# extract one frame at the end of a video ffmpeg -sseof -0.1 -i intro_1.mp4 -frames:v 1 -q:v 1 intro_end.jpg
-sseof -0.1: This option tells FFmpeg to seek to 0.1 seconds before the end of the file. This approach is often more reliable for extracting the last frame, especially if the video’s duration isn’t an exact multiple of the frame interval. Super User -frames:v 1: Extracts a single frame. -q:v 1: Sets the quality of the output image; 1 is the highest quality.
# extract one frame at the beginning of a video ffmpeg -i speaking_4.mp4 -frames:v 1 speaking_beginning.jpg
# check video length ffmpeg -i C:\myvideo.mp4 -f null –
# Convert mov/mp4 to animated gifEdit ffmpeg -i input.mp4 -pix_fmt rgb24 output.gif Other useful ffmpeg commandsEdit
There’s been no statements as to when Midjourney’s technology will start showing up in Meta’s products, or to what degree it will be baked into the company’s AI strategy.
Tired of having iTunes messing up your mp3 library? … Time to try MiniTunes!
– Arrange your library by Genre, Artists or Albums. – Change UI colors at will. – Edit tags and create playlists. – Consolidate your library once for all. – Windows 64 only
Qwen-Image-Edit is the image editing version of Qwen-Image. It is further trained based on the 20B Qwen-Image model, successfully extending Qwen-Image’s unique text rendering capabilities to editing tasks, enabling precise text editing. In addition, Qwen-Image-Edit feeds the input image into both Qwen2.5-VL (for visual semantic control) and the VAE Encoder (for visual appearance control), thus achieving dual semantic and appearance editing capabilities.
PixiEditor is a universal 2D editor that was made to provide you with tools and features for all your 2D needs. Create beautiful sprites for your games, animations, edit images, create logos. All packed up in an intuitive and familiar interface.
The goal was ambitious: to generate a hyper-detailed 3DGS scan from a massive dataset—20,000 drone photos at full resolution (5280x3956px). All of this on a single machine with just one RTX 4090 GPU.
What was the problem? Most existing tools simply can’t handle this volume of data. For instance, Postshot, which is excellent for many tasks, confidently processed up to 7,000 photos but choked on 20,000—it ran for two days without even starting the model training. The Breakthrough Solution. The real discovery was the software from GreenValley International
Their approach is brilliant: instead of trying to swallow the entire dataset at once, the program intelligently divides it into smaller, manageable chunks, trains each one individually, and then seamlessly merges them into one giant, detailed scene. After 40 hours of rendering, we got this stunning 103 million splats PLY result:
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
Loss of Connection with Creation:
AI-assisted creation diminishes the creator’s role in the decision-making process.
The resulting creation lacks the personal, intentional choices that contribute to meaningful expression.
AI is considered a tool that, when misused, turns creation into automated button-pushing, stripping away the purpose of human expression.
Difficulty in Assessing Authenticity:
It becomes challenging to distinguish between human and AI contributions within a creation.
AI-generated content lacks transparency regarding the intent behind specific choices or expressions.
The author asserts that AI-generated content often falls short in providing the depth and authenticity required for meaningful communication.
Stable Diffusion is a latent diffusion model that generates AI images from text. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space.
Stable Diffusion belongs to a class of deep learning models called diffusion models. They are generative models, meaning they are designed to generate new data similar to what they have seen in training. In the case of Stable Diffusion, the data are images.
Why is it called the diffusion model? Because its math looks very much like diffusion in physics. Let’s go through the idea.
Blind people who regain their sight may find themselves in a world they don’t immediately comprehend. “It would be more like a sighted person trying to rely on tactile information,” Moore says.
Learning to see is a developmental process, just like learning language, Prof Cathleen Moore continues. “As far as vision goes, a three-and-a-half year old child is already a well-calibrated system.”
