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GGUF builds allow users to run demanding 7B-parameter image generation architectures on standard consumer hardware. The release highlights the accessibility of unmoderated local image pipelines via modular tools like ComfyUI.
The repository by abenzerps converts official Qwen/Qwen-Image-2.1 weights via stable-diffusion.cpp without modifying base behaviour. While branded as uncensored, replies clarify that local implementations of the base model inherently lack prompt refusals or safety checkers. The primary practical benefit is resource efficiency: combining Q4_K_M quantisation with CPU RAM offloading for the text encoder enables smooth generation on GPUs with limited VRAM.
Mia
@MiaAI_lab · 4 October 2026
Uncensored Qwen Image 2.1 for anyone interested t.co/Krur6K3vPR
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Link:abenzerps
GGUF quantisations of Qwen/Qwen-Image-2.1 have been released on Hugging Face. The model files are designed for local image generation in ComfyUI and omit built-in safety filters or content blockers.
Link:PocketAiHub
PocketAiHub has released an unofficial MLX derivative family of Qwen3.8-27B with suppressed refusal behaviour ('abliteration'). The consolidated repository provides quantized builds ranging from experimental 2-bit AWQ to full BF16 optimised for Apple Silicon.
Link:Unsloth AI
Unsloth AI has published GGUF quantizations for the Qwen3.8-27B model under an Apache-2.0 licence. Utilizing their new Dynamic V3.0 quantization format, Unsloth claims over 10% higher top-1 accuracy at identical sizes compared to alternative quants. Qwen3.8-27B is a 27-billion parameter dense vision-language model featuring native text, image, and video comprehension alongside granular thinking controls.
Link:Qwen
The Qwen team has released Qwen3.8-27B, a compact, dense vision-language model under the Apache-2.0 licence. The 27-billion-parameter model pairs Gated DeltaNet with conventional gated attention, handles images and videos natively, and introduces flexible reasoning controls.
Link:kodee
Empero has released Qwythos-27B-v1, an open-weights bf16 reasoning model published under the Apache-2.0 licence. Sized up from Qwythos-9B on a Qwen3.5-27B base, it preserves native function calling, multimodal vision, MTP, and a 1M context window.
Link:Empero AI
Empero AI has released Qwythos-27B-v1, an open-weight reasoning model based on Qwen3.5-27B. The checkpoint preserves multi-token prediction (MTP), vision capabilities, and a 1M-token context window while specialising in terminal and agentic workflows.
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