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Qwen-Image 2.1 All-In-One ComfyUI One-Click Windows Installer

Qwen-Image 2.1 All-In-One ComfyUI One-Click Windows Installer

The complete Qwen-Image 2.1 setup in one double-click — text-to-image and image editing from a single unified model. This all-in-one installer sets up ComfyUI Portable, the custom nodes and the models for both workflows, with a download menu that lets you pick your VRAM tier.
Qwen-Image 2.1 is the Qwen team's new unified model — just 7B parameters in its visual component, smaller and more efficient than the previous open Qwen release — with native transparent (RGBA) images and editing with up to 10 reference images. It's strong at prompt adherence and typography.
Runs on 6–8 GB of VRAM with the GGUF build and the low-VRAM text encoder, or pick FP8 for full quality on 12 GB.
What the installer does: sets up ComfyUI Windows Portable with the CUDA 12.8 PyTorch build, installs ComfyUI Manager, ComfyUI-GGUF and rgthree, preloads both workflows, then shows a menu to choose your base model, text encoder and optional turbo LoRAs. Git, 7-Zip and the correct PyTorch build are handled automatically. The installer will be updated as new Qwen-Image 2.1 tools and models drop.

  • What you get

    A one-click Windows installer for ComfyUI Portable, both Qwen-Image 2.1 workflows — Text-to-Image and Image Edit (I2I) — and the custom nodes they need: ComfyUI Manager, ComfyUI-GGUF and rgthree.

    The download menu — pick your VRAM tier:

    Base model: FP8 (int8, ~7 GB, higher quality) into diffusion_models/, or GGUF Q5_K_M (~4–7 GB by quant, low VRAM) into unet/
    Text encoder: qwen3vl_8b int8 (standard) or w4a8 (extra low VRAM) into text_encoders/
    VAE: qwen_image_2.1_vae_bf16 into vae/ — downloads with any choice

    Optional turbo LoRAs into loras/: none, Viggle 6-step, Alibaba-PAI 4-step, or both to compare. They cut editing to 4–6 steps at cfg 1. They're consistently great for image editing, but text-to-image on them is hit-or-miss — use turbo for edits, and the standard model at full steps for reliable text-to-image. The matching free turbo edit workflow runs them.

  • System requirements

    OS: Windows 10 or 11, 64-bit.

    GPU: NVIDIA RTX 30XX / 40XX / 50XX.
    FP8: 12 GB. On a 16 GB card a 1024×1024 image takes about 1–2 minutes at 25 steps.
    GGUF + w4a8: runs on 6–8 GB and still produces good quality.

    Disk: at least 30 GB free.

    Handled for you: Git, 7-Zip and the correct PyTorch build are installed automatically.

  • Getting started

    1. Put the install .bat in its own dedicated folder and double-click. Use a simple local path like C:\Qwen\ — avoid Desktop, Documents, and cloud-synced folders.

    2. At the menu, pick the base model and text encoder for your card: 8 GB → GGUF + w4a8; 12 GB+ → FP8 + int8 for best quality. Add a turbo LoRA if you'll mostly be editing.

    3. Load either workflow (Text-to-Image or Edit) and confirm the models are selected.

    4. Start around 25 steps at 1024×1024.

    5. For fast edits, run a turbo LoRA with the free turbo edit workflow at 4–6 steps and cfg 1.

  • Troubleshooting

    Install fails or behaves oddly: check the install path first. A cloud-synced folder (OneDrive, Dropbox) or a deep path with spaces is the most common cause. Move to C:\Qwen\ and re-run.

    Out of memory: choose the GGUF base and the w4a8 text encoder at the download menu — that's the 6–8 GB tier.

    Text-to-image comes out smeared or incomplete: check whether a turbo LoRA is active. They're reliable for editing but hit-or-miss for text-to-image — turn it off and run the standard model at full steps.

    A model isn't showing in a loader: the FP8 base lives in diffusion_models/ and the GGUF base in unet/, and they're loaded by different loader nodes — select the one that matches what you downloaded.

  • Licence & credit

    Qwen-Image 2.1 is by the Qwen team (Alibaba). ComfyUI support is by Comfy-Org, the GGUF quants by Abiray, and the turbo LoRAs by Viggle and Alibaba-PAI. This product is the installer and workflow packaging — it automates the setup of their work into a one-click process.

    Licence note: Qwen-Image 2.1 is released under the Qwen Research License — non-commercial use only. Unlike the earlier Apache-licensed Qwen-Image release, commercial use requires a separate licence from Tongyi Lab (Alibaba). The GGUF quants and turbo LoRAs are derived from it and carry the same licence. Read the terms on the model's Hugging Face page before any commercial use.

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