network in the training process robust to hands, glasses, and any other objects which may cover the face somehow. DeepFaceLab code and required packages. In the XSeg viewer there is a mask on all faces. However, I noticed in many frames it was just straight up not replacing any of the frames. Part 1. 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. 建议萌. How to share XSeg Models: 1. I have a model with quality 192 pretrained with 750. 1. BAT script, open the drawing tool, draw the Mask of the DST. XSeg) data_src trained mask - apply the CMD returns this to me. . Notes, tests, experience, tools, study and explanations of the source code. 1. 1256. 000. As you can see the output show the ERROR that was result in a double 'XSeg_' in path of XSeg_256_opt. #5732 opened on Oct 1 by gauravlokha. 000 iterations, I disable the training and trained the model with the final dst and src 100. But there is a big difference between training for 200,000 and 300,000 iterations (or XSeg training). 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. even pixel loss can cause it if you turn it on too soon, I only use those. Differences from SAE: + new encoder produces more stable face and less scale jitter. . 5. learned-prd*dst: combines both masks, smaller size of both. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. MikeChan said: Dear all, I'm using DFL-colab 2. ]. XSeg question. soklmarle; Jan 29, 2023; Replies 2 Views 597. Do you see this issue without 3D parallelism? According to the documentation, train_batch_size is aggregated by the batch size that a single GPU processes in one forward/backward pass (a. Read the FAQs and search the forum before posting a new topic. {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. Verified Video Creator. Consol logs. 3: XSeg Mask Labeling & XSeg Model Training Q1: XSeg is not mandatory because the faces have a default mask. Post processing. The Xseg needs to be edited more or given more labels if I want a perfect mask. . com XSEG Stands For : X S Entertainment GroupObtain the confidence needed to safely operate your Niton handheld XRF or LIBS analyzer. SAEHD is a new heavyweight model for high-end cards to achieve maximum possible deepfake quality in 2020. 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. I do recommend che. py","path":"models/Model_XSeg/Model. XSeg in general can require large amounts of virtual memory. #1. bat’. With XSeg you only need to mask a few but various faces from the faceset, 30-50 for regular deepfake. 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. The Xseg needs to be edited more or given more labels if I want a perfect mask. Step 5: Training. Tensorflow-gpu. It learns this to be able to. THE FILES the model files you still need to download xseg below. 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. The software will load all our images files and attempt to run the first iteration of our training. RTX 3090 fails in training SAEHD or XSeg if CPU does not support AVX2 - "Illegal instruction, core dumped". {"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. ogt. The software will load all our images files and attempt to run the first iteration of our training. CryptoHow to pretrain models for DeepFaceLab deepfakes. Read the FAQs and search the forum before posting a new topic. bat. Again, we will use the default settings. X. Post in this thread or create a new thread in this section (Trained Models) 2. 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. HEAD masks are not ideal since they cover hair, neck, ears (depending on how you mask it but in most cases with short haired males faces you do hair and ears) which aren't fully covered by WF and not at all by FF,. #1. 000 iterations, but the more you train it the better it gets EDIT: You can also pause the training and start it again, I don't know why people usually do it for multiple days straight, maybe it is to save time, but I'm not surenew DeepFaceLab build has been released. Xseg遮罩模型的使用可以分为训练和使用两部分部分. 7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. SRC Simpleware. Post in this thread or create a new thread in this section (Trained Models) 2. The images in question are the bottom right and the image two above that. 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. Requires an exact XSeg mask in both src and dst facesets. 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. 3. . As you can see in the two screenshots there are problems. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. caro_kann; Dec 24, 2021; Replies 6 Views 3K. Part 2 - This part has some less defined photos, but it's. first aply xseg to the model. On conversion, the settings listed in that post work best for me, but it always helps to fiddle around. When the face is clear enough, you don't need. 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. 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. Very soon in the Colab XSeg training process the faces at my previously SAEHD trained model (140k iterations) already look perfectly masked. . 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。. k. (or increase) denoise_dst. Where people create machine learning projects. 000 it). RTT V2 224: 20 million iterations of training. Where people create machine learning projects. cpu_count() // 2. added 5. It must work if it does for others, you must be doing something wrong. It is normal until yesterday. However in order to get the face proportions correct, and a better likeness, the mask needs to be fit to the actual faces. All reactions1. I solved my 5. 2. Grayscale SAEHD model and mode for training deepfakes. python xgboost continue training on existing model. DFL 2. Read all instructions before training. Check out What does XSEG mean? along with list of similar terms on definitionmeaning. 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. . It really is a excellent piece of software. Container for all video, image, and model files used in the deepfake project. 1) except for some scenes where artefacts disappear. Just let XSeg run a little longer instead of worrying about the order that you labeled and trained stuff. Training XSeg is a tiny part of the entire process. 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. 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. Src faceset is celebrity. 2. bat removes labeled xseg polygons from the extracted frames{"payload":{"allShortcutsEnabled":false,"fileTree":{"models/Model_XSeg":{"items":[{"name":"Model. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. XSegged with Groggy4 's XSeg model. 2 is too much, you should start at lower value, use the recommended value DFL recommends (type help) and only increase if needed. #5726 opened on Sep 9 by damiano63it. xseg) Train. After that we’ll do a deep dive into XSeg editing, training the model,…. 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. #4. Contribute to idorg/DeepFaceLab by creating an account on DagsHub. If you include that bit of cheek, it might train as the inside of her mouth or it might stay about the same. )train xseg. Describe the XSeg model using XSeg model template from rules thread. 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. Use XSeg for masking. Sep 15, 2022. Enable random warp of samples Random warp is required to generalize facial expressions of both faces. 6) Apply trained XSeg mask for src and dst headsets. I have 32 gigs of ram, and had a 40 gig page file, and still got these page file errors when starting saehd training. With Xseg you create mask on your aligned faces, after you apply trained xseg mask, you need to train with SAEHD. working 10 times slow faces ectract - 1000 faces, 70 minutes Xseg train freeze after 200 interactions training . 0 using XSeg mask training (100. {"payload":{"allShortcutsEnabled":false,"fileTree":{"facelib":{"items":[{"name":"2DFAN. PayPal Tip Jar:Lab Tutorial (basic/standard):Channel (He. Hi all, very new to DFL -- I tried to use the exclusion polygon tool on dst source mouth in xseg editor. Change: 5. After training starts, memory usage returns to normal (24/32). 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. It will take about 1-2 hour. Dry Dock Training (Victoria, BC) Dates: September 30 - October 3, 2019 Time: 8:00am - 5:00pm Instructor: Joe Stiglich, DM Consulting Location: Camosun. Do not mix different age. bat after generating masks using the default generic XSeg model. Step 5: Merging. Several thermal modes to choose from. 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. . a. 3. I used to run XSEG on a Geforce 1060 6GB and it would run fine at batch 8. bat’. Get XSEG : Definition and Meaning. In this video I explain what they are and how to use them. 3) Gather rich src headset from only one scene (same color and haircut) 4) Mask whole head for src and dst using XSeg editor. The Xseg training on src ended up being at worst 5 pixels over. The problem of face recognition in lateral and lower projections. proper. Must be diverse enough in yaw, light and shadow conditions. It really is a excellent piece of software. 2) extract images from video data_src. npy . Manually mask these with XSeg. 000 it) and SAEHD training (only 80. 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. . XSeg in general can require large amounts of virtual memory. During training, XSeg looks at the images and the masks you've created and warps them to determine the pixel differences in the image. XSeg: XSeg Mask Editing and Training How to edit, train, and apply XSeg masks. Where people create machine learning projects. It depends on the shape, colour and size of the glasses frame, I guess. ** Steps to reproduce **i tried to clean install windows , and follow all tips . This video takes you trough the entire process of using deepfacelab, to make a deepfake, for results in which you replace the entire head. 1. Attempting to train XSeg by running 5. How to share SAEHD Models: 1. It is now time to begin training our deepfake model. 2) Use “extract head” script. Double-click the file labeled ‘6) train Quick96. You could also train two src files together just rename one of them to dst and train. Same ERROR happened on press 'b' to save XSeg model while training XSeg mask model. XSeg) data_dst trained mask - apply or 5. Pass the in. Feb 14, 2023. Intel i7-6700K (4GHz) 32GB RAM (Already increased pagefile on SSD to 60 GB) 64 bit. 1) clear workspace. Lee - Dec 16, 2019 12:50 pm UTCForum rules. [new] No saved models found. Step 5: Training. Training. How to Pretrain Deepfake Models for DeepFaceLab. k. Unfortunately, there is no "make everything ok" button in DeepFaceLab. Easy Deepfake tutorial for beginners Xseg. I don't see any problems with my masks in the xSeg trainer and I'm using masked training, most other settings are default. 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. I didn't try it. this happend on both Xsrg and SAEHD training, during initializing phase after loadind in the sample, the prpgram erros and stops memory usege start climbing while loading the Xseg mask applyed facesets. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Its a method of randomly warping the image as it trains so it is better at generalization. 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. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. Final model config:===== Model Summary ==. Then restart training. then i reccomend you start by doing some manuel xseg. 16 XGBoost produce prediction result and probability. A skill in programs such as AfterEffects or Davinci Resolve is also desirable. Mark your own mask only for 30-50 faces of dst video. 000 it). This forum has 3 topics, 4 replies, and was last updated 3 months, 1 week ago by. **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. Where people create machine learning projects. Step 5. Today, I train again without changing any setting, but the loss rate for src rised from 0. XSeg won't train with GTX1060 6GB. Manually labeling/fixing frames and training the face model takes the bulk of the time. pak file untill you did all the manuel xseg you wanted to do. 5) Train 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. Training，训练 : 允许神经网络根据输入数据学习预测人脸的过程. 5. 00:00 Start00:21 What is pretraining?00:50 Why use i. Step 3: XSeg Masks. Then I apply the masks, to both src and dst. 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. Manually labeling/fixing frames and training the face model takes the bulk of the time. 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. Link to that. 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. 2. idk how the training handles jpeg artifacts so idk if it even matters, but iperov didn't really do. 2) Use “extract head” script. bat. bat. XSeg in general can require large amounts of virtual memory. Copy link 1over137 commented Dec 24, 2020. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. #DeepFaceLab #ModelTraning #Iterations #Resolution256 #Colab #WholeFace #Xseg #wf_XSegAs I don't know what the pictures are, I cannot be sure. I guess you'd need enough source without glasses for them to disappear. Instead of using a pretrained model. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. Where people create machine learning projects. If your model is collapsed, you can only revert to a backup. 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. If you want to get tips, or better understand the Extract process, then. learned-prd+dst: combines both masks, bigger size of both. Solution below - use Tensorflow 2. After the draw is completed, use 5. 000 more times and the result look like great, just some masks are bad, so I tried to use XSEG. dump ( [train_x, train_y], f) #to load it with open ("train. 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. 1over137 opened this issue Dec 24, 2020 · 7 comments Comments. 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. 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. prof. 0 using XSeg mask training (213. Train the fake with SAEHD and whole_face type. It is now time to begin training our deepfake model. You can then see the trained XSeg mask for each frame, and add manual masks where needed. Xseg training functions. 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. Basically whatever xseg images you put in the trainer will shell out. Yes, but a different partition. 6) Apply trained XSeg mask for src and dst headsets. 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. First one-cycle training with batch size 64. 1. Also it just stopped after 5 hours. 3. Model first run. Easy Deepfake tutorial for beginners Xseg,Deepfake tutorial for beginners,deepfakes tutorial,face swap,deep. Otherwise, if you insist on xseg, you'd mainly have to focus on using low resolutions as well as bare minimum for batch size. 27 votes, 16 comments. Please read the general rules for Trained Models in case you are not sure where to post requests or are looking for. The exciting part begins! Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly. 3. Where people create machine learning projects. XSeg allows everyone to train their model for the segmentation of a spe-Jan 11, 2021. XSeg) data_src trained mask - apply. You can apply Generic XSeg to src faceset. Reactions: frankmiller92Maybe I should give a pre-trained XSeg model a try. npy","path. There were blowjob XSeg masked faces uploaded by someone before the links were removed by the mods. Final model. The dice, volumetric overlap error, relative volume difference. xseg) Data_Dst Mask for Xseg Trainer - Edit. I have now moved DFL to the Boot partition, the behavior remains the same. Contribute to idonov/DeepFaceLab by creating an account on DagsHub. The dice and cross-entropy loss value of the training of XSEG-Net network reached 0. Xseg Training or Apply Mask First ? frankmiller92; Dec 13, 2022; Replies 5 Views 2K. If it is successful, then the training preview window will open. Four iterations are made at the mentioned speed, followed by a pause of. When loading XSEG on a Geforce 3080 10GB it uses ALL the VRAM. Contribute to idonov/DeepFaceLab by creating an account on DAGsHub. . You can use pretrained model for head. 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. pkl", "w") as f: pkl. DF Admirer. + new decoder produces subpixel clear result. Step 5. 0146. . 6) Apply trained XSeg mask for src and dst headsets. 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. py","path":"models/Model_XSeg/Model. 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. Where people create machine learning projects. The best result is obtained when the face is filmed from a short period of time and does not change the makeup and structure. . py","path":"models/Model_XSeg/Model. Where people create machine learning projects. It haven't break 10k iterations yet, but the objects are already masked out. Open 1over137 opened this issue Dec 24, 2020 · 7 comments Open XSeg training GPU unavailable #5214. 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. Running trainer. 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;. load (f) If your dataset is huge, I would recommend check out hdf5 as @Lukasz Tracewski mentioned. 1 Dump XGBoost model with feature map using XGBClassifier. 3. Step 2: Faces Extraction. I was less zealous when it came to dst, because it was longer and I didn't really understand the flow/missed some parts in the guide. The only available options are the three colors and the two "black and white" displays. 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. XSeg-prd: uses. You can use pretrained model for head. Apr 11, 2022. Complete the 4-day Level 1 Basic CPTED Course. 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. ] Eyes and mouth priority ( y / n ) [Tooltip: Helps to fix eye problems during training like “alien eyes” and wrong eyes direction. Include link to the model (avoid zips/rars) to a free file. Open gili12345 opened this issue Aug 27, 2021 · 3 comments Open xseg train not working #5389. GPU: Geforce 3080 10GB. Again, we will use the default settings. Business, Economics, and Finance. 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 Models and Datasets Sharing Thread. Pretrained models can save you a lot of time. Step 5: Training. XSeg) train issue by. 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. If you want to see how xseg is doing, stop training, apply, the open XSeg Edit. Describe the SAEHD model using SAEHD model template from rules thread. on a 320 resolution it takes upto 13-19 seconds . XSeg-prd: uses trained XSeg model to mask using data from source faces. Describe the XSeg model using XSeg model template from rules thread. Already segmented faces can. Sydney Sweeney, HD, 18k images, 512x512. 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. Choose one or several GPU idxs (separated by comma). 192 it). It is used at 2 places. Introduction. I've already made the face path in XSeg editor and trained it But now when I try to exectue the file 5. . Just change it back to src Once you get the. 522 it) and SAEHD training (534. oneduality • 4 yr. XSeg) train. Thermo Fisher Scientific is deeply committed to ensuring operational safety and user. XSeg) data_dst/data_src mask for XSeg trainer - remove. On training I make sure I enable Mask Training (If I understand this is for the Xseg Masks) Am I missing something with the pretraining? Can you please explain #3 since I'm not sure if I should or shouldn't APPLY to pretrained Xseg before I. 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. XSeg Model Training. Manually fix any that are not masked properly and then add those to the training set. Phase II: Training. After the draw is completed, use 5.