---

license: other

license_name: proprietary-research

license_link: https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO-NSFW-v23

library_name: gguf

tags:

• gguf
• nvfp4
• quantized
• diffusion
• image-editing
• nsfw
• qwen
• comfyui

base_model: Qwen/Qwen-Image-Edit-Rapid-AIO-NSFW-v23

---

Qwen-Image-Edit-Rapid-AIO-NSFW-v23 — NVFP4 GGUF

This repository contains NVFP4 (4-bit NVIDIA FP4) quantized GGUF components of the Phr00t/Qwen-Image-Edit-Rapid-AIO-NSFW-v23 merged checkpoint, converted for use with ComfyUI-GGUF.

Components

| File | Size | Description |

|---|---|---|

| qwen-v23-diffusion-NVFP4.gguf | 11.5 GB | MMDiT diffusion backbone (~20B params) — NVFP4 quantized |

| text_encoder/text_encoder-NVFP4.gguf | 3.84 GB | Qwen2.5-VL-7B text encoder — NVFP4 quantized |

| vae/vae.safetensors | 0.25 GB | VAE decoder (BF16, original format) |

Hardware Requirements

• GPU: NVIDIA Blackwell (RTX 50-series) with CUDA 13.0+
• VRAM: ~10 GB for diffusion model + text encoder + VAE (NVFP4 saves ~4 GB vs FP8)
• System RAM: 32 GB+ recommended

Usage with ComfyUI

1. Install ComfyUI-GGUF
2. Place qwen-v23-diffusion-NVFP4.gguf in ComfyUI/models/checkpoints/
3. Place text_encoder/text_encoder-NVFP4.gguf in ComfyUI/models/clip/
4. Place vae/vae.safetensors in ComfyUI/models/vae/
5. Load the checkpoint normally — ComfyUI-GGUF will automatically detect the NVFP4 format

Quantization Details

| Property | Value |

|---|---|

| Quantization (Diffusion) | NVFP4 (NVIDIA FP4, 4-bit) |

| Quantization (Text Encoder) | NVFP4 (NVIDIA FP4, 4-bit) |

| GGML Quant Type | 40 (NVFP4) |

| GGUF File Type | 39 (LLAMA_FTYPE_MOSTLY_NVFP4) |

| Bits Per Weight (Text Encoder) | ~4.66 |

| Backend | CUDA 13.2, compute 12.0 |

| llama.cpp Commit | dd7cad7 |

| Source Precision | F8_E4M3 → F16 → NVFP4 |

Conversion Process

The original FP8 merged checkpoint was:

1. Split into three components (diffusion, text encoder, VAE)
2. Each component's FP8 tensors manually converted to F16 via streaming binary I/O with row-chunked FP8→F16 conversion to avoid OOM on large tensors
3. Written as F16 GGUF using gguf Python library (v0.19.0)
4. Quantized to NVFP4 using llama-quantize.exe (commit dd7cad7, CUDA 13.2)
5. Architecture binary-patched in output GGUF to match expected ComfyUI-GGUF target

Text Encoder Notes

• The text encoder (Qwen2.5-VL-7B, 7.75 GB F8_E4M3 → 3.84 GB NVFP4) uses the qwen2 GGUF architecture
• Input GGUF was written with both qwen2. and llama. metadata keys for quantizer compatibility, then patched to qwen2 after quantization
• Row-chunked FP8→F16 conversion (256 rows at a time) prevents OOM on the 545M-element embedding table
• Embedding weights are tied (lm_head shares weights with token_embd) — standard for Qwen2 models

Available Quantizations

Currently only NVFP4 is provided. Other quantizations (Q4_K_M, Q5_K_M, Q6_K, Q8_0) are possible using the same toolchain.

Notes

• This is a NSFW checkpoint. Use at your own discretion.
• The text encoder is now also NVFP4 GGUF, saving ~4 GB (was 7.75 GB as safetensors) while remaining compatible with ComfyUI-GGUF.
• The VAE remains in safetensors format (BF16) for maximum compatibility.
• NVFP4 is a lossy quantization — expect minor quality degradation compared to the original FP8 model.
• Requires NVIDIA Blackwell GPU (RTX 50-series) with CUDA 13.0+ for NVFP4 compute support.

Links

• Original model: Phr00t/Qwen-Image-Edit-Rapid-AIO-NSFW-v23
• ComfyUI-GGUF: city96/ComfyUI-GGUF
• llama.cpp: ggml-org/llama.cpp