#ComfyUI INT4 quantization
19 related articles
·4 min
INT4 ConvRot W4A4 Quantization: Running Large Image Models on Low-VRAM GPUs in ComfyUI
An in-depth look at INT4 ConvRot W4A4 quantization, covering conversions of Krea2, Qwen-Image, and other diffusion models to help ComfyUI users run large image models on 8GB GPUs.
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·2 min
ComfyUI KREA 2 v1.2 Identity Edit Workflow: A Practical Guide to Low-VRAM Identity-Preserving Editing
A detailed guide to ComfyUI KREA 2 Identity Edit v1.2 workflow installation and usage, supporting low-VRAM GPUs for identity-preserving editing including pose changes, style transfer, face swap, and virtual try-on.
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·3 min
Bernini Tile-Based Generation: An Engineered Approach to Video Super-Resolution in ComfyUI
Bernini is a ComfyUI video super-resolution node package using Tile Split/Select/Merge to solve seams, drift, and VRAM overflow. Benchmarked at 325s for 39 frames at 1920×1080.
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·2 min
Abliteration Explained: Building a Custom Uncensored Text Encoder for Krea 2
How a developer ran 4,000 multi-seed abliteration trials to build a 100% HarmBench-compliant uncensored text encoder for Krea 2's Qwen3-VL-4B, with five quantization formats and ComfyUI deployment guide.
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·2 min
The Complete Optimization Guide for Local AI Image & Video Generation with 16GB VRAM
What can 16GB VRAM do? This guide covers FLUX, SDXL, Wan video models, ComfyUI workflows, GGUF quantization, and VRAM optimization to max out your RTX 16GB GPU.
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·2 min
ComfyUI v0.28.0: Native Video Super-Resolution, 3D Support, and PixelDiT Architecture — A Complete Breakdown
ComfyUI v0.28.0 adds SeedVR2 native video super-resolution, PixelDiT architecture, 3D Gaussian Splatting export, int4 quantization, and lip-sync integration for a major multimodal AI workflow upgrade.
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·2 min
LingBot-Video 1.3B Selective FP8 Quantization Benchmark: 22% Faster Sampling
Reddit developer ALX-CODE shares a selective FP8 quantization scheme for LingBot-Video 1.3B, achieving ~22% faster sampling (4.65s→3.65s) on an RTX 5080. This article breaks down the mixed-precision strategy, open-source resources, and ComfyUI adaptation.
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·2 min
LTX 2.3 Hands-On: How Efficient Is Local AI Video Generation on the RTX 5080?
Hands-on with LTX 2.3 and ComfyUI for local AI video generation on the RTX 5080: 8-second clips in just 2-3 minutes while running DaVinci Resolve simultaneously. Covers hardware, workflow setup, and multi-tool creation.
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·4 min
AMD Ryzen AI Halo Hands-On: Can x86 Architecture Challenge NVIDIA's DGX Spark?
In-depth review of the AMD Ryzen AI Halo mini AI box: powered by the Ryzen AI Max Plus 395 (Strix Halo) chip with 128GB unified memory, priced at $4,000. Compared against NVIDIA's DGX Spark across token generation, prefill speed, and x86 advantages.
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·2 min
Directly Optimizing Face Similarity: A Practical Guide to Accelerating Character LoRA Training
Ditch inefficient noise prediction and train character LoRAs by directly optimizing face similarity. Using the DRaFT differentiable reward method, training completes in 10-12 minutes on an RTX 4090, far outperforming traditional SFT. Includes open-source code.
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·3 min
ComfyUI-INT4-Fast: Running Flux Models Smoothly on 6GB GPUs
ComfyUI-INT4-Fast brings W4A4 quantized inference to ComfyUI. RTX 3060 (6GB VRAM) generates 1024×1024 images in 17s. Per-layer mixed-precision routing balances speed and quality for Flux models.
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·5 min
Runs on Low VRAM! A Hands-On Look at the ID LoRA Face-to-Video Workflow
A face-to-video workflow built on GGUF quantized models and ID LoRA runs on just an RTX 3060 with 6GB VRAM. This article breaks down its core principles, four-step process, and how it tackles facial consistency in AI video.
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·7 min
Krea2 Tested: Does the FP8 vs BF16 Quality Gap Still Exist?
A Reddit user compared FP8 and BF16 precision on the Krea2 model and found almost no difference in image quality. This article analyzes the technical reasons behind the shrinking quantization gap and its real-world impact on VRAM usage and inference speed.
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·5 min
Ideogram 4 Local Deployment in Practice: One-Click Image Generation with a Fully Automated ComfyUI Workflow
Deep dive into Ideogram 4's core strengths: realistic photographic quality, powerful text generation, and Chinese prompt support. Learn to skip complex JSON prompts with a fully automated ComfyUI workflow + Qwen3—input your idea, get an image instantly, deployable locally at just 8GB.
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·3 min
Ideogram 4 Fully Automated Workflow: Run Locally with Just 8GB VRAM, No Complex Prompts Needed
Ideogram 4 automated ComfyUI workflow using Qwen2.5 VL-8B: run locally with 8GB VRAM, auto-generate structured JSON prompts from simple descriptions, with image reverse-engineering support.
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·3 min
Deploying a Multimodal AI Agent Locally on a 3080Ti: VRAM Management and a Deep Dive into All Five Modules
A detailed guide to deploying a multimodal AI Agent on a 3080Ti with 12GB VRAM, covering LLM, STT, TTS, image and video generation module selection, dynamic VRAM loading, and real-world performance.
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Tutorials·4 min
Practical Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Step-by-step guide to building a local RAG knowledge base using RAGFlow, Ollama, and LM Studio with Docker, covering Embedding model deployment and network troubleshooting for private AI Q&A.
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Tutorials·3 min
Stable Diffusion Local Deployment Guide: Run AI Image Generation Free with 8GB RAM
Complete guide to deploying Stable Diffusion locally. Covers hardware requirements, one-click installation, and model setup. Run AI image generation free with 8GB RAM.
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