Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. learned-dst: uses masks learned during training. After that we’ll do a deep dive into XSeg editing, training the model,…. . But I have weak training. . 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. Model training is consumed, if prompts OOM. Step 5: Training. Change: 5. I've posted the result in a video. 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. Problems Relative to installation of "DeepFaceLab". 5. I didn't try it. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). prof. 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. 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. . 2) Use “extract head” script. 000 it) and SAEHD training (only 80. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. I solved my 5. Where people create machine learning projects. Expected behavior. 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. Video created in DeepFaceLab 2. It will take about 1-2 hour. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. Timothy B. 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. 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. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. 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. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. , gradient_accumulation_ste. The images in question are the bottom right and the image two above that. I mask a few faces, train with XSeg and results are pretty good. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety. DeepFaceLab 2. Already segmented faces can. 9 XGBoost Best Iteration. 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. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Run: 5. 5) Train XSeg. Python Version: The one that came with a fresh DFL Download yesterday. With the first 30. Describe the XSeg model using XSeg model template from rules thread. Sydney Sweeney, HD, 18k images, 512x512. 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. Describe the XSeg model using XSeg model template from rules thread. Manually mask these with XSeg. This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. Model training is consumed, if prompts OOM. npy","path":"facelib/2DFAN. . 522 it) and SAEHD training (534. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. **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. XSeg in general can require large amounts of virtual memory. Just change it back to src Once you get the. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. 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 1over137 commented Dec 24, 2020. thisdudethe7th Guest. with XSeg model you can train your own mask segmentator of dst (and src) faces that will be used in merger for whole_face. With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. k. I have to lower the batch_size to 2, to have it even start. Step 5: Training. 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. py","path":"models/Model_XSeg/Model. npy","path. Link to that. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. How to share SAEHD Models: 1. Remove filters by clicking the text underneath the dropdowns. idk how the training handles jpeg artifacts so idk if it even matters, but iperov didn't really do. How to share AMP Models: 1. DLF installation functions. Aug 7, 2022. py","contentType":"file"},{"name. Dst face eybrow is visible. If your model is collapsed, you can only revert to a backup. 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. Post processing. 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;. If it is successful, then the training preview window will open. XSeg) data_dst trained mask - apply or 5. 1. #5726 opened on Sep 9 by damiano63it. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. It is now time to begin training our deepfake model. after that just use the command. Manually fix any that are not masked properly and then add those to the training set. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. 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. Then I apply the masks, to both src and dst. 3. Where people create machine learning projects. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. That just looks like "Random Warp". 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. 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. 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. Download Gibi ASMR Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 38,058 / Size: GBDownload Lee Ji-Eun (IU) Faceset - Face: WF / Res: 512 / XSeg: Generic / Qty: 14,256Download Erin Moriarty Faceset - Face: WF / Res: 512 / XSeg: Generic / Qty: 3,157Artificial human — I created my own deepfake—it took two weeks and cost $552 I learned a lot from creating my own deepfake video. 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. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. Step 1: Frame Extraction. #1. Sometimes, I still have to manually mask a good 50 or more faces, depending on. . Definitely one of the harder parts. 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). SRC Simpleware. You can use pretrained model for head. It might seem high for CPU, but considering it wont start throttling before getting closer to 100 degrees, it's fine. 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 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 ". However, when I'm merging, around 40 % of the frames "do not have a face". DST and SRC face functions. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep. I do recommend che. In this video I explain what they are and how to use them. Pickle is a good way to go: import pickle as pkl #to save it with open ("train. Use the 5. com! 'X S Entertainment Group' is one option -- get in to view more @ The. Double-click the file labeled ‘6) train Quick96. On conversion, the settings listed in that post work best for me, but it always helps to fiddle around. bat. py by just changing the line 669 to. PayPal Tip Jar:Lab:MEGA:. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. 0146. #5727 opened on Sep 19 by WagnerFighter. 6) Apply trained XSeg mask for src and dst headsets. Only deleted frames with obstructions or bad XSeg. With the help of. The software will load all our images files and attempt to run the first iteration of our training. 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. 5. The Xseg needs to be edited more or given more labels if I want a perfect mask. However, I noticed in many frames it was just straight up not replacing any of the frames. SAEHD is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020. 3. bat. 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. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. 2. 000 iterations, I disable the training and trained the model with the final dst and src 100. Business, Economics, and Finance. 000. Read all instructions before training. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. I have an Issue with Xseg training. 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. Post in this thread or create a new thread in this section (Trained Models) 2. Part 2 - This part has some less defined photos, but it's. Share. Hello, after this new updates, DFL is only worst. Mark your own mask only for 30-50 faces of dst video. 0 Xseg Tutorial. 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. I have now moved DFL to the Boot partition, the behavior remains the same. xseg) Train. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. fenris17. I have an Issue with Xseg training. XSeg) data_dst/data_src mask for XSeg trainer - remove. . py","path":"models/Model_XSeg/Model. XSeg) data_src trained mask - apply the CMD returns this to me. Four iterations are made at the mentioned speed, followed by a pause of. . 000 it). After the draw is completed, use 5. Video created in DeepFaceLab 2. Frame extraction functions. py","contentType":"file"},{"name. GPU: Geforce 3080 10GB. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. GPU: Geforce 3080 10GB. 5. Post in this thread or create a new thread in this section (Trained Models). Put those GAN files away; you will need them later. ]. 000 it), SAEHD pre-training (1. 建议萌. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. The software will load all our images files and attempt to run the first iteration of our training. Sometimes, I still have to manually mask a good 50 or more faces, depending on material. The software will load all our images files and attempt to run the first iteration of our training. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. Pretrained models can save you a lot of time. Plus, you have to apply the mask after XSeg labeling & training, then go for SAEHD training. XSeg-prd: uses. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. Its a method of randomly warping the image as it trains so it is better at generalization. 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. learned-prd*dst: combines both masks, smaller size of both. 0 using XSeg mask training (213. Xseg Training or Apply Mask First ? frankmiller92; Dec 13, 2022; Replies 5 Views 2K. bat’. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). npy","contentType":"file"},{"name":"3DFAN. #1. Step 3: XSeg Masks. It haven't break 10k iterations yet, but the objects are already masked out. . when the rightmost preview column becomes sharper stop training and run a convert. network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. Does model training takes into account applied trained xseg mask ? eg. The Xseg training on src ended up being at worst 5 pixels over. Step 5: Merging. For DST just include the part of the face you want to replace. in xseg model the exclusions indeed are learned and fine, the issue new is in training preview, it doesn't show that , i haven't done yet, so now sure if its a preview bug what i have done so far: - re checked frames to see if. The Xseg needs to be edited more or given more labels if I want a perfect mask. gili12345 opened this issue Aug 27, 2021 · 3 comments Comments. Basically whatever xseg images you put in the trainer will shell out. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. If it is successful, then the training preview window will open. 2) Use “extract head” script. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. Attempting to train XSeg by running 5. 1 Dump XGBoost model with feature map using XGBClassifier. + pixel loss and dssim loss are merged together to achieve both training speed and pixel trueness. Training. You should spend time studying the workflow and growing your skills. Post in this thread or create a new thread in this section (Trained Models). 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. XSeg apply takes the trained XSeg masks and exports them to the data set. When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. 1. run XSeg) train. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. DFL 2. 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. 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. Notes, tests, experience, tools, study and explanations of the source code. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. During training check previews often, if some faces have bad masks after about 50k iterations (bad shape, holes, blurry), save and stop training, apply masks to your dataset, run editor, find faces with bad masks by enabling XSeg mask overlay in the editor, label them and hit esc to save and exit and then resume XSeg model training, when. Tensorflow-gpu 2. Step 5. Please mark. Consol logs. And for SRC, what part is used as face for training. Increased page file to 60 gigs, and it started. Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. bat compiles all the xseg faces you’ve masked. Xseg遮罩模型的使用可以分为训练和使用两部分部分. . Final model config:===== Model Summary ==. I'm facing the same problem. DeepFaceLab is the leading software for creating deepfakes. 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. As you can see in the two screenshots there are problems. For this basic deepfake, we’ll use the Quick96 model since it has better support for low-end GPUs and is generally more beginner friendly. When it asks you for Face type, write “wf” and start the training session by pressing Enter. If you include that bit of cheek, it might train as the inside of her mouth or it might stay about the same. Describe the SAEHD model using SAEHD model template from rules thread. Grab 10-20 alignments from each dst/src you have, while ensuring they vary and try not to go higher than ~150 at first. Keep shape of source faces. The Xseg training on src ended up being at worst 5 pixels over. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. 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. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. #4. 3. It really is a excellent piece of software. It will likely collapse again however, depends on your model settings quite usually. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. 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. Final model. caro_kann; Dec 24, 2021; Replies 6 Views 3K. This seems to even out the colors, but not much more info I can give you on the training. First one-cycle training with batch size 64. Make a GAN folder: MODEL/GAN. py","contentType":"file"},{"name. The dice, volumetric overlap error, relative volume difference. - GitHub - Twenkid/DeepFaceLab-SAEHDBW: Grayscale SAEHD model and mode for training deepfakes. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. I was less zealous when it came to dst, because it was longer and I didn't really understand the flow/missed some parts in the guide. XSeg-prd: uses trained XSeg model to mask using data from source faces. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). I solved my 6) train SAEHD issue by reducing the number of worker, I edited DeepFaceLab_NVIDIA_up_to_RTX2080ti_series _internalDeepFaceLabmodelsModel_SAEHDModel. How to share SAEHD Models: 1. Where people create machine learning projects. 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. After training starts, memory usage returns to normal (24/32). Instead of using a pretrained model. 3. I wish there was a detailed XSeg tutorial and explanation video. 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. oneduality • 4 yr. The problem of face recognition in lateral and lower projections. Choose the same as your deepfake model. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. 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. Read the FAQs and search the forum before posting a new topic. 5) Train XSeg. This forum is for reporting errors with the Extraction process. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 0 XSeg Models and Datasets Sharing Thread. I guess you'd need enough source without glasses for them to disappear. Does the model differ if one is xseg-trained-mask applied while. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. ProTip! Adding no:label will show everything without a label. BAT script, open the drawing tool, draw the Mask of the DST. Oct 25, 2020. Where people create machine learning projects. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. + new decoder produces subpixel clear result. 0rc3 Driver. Tensorflow-gpu. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. 3. Manually labeling/fixing frames and training the face model takes the bulk of the time. 6) Apply trained XSeg mask for src and dst headsets. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. XSeg) data_dst mask - edit. Get XSEG : Definition and Meaning. XSeg) data_dst/data_src mask for XSeg trainer - remove. It should be able to use GPU for training. X. 0 to train my SAEHD 256 for over one month. DF Admirer. From the project directory, run 6. DF Vagrant. I actually got a pretty good result after about 5 attempts (all in the same training session). The next step is to train the XSeg model so that it can create a mask based on the labels you provided. Increased page file to 60 gigs, and it started. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. (or increase) denoise_dst. Train the fake with SAEHD and whole_face type. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. Do not mix different age. then i reccomend you start by doing some manuel xseg. 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. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. Normally at gaming temps reach high 85-90, and its confirmed by AMD that the Ryzen 5800H is made that way. )train xseg. I turn random color transfer on for the first 10-20k iterations and then off for the rest. 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. 000. You can use pretrained model for head. 4. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. This one is only at 3k iterations but the same problem presents itself even at like 80k and I can't seem to figure out what is causing it. In a paper published in the Quarterly Journal of Experimental. 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. First one-cycle training with batch size 64. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. 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. Consol logs. DeepFaceLab code and required packages. What's more important is that the xseg mask is consistent and transitions smoothly across the frames. . 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. Post_date. train untill you have some good on all the faces. 0 using XSeg mask training (100. bat train the model Check the faces of 'XSeg dst faces' preview. Describe the SAEHD model using SAEHD model template from rules thread. Where people create machine learning projects. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub.