--- title: Qwen-Image-2.1-Turbo-GGUF canonical_url: "https://www.modelscope.cn/models/Abiray/Qwen-Image-2.1-Turbo-GGUF" md_url: "https://www.modelscope.cn/models/Abiray/Qwen-Image-2.1-Turbo-GGUF.md" repository: Abiray/Qwen-Image-2.1-Turbo-GGUF last_updated: 2026-10-11 license: other pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis base_model: - Qwen/Qwen-Image-2.1-Turbo base_model_relation: quantized library_name: - lora - gguf - pytorch frameworks: - pytorch downloads: 625 stars: 6 tags: - text-to-image - image-to-image - gguf - qwen - qwen-image - turbo - comfyui --- # Qwen-Image-2.1-Turbo-GGUF > Qwen-Image-2.1-Turbo-GGUF - Abiray 在 ModelScope 开源的模型。Qwen-Image-2.1-Turbo GGUF Abiray/Qwen-Image-2.1-Turbo-GGUF 是 ModelScope 魔搭社区上的text-to-image-synthesis模型，采用 other 许可，基于 Qwen/Qwen-Image-2.1-Turbo 构建。 - **Repository**: Abiray/Qwen-Image-2.1-Turbo-GGUF - **License**: other - **Tasks**: text-to-image-synthesis - **Base model**: Qwen/Qwen-Image-2.1-Turbo - **Tags**: text-to-image, image-to-image, gguf, qwen, qwen-image, turbo, comfyui - **Downloads**: 625 - **Stars**: 6 - **Last updated**: 2026-10-11 Source: https://www.modelscope.cn/models/Abiray/Qwen-Image-2.1-Turbo-GGUF --- # Qwen-Image-2.1-Turbo GGUF Quantized GGUF weights for Alibaba's official **[Qwen-Image-2.1-Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo)** distilled diffusion model. These checkpoints enable lightning-fast text-to-image and image-editing generation in ComfyUI at a fraction of the native VRAM requirement, taking full advantage of 4–8 step Turbo sampling. --- ## 📦 Quantization Breakdown All models were converted using `ComfyUI-GGUF` tools and quantized using a patched multi-threaded `llama-quantize` build natively supporting the 297-tensor `qwen_image` architecture. | Filename | File Size | Recommended GPU / VRAM | Quality Tier & Notes | | :--- | :--- | :--- | :--- | | **`qwen_image_2.1_turbo_Q8_0.gguf`** | 7.59 GB | 12 GB – 16 GB+ | Reference grade; indistinguishable from native BF16. | | **`qwen_image_2.1_turbo_Q6_K.gguf`** | 5.88 GB | 10 GB – 12 GB | High-fidelity; preserves complex typography and prompt nuance. | | **`qwen_image_2.1_turbo_Q5_K_M.gguf`** | 5.01 GB | 8 GB – 12 GB | Excellent texture detail and micro-contrast. | | **`qwen_image_2.1_turbo_Q5_K_S.gguf`** | 4.94 GB | 8 GB – 10 GB | Balanced 5-bit quant; slight memory savings over Q5_K_M. | | **`qwen_image_2.1_turbo_Q5_0.gguf`** | 4.94 GB | 8 GB – 10 GB | Standard uniform 5-bit quantization; consistent quality across layers. | | **`qwen_image_2.1_turbo_Q4_K_M.gguf`** | 4.19 GB | 6 GB – 8 GB | **⭐ Recommended Sweet Spot**: Best quality-to-speed ratio. | | **`qwen_image_2.1_turbo_Q4_K_S.gguf`** | 4.06 GB | 6 GB – 8 GB | Compact 4-bit k-quant; ideal balance for strict 6 GB/8 GB envelopes. | | **`qwen_image_2.1_turbo_Q4_0.gguf`** | 4.05 GB | 6 GB – 8 GB | Standard legacy 4-bit format; clean and fast dequantization. | | **`qwen_image_2.1_turbo_Q3_K_M.gguf`** | 3.19 GB | 6 GB VRAM / Laptops | Ultra-compact footprint with strong core composition retention. | | **`qwen_image_2.1_turbo_Q3_K_S.gguf`** | 3.11 GB | 4 GB – 6 GB | Lightweight 3-bit quant tailored for tight memory limits. | | **`qwen_image_2.1_turbo_Q2_K.gguf`** | 2.45 GB | 4 GB VRAM / Extreme Low-Spec | Minimal footprint; noticeable texture loss, but fits on almost any system. | --- ## 🖼️ Sample Generations (Q4_K_M) --- ## ⚡ Recommended Generation Parameters Because this is the distilled **Turbo** variant, your sampling settings must match the accelerated schedule: * **Sampler**: `euler` * **Scheduler**: `simple` or `normal` * **Steps**: **4 to 8 steps** (6 steps is ideal for general fidelity) * **CFG Scale**: **`1.0`** > ⚠️ **Important**: Do not increase CFG above 1.0 on this distilled checkpoint. Distillation collapses guidance requirements; CFG values > 1.0 will cause harsh contrast clipping and visual artifacts. * **Negative Prompt**: Leave empty (ignored when CFG = 1.0). --- ## 🛠️ ComfyUI Setup & Required Components ### 1. Install Node Make sure you have the latest version of [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) installed in your `ComfyUI/custom_nodes/` directory. ### 2. File Placement * **Diffusion Model (GGUF)**: Download your chosen `.gguf` file from this repo and place it in: `ComfyUI/models/diffusion_models/` (or `ComfyUI/models/unet/`) * **Text Encoder**: Download `qwen3vl_8b_int8_convrot.safetensors` or `qwen3vl_8b_w4a8.safetensors` from [Comfy-Org/Qwen-Image-2.1](https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/text_encoders) and place it in: `ComfyUI/models/text_encoders/` * **VAE**: Download `qwen_image_2.1_vae_bf16.safetensors` from [Comfy-Org/Qwen-Image-2.1](https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/vae) and place it in: `ComfyUI/models/vae/` --- ## 🎨 Ready-to-Use Workflows You can drag and drop either of these pre-built workflow JSONs straight into ComfyUI to start generating: 1. **Text-to-Image Workflow**: [Download Text2Image Workflow JSON](https://huggingface.co/Abiray/Qwen-Image-2.1-GGUF/blob/main/Qwen_Image_2.1_GGUF_Text2Image.json) 2. **Image-to-Image / Editing Workflow**: [Download Image2Image Edit Workflow JSON](https://huggingface.co/Abiray/Qwen-Image-2.1-GGUF/blob/main/Qwen_Image_2.1_GGUF_Image2Image_Edit.json) *(Just switch the model loader node to point to your new `qwen_image_2.1_turbo_Q*.gguf` file and adjust steps to 4–8 with CFG set to 1.0!)* --- ## 📑 Acknowledgements & Credits * Base model developed and released by the **[Qwen Team / Alibaba](https://huggingface.co/Qwen)**. * GGUF diffusion support enabled via **[city96/ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF)** and **[llama.cpp](https://github.com/ggerganov/llama.cpp)**.