You should spend time studying the workflow and growing your skills. DFL 2. XSeg) data_dst/data_src mask for XSeg trainer - remove. 2. I just continue training for brief periods, applying new mask, then checking and fixing masked faces that need a little help. With the first 30. py","contentType":"file"},{"name. Do not mix different age. == Model name: XSeg ==== Current iteration: 213522 ==== face_type: wf ==== p. v4 (1,241,416 Iterations). 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. 5) Train XSeg. 000 it), SAEHD pre-training (1. XSegged with Groggy4 's XSeg model. 0 How to make XGBoost model to learn its mistakes. 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. 5. You can use pretrained model for head. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). bat’. Read all instructions before training. 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. Enter a name of a new model : new Model first run. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Enjoy it. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. Usually a "Normal" Training takes around 150. 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 ). With the help of. I actually got a pretty good result after about 5 attempts (all in the same training session). . Xseg Training or Apply Mask First ? frankmiller92; Dec 13, 2022; Replies 5 Views 2K. I do recommend che. Where people create machine learning projects. 3. bat训练遮罩，设置脸型和batch_size，训练个几十上百万，回车结束。 XSeg遮罩训练素材是不区分是src和dst。 2. I'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. learned-prd+dst: combines both masks, bigger size of both. The Xseg training on src ended up being at worst 5 pixels over. XSeg won't train with GTX1060 6GB. Where people create machine learning projects. BAT script, open the drawing tool, draw the Mask of the DST. 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). Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. 0 XSeg Models and Datasets Sharing Thread. 000 it). Xseg遮罩模型的使用可以分为训练和使用两部分部分. ProTip! Adding no:label will show everything without a label. Xseg training functions. 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. py","path":"models/Model_XSeg/Model. Where people create machine learning projects. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. How to share XSeg Models: 1. 5) Train XSeg. idk how the training handles jpeg artifacts so idk if it even matters, but iperov didn't really do. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. A skill in programs such as AfterEffects or Davinci Resolve is also desirable. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. 1) except for some scenes where artefacts disappear. 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. Curiously, I don't see a big difference after GAN apply (0. Where people create machine learning projects. The software will load all our images files and attempt to run the first iteration of our training. 2. Post in this thread or create a new thread in this section (Trained Models). 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. 192 it). 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. Notes; Sources: Still Images, Interviews, Gunpowder Milkshake, Jett, The Haunting of Hill House. Notes, tests, experience, tools, study and explanations of the source code. 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. This video was made to show the current workflow to follow when you want to create a deepfake with DeepFaceLab. 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. The training preview shows the hole clearly and I run on a loss of ~. This seems to even out the colors, but not much more info I can give you on the training. If it is successful, then the training preview window will open. , gradient_accumulation_ste. Where people create machine learning projects. Pass the in. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Video created in DeepFaceLab 2. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. bat after generating masks using the default generic XSeg model. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. Again, we will use the default settings. 0 using XSeg mask training (100. The images in question are the bottom right and the image two above that. I didn't filter out blurry frames or anything like that because I'm too lazy so you may need to do that yourself. Describe the SAEHD model using SAEHD model template from rules thread. Where people create machine learning projects. You can use pretrained model for head. Oct 25, 2020. #5726 opened on Sep 9 by damiano63it. DLF installation functions. It will take about 1-2 hour. Sometimes, I still have to manually mask a good 50 or more faces, depending on material. Manually labeling/fixing frames and training the face model takes the bulk of the time. Python Version: The one that came with a fresh DFL Download yesterday. 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. 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. And the 2nd column and 5th column of preview photo change from clear face to yellow. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. xseg) Data_Dst Mask for Xseg Trainer - Edit. . When the face is clear enough, you don't need to do manual masking, you can apply Generic XSeg and get. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. How to share SAEHD Models: 1. 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。. GPU: Geforce 3080 10GB. 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 face. Then restart training. 0 using XSeg mask training (213. on a 320 resolution it takes upto 13-19 seconds . 3. It must work if it does for others, you must be doing something wrong. 1. PayPal Tip Jar:Lab:MEGA:. 0 instead. learned-dst: uses masks learned during training. XSeg apply takes the trained XSeg masks and exports them to the data set. 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. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. bat. 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". Post in this thread or create a new thread in this section (Trained Models). Does the model differ if one is xseg-trained-mask applied while. 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 Model Training. Step 3: XSeg Masks. com XSEG Stands For : X S Entertainment GroupObtain the confidence needed to safely operate your Niton handheld XRF or LIBS analyzer. . py","path":"models/Model_XSeg/Model. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. XSeg training GPU unavailable #5214. 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. Yes, but a different partition. added XSeg model. And for SRC, what part is used as face for training. 1. XSeg in general can require large amounts of virtual memory. Step 5. The next step is to train the XSeg model so that it can create a mask based on the labels you provided. 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. also make sure not to create a faceset. pkl", "w") as f: pkl. How to share SAEHD Models: 1. 3. 2) Use “extract head” script. XSeg) train; Now it’s time to start training our XSeg model. 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. Include link to the model (avoid zips/rars) to a free file sharing of your choice (google drive, mega). Xseg Training is a completely different training from Regular training or Pre - Training. XSEG DEST instead cover the beard (Xseg DST covers it) but cuts the head and hair up. After that we’ll do a deep dive into XSeg editing, training the model,…. Unfortunately, there is no "make everything ok" button in DeepFaceLab. 3. Download Celebrity Facesets for DeepFaceLab deepfakes. After the draw is completed, use 5. 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. even pixel loss can cause it if you turn it on too soon, I only use those. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. Post in this thread or create a new thread in this section (Trained Models) 2. )train xseg. Attempting to train XSeg by running 5. 4. I wish there was a detailed XSeg tutorial and explanation video. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Download Nimrat Khaira Faceset - Face: WF / Res: 512 / XSeg: None / Qty: 18,297Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. Describe the XSeg model using XSeg model template from rules thread. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. . Xseg apply/remove functions. If it is successful, then the training preview window will open. 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. 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. . Training XSeg is a tiny part of the entire process. 1. cpu_count() // 2. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. 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. Post in this thread or create a new thread in this section (Trained Models) 2. 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. 5. Keep shape of source faces. Windows 10 V 1909 Build 18363. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 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. when the rightmost preview column becomes sharper stop training and run a convert. Step 4: Training. Final model config:===== Model Summary ==. 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 ". 2) Use “extract head” script. + new decoder produces subpixel clear result. When the face is clear enough, you don't need. 000 it). XSeg) data_dst/data_src mask for XSeg trainer - remove. #5732 opened on Oct 1 by gauravlokha. Xseg editor and overlays. 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. Post_date. Could this be some VRAM over allocation problem? Also worth of note, CPU training works fine. Instead of using a pretrained model. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. updated cuda and cnn and drivers. 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. - Issues · nagadit/DeepFaceLab_Linux. Step 5: Training. 27 votes, 16 comments. A pretrained model is created with a pretrain faceset consisting of thousands of images with a wide variety. The only available options are the three colors and the two "black and white" displays. Applying trained XSeg model to aligned/ folder. bat scripts to enter the training phase, and the face parameters use WF or F, and BS use the default value as needed. 训练Xseg模型. 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. oneduality • 4 yr. SAEHD looked good after about 100-150 (batch 16), but doing GAN to touch up a bit. XSeg in general can require large amounts of virtual memory. 3. py","contentType":"file"},{"name. Download Megan Fox Faceset - Face: F / Res: 512 / XSeg: Generic / Qty: 3,726Contribute to idonov/DeepFaceLab by creating an account on DagsHub. #1. 6) Apply trained XSeg mask for src and dst headsets. Where people create machine learning projects. ]. GPU: Geforce 3080 10GB. Where people create machine learning projects. , train_step_batch_size), the gradient accumulation steps (a. XSeg-dst: uses trained XSeg model to mask using data from destination faces. 5. It is now time to begin training our deepfake model. 05 and 0. Post in this thread or create a new thread in this section (Trained Models) 2. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. pak file untill you did all the manuel xseg you wanted to do. Use Fit Training. XSeg allows everyone to train their model for the segmentation of a spe-Jan 11, 2021. Double-click the file labeled ‘6) train Quick96. Tensorflow-gpu 2. . I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. 0 Xseg Tutorial. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. Src faceset should be xseg'ed and applied. Tensorflow-gpu. npy","path. Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. Training; Blog; About; You can’t perform that action at this time. SAEHD is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020. Mar 27, 2021 #1 (account deleted) Groggy4 NotSure. learned-prd+dst: combines both masks, bigger size of both. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by nebelfuerst. soklmarle; Jan 29, 2023; Replies 2 Views 597. Sydney Sweeney, HD, 18k images, 512x512. 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. Only deleted frames with obstructions or bad XSeg. 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. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. Face type ( h / mf / f / wf / head ): Select the face type for XSeg training. gili12345 opened this issue Aug 27, 2021 · 3 comments Comments. Please read the general rules for Trained Models in case you are not sure where to post requests or are looking for. Hi everyone, I'm doing this deepfake, using the head previously for me pre -trained. You can apply Generic XSeg to src faceset. xseg) Data_Dst Mask for Xseg Trainer - Edit. I've posted the result in a video. Train the fake with SAEHD and whole_face type. 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. Again, we will use the default settings. Must be diverse enough in yaw, light and shadow conditions. (or increase) denoise_dst. And this trend continues for a few hours until it gets so slow that there is only 1 iteration in about 20 seconds. All images are HD and 99% without motion blur, not Xseg. 9794 and 0. 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. Manually labeling/fixing frames and training the face model takes the bulk of the time. 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. Where people create machine learning projects. The software will load all our images files and attempt to run the first iteration of our training. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep fakes,d. Problems Relative to installation of "DeepFaceLab". 建议萌. In addition to posting in this thread or the general forum. Make a GAN folder: MODEL/GAN. First one-cycle training with batch size 64. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. slow We can't buy new PC, and new cards, after you every new updates ))). The Xseg needs to be edited more or given more labels if I want a perfect mask. Easy Deepfake tutorial for beginners Xseg. Video created in DeepFaceLab 2. DeepFaceLab Model Settings Spreadsheet (SAEHD) Use the dropdown lists to filter the table. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. 运行data_dst mask for XSeg trainer - edit. Double-click the file labeled ‘6) train Quick96. py","contentType":"file"},{"name. . npy . Train XSeg on these masks. 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. XSeg-prd: uses. 000 it) and SAEHD training (only 80. 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. 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. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. RTT V2 224: 20 million iterations of training. After training starts, memory usage returns to normal (24/32). This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. 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. 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. Describe the XSeg model using XSeg model template from rules thread. Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. 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. 00:00 Start00:21 What is pretraining?00:50 Why use i. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. DeepFaceLab code and required packages. Where people create machine learning projects. 9 XGBoost Best Iteration. From the project directory, run 6. Training speed. 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). 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. 0146. DeepFaceLab is the leading software for creating deepfakes. 000 it), SAEHD pre-training (1. A lot of times I only label and train XSeg masks but forgot to apply them and that's how they looked like. In addition to posting in this thread or the general forum. a. I'm facing the same problem. It has been claimed that faces are recognized as a “whole” rather than the recognition of individual parts. Part 2 - This part has some less defined photos, but it's. 5. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. 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. py","path":"models/Model_XSeg/Model. Where people create machine learning projects. 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. For those wanting to become Certified CPTED Practitioners the process will involve the following steps: 1. However in order to get the face proportions correct, and a better likeness, the mask needs to be fit to the actual faces. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. 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. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. I turn random color transfer on for the first 10-20k iterations and then off for the rest. 000 iterations, I disable the training and trained the model with the final dst and src 100. pak” archive file for faster loading times 47:40 – Beginning training of our SAEHD model 51:00 – Color transfer. I have to lower the batch_size to 2, to have it even start. It might seem high for CPU, but considering it wont start throttling before getting closer to 100 degrees, it's fine. Xseg editor and overlays. If you want to get tips, or better understand the Extract process, then. workspace. You can then see the trained XSeg mask for each frame, and add manual masks where needed. 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. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. Lee - Dec 16, 2019 12:50 pm UTCForum rules. Step 2: Faces Extraction. py","contentType":"file"},{"name. 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. However, when I'm merging, around 40 % of the frames "do not have a face". Final model. run XSeg) train. The problem of face recognition in lateral and lower projections. I have to lower the batch_size to 2, to have it even start. . 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. Describe the AMP model using AMP model template from rules thread. Hello, after this new updates, DFL is only worst. Include link to the model (avoid zips/rars) to a free file. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep. DF Admirer. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. Mark your own mask only for 30-50 faces of dst video.