The BEST AI for 4K images. Free & fast
PiD provides AI filmmakers with a fast, locally-runnable alternative for high-resolution image upscaling that bypasses the limitations of traditional latent decoders.
What this lesson covers
This video introduces Pixel Diffusion (PiD) by Nvidia, a lightweight and extremely fast open-source model capable of generating and upscaling 4K resolution images directly in pixel space. The creator demonstrates how to install and run PiD locally using ComfyUI workflows, comparing its speed and consistency favorably against traditional upscalers like SeedVR2.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 0:00 ↗
Pixel Diffusion (PiD) by Nvidia is an open-source model capable of creating 4K resolution images in under 5 seconds.
- 2:00 ↗
PiD works in pixel space rather than a compressed latent space, performing direct denoising in high resolution to minimize artifacts.
- 2:52 ↗
Compared to SeedVR2, PiD offers higher consistency, sharper detail preservation, and up to 5.9x faster latency.
- 4:30 ↗
ComfyUI serves as the primary offline platform for running PiD workflows locally.
- 6:24 ↗
The ComfyUI integration allows users to either upscale existing images or chain outputs from models like Flux and Z-Image directly into PiD.
Workflow outlined in the video
- Update ComfyUI to the latest version using the local update batch script.
- Download the required text encoder, such as Gemma 2 2B, and place it in the appropriate ComfyUI text encoders folder.
- Download the PiD upscaler model matching your preferred base latent model.
- Import the O2 or O3 workflow JSON file into ComfyUI via drag and drop.
- Load your target image into the ComfyUI workflow and execute the graph to generate the 2K or 4K upscaled result.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
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101,801 views captured 23 October 2026. ReelStack recommendation score: 46/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
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- 45% performance: 0.86× views vs 10 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 37 views/day vs 7 channel baseline (5 comparable uploads), with the same view-evidence factor
- 10% engagement: 83/100; likes + 4× comments, smoothed with a 500-view neutral prior