Video Tutorialsmac's History

Video Tutorialsmac's History Timeline
Video Tutorialsmac's History Youtube
Video Tutorialsmac's History Documentaries

Ho-Chunk History Special 26m 45s Video has closed captioning. By the banks of the Lemonweir River in what for ages had been Ho-Chunk territory, Andy Thundercloud shares the oral tradition of. Dec 31, 2019 - Explore Albertina's board 'Best mac makeup' on Pinterest. See more ideas about african fashion dresses, african fashion, african dress. When video games break into reality, rivalries only become more bitter — and today I’m going to talk about some of the biggest battles of personality in video game history. Video is an electronic medium for the recording, copying, playback, broadcasting, and display of moving visual media. Video was first developed for mechanical television systems, which were quickly replaced by cathode ray tube (CRT) systems which were later replaced by flat panel displays of several types.

The great rivalries of gaming aren’t always fictional. When video games break into reality, rivalries only become more bitter — and today I’m going to talk about some of the biggest battles of personality in video game history. Whether it’s one speedrunner versus many, or two armies of online players duking it out for space bound survival, these rivalries are so huge they’ve escaped the digital confines of video games and leaked all over the real world.

For a rivalry to truly be legendary, the story has to inspire. And there are some truly inspiring stories to tell — mostly about how much we can accomplish when we’re way too pissed off. Some of these rivalries are friendly, and some are venomous. Some have been over for years, while others are still going to this day. These stories are completely insane, and most of them I’ve only just barely touched on. I highly recommend clicking the links and watching the videos that accompany each entry. They’re absolutely worth your time.

Revisit more game lists on Gameranx:

Goonswarm vs. PAPI [EVE Online]

You’ve probably heard about this — the biggest battle in EVE Online history — and maybe the biggest battle in the history of online games. It’s certainly the most expensive. Warships in EVE Online cost real-world dollars, and the latest battle between Goonswarm and a coalition that call themselves PAPI involved thousands of players — previous battles between the two groups have resulted in thousands of dollars in in-game damages, and even real-life repercussions when developer CCP’s servers crashed due to the immense player load.

Goonswarm originally formed on The Something Awful Forums and has grown into a domination faction in EVE Online Maccleaner pro. — Goonswarm controls vast regions of space with thousands of players. Lead by Alex “The Mittani” Gianturco, the Goonswarm dominated the EVE Online scene way back in 2005 when they destroyed rival coalitions through sneaky tactics — they literally sent a double agent into their enemy’s ranks.

And now there’s PAPI. A loose coalition of allies lead by a player called Vily, the group wants to exterminate and dissolve the forces of Goonswarm. Why the beef? It all comes down to a disgruntled former employee. As detailed in this interview by Polygon, Vily was once a Fleet Commander in Goonswarm — but he had to take a long break for a real-life military deployment. When he returned, Goonswarm didn’t want him back.

So Vily decided to create his own thing. And it’s sparked one of the biggest rivalries in video game history. The latest battle in “World War Bee 2” isn’t the last, and we’re going to see even more money burned in an endless feud. But that’s kind of the whole point of EVE Online, isn’t it?

Rivals never back down. Learn more about the biggest rivalries in gaming on the next page.

Oct 25th, 2019 by DFBlue

tutorial
guide
how to
deepfacelab
deepfakes

👀 Update 10/25/19: Added instructions for the new SAEHD model (amazing)

👽 Update 9/24/19: New post for AVATAR mode

Source and destination videos requirements

High resolution (4k webm is best, lower than 1080p is not recommended)
Faces not too far from camera and unobstructed
Multiple angles, facial expressions
Brightly and evenly lit
Faces should somewhat match (beard, hat, hair, skin color, shape, glasses)
Need at least 2 mins of good quality video, interview videos work well

Downloading the software

Video Tutorialsmac's History Timeline

We will use DeepFaceLab to create the deepfakes. Another software, FaceSwap is also available, and will have a separate tutorial.

Download DeepFaceLab
- Make sure to pick the right build for your GPU. If you don’t have a GPU, use the CLSSE build
- Here’s the direct link
- In that folder, you will find some pre-compiled face-sets. Go ahead and download one of them to get started quickly (otherwise you will have to build your own face-set from videos / images)
The downloaded .exe will extract and install the program to the location of your choosing.
- A workspace folder will be created. This is the folder where all the action will happen.

Extracting faces from source video

Name the source video data_src and place it in the workspace folder.
- Most formats that ffmpeg supports will work
Run 2) extract images from video data_src
- Use PNG (better quality)
- FPS <= 10 that gets you at least 2000 images (4k-6k is ideal)
Run 4) data_src extract faces S3FD best GPU
- Extracted faces saved to data_srcaligned.
Run 4.2.2) data_src sort by similar histogram
- Groups similar detected faces together
Run 4.1) data_src check result
- Delete faces that are not the right person, super blurry, cut off, upside down or sideways, or obstructed
Run 4.2.other) data_src util add landmarks debug images
- New images with _debug suffix are created in data_src/aligned which allow you to see the detected facial landmarks
- Look for faces where landmarks are misaligned and delete the _debug and original images for those
- Once you’re done, delete all _debug images by using the search bar to filter for _debug
Run 4.2.6) data_src sort by final
- Choose a target image number around 90% of your total faces

Extracting faces from destination video

Video Tutorialsmac's History Youtube

You may choose to either extract from (1) the final video clip you want, or (2) one that is cut to include only the face you want to swap. If you choose 1, you may have to spend more time cleaning the extracted faces. If you choose 2 you will have to edit back the final video (and audio) after the swap.

Name your final video data_dst and put it in the workspace folder
Run 3.2) extract PNG from video data_dst FULL FPS
Run 5) data_dst extract faces S3FD best GPU
Run 5.2) data_dst sort by similar histogram
Run 5.1) data_dst check results
- Delete all faces that are not the target face to swap, or are the target face but upside down or sideways. Every face that you leave in will be swapped in the final video.
Run 5.1) data_dst check results debug
- Delete any faces that are not correctly aligned or missing alignment, paying special attention to the jawline. We will manually align these frames in the next step.
Run 5) data_dst extract faces MANUAL RE-EXTRACT DELETED RESULTS DEBUG
- We run this step to manually align frames that we deleted in the last step. The manually aligned faces will be automatically extracted and used for converting. You must manually align frames you want converted (swapped) even if it’s a lot of work. If you fail to do so, your swap will use the original face for those frames.
- Manual alignment instructions:
  - For each face, move your cursor around until it aligns correctly onto the face
  - If it’s not aligning, use the mouse scroll wheel / zoom to change the size of the boxes
  - When alignment is correct, hit enter
  - Go back and forth with , and .. If you don’t want to align a frame just skip it with .
  - Mouse left click will lock/unlock landmarks. You can either lock it by clicking or hitting enter.

Training

Run 6) train SAEHD 7 starter blog post ideas post.

For an NVIDIA GTX 1080 8gb GPU
Setting	Value	Notes
iterations	100000	Or until previews are sharp with eyes and teeth details.
resolution	128	Increasing resolution requires significant VRAM increase
face_type	f
learn_mask	y
optimizer_mode	2 or 3	Modes 2/3 place work on the gpu and system memory. For a 8gb card you can place on mode 3 and still most likely be able to do 160 res fakes with small batch size.
architecture	df
ae_dims	512	Reduce if less GPU memory (256)
ed_ch_dims	21	Reduce if less GPU memory
random_warp	y
trueface	n
face_style_power	0	Can enable if you want to morph src more to dst. But disable after 15k iterations.
bg_style_power	10	Turn off at 15k iterations. Styles on consume ~30% more vram so you will need to change batch size accordingly.
color_transfer	varies	Try all modes in the interactive converter
clipgrad	n
batch_size	8	Higher if you don't run out of memory
sort_by_yaw	n	No, unless you have very few src faces
random_flip	y

Optional: History timelapse

Before converting, you can make a timelapse of the preview history (if you saved it during training). Do this only if you understand what ffmpeg is.

Convert

Run 7) convert SAEHD

Use the interactive converter and memorize the shortcut keys, it will speed up the process a lot.

Setting	Value	Notes
interactive_converter	y	Definitely use the interactive converter since you can try out all the different settings before converting all the frames
mode	overlay
mask_mode	learned
erode_modifier	0-50	If src face is bleeding outside the edge of dst face increase this to 'erode' away the src face on the outside
blur_modifier	10-200	The more similar the face the lower you can set erode and blur and get great results.
motion_blur	0
color_transfer	ebs	Try all of them, can even use different ones for different scenes / lighting
sharpen_mode	box
sharpen_amount	1-3
super_resolution	RankSRGAN	Enhances detail, especially around the eyes
color_degrade_power	n
export_alpha_mask	n	Outputs transparent PNGs for use in post-production tools if you need it

While conversion is running, you can preview the final images data_dstmerged folder to make sure it’s correct. If it’s not, just close the convert window, delete /merged and start conversion again.
Run 8) converted to mp4
- Bitrate of 3-8 is sufficient for most

Done 🤡

Contributions by DFBlue, PlanetOfTheFakes

Video Tutorialsmac's History Documentaries

Follow us on Twitter @dfblue or Reddit u/deepfakeblue to keep up with everything that is going on in the world of deepfakes. Think Blue.