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chfm/
Boogu-Image-Turbo_GGUFs

Name: Boogu-Image-Turbo_GGUFs
Availability: InStock

Boogu/Boogu-Image-0.1-Turbo

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Boogu-Image-0.1-Turbo GGUF

Required Text Encoder & VAE

Text Encoder (Qwen3-VL): You must use an FP8 scaled version of the Qwen3-VL encoder.
- Download the FP8 encoder from the Comfy-Org Boogu Repo.
VAE (Flux): The Boogu pipeline utilizes the standard Flux VAE.
- Download flux1_vae_bf16.safetensors from the Comfy-Org Boogu Repo

⚠️ Why are there no Q2 or Q3 K-Quants?

Currently, this repo only provides Flat Quants (Q4_0, Q4_1, Q5_0, Q5_1, Q8_0).

Standard K-quants (like Q2_K or Q3_K_M) require a hardcoded architectural mapping blueprint inside the llama.cpp source code. Because the Boogu/OmniGen architecture is brand new, those K-quant blueprints do not exist in the compiler yet. Flat quants bypass this requirement by forcing all 2D tensors to the target bit-depth.

If you are running an 8GB VRAM setup (like an RTX 3070), the Q4_0 is the recommended sweet spot for VRAM savings and quality.

How to use in ComfyUI

Prerequisite: Core Update Override Native support for the Boogu/OmniGen architecture has been merged from Pull Request #14523. If your Load Clip node doesnt have the Boogu architecture, you must manually fetch the PR into your ComfyUI installation.

Open a command prompt directly inside your ComfyUI folder.
Run the following commands to fetch and switch to the PR branch:

cmd

   git fetch origin pull/14523/head:boogu-pr
   git checkout boogu-pr

chfm/Boogu-Image-Turbo_GGUFs

Boogu-Image-0.1-Turbo GGUF

Required Text Encoder & VAE

⚠️ Why are there no Q2 or Q3 K-Quants?

How to use in ComfyUI

chfm/
Boogu-Image-Turbo_GGUFs