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Krea-2-SVDQuant-ComfyUI

AlperKTS

Quantized **Krea 2** checkpoints for ComfyUI — about **2x faster** and **a third the size** of the usual FP8 version, with no calibration dataset needed, on both **Krea 2 Turbo** (distilled, 8 steps) and the **base** release. This repo holds the custom nodes, the All-in-One builder, and the `quantize_krea2.py` conversion script; the `.safetensors` files live on Hugging Face. Works on **any modern NVIDIA GPU** — INT8/W4A4 tensor cores go back to Turing (RTX 20-series). Benchmarked on an RTX 3090 (Ampere). Everything here was built from scratch against ComfyUI's own quantization backend and is 100% reproducible. > **Requires a cu130 (CUDA 13) or newer PyTorch build.** ComfyUI disables `comfy_kitchen`'s > CUDA backend on older torch builds, which silently drops every quantized checkpoint onto a > pure-Python fallback that is *slower than bf16*. If these checkpoints are slower than FP8 > for you, see TROUBLESHOOTING.md.

Image generation

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Krea2_FP8

AlperKTS

This repository provides an optimized **FP8 (float8_e4m3fn) weight-only quantized version** of the newly released **Krea 2 OSS (Turbo)** transformer. This optimization reduces the model size from the original **24.76 GiB (BF16)** down to **12.01 GiB**, making it highly accessible and runnable on standard consumer hardware (such as 16GB and 24GB GPUs) without sacrificing output quality. - **Original Model Creators**: All credit goes to KREA.ai for the original research, architecture, and weights. - **License**: This model is subject to the **KREA 2 License Agreement**. Please read and comply with the official license terms before using these weights: KREA 2 Licensing Terms. - **Purpose**: This repository is a community-contributed utility. It does not claim ownership of the original model or architecture. Its sole purpose is to provide optimized, consumer-hardware-friendly weights for the open-source community.

Image generation

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Qwen-Image-2.1-GGUF

AlperKTS

Quantized GGUF weights of the state-of-the-art **Qwen-Image 2.1** diffusion model for **ComfyUI** using ComfyUI-GGUF. These models allow running Qwen-Image 2.1 on consumer GPUs with significantly reduced VRAM footprint while preserving maximum visual quality, text-rendering capabilities, and composition fidelity. 1. **Diffusion Model (GGUF):** - Download any of the `.gguf` files from this repo (e.g. `qwen_image_2.1_Q8_0.gguf`). - Place it in: `ComfyUI/models/diffusion_models/` (or `ComfyUI/models/unet/`)

Image generation

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 3

Krea-2-SVDQuant-ComfyUI

Krea2_FP8

Qwen-Image-2.1-GGUF

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