npy . Where people create machine learning projects. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. In addition to posting in this thread or the general forum. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. In a paper published in the Quarterly Journal of Experimental. py","contentType":"file"},{"name. . bat train the model Check the faces of 'XSeg dst faces' preview. == Model name: XSeg ==== Current iteration: 213522 ==== face_type: wf ==== p. Run: 5. After that we’ll do a deep dive into XSeg editing, training the model,…. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). 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. 3. 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. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. The software will load all our images files and attempt to run the first iteration of our training. Lee - Dec 16, 2019 12:50 pm UTCForum rules. 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. For a 8gb card you can place on. Oct 25, 2020. Xseg editor and overlays. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. slow We can't buy new PC, and new cards, after you every new updates ))). With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. Pass the in. traceback (most recent call last) #5728 opened on Sep 24 by Ujah0. 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. DF Admirer. Face type ( h / mf / f / wf / head ): Select the face type for XSeg training. In addition to posting in this thread or the general forum. 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. However, I noticed in many frames it was just straight up not replacing any of the frames. Training XSeg is a tiny part of the entire process. As you can see in the two screenshots there are problems. Model training is consumed, if prompts OOM. + new decoder produces subpixel clear result. 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. Only deleted frames with obstructions or bad XSeg. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. Requires an exact XSeg mask in both src and dst facesets. pak” archive file for faster loading times 47:40 – Beginning training of our SAEHD model 51:00 – Color transfer. 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. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. 5. bat. 6) Apply trained XSeg mask for src and dst headsets. Yes, but a different partition. py","path":"models/Model_XSeg/Model. It really is a excellent piece of software. 1256. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. This seems to even out the colors, but not much more info I can give you on the training. Sep 15, 2022. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. Training. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. I have to lower the batch_size to 2, to have it even start. Use Fit Training. #1. 2. Please mark. Download Celebrity Facesets for DeepFaceLab deepfakes. then i reccomend you start by doing some manuel xseg. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. Where people create machine learning projects. 1 Dump XGBoost model with feature map using XGBClassifier. after that just use the command. Also it just stopped after 5 hours. Post in this thread or create a new thread in this section (Trained Models) 2. Read the FAQs and search the forum before posting a new topic. You can then see the trained XSeg mask for each frame, and add manual masks where needed. Where people create machine learning projects. a. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. 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. Training; Blog; About; You can’t perform that action at this time. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Today, I train again without changing any setting, but the loss rate for src rised from 0. 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. 0146. Where people create machine learning projects. Sometimes, I still have to manually mask a good 50 or more faces, depending on. 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. learned-prd*dst: combines both masks, smaller size of both. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. bat after generating masks using the default generic XSeg model. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Video created in DeepFaceLab 2. XSeg-dst: uses trained XSeg model to mask using data from destination faces. 2. Phase II: Training. DFL 2. BAT script, open the drawing tool, draw the Mask of the DST. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. bat compiles all the xseg faces you’ve masked. 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. What's more important is that the xseg mask is consistent and transitions smoothly across the frames. 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 ). The Xseg needs to be edited more or given more labels if I want a perfect mask. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . #1. Sometimes, I still have to manually mask a good 50 or more faces, depending on material. Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. Read all instructions before training. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. Src faceset should be xseg'ed and applied. pkl", "w") as f: pkl. if i lower the resolution of the aligned src , the training iterations go faster , but it will STILL take extra time on every 4th iteration. You can see one of my friend in Princess Leia ;-) I've put same scenes with different. XSeg) data_src trained mask - apply the CMD returns this to me. Step 5. Xseg遮罩模型的使用可以分为训练和使用两部分部分. 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;. Its a method of randomly warping the image as it trains so it is better at generalization. 建议萌. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Deepfake native resolution progress. pak file untill you did all the manuel xseg you wanted to do. when the rightmost preview column becomes sharper stop training and run a convert. After the draw is completed, use 5. Again, we will use the default settings. . 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. It is now time to begin training our deepfake model. Where people create machine learning projects. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by. Intel i7-6700K (4GHz) 32GB RAM (Already increased pagefile on SSD to 60 GB) 64 bit. The images in question are the bottom right and the image two above that. ]. DST and SRC face functions. 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). 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. X. Describe the SAEHD model using SAEHD model template from rules thread. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I've posted the result in a video. Model training is consumed, if prompts OOM. (or increase) denoise_dst. k. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. 5. In this video I explain what they are and how to use them. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). soklmarle; Jan 29, 2023; Replies 2 Views 597. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. k. Where people create machine learning projects. Describe the XSeg model using XSeg model template from rules thread. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. How to Pretrain Deepfake Models for DeepFaceLab. 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. The result is the background near the face is smoothed and less noticeable on swapped face. v4 (1,241,416 Iterations). DeepFaceLab is the leading software for creating deepfakes. with XSeg model you can train your own mask segmentator of dst (and src) faces that will be used in merger for whole_face. 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. The problem of face recognition in lateral and lower projections. If your model is collapsed, you can only revert to a backup. 0 to train my SAEHD 256 for over one month. I understand that SAEHD (training) can be processed on my CPU, right? Yesterday, "I tried the SAEHD method" and all the. 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. dump ( [train_x, train_y], f) #to load it with open ("train. Model training fails. XSeg allows everyone to train their model for the segmentation of a spe-Jan 11, 2021. proper. 0 instead. 2) Use “extract head” script. 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". The exciting part begins! Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. 192 it). Blurs nearby area outside of applied face mask of training samples. 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. 2) Use “extract head” script. 0 XSeg Models and Datasets Sharing Thread. Copy link 1over137 commented Dec 24, 2020. I have now moved DFL to the Boot partition, the behavior remains the same. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. Double-click the file labeled ‘6) train Quick96. . 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. 运行data_dst mask for XSeg trainer - edit. 5) Train XSeg. 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 have found a bug are having issues with the Training process not working, then you should post in the Training Support forum. Use the 5. Dst face eybrow is visible. It is now time to begin training our deepfake model. XSeg) data_dst trained mask - apply or 5. The Xseg training on src ended up being at worst 5 pixels over. 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 normal until yesterday. Video created in DeepFaceLab 2. It will likely collapse again however, depends on your model settings quite usually. XSeg Model Training. Run 6) train SAEHD. Feb 14, 2023. I mask a few faces, train with XSeg and results are pretty good. However, when I'm merging, around 40 % of the frames "do not have a face". 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. Describe the SAEHD model using SAEHD model template from rules thread. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. first aply xseg to the model. 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 didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. The Xseg needs to be edited more or given more labels if I want a perfect mask. 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. 5) Train XSeg. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. . ago. The software will load all our images files and attempt to run the first iteration of our training. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. 1. gili12345 opened this issue Aug 27, 2021 · 3 comments Comments. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. For DST just include the part of the face you want to replace. Double-click the file labeled ‘6) train Quick96. . 1. . 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 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. prof. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. XSeg) train. Use XSeg for masking. Keep shape of source faces. 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。. 3. It is used at 2 places. Differences from SAE: + new encoder produces more stable face and less scale jitter. Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. 1. Choose one or several GPU idxs (separated by comma). 000 it). GPU: Geforce 3080 10GB. XSeg) data_dst/data_src mask for XSeg trainer - remove. You can use pretrained model for head. How to share SAEHD Models: 1. XSeg apply takes the trained XSeg masks and exports them to the data set. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. 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. caro_kann; Dec 24, 2021; Replies 6 Views 3K. 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. Windows 10 V 1909 Build 18363. S. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd. 0 using XSeg mask training (100. 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, then we’ll use the generic mask to shortcut the entire process. . Contribute to idorg/DeepFaceLab by creating an account on DagsHub. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep. Final model config:===== Model Summary ==. The images in question are the bottom right and the image two above that. 1. learned-dst: uses masks learned during training. py","path":"models/Model_XSeg/Model. Enjoy it. Complete the 4-day Level 1 Basic CPTED Course. And for SRC, what part is used as face for training. This forum is for discussing tips and understanding the process involved with Training a Faceswap model. npy","contentType":"file"},{"name":"3DFAN. Where people create machine learning projects. 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. Choose the same as your deepfake model. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. [new] No saved models found. 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. 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. . Again, we will use the default settings. 000 iterations many masks look like. When the face is clear enough, you don't need. 3X to 4. Training speed. Train the fake with SAEHD and whole_face type. In addition to posting in this thread or the general forum. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Tensorflow-gpu. Running trainer. It really is a excellent piece of software. 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. Requesting Any Facial Xseg Data/Models Be Shared Here. Pickle is a good way to go: import pickle as pkl #to save it with open ("train. bat I don’t even know if this will apply without training masks. Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. Basically whatever xseg images you put in the trainer will shell out. [Tooltip: Half / mid face / full face / whole face / head. py by just changing the line 669 to. 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. oneduality • 4 yr. Step 5. 2. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. Make a GAN folder: MODEL/GAN. And the 2nd column and 5th column of preview photo change from clear face to yellow. 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. Verified Video Creator. 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. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety. 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. THE FILES the model files you still need to download xseg below. Where people create machine learning projects. SRC Simpleware. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. . Do not mix different age. a. 3. SAEHD Training Failure · Issue #55 · chervonij/DFL-Colab · GitHub. 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. 1 participant. 9794 and 0. Notes, tests, experience, tools, study and explanations of the source code. 6) Apply trained XSeg mask for src and dst headsets. py","contentType":"file"},{"name. , train_step_batch_size), the gradient accumulation steps (a. Video created in DeepFaceLab 2. In the XSeg viewer there is a mask on all faces. With the first 30. Share. Even though that. The training preview shows the hole clearly and I run on a loss of ~. npy","path. Part 2 - This part has some less defined photos, but it's. Xseg apply/remove functions. The only available options are the three colors and the two "black and white" displays. 5. 000. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. 00:00 Start00:21 What is pretraining?00:50 Why use i. xseg train not working #5389. . Where people create machine learning projects. Post in this thread or create a new thread in this section (Trained Models). 3. It really is a excellent piece of software. then copy pastE those to your xseg folder for future training. Curiously, I don't see a big difference after GAN apply (0. , gradient_accumulation_ste. Put those GAN files away; you will need them later. After training starts, memory usage returns to normal (24/32). Aug 7, 2022. 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. bat. I wish there was a detailed XSeg tutorial and explanation video. Normally at gaming temps reach high 85-90, and its confirmed by AMD that the Ryzen 5800H is made that way. - GitHub - Twenkid/DeepFaceLab-SAEHDBW: Grayscale SAEHD model and mode for training deepfakes. 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. Deletes all data in the workspace folder and rebuilds folder structure. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Read the FAQs and search the forum before posting a new topic. 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. 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. Python Version: The one that came with a fresh DFL Download yesterday. Where people create machine learning projects. 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. XSeg) train. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. First one-cycle training with batch size 64. . 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. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. Copy link. Grab 10-20 alignments from each dst/src you have, while ensuring they vary and try not to go higher than ~150 at first. cpu_count() // 2. 000 it) and SAEHD training (only 80. 000. If you want to get tips, or better understand the Extract process, then. Step 1: Frame Extraction. XSeg) data_src trained mask - apply. It must work if it does for others, you must be doing something wrong. Does the model differ if one is xseg-trained-mask applied while. The software will load all our images files and attempt to run the first iteration of our training. Final model. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. 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). Several thermal modes to choose from. even pixel loss can cause it if you turn it on too soon, I only use those. The more the training progresses, the more holes in the SRC model (who has short hair) will open up where the hair disappears. I actually got a pretty good result after about 5 attempts (all in the same training session). 192 it). #4. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. 2. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I have to lower the batch_size to 2, to have it even start.