Models) 2. 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. Model training is consumed, if prompts OOM. Actual behavior. Where people create machine learning projects. on a 320 resolution it takes upto 13-19 seconds . caro_kann; Dec 24, 2021; Replies 6 Views 3K. Xseg Training is a completely different training from Regular training or Pre - Training. Manually labeling/fixing frames and training the face model takes the bulk of the time. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. This forum is for reporting errors with the Extraction process. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. 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. I solved my 5. It might seem high for CPU, but considering it wont start throttling before getting closer to 100 degrees, it's fine. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. bat. 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. Tensorflow-gpu 2. All images are HD and 99% without motion blur, not Xseg. 0 XSeg Models and Datasets Sharing Thread. after that just use the command. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. However, when I'm merging, around 40 % of the frames "do not have a face". Step 5: Merging. 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. Xseg editor and overlays. bat after generating masks using the default generic XSeg 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. THE FILES the model files you still need to download xseg below.