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realrebelai/
Boogu-Image-Edit-Turbo_GGUFs

Name: Boogu-Image-Edit-Turbo_GGUFs
Availability: InStock

Boogu/Boogu-Image-0.1-Edit-Turbo

quantized

apache-2.0

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Boogu-Image-0.1-Edit-Turbo — GGUF (Flat Quants)

GGUF quantizations of Boogu/Boogu-Image-0.1-Edit-Turbo, the distilled (Turbo) reference-image edit variant of the Boogu-Image family. Quantized for low-VRAM ComfyUI use — an 8GB card (RTX 3070 class) with 16GB system RAM can run these.

Quantized by realrebelai. These are the DiT only — you supply the text encoder and VAE separately (see below).

Files

File	Quant	Size
`boogu-edit-turbo-dit-Q8_0.gguf`	Q8_0	~11.6 GB
`boogu-edit-turbo-dit-Q5_1.gguf`	Q5_1	~8.64 GB
`boogu-edit-turbo-dit-Q5_0.gguf`	Q5_0	~8.04 GB
`boogu-edit-turbo-dit-Q4_1.gguf`	Q4_1	~7.44 GB
`boogu-edit-turbo-dit-Q4_0.gguf`	Q4_0	~6.84 GB

On 8GB VRAM, Q4_0 is the recommended sweet spot for the balance of VRAM savings and quality. Step up to Q5_1 or Q8_0 if you have the headroom and want maximum fidelity.

Why only flat quants (Q4_0 / Q4_1 / Q5_0 / Q5_1 / Q8_0)?

This repo provides flat quants only. Standard K-quants (Q2_K, Q3_K_M, etc.) require a hardcoded architectural mapping blueprint inside the llama.cpp source. 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, so they quantize cleanly where K-quants would fall back to near-full precision.

Required components (not included here)

Boogu will not run with standard SD or Flux encoders. You must download the specific text encoder and VAE:

Text Encoder (Qwen3-VL): the FP8 scaled Qwen3-VL encoder from the Comfy-Org Boogu repo. In your CLIPLoader, set type = boogu.
VAE (Flux): flux1_vae_bf16.safetensors from the Comfy-Org Boogu repo.

⚠️ Most "it looks low-res / soft / noisy" reports come from loading the wrong encoder or VAE (e.g. a different Qwen3-VL size), or from the CLIPLoader type not being set to boogu. Verify these two files before reporting an issue.

Prerequisite: Core Update (PR #14523)

Native support for the Boogu/OmniGen architecture was merged in Pull Request #14523. If your Load CLIP node has no boogu architecture option, fetch the PR into your ComfyUI install. Open a command prompt inside your ComfyUI folder:

bash

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

Install

Download one boogu-edit-turbo-dit-*.gguf and place it in ComfyUI/models/unet/.
Download the Qwen3-VL FP8 encoder → ComfyUI/models/text_encoders/ (or clip/).
Download flux1_vae_bf16.safetensors → ComfyUI/models/vae/.
Load the DiT with the Unet Loader (GGUF), the encoder with CLIPLoader (type = boogu), and the VAE with Load VAE.
As an edit model, feed your reference image into the workflow's edit/reference-image input.

Notes

Turbo is the distilled variant — run it at its reduced step count (follow the step/CFG guidance on the base Edit Turbo model card); the full 50-step schedules used for non-Turbo models are unnecessary here.
These files are the diffusion transformer only. The encoder and VAE are shared across the Boogu-Image family — if you already run Boogu Base or Turbo, you have them.

Quantized and published by realrebelai. Boogu-Image is created by Boogu; all credit for the base model to the original authors. Released under Apache-2.0, matching the base model license.

realrebelai/Boogu-Image-Edit-Turbo_GGUFs