Open Source Video Upscaler with Temporal Smoothing

Free.ai Video Upscaler Self-hosted Real-ESRGAN x4 video upscaler with temporal smoothing — a free, GPU-backed alternative to Topaz Video Upscaler. Powers the Free.ai video upscaler tool. What it does Takes a video, upscales every frame 4× with Real-ESRGAN, applies a temporal smoothing pass to eliminate frame-to-frame flicker, then re-encodes preserving the source audio. The output is competitive with commercial video upscalers on per-frame detail and temporal coherence. Why temporal smoothing matters Frame-by-frame neural upscalers (the cheap way) introduce high-frequency flicker because each frame is upscaled independently — a slight texture detail in pixel (12,34) might be reconstructed differently by the model on two consecutive frames, producing a shimmer. Topaz solves this by training a video-aware model with temporal context. We solve it cheaper: a 3-tap weighted average over consecutive output frames (0.15 × prev + 0.7 × current + 0.15 × next) kills the high-freq flicker without smearing motion. A scene-cut detector (per-pixel mean-abs-diff > 60) skips smoothing when adjacent frames are too different — preserves crisp edits, scene transitions, and very fast motion. Don't want to self-host? Use our hosted API If you don't have a GPU or just want to try the upscaler without setting anything up, hit our hosted endpoint — same code, our infra: curl -X POST https://api.free.ai/v1/video/upscale/ \ -H "Authorization: Bearer sk-free-..." \ -F "[email protected]" \ -F "model=realesrgan" \ -F "scale=2" # Returns: {"video_url": "https://gpu4.free.ai/static/outputs/<job>.mp4", ...} Get an API key at free.ai/api/ Free pool covers small clips daily; longer clips deduct from your token balance Same temporal-smoothed pipeline as this repo, just running on our GPUs Premium video-aware upscalers (Topaz, etc.) also available via the same endpoint with model=premium/topaz/upscale/video For high-volume integrations, contact us — we do volume pricing for partners. Install (self-hosted) # 1. Install torch with the right CUDA version for your driver. # Common choice for CUDA 12.x systems: pip install torch==2.5.1 torchvision==0.20.1 \ --index-url https://download.pytorch.org/whl/cu121 # 2. Install the upscaler: pip install free-video-upscaler # 3. Patch basicsr's torchvision import (newer torchvision removed # `functional_tensor` — known basicsr issue, no fix released yet): python -c "import basicsr.data.degradations as _d, re, pathlib; \ p = pathlib.Path(_d.__file__); \ p.write_text(p.read_text().replace('torchvision.transforms.functional_tensor', \ 'torchvision.transforms.functional'))" # 4. Download model weights: mkdir -p ~/.realesrgan/weights && cd ~/.realesrgan/weights wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth CLI free-upscaler --input my-video.mp4 --output upscaled.mp4 --scale 2 free-upscaler [-h] --input INPUT --output OUTPUT [--scale {2,4}] [--model {x4plus,anime}] [--tile TILE] [--no-temporal] [--fp32] [--weights WEIGHTS] [--quiet] [--version] Python API from free_video_upscaler import upscale_video upscale_video( "in.mp4", "out.mp4", scale=2, # 2 or 4 model="x4plus", # "x4plus" or "anime" temporal_smooth=True, progress=lambda i, n: print(f"{i}/{n}"), ) How it compares Feature This package Topaz Video AI Naive Real-ESRGAN Per-frame upscale Real-ESRGAN x4plus Proteus / Iris Real-ESRGAN x4plus…

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