DeepDream for Video with Temporal Consistency
I forked a PyTorch DeepDream implementation and added video support with temporal consistency. It produces smooth DeepDream videos with minimal flickering, and is highly flexible including many parameters and supports multiple pretrained image classifiers including GoogLeNet. Check out the repo for sample videos! Features: - Optical flow warps previous hallucinations into the current frame - Occlusion masking prevents ghosting and hallucination transfer when objects move - Advanced parameters (layers, octaves, iterations) still work - Works on GPU, CPU, and Apple Silicon
DeepDream for Video with Temporal Consistency launched on January 8, 2026 with 72 votes, #469 of 14,585 launches that month and more than 95% of that year's launches.
In plain words
DeepDream for Video with Temporal Consistency is a PyTorch tool that applies DeepDream visual effects to video while maintaining smooth, flicker-free results. It uses optical flow and occlusion masking to prevent ghosting and visual artifacts as objects move. The tool supports multiple pretrained classifiers including GoogLeNet and offers advanced parameters for customization. It runs on GPU, CPU, and Apple Silicon hardware, making it accessible to users with different computing setups.
written from the facts on this page · September 2026
From the sources
DeepDream for video with temporal consistency. Features RAFT optical flow estimation and occlusion masking to prevent ghosting. A PyTorch implementation. - jeremicna/deepdream-video-pytorch
This is a fork of neural-dream , a PyTorch implementation of DeepDream. This fork introduces optical flow estimation and occlusion masking to apply DeepDream to videos with temporalfrom github.com
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